gene disorder research paper

The genetic basis of disease

• December 2018
• Essays in Biochemistry 62(5):643-723
• 62(5):643-723
• CC BY-NC-ND 4.0
• This person is not on ResearchGate, or hasn't claimed this research yet.

• University of Glasgow

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• DOI: 10.30970/vlubs.2024.91.04
• Corpus ID: 270350001

Snijders Blok-Campeau syndrome: a novel neurodevelopmental genetic disorder

• O. Yushchuk , I. Ruda , V. Fedorenko
• Published in Visnyk of Lviv University… 7 June 2024

34 References

Snijders blok–campeau syndrome: description of 20 additional individuals with variants in chd3 and literature review, a severe neurocognitive phenotype caused by biallelic chd3 variants in two siblings, hypersociability associated with developmental delay, macrocephaly and facial dysmorphism points to chd3 mutations., a novel chd3 variant in a patient with central precocious puberty: expanded phenotype of snijders blok‐campeau syndrome, inherited variants in chd3 show variable expressivity in snijders blok-campeau syndrome., a second cohort of chd3 patients expands the molecular mechanisms known to cause snijders blok-campeau syndrome, a de novo chd3 variant in a child with intellectual disability, autism, joint laxity, and dysmorphisms, mutations in chd7, encoding a chromatin-remodeling protein, cause idiopathic hypogonadotropic hypogonadism and kallmann syndrome., chd2-related cns pathologies, chd3 helicase domain mutations cause a neurodevelopmental syndrome with macrocephaly and impaired speech and language, related papers.

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Unraveling the genetic landscape of neurological disorders: insights into pathogenesis, techniques for variant identification, and therapeutic approaches.

1. Introduction

2. genetic techniques in addressing neurodegenerative disorders, 3. genetic mutations and corresponding cellular alterations in neurodegenerative disorders, 3.1. amyotrophic lateral sclerosis (als), 3.1.1. genetic and pathological overlap between als and ftd, 3.1.2. epidemiology, 3.1.3. genetic causes and risk factor.

• Superoxide dismutase ( SOD1 )
• Chromosome 9 open reading frame 72 ( C9ORF72 )

3.1.4. Additional Risk Loci from Genome-Wide Association Studies

3.2. alzheimer’s disease (ad), 3.2.1. epidemiology, 3.2.2. genetic causes and risk factors, 3.2.3. additional risk loci from genome-wide association studies (gwass), 3.3. parkinson’s disease (pd), 3.3.1. epidemiology, 3.3.2. genetic causes and risk factors, 3.3.3. additional risk loci from genome-wide association studies, 3.3.4. other neurodegenerative disorders, 4. gene therapy for neurodegenerative diseases, 5. gene expression, 5.1. the exogenous introduction of genes into the cns, 5.2. dna editing, 5.3. crispr-mediated base editing and prime editing, 6. genetic therapy for ad, 6.1. targeting mapt, 6.2. targeting apoe, 6.3. targeting app, 7. gene therapy for pd, 7.1. modulating neuronal signaling, 7.2. targeting disease genes—snca, gba, and lrrk2, 8. gene therapy for als, targeting sod1, 9. conclusions and future perspectives, author contributions, acknowledgments, conflicts of interest.

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Firdaus, Z.; Li, X. Unraveling the Genetic Landscape of Neurological Disorders: Insights into Pathogenesis, Techniques for Variant Identification, and Therapeutic Approaches. Int. J. Mol. Sci. 2024 , 25 , 2320. https://doi.org/10.3390/ijms25042320

Firdaus Z, Li X. Unraveling the Genetic Landscape of Neurological Disorders: Insights into Pathogenesis, Techniques for Variant Identification, and Therapeutic Approaches. International Journal of Molecular Sciences . 2024; 25(4):2320. https://doi.org/10.3390/ijms25042320

Firdaus, Zeba, and Xiaogang Li. 2024. "Unraveling the Genetic Landscape of Neurological Disorders: Insights into Pathogenesis, Techniques for Variant Identification, and Therapeutic Approaches" International Journal of Molecular Sciences 25, no. 4: 2320. https://doi.org/10.3390/ijms25042320

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• Published: 08 January 2020

A brief history of human disease genetics

• Melina Claussnitzer 1 , 2 , 3 ,
• Judy H. Cho 4 , 5 , 6 ,
• Rory Collins 7 , 8 ,
• Nancy J. Cox 9 ,
• Emmanouil T. Dermitzakis 10 , 11 ,
• Matthew E. Hurles 12 ,
• Sekar Kathiresan 2 , 13 , 14 ,
• Eimear E. Kenny 4 , 6 , 15 ,
• Cecilia M. Lindgren 2 , 16 , 17 ,
• Daniel G. MacArthur 2 , 13 , 18 ,
• Kathryn N. North 19 , 20 ,
• Sharon E. Plon 21 , 22 ,
• Heidi L. Rehm 2 , 13 , 18 , 23 ,
• Neil Risch 24 ,
• Charles N. Rotimi 25 ,
• Jay Shendure 26 , 27 , 28 ,
• Nicole Soranzo 12 , 29 &
• Mark I. McCarthy 17 , 30 , 31 , 32  

Nature volume  577 ,  pages 179–189 ( 2020 ) Cite this article

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• Functional genomics
• Medical genetics

A primary goal of human genetics is to identify DNA sequence variants that influence biomedical traits, particularly those related to the onset and progression of human disease. Over the past 25 years, progress in realizing this objective has been transformed by advances in technology, foundational genomic resources and analytical tools, and by access to vast amounts of genotype and phenotype data. Genetic discoveries have substantially improved our understanding of the mechanisms responsible for many rare and common diseases and driven development of novel preventative and therapeutic strategies. Medical innovation will increasingly focus on delivering care tailored to individual patterns of genetic predisposition.

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The road ahead in genetics and genomics

Genetic association analysis of 77,539 genomes reveals rare disease etiologies

Genetic associations of protein-coding variants in human disease

For almost all human diseases, individual susceptibility is, to some degree, influenced by genetic variation. Consequently, characterizing the relationship between sequence variation and disease predisposition provides a powerful tool for identifying processes fundamental to disease pathogenesis and highlighting novel strategies for prevention and treatment.

Over the past 25 years, advances in technology and analytical approaches, often building on major community projects—such as those that generated the human genome sequence 1 and elaborated on that reference to capture sites of genetic variation 2 , 3 , 4 , 5 , 6 —have enabled many of the genes and variants that are causal for rare diseases to be identified and enabled a systematic dissection of the genetic basis of common multifactorial traits. There is growing momentum behind the application of this knowledge to drive innovation in clinical care, most obviously through developments in precision medicine. Genomic medicine, which was previously restricted to a few specific clinical indications, is poised to go mainstream.

This Review charts recent milestones in the history of human disease genetics and provides an opportunity to reflect on lessons learned by the human genetics community. We focus first on the long-standing division between genetic discovery efforts targeting rare variants with large effects and those seeking alleles that influence predisposition to common diseases. We describe how this division, with its echoes of the century-old debate between Mendelian and biometric views of human genetics, has obscured the continuous spectrum of disease risk alleles—across the range of frequencies and effect sizes—observed in the population, and outline how genome-wide analyses in large biobanks are transforming genetic research by enabling a comprehensive perspective on genotype–phenotype relationships. We describe how the expansion in the scale and scope of strategies for enumerating the functional consequences of genetic variation is transforming the torrent of genetic discoveries from the past decade into mechanistic insights, and the ways in which this knowledge increasingly underpins advances in clinical care. Finally, we reflect on some of the challenges and opportunities that confront the field, and the principles that will, over the coming decade, drive the application of human genetics to enhance understanding of health and disease and maximize clinical benefit.

Rare diseases, rare variants

During the 1980s and 1990s, efforts to map disease genes were focused on rare, monogenic and syndromic diseases and were mostly driven by linkage analysis and fine mapping within large multiplex pedigrees. Localization of genetic signals was typically followed by Sanger sequencing of the genes found to map within the linked locus to identify disease-causing alleles. Assessments of pathogenicity, based on segregation of a putatively causal variant with disease across multiple families and evidence that the risk genotype was absent in healthy individuals, were typically followed by confirmatory functional studies in cellular and animal models. This path to gene identification was laborious; nevertheless, by 2000, around 1,000 of the estimated 7,000 single-gene inherited diseases had been characterized, including many with substantial biomedical impact, such as Huntington’s disease and cystic fibrosis 7 , 8 , 9 .

Completion of the draft human genome sequence 1 reduced many of the obstacles to disease-gene mapping and propelled a fourfold increase in the genes implicated as causal for rare, single-gene disorders (Fig. 1 ). Microarray-based detection of structural variation 10 and exome- and genome-wide sequencing 11 , 12 have been pivotal, bolstered by in silico analysis and prioritization of the discovered genetic variants. Increasing availability of reference datasets cataloguing population genetic variation across diverse ethnic backgrounds has supported robust causal inference 2 , 3 , 5 , 6 . More recently, the adoption of high-throughput sequencing technologies has enabled the full range of causal genetic variation, from single mutations to large structural rearrangements, to be identified in a single assay. These technologies have extended from research into clinical usage, driving earlier and faster diagnosis for genetic disorders.

The cumulative numbers of genes harbouring variants causal for rare, monogenic diseases and traits and of significant GWAS associations implicated in common, complex diseases and traits are shown. Left, the advent of high-throughput sequencing technologies and availability of reference genomes from diverse populations has supported a fourfold increase in the discovery of rare disease-causing genes between 1999 and 2019. Right, international efforts such as the Human Genome Project and the HapMap Project, combined with GWAS and sequencing studies, have supported identification of more than 60,000 genetic associations across thousands of human diseases and traits. Centre, more recent developments have brought a synthesis of the rare- and common-variant approaches based around the combination of sequence-informed analyses in large cohorts. Key events contributing to these themes are depicted in the timeline. GA4GH, Global Alliance for Genomics and Health 160 ; ExAC, Exome Aggregation Consortium 5 .

Reduced reliance on multiplex pedigrees in favour of collections of affected cases, often with parents 13 , has proven decisive in identifying new dominant disorders, many of which were previously considered recessive 14 . Increasingly, discovery of rare disease genes has transitioned from genetic characterization of small numbers of individuals with similar clinical presentations to genome-wide sequencing of larger cohorts of phenotypically diverse patients. This genotype-driven approach has revealed new disorders associated with more variable clinical presentation 15 , 16 .

A more systematic approach to data sharing has been critical, both for the characterization of new disorders and diagnostic interpretation of potential causal alleles. The value of sharing genetic and phenotypic data from those thought to harbour rare undiagnosed genetic diseases has fostered global collaborative networks (for example, Matchmaker Exchange, DECIPHER and GeneMatcher) designed to match patients with similar genetic variants and/or phenotypic manifestations, even across continents 17 , 18 , 19 . Interactions between researchers and families with rare disease have enabled natural history studies to be driven by family support groups positioned to initiate data collection from patient cohorts once a causal gene is discovered 20 .

Clinical translation of these technologies has benefited from a series of information resources, including open databases of genes associated with rare disorders (for example, OMIM and ORPHANET) 21 , clinically interpreted variants (for example, ClinVar and ClinGen) 22 , 23 and patient records (for example, DECIPHER and MyGene2 ( https://mygene2.org/MyGene2 )) 17 . Access to resources that catalogue genetic variation across populations (such as ExAC and its successor gnomAD) 5 , 6 has enabled the confident exclusion of genetic variants too common in population-level data to be plausible causes of rare, penetrant early-onset genetic diseases 24 . These analyses have reduced the contamination of databases with variants erroneously interpreted as causal for disease, and are addressing the overestimation of disease penetrance arising from the historical focus on multiplex pedigrees 25 . Improved recognition of the variable penetrance of many ‘monogenic’ disease alleles has invigorated efforts to identify the genetic and environmental modifiers responsible 26 , 27 .

Although huge strides have been made in associating specific genes with particular disorders, establishing the causal role of individual variants within those genes remains problematic, and many patients with suspected rare genetic diseases are left without a definitive diagnosis 28 . Even for variants with established causality, the penetrance is often unclear. Resolving these uncertainties represents the central challenge for the field. Aggregation of sequencing data from large numbers of affected cases and population reference samples will provide the evidence base required for robust interpretation of variants. Highly parallelized in vitro cellular assays that allow assessment of the functional effects of all variants in a disease-associated gene can transform interpretation of novel variants 29 , although developing well-calibrated functional assays predictive of pathogenicity for all disease genes represents a daunting prospect. Direct functional genomic exploration of accessible and disease-relevant tissues from patients using RNA sequencing and DNA methylation assays 30 , 31 can identify previously cryptic causal genetic variants, particularly in under-explored regions outside protein-coding genes 32 , 33 . Developments in each of these areas will extend the range of variants and genes for which diagnostic and prognostic clinical information can be provided to patients and their families.

Common diseases, common variants

Efforts to apply the approach—linkage analysis in multiplex pedigrees—that had been so successful for the high-penetrance variants responsible for Mendelian disease were, with notable exceptions 34 , 35 , 36 , largely unsuccessful for common, later-onset traits with more complex multifactorial aetiologies, such as asthma, diabetes and depression. Recognition that association-based methods, focused on detecting phenotype-related differences in variant allele frequencies might have greater traction for identifying less penetrant common alleles redirected attention to analysis of case–control samples 37 . However, initial efforts targeting variants within ‘candidate’ genes were plagued by inadequate power, unduly liberal thresholds for declaring significance and scant attention to sources of bias and confounding, resulting in overblown claims and failed replication.

Systematic efforts to characterize genome-wide patterns of genomic variation, initially through the HapMap Consortium 2 , proved catalytic, demonstrating that the allelic structure of the genome was segmented into haplotype blocks, each containing sets of correlated variants. Recognition that this configuration could support genome-wide surveys of association energized the technological innovation—in the form of massively parallel genotyping arrays—to make such studies possible (Fig. 1 ). Early wins in acute macular degeneration 38 and inflammatory bowel disease 39 were encouraging, and progress on several fronts—expansion of study size, denser genotyping arrays, novel strategies for imputation, attention to biases and appropriate significance thresholds—delivered robust associations across a range of diseases 40 . Most variants uncovered by these early genome-wide association studies (GWAS) were common, with more subtle effects than many had anticipated. A host of trait-specific consortia formed, covering diverse dichotomous and quantitative phenotypes, to accelerate genetic discovery through the aggregation and meta-analysis of data from multiple GWAS studies 41 , 42 , 43 . Many tens of thousands of robust associations were identified 44 . Recently, increased access to exome and whole-genome sequence data has, through both direct association analysis 45 , 46 and imputation 3 , 4 , extended discovery to low-frequency and rare alleles previously inaccessible to GWAS.

In the decade since the first GWAS, understanding of the genetic basis of common human disease has been transformed. The disparity between the observed effects of the variants first identified by GWAS and estimates of overall trait heritability (the ‘missing heritability’ conundrum) is now largely resolved 47 . Common diseases are not simply aggregations of related Mendelian conditions: for most complex traits, genetic predisposition is shared across thousands of mostly common variants with individually modest effects on population risk 41 , 43 .

Although the collective contribution of low-frequency and rare risk alleles to overall trait variability appears modest compared with that attributable to common variants 45 , 48 , the rare risk alleles detected in current sample sizes necessarily have large phenotypic effects and are proportionately more likely to be coding, enhancing their value for biological inference. Founder populations (such as those from Finland and Iceland) have provided multiple examples of otherwise rare risk alleles driven to higher frequency locally through drift and/or selection 49 , 50 , 51 , 52 . In addition, studies in populations with high rates of consanguinity make it possible to identify individuals homozygous for otherwise rare loss-of-function alleles, the basis for a ‘human knockout’ project to systematically investigate the phenotypic consequences of gene disruption in humans 53 , 54 .

For most diseases, large-scale GWAS-aggregation efforts have been disproportionately powered by information from individuals of European descent 55 . Whereas patterns of genetic predisposition appear broadly similar across major population groups and many common risk alleles discovered in one population group are detectable in others, allele frequencies can vary substantially; extending GWAS and sequencing studies to diverse populations will surely generate a rich harvest of novel risk alleles.

The relative contributions of common and rare variants indicate that, for many traits, particularly those with post-reproductive onset, purifying selection has had only limited effect 45 , 56 . For a few risk alleles, hallmarks of balancing selection reflect increased carrier survival, usually through protection from infectious diseases. This includes well-known examples of alleles maintained at high frequency in populations of African descent 57 , 58 .

While the extensive linkage disequilibrium within human populations has been essential to discovery in GWAS, high correlation between adjacent variants frustrates mapping of the specific variants responsible for these associations. Increasing sample size, improved access to trans-ethnic data, and more representative imputation reference panels 3 provide a path to improved resolution of the causal variants 59 and clues to the molecular mechanisms through which they operate. Functional interpretation is easiest for causal variants within coding sequences; however, most common disease-risk variants map to noncoding sequences, and are presumed to influence predisposition through effects on transcriptional regulation. In these cases, mechanistic inference depends on connecting association signals to their downstream targets (see below). For many traits, there is clear convergence between common-variant association signals and genes implicated in monogenic forms of the same disease, as well as enrichment of GWAS signals in regulatory elements specifically active in cell types consistent with known disease biology 60 , 61 . This provides reassurance that, even as the number of association signals for a given disease proliferates, the genetic associations uncovered will coalesce around molecular and cellular processes with a core role in pathogenesis 62 , 63 .

Importantly, the signals discovered by GWAS have revealed many unexpected insights into the biological basis of complex disease. Examples include the role of complement in the pathogenesis of acute macular degeneration 38 , synaptic pruning in schizophrenia 64 and autophagy in inflammatory bowel disease 65 . In addition, as inherited sequence variation is a prominent cause of phenotypic variation (but the reverse is not true), risk variants identified by GWAS have value as genetic instruments, mapping causal relationships between traits and inferring contributions made by circulating biomarkers and environmental exposures to disease development 66 .

As described below, findings from GWAS have increasing translational impact through identification of novel therapeutic targets 67 , prioritization (and deprioritization) of existing ones 68 and development of polygenic scores that quantify individual genetic risk 69 .

Comprehensive genotype–phenotype maps

The historical division of disease-gene discovery into monogenic and polygenic strands arose from development and implementation of analytical approaches—family-based linkage and case–control association 37 —that are best-suited for detecting particular subsets of causal alleles. This obscured the true state of nature, with disease-risk alleles being distributed across a continuous spectrum of frequencies and effect sizes. In addition, the trait- and disease-specific perspective of early GWAS discovery (mostly reliant on case–control studies) was poorly equipped to investigate the contribution of genetic variants to phenotypic effects that are nested within or spread across classical disease definitions. Recent developments have enabled a more holistic perspective on genotype–phenotype relationships (Fig. 1 ).

One major advance has been the increasing availability of large prospective population-based cohorts. These biobank efforts, pioneered in studies such as the Framingham Cohort 70 and the efforts of DeCODE in Iceland 71 , 72 , now encompass a growing inventory of national cohorts in North America, Europe, Asia and beyond 73 , 74 , 75 , 76 . The UK Biobank study, including 500,000 largely healthy, middle-aged participants has been particularly influential, transforming human genetic research in part through permissive data-sharing policies that have allowed multiple research groups to analyse the data 74 . Efforts to make clinical data embedded in electronic health records and registries available for research 77 , 78 mean that biobanks increasingly provide access to a wide range of demographic, clinical and lifestyle data, captured in harmonized, systematic fashion from large, often multi-ethnic collections of individuals. For millions of biobank participants, this rich phenotypic information has been combined with genome-wide genetic data. There are nascent efforts to capture transcriptomic, proteomic and metabolomic phenotypes, although these are not yet at equivalent scale to the genetic data 79 , 80 . Biobank analyses have provided more generalizable estimates of the relevance of genetic risk factors in the context of the separate and joint effects of non-genetic factors 81 . Increasingly, integration with healthcare data brings a longitudinal dimension to phenotypic characterization, which facilitates analyses of disease progression and lifelong disease risk 82 .

The rich phenotypic scope of these cohorts has enabled variants of interest to be interrogated for associations across the gamut of available phenotypes. These phenome-wide association studies (PheWAS) have revealed the extent to which many variants have pleiotropic effects across multiple traits 83 . Some of these relationships are expected, such as the impact of obesity variants on risk of hepatic steatosis and type 2 diabetes 84 or variants that influence multiple autoimmune conditions 85 . Others connect diseases and traits in surprising ways, highlighting shared polygenic, pleiotropic effects and cell-type specificity, and delivering insights into shared biology and overlapping mechanisms 86 , 87 . These findings inform the prioritization of therapeutic targets, providing clues to potential on-target side effects and opportunities for drug repurposing 87 , 88 , 89 .

The second enabler of inclusive, systematic analysis of genotype–phenotype relationships has been access to whole-genome sequence data. The scale of genetic analysis based on sequence data still lags behind that of genome-wide genotyping data (the largest sequence-based datasets are one tenth the size of the largest GWAS 90 , 91 , 92 ), although reductions in sequencing costs are decreasing the differential. Most direct analysis of high-throughput sequence data has focused on the coding regions. Strategies for assigning variant function and jointly analysing sets of variants of similar functional effect have enabled aggregate, gene-level tests of rare functional-variant association that are often better powered than single-variant tests 91 , 92 . However, the principal benefit to date of whole-genome sequence data to genetic discovery has been to bolster array-based access to lower-frequency alleles, either directly, through their inclusion on genotyping platforms, or indirectly, through imputation from sequence-based reference samples 3 , 4 .

These developments have enabled researchers to bridge the gap between the monogenic and polygenic realms, identifying common variant modifiers of monogenic phenotypes contributing to the variable expression of rare, large-effect alleles 26 , 93 , and low-frequency and rare variants that influence common multifactorial traits 94 , 95 . This enables more rigorous evaluation of the contribution of rare and common variants to trait susceptibility 48 and supports the enumeration of ‘allelic series’ (sets of alleles of varying frequency, effect size and direction that disrupt the same gene) critical for studies of disease mechanism and therapeutic target optimization 89 , 96 . These developments are rapidly converging towards the ultimate destination: a comprehensive matrix of the effect of all observable genetic variants across the widest possible range of cross-sectional and longitudinal biomedical phenotypes. Success in this endeavour depends on ever greater harmonization between, and integration of results from, individual studies through sustained investments in data sharing.

Adding function

From the first linkage maps to whole-genome sequencing of large cohorts, human genetics has deployed increasingly sophisticated and inherently systematic approaches for mapping the genetic factors that underlie traits and diseases. However, progress in determining how these variants influence disease, through systematic interrogation of their functional effects on molecular, cellular and physiological processes, has been far slower.

For monogenic diseases, for which the alleles responsible are typically rare, penetrant and coding, genetic approaches have generally been both necessary and sufficient to implicate a gene as causal 28 . However, as efforts to elucidate the genetic basis of Mendelian disorders progress towards completion 97 , functional studies remain important to understand the mechanisms by which disruptive variation within a causal gene leads to disease phenotypes. Unlike common diseases, the clarity of causation for Mendelian disorders usually simplifies the task of generating models (including human cells and organoids or rodents) to connect genotype to organismal phenotype; these have led to many critical insights into the biology of health and disease in humans 98 , 99 . In addition, for genes harbouring variants with medically actionable consequences (as with the BRCA1 and BRCA2 mutations that are causal for early-onset breast and ovarian cancer), functional studies can support the translational interpretation of novel alleles identified by medical sequencing 29 .

For common diseases, functional studies have a more fundamental role. Although tens of thousands of associations have been discovered across thousands of common human diseases and traits 44 , multiple factors have frustrated efforts to convert these genetic signals to knowledge about causal variants, genes and mechanisms. For the common variants that underlie the bulk of complex-disease risk, the resolution of association mapping is often limited by the haplotype structure of the human genome 2 , 3 , 4 . Furthermore, most GWAS associations map to the noncoding genome and thus lack a direct address to the gene that mediates their effects. Growing appreciation of the pervasive role of pleiotropy complicates matters: many variants identified by GWAS are associated with multiple traits and exert diverse effects across multiple cell types 100 .

To date, relatively few studies have achieved the goal of connecting variants causal for complex traits to the molecular and cellular functions that mediate that predisposition. One early success described how regulatory variants that modulate SORT1 expression influence low-density lipoprotein cholesterol and myocardial infarction risk 101 . More recent examples have focused on the relationship between obesity-associated variants intronic to FTO , altered expression of IRX3 and IRX5 , and adipocyte 102 and hypothalamic 103 function. Similar functional descriptions have been reported for individual loci implicated in schizophrenia 64 , cardiovascular disease 104 , type 2 diabetes 105 and Alzheimer’s disease 106 , among others.

Over the past decade, the challenge for the functional genomics community has been to convert this ‘one-locus-at-a-time’ workflow to a systematic, multidimensional, integrative approach able to deliver genome-scale functional analyses to match genome-wide variant discovery (Fig. 2 ). At the molecular level, one cornerstone has been generation of genome-wide catalogues of functional activity. For example, the ENCODE and Roadmap Epigenomics projects have generated maps of histone modifications, transcription-factor binding, chromatin accessibility, three-dimensional genome structure and other regulatory annotations across hundreds of cell types and tissues 107 , 108 . The patterns of genomic overlap between these data and GWAS results enable the functional inference of risk variants, deliver clues to the specific cell types driving disease pathogenesis 60 , 109 and accelerate locus-specific mechanistic insights.

Top, the growth in the number of genetic loci associated by GWAS with human traits and diseases (bars) and of variant-to-function studies (area under line, not to scale). Bottom, foundational technological and computational advances over the last decade that enabled (1) development of systematic, genome-wide catalogues of functional elements across multiple cell types and tissues (blue); (2) mapping of QTLs in the context of gene expression, metabolites, proteins and regulatory elements (red); (3) engineering of genes, genetic elements and genetic variation at increasing scale (orange); and (4) systematic tissue-specific surveys of regulatory elements and transcription (grey). scRNA-seq, single-cell RNA-sequencing analysis; ChIA-PET, chromatin interaction analysis by paired-end tag sequencing; ChIP–seq, chromatin immunoprecipitation followed by sequencing; FAIRE-seq, formaldehyde-assisted isolation of regulatory elements with sequencing; DHS-seq, DNase I-hypersensitive sites sequencing; ATAC-seq, assay for transposase-accessible chromatin using sequencing; MPRA, massively parallel reporter assay; STARR-seq, self-transcribing active regulatory region sequencing; CNN: convolutional neural networks. For further details and primary literature on many of these assays, see ref. 173 .

In parallel, there has been a scaling of efforts to connect trait-associated regulatory variants to the genes and processes that they regulate in cell types relevant to the disease of interest 110 , 111 . For example, the GTEx (Genotype-Tissue Expression) consortium has mapped thousands of expression quantitative trait loci (QTLs) across hundreds of individuals and dozens of tissues 112 . Further clues to the relationships between regulatory variants and their effector genes can be gathered from DNA proximity assays (such as Hi-C) and single-cell data 113 (Fig. 2 ). Programs such as HubMAP 114 and the Human Cell Atlas 115 are set to deliver comprehensive, high-resolution reference maps of individual human cell types across diverse developmental stages, providing new opportunities to understand how regulatory genetic variation results in cellular and organismal phenotypes.

Efforts to probe the clinical consequences of coding alleles with large phenotypic effects (particularly null alleles) in humans 53 , 54 and across diverse animal models 116 represent powerful strategies for extending functional analyses to the whole-body level. Connections between genetic variation and circulating proteomic and metabolomic data provide additional mechanistic links between cellular events and whole-body physiology 79 , 80 . These efforts are paralleled by PheWAS approaches 83 , which, by mapping variant effects across the range of traits available in biobanks and EMRs, can inform priors for cell types and pathways at individual loci. Importantly, whereas early studies typically linked GWAS risk alleles to data from a single functional assay, the focus is increasingly on maximizing biological insight through the multi-dimensional integration of multiple genome-wide data types using approaches such as heritability partitioning 117 , functional enrichment analyses 60 , 109 , integration of the three-dimensional genome structure 118 and deep convolutional neural networks 119 , 120 .

Although QTL analyses can implicate a haplotype in a molecular, cellular or organismal phenotype, they are, in isolation, insufficient to define the specific causal variants responsible. To address this, there has been rapid maturation of technologies, such as massively parallel reporter assays 121 , 122 , 123 and CRISPR genome editing, to support functional characterization of targeted sequence perturbations at scale. Variations on these methods enable the functional evaluation of genes (via knockout screens 124 ), regulatory elements (using CRISPR interference and CRISPR activation screens 125 , 126 ), and genetic variants (base editors 127 ) at increasing scale and resolution 29 . Combined with complex readouts—including high-content imaging 128 and single-cell transcriptomics and epigenomics 129 , 130 —these methods can generate empirical ‘truth’ data, supporting the development of in silico models to predict causal variants, effector transcripts 126 and cellular effects. In due course, such models should reduce the need for exhaustive experimental characterization of function for all variants across all cell types.

The goal of such efforts is to enumerate the cascade of molecular events that underlie observed genotype–phenotype associations using physiologically relevant cellular systems (from primary cells to organoids and ‘organ-on-chip’ designs) and whole-body assays appropriate to the disease of interest. Collectively, strategies that offer large-scale functional evaluation of variants and genes of interest will reduce (but probably not eliminate) the intensive effort required for ‘final mile’ validation of disease mechanisms in dedicated systems, thereby accelerating downstream translational application.

Clinical implementation

Medical genetics, as applied to rare diseases, has been characterized by the rapid application in the clinic of the transformative genomic technologies that drove initial research discoveries. There are now targeted genetic tests for nearly all clinical presentations attributable to large-impact alleles, alongside more extensive genome-sequencing assays that, when necessary, enable interrogation of a longer list of relevant genes. Genetic testing for symptomatic individuals and at-risk relatives occurs routinely in many medical specialties. In parallel, the use of somatic cancer testing has increased as therapies targeted to specific mutational events have entered clinical practice (these developments are reviewed elsewhere 131 , 132 ).

For patients with symptoms that indicate a probable monogenic aetiology (such as retinal degeneration, hearing loss or cardiomyopathy), targeted panels are typically the platform of choice 133 , although they are increasingly performed on a more extensive sequence backbone. For more complex phenotypes—those without a clear match to a specific syndrome, such as neurodevelopmental disorders and multiple congenital anomalies—testing has gravitated towards early deployment of exome and genome-sequencing platforms that offer speedy resolution of what has historically often been a traumatic diagnostic odyssey 15 , 134 . The power of genomic diagnosis is especially clear for those presenting with monogenic neurodevelopmental disorders and critically ill infants 135 , 136 . Sequencing of the parent–offspring trio can detect de novo variation in dominant disorders and phase biallelic rare variants in recessive disease 13 .

The transition from targeted gene tests to genomic sequencing enables recursive reanalysis, including reinterpretation of individual sequences on the basis of subsequent discoveries regarding causal disease alleles and their phenotypic consequences 137 . However, improved molecular diagnostics are required to ensure reliable detection of a subset of genetic disorders, including those arising from triplet repeats and complex rearrangements 138 . Deep sequencing of affected tissues for mosaic variants and the use of RNA sequencing to detect noncoding variants that drive early-onset disease (for example, through effects on splicing) represent new fronts for clinical diagnostics 30 .

Other examples of the rapid adoption of new genomic technologies include noninvasive prenatal testing (more than ten million tests by 2018 across multiple countries 139 , 140 , 141 ) and the use of recessive carrier panels for couples planning pregnancies. Newborn screening is now universal in many countries, although it is limited to disorders combining high-throughput low-cost detection with effective early interventions (such as diet restrictions or enzyme replacement) 142 . Genetic diagnostics are also increasingly applied to newborn screening as a reflex test following an abnormal (for example, metabolic) screening test 143 . Over the next decade, the repertoire of disorders captured by neonatal screening and prenatal testing is likely to expand markedly. Whereas prenatal testing may be more effective at avoiding disease, the associated ethical issues are more complex 144 .

Although genetic testing for rare disease and cancer has exploded, there has been more limited uptake of genetic information in other aspects of healthcare. For example, despite multiple examples of clinically important genetic markers related to drug efficacy and side-effect profile 145 , the roll-out of pharmacogenetics has been hampered by a range of factors, including lack of clinical decision support in electronic medical systems to guide the drug choice or dosing by the physician. This has been compounded by challenges in diagnostic testing: complex haplotype structures and structural variants at some key drug metabolism loci necessitate genome sequencing or specific targeted panels to detect all clinically relevant variants.

For common diseases, translational attention is currently focused on the clinical potential of polygenic risk scores. The development of robust polygenic scores for several common diseases has been catalysed by more precise per-variant effect estimates from larger GWAS datasets, improved algorithms for combining information across millions of single-nucleotide polymorphisms, and large-scale biobanks that support score validation 69 , 146 , 147 . For example, a genome-wide polygenic score for heart attack, incorporating 6.6 million variants, indicates that 5% of European-descent individuals have a risk of future cardiac events equivalent to that seen in those with less frequent monogenic forms of hypercholesterolaemia 69 . Increasingly, the shift from array-based genotyping to sequence-based analysis is facilitating risk prediction, which integrates information from rare, large-effect alleles with that from polygenic scores 93 . By improving the capture of genetic risk, particularly in non-European populations, and integrating environmental and biomarker data to quantify aspects of non-genetic risk, it should be possible to achieve increasingly accurate prediction of individual disease risk, and to use this information to tailor screening, prevention and treatment. Success will depend on developing models of risk that robustly integrate these diverse data types and on optimizing the strategies deployed to ensure effective implementation.

The absence of evidence-based guidelines to support healthcare recommendations continues to hinder the clinical applications of genetic data. In some countries, this is compounded by confusion over reimbursement and disparities in testing across society 148 . Many healthcare professionals lack experience in genomic medicine and need education and guidance to practice in the rapidly evolving space of genetic and genomic testing 149 . One consequence of these difficulties has been an expanding direct-to-consumer testing market, variably controlled by country-specific regulations 150 , which is moving beyond a focus on ancestry and personal traits, towards models in which individuals have direct access to ordering physicians and genetic counselors 151 . The risk of commercial influence in this model remains high. There are concerns about the consequences of unfettered release of genetic data of dubious or inflated clinical relevance, and limited infrastructure to pull these results into mainstream medical systems.

These advances have fostered debate about the value of genetics for population screening, for both monogenic and complex disorders. Population screening for monogenic disorders is most likely to be initiated for conditions for which risk estimates are well-understood and there are actionable interventions (for example, Lynch syndrome and familial hypercholesterolaemia). Expansion to other disorders requires better understanding of the penetrance of pathogenic alleles in unselected populations 152 and caution before extending screening to longer lists of genes that are less securely implicated in disease causation 153 . As certain countries consider universal capture of genome-wide genetic data at birth or later in life, key questions concern the strategies for releasing this information to citizens and their medical teams to support individual healthcare.

Ultimately, barriers to genomic medicine are most directly overcome by demonstrating clinical utility in disease management and therapeutic decision-making, with evidence for improved patient outcomes. Hereditary cancers provide multiple examples, such as the use of BRCA1/BRCA2 testing to inform PARP inhibitor treatment in patients with cancer 154 . There is a growing list of diseases for which a molecular diagnosis results in specific interventions designed to improve patient outcomes ( https://www.ncbi.nlm.nih.gov/books/NBK1116/ ) (some examples are listed in Table 1 ), and there are currently more than 50 FDA-approved drugs for genetic disorders 155 . Although gene therapy has been slow to evolve since its early introduction, recent advances in gene editing are reinvigorating approaches to treat disorders by manipulation of the underlying genetic defects 156 .

Looking forward

Over the coming decade, the challenge will be to optimize and to implement at scale, strategies that use human genetics to further the understanding of health and disease, and to maximize the clinical benefit of those discoveries. Realizing these goals will require the concerted effort of researchers in academia and industry to bring about transformational change across a range of highly interconnected domains, for example, through the auspices of the recently established International Common Disease Alliance ( https://www.icda.bio ). Such efforts will be directed towards establishing: (a) comprehensive inventories of genotype–phenotype relationships across populations and environments; (b) systematic assays of variant- and gene-level function across cell types, states and exposures; (c) improved scalable strategies for turning this basic knowledge into fully developed molecular, cellular and physiological models of disease pathogenesis; and (d) application of those biological insights to drive novel preventative and therapeutic options.

The first of these will involve documenting the full spectrum of natural genetic variation across all human populations, including capture of structural variants, and somatic mutations that accumulate with aging 157 , 158 , and associating these variations with the ever-richer disease-related intermediate and clinical traits available through biobanks and electronic health records. It will be particularly important to include populations historically under-represented in genomic research, following the pioneering work of the H3Africa consortium 159 . As over time, clinically sequenced genomes will outnumber those collected in academia, research and healthcare communities will need to develop a harmonized approach to genomics to transcend historical boundaries. Progress will be critically dependent on platforms and governance that lower barriers to the integration of genetic and phenotypic data across studies and countries, along with technical standards that are reliable, secure and compatible with the international regulatory landscape 160 .

Mechanistic interpretation of genetic associations, particularly those in regulatory regions, will be driven by the systematic annotation of sequence variants and genes for functional impact across disease-relevant cell types, enabling mapping of processes contributing to disease development with respect to place (tissue and cell type), time (developmental stage) and context (external influences) 161 . Accelerating efforts to characterize the cellular composition of tissues through single-cell assays 115 will increase the granularity of these observations. Large-scale perturbation studies across diverse cellular and animal models will, together with analyses of coding variants in humans 53 , 54 , provide confidence in causal inference. Large-scale proteomic and metabolomic analyses (in tissues and biological fluids) will provide a bridge to downstream pathways 79 , 80 . Research access to such functional data, generated at scale, should lower the barriers to mechanistic inference, provide system-wide context and enable researchers to focus wet-laboratory validation on the most critical experiments. Collectively, these efforts will support compilation of a systematic catalogue of key networks and processes that influence normal physiology and disease development and inform a revised molecular taxonomy of disease.

This knowledge will reinforce the essential contribution of human genetics to the identification and prioritization of targets for therapeutic development 89 , 162 . Insights into the efficacy of target perturbation and potential for adverse events, allied to characterization of translatable biomarkers, provide ways to boost the efficiency of drug-development pipelines 162 . Given the clinical importance of slowing disease progression 163 , target-discovery efforts will increasingly need to embrace the genetics of disease progression and treatment response, as these may involve processes distinct from those captured by studies of disease onset.

In parallel, the clinical use of human genetics will benefit from progress towards universal determination of individual genome sequences built through a combination of biobank expansion and direct access within healthcare systems. This will power clinical applications that extend beyond the current focus on neonatal sequencing, Mendelian diagnostics and somatic tumour sequencing 164 . In particular, improvements in polygenic score derivation will boost risk prediction for multifactorial traits, provide a molecular basis for disease classification, support biomarker discovery and therapeutic optimization and contribute to understanding of the variable penetrance of monogenic conditions 69 . Implementing genomic medicine as a routine component of clinical care across diverse healthcare environments will inevitably require investment in the training of healthcare professionals and attention to optimal strategies for returning genetic findings to patients.

The limited heritability of many multifactorial traits constrains the clinical precision available from genetic data alone. This will drive efforts to integrate information on personal environment, lifestyle and behaviour, and to combine prognostic, predictive information on disease risk with longitudinal measures of molecular and clinical state that track an individual’s journey from health to disease. Human genetics will also, given its unique potential for causal inference, support identification of the non-genetic risk factors (often modifiable) that directly contribute to disease predisposition and development 165 . As polygenic score performance improves, analysis of individuals who show marked divergence between genetic predisposition and real-world clinical outcomes should define exposures (such as lifestyle choices or gut microbiome) the contribution of which to disease causation remains unclear 166 .

Collectively, these developments can be expected to accelerate personalization of healthcare delivery. Provided costs are sustainable, a more preventative perspective on health could emerge, managed through proactive genomic, clinical and lifestyle surveillance using risk scores, complex biomarkers, liquid biopsies and wearables. Improved understanding of aetiological heterogeneity, patterns of sharing of genetic risk across diseases, variation in therapeutic response and risk of adverse events will enhance targeting of preventative and therapeutic interventions 167 . At the population level, intervention strategies will seek to combine population-wide and targeted strategies to best effect 168 . It will be critical to ensure that these benefits are available to as many as possible, so that genomics reduces, rather than exacerbates, national and global health disparities 55 , 169 (Box 1 ).

The developments described above, represent variations on the theme of ‘reading’ the genome. The emerging capacity to block this reading (for example, through siRNA therapies 170 ) or even to ‘write’ the genome (through CRISPR editing) promises to be equally transformative, providing new opportunities to correct, and even cure, Mendelian disease. Spectacular advances in developing novel therapeutic strategies are likely for many diseases, based, for example, on ex vivo cellular manipulation 171 or in vivo somatic cell editing 172 .

Importantly, developments in genomic medicine need to proceed in a bioethical framework for research and clinical use that recognizes the personal relevance of human genetics and the critical importance of autonomous consent and the protection of privacy, while minimizing the adverse consequences of genetic exceptionalism. Governance needs to reaffirm the rights of citizens to make individual contributions to scientific progress through research participation and encourage the responsible exchange of data for clinical and research purposes.

Box 1 Global genomics

Present and future advances in genetics and genomics have the potential to provide benefits to individuals and societies across the world, but equitable and fair access to those benefits will require proactive measures to address entrenched disparities in scientific capacity and clinical opportunities. This includes:

Global characterization of genetic variation. Systematic catalogues of human genome variation from a broad range of global populations will maximize the genetic diversity available for genetic discovery and clinical implementation. This will support more accurate imputation, enable more effective use of polygenic scores, and improve detection and interpretation of rare large-effect alleles.

Genetic discovery for diseases with restricted geographical coverage. Genetic studies conducted in diverse populations will support genetic discovery as well as functional characterization of hitherto-neglected diseases with disproportionate regional impact and may lead to novel preventative and therapeutic strategies.

An ethical framework for global research. Genetic research needs to proceed on the expectation that the benefits of research will be available to those who have participated, and that relationships between researchers and patients are based on robust expressions of accountability and governance. Participation should be based on appropriate informed individual consent in the context of community involvement, with mechanisms to support equitable data sharing with researchers in participating countries.

Support for local research capacity. Collection of biological specimens and data are dependent on adequate support for data collection, generation and analysis in participating countries, with infrastructure, training and career structures in place to support local researchers and equitable credit for shared research outputs.

Equitable translation. Participants and their communities should have fair and equitable access to the biomedical information arising from the studies in which they are involved to support population-relevant clinical diagnostics and implementation (for example, of polygenic scores). There should be commitment to the development of technologies and strategies that enable the clinical benefits of genomic medicine to be accessible to those in less-developed healthcare environments.

Future prospects

Over the past two decades, understanding of the genetic basis of human disease has been transformed by a combination of spectacular technological and analytical advances, collaborative commitment to the development of foundational resources and the collection and analysis of vast amounts of genetic, molecular and clinical data. The biological insights derived from these data are, increasingly, drivers of translational innovation, and widening personal access to large-scale genetic and molecular data promises to reshape medical care.

However, for the full potential of genomic medicine to be realized, there will need to be sustained collaborative endeavour on several fronts to ensure that the capacity to generate ever more detailed maps of the relationships between sequence variation and biomedical phenotypes delivers a comprehensive understanding of disease mechanisms that can be translated into the medicines of tomorrow.

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Acknowledgements

We acknowledge grant funding from the following funders. J.H.C.: NIH (U01DK062429, U01DK062422 and R01DK106593); N.J.C.: NIH (U01HG009086, U54MD010722 and R01MH113362); E.T.D.: Swiss National Science Foundation, Louis Jeantet Foundation; E.E.K.: NIH (R01HG010297, R01HL104608, U01HG009610, R01DK110113, U01HG009080, X01HL1345 and UM1HG0089001); C.M.L.: NIHR Oxford Biomedical Research Centre and NIH (5P50HD028138-27); K.N.N.: NHMRC (APP1113531); S.E.P.: NIH (U01HG006485 and 1U41HG009649); C.N.R.: NIH Intramural Program at the Center for Research on Genomics and Global Health and National Human Genome Research Institute; and M.I.M.: Wellcome (090532, 098381, 106130, 203141 and 212259) and NIH (U01-DK105535, DK085545 and DK098032). Personal funding comes from the following sources. M.C.: Next Generation Fund at the Broad Institute of MIT and Harvard; J.H.C.: Sanford Grossman Charitable Trust and Helmsley Charitable Trust; R.C.: UKBiobank; C.M.L.: Li Ka Shing Foundation; J.S.: Howard Hughes Medical Institute; and M.I.M.: Wellcome and NIHR.

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Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA

Melina Claussnitzer

Broad Institute of MIT and Harvard Cambridge, Cambridge, MA, USA

Melina Claussnitzer, Sekar Kathiresan, Cecilia M. Lindgren, Daniel G. MacArthur & Heidi L. Rehm

Institute of Nutritional Science, University of Hohenheim, Stuttgart, Germany

Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Judy H. Cho & Eimear E. Kenny

Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Judy H. Cho

Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, UK

Rory Collins

UK Biobank, Stockport, UK

Vanderbilt Genetics Institute and Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA

Nancy J. Cox

Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland

Emmanouil T. Dermitzakis

Health 2030 Genome Center, Geneva, Switzerland

Wellcome Sanger Institute, Hinxton, UK

Matthew E. Hurles & Nicole Soranzo

Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA

Sekar Kathiresan, Daniel G. MacArthur & Heidi L. Rehm

Verve Therapeutics, Cambridge, MA, USA

Sekar Kathiresan

Center for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Eimear E. Kenny

Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK

Cecilia M. Lindgren

Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK

Cecilia M. Lindgren & Mark I. McCarthy

Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA

Daniel G. MacArthur & Heidi L. Rehm

Murdoch Children’s Research Institute, Parkville, Victoria, Australia

Kathryn N. North

University of Melbourne, Parkville, Victoria, Australia

Departments of Pediatrics and Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA

Sharon E. Plon

Texas Children’s Cancer Center, Texas Children’s Hospital, Houston, TX, USA

Department of Pathology, Harvard Medical School, Boston, MA, USA

Heidi L. Rehm

Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA

Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, MD, USA

Charles N. Rotimi

Department of Genome Sciences, University of Washington, Seattle, WA, USA

Jay Shendure

Brotman Baty Institute for Precision Medicine, Magnuson Health Sciences Building, Seattle, WA, USA

Howard Hughes Medical Institute, Seattle, WA, USA

Department of Haematology, University of Cambridge, Cambridge, UK

Nicole Soranzo

Oxford Centre for Diabetes, Endocrinology and Metabolism, Oxford, UK

Mark I. McCarthy

Oxford NIHR Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK

Human Genetics, Genentech, South San Francisco, CA, USA

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M.C. and M.I.M. coordinated the drafting of the Review. All other authors contributed to drafting of the text and approved the final version.

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Correspondence to Mark I. McCarthy .

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Competing interests.

R.C. has received research grants from British Heart Foundation, Cancer Research UK, Medical Research Council, Merck & Co, UKBiobank, Wellcome and Medco; a Pfizer Prize Award (to NDPH) and is named on a patent for a statin-related myopathy genetic test. R.C. receives no personal remuneration from these: any share of royalties or other payments have been waived in favour of NDPH. E.T.D. is chairman and board member of Hybridstat and on the advisory board of DNAnexus. M.E.H. is a co-founder, shareholder and director of Congenica. S.K. has received research grants from Bayer and Novartis; is on Scientific Advisory Boards of Regeneron Genetics Center, Corvidia Therapeutics and Maze Therapeutics; has equity in San Therapeutics, Catabasis, Verve and Maze Therapeutics and is a consultant for Maze Therapeutics, Alynlam, ExpertConnect, Leerink Partners, Noble Insights, Bayer and Novo Ventures. E.E.K. receives honoraria from Illumina and Regeneron Pharmaceuticals. C.M.L. has research collaborations with Novo Nordisk and Bayer, receiving no personal payment. D.G.M. is co-founder and shareholder of Goldfinch Bio and has received research funding from AbbVie, Biogen, BioMarin, Merck, Pfizer and Sanofi-Genzyme. S.E.P. is a member of the Scientific Advisory Board of Baylor Genetics. J.S. is a member of the Scientific Advisory Board for Maze Therapeutics, Camp4 Therapeutics, Nanostring, Phase Genomics, Adaptive Biotechnology and Stratos Genomics, a founder of Phase Genomics and a consultant for Guardant Health. M.I.M. was a member of advisory panels for Pfizer, NovoNordisk and Zoe Global; received honoraria from Merck, Pfizer, NovoNordisk and Eli Lilly and research funding from Abbvie, Astra Zeneca, Boehringer Ingelheim, Eli Lilly, Janssen, Merck, NovoNordisk, Pfizer, Roche, Sanofi Aventis, Servier and Takeda. As of June 2019, M.I.M. is an employee of Genentech and a holder of Roche stock. The views expressed in this article are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health.

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Claussnitzer, M., Cho, J.H., Collins, R. et al. A brief history of human disease genetics. Nature 577 , 179–189 (2020). https://doi.org/10.1038/s41586-019-1879-7

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The genetics of bipolar disorder

Affiliations.

• 1 Human Genetics Branch, National Institute of Mental Health Intramural Research Program, Department of Health and Human Services, National Institutes of Health, Bethesda, MD, USA.
• 2 College of Medicine, University of the Philippines Manila, 1000, Ermita, Manila, Philippines.
• 3 Human Genetics Branch, National Institute of Mental Health Intramural Research Program, Department of Health and Human Services, National Institutes of Health, Bethesda, MD, USA. [email protected].
• PMID: 31907381
• DOI: 10.1038/s41380-019-0634-7

Bipolar disorder (BD) is one of the most heritable mental illnesses, but the elucidation of its genetic basis has proven to be a very challenging endeavor. Genome-Wide Association Studies (GWAS) have transformed our understanding of BD, providing the first reproducible evidence of specific genetic markers and a highly polygenic architecture that overlaps with that of schizophrenia, major depression, and other disorders. Individual GWAS markers appear to confer little risk, but common variants together account for about 25% of the heritability of BD. A few higher-risk associations have also been identified, such as a rare copy number variant on chromosome 16p11.2. Large scale next-generation sequencing studies are actively searching for other alleles that confer substantial risk. As our understanding of the genetics of BD improves, there is growing optimism that some clear biological pathways will emerge, providing a basis for future studies aimed at molecular diagnosis and novel therapeutics.

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Neurofibromatosis type 1 (NF1) is a genetic condition that causes changes in skin pigment and tumors on nerve tissue. Skin changes include flat, light brown spots and freckles in the armpits and groin. Tumors can grow anywhere in the nervous system, including the brain, spinal cord and nerves. NF1 is rare. About 1 in 2,500 is affected by NF1.

The tumors often are not cancerous, known as benign tumors. But sometimes they can become cancerous. Symptoms often are mild. But complications can occur and may include trouble with learning, heart and blood vessel conditions, vision loss, and pain.

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Neurofibromatosis type 1 (NF1) usually is diagnosed during childhood. Symptoms are seen at birth or shortly afterward and almost always by age 10. Symptoms tend to be mild to moderate, but they can vary from person to person.

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• Flat, light brown spots on the skin, known as cafe au lait spots. These harmless spots are common in many people. But having more than six cafe au lait spots suggests NF1. They often are present at birth or appear during the first years of life. After childhood, new spots stop appearing.
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• Soft, pea-sized bumps on or under the skin called neurofibromas. These benign tumors usually grow in or under the skin but can also grow inside the body. A growth that involves many nerves is called a plexiform neurofibroma. Plexiform neurofibromas, when located on the face, can cause disfigurement. Neurofibromas may increase in number with age.
• Bone changes. Changes in bone development and low bone mineral density can cause bones to form in an irregular way. People with NF1 may have a curved spine, known as scoliosis, or a bowed lower leg.
• Tumor on the nerve that connects the eye to the brain, called an optic pathway glioma. This tumor usually appears by age 3. The tumor rarely appears in late childhood and among teenagers, and almost never in adults.
• Learning disabilities. It's common for children with NF1 to have some trouble with learning. Often there is a specific learning disability, such as trouble with reading or math. Attention-deficit/hyperactivity disorder (ADHD) and speech delay also are common.
• Larger than average head size. Children with NF1 tend to have a larger than average head size due to increased brain volume.
• Short stature. Children who have NF1 often are below average in height.

When to see a doctor

See a healthcare professional if your child has symptoms of neurofibromatosis type 1. The tumors are often not cancerous and are slow growing, but complications can be managed. If your child has a plexiform neurofibroma, a medicine is available to treat it.

Neurofibromatosis type 1 is caused by an altered gene that either is passed down by a parent or occurs at conception.

The NF1 gene is located on chromosome 17. This gene produces a protein called neurofibromin that helps regulate cell growth. When the gene is altered, it causes a loss of neurofibromin. This allows cells to grow without control.

Risk factors

Autosomal dominant inheritance pattern

In an autosomal dominant disorder, the changed gene is a dominant gene. It's located on one of the nonsex chromosomes, called autosomes. Only one changed gene is needed for someone to be affected by this type of condition. A person with an autosomal dominant condition — in this example, the father — has a 50% chance of having an affected child with one changed gene and a 50% chance of having an unaffected child.

The biggest risk factor for neurofibromatosis type 1 (NF1) is a family history. For about half of people who have NF1, the disease was passed down from a parent. People who have NF1 and whose relatives aren't affected are likely to have a new change to a gene.

NF1 has an autosomal dominant inheritance pattern. This means that any child of a parent who is affected by the disease has a 50% chance of having the altered gene.

Complications

Complications of neurofibromatosis type 1 (NF1) vary, even within the same family. Generally, complications occur when tumors affect nerve tissue or press on internal organs.

Complications of NF1 include:

• Neurological symptoms. Trouble with learning and thinking are the most common neurological symptoms associated with NF1. Less common complications include epilepsy and the buildup of excess fluid in the brain.
• Concerns with appearance. Visible signs of NF1 can include widespread cafe au lait spots, many neurofibromas in the facial area or large neurofibromas. In some people this can cause anxiety and emotional distress, even if they're not medically serious.
• Skeletal symptoms. Some children have bones that didn't form as usual. This can cause bowing of the legs and fractures that sometimes don't heal. NF1 can cause curvature of the spine, known as scoliosis, that may need bracing or surgery. NF1 also is associated with lower bone mineral density, which increases the risk of weak bones, known as osteoporosis.
• Changes in vision. Sometimes a tumor called an optic pathway glioma develops on the optic nerve. When this happens, it can affect vision.
• Increase in symptoms during times of hormonal change. Hormonal changes associated with puberty or pregnancy might cause an increase in neurofibromas. Most people who have NF1 have healthy pregnancies but will likely need monitoring by an obstetrician who is familiar with NF1.
• Cardiovascular symptoms. People who have NF1 have an increased risk of high blood pressure and may develop blood vessel conditions.
• Trouble breathing. Rarely, plexiform neurofibromas can put pressure on the airway.
• Cancer. Some people who have NF1 develop cancerous tumors. These usually arise from neurofibromas under the skin or from plexiform neurofibromas. People who have NF1 also have a higher risk of other forms of cancer. They include breast cancer, leukemia, colorectal cancer, brain tumors and some types of soft tissue cancer. Screening for breast cancer should begin earlier, at age 30, for women with NF1 compared to the general population.
• Benign adrenal gland tumor, known as a pheochromocytoma. This noncancerous tumor produces hormones that raise your blood pressure. Surgery often is needed to remove it.

Neurofibromatosis type 1 care at Mayo Clinic

• Ferri FF. Neurofibromatosis. In: Ferri's Clinical Advisor 2024. Elsevier; 2024. https://www.clinicalkey.com. Accessed Feb. 21, 2024.
• Neurofibromatosis. National Institute of Neurological Disorders and Stroke. https://www.ninds.nih.gov/Disorders/Patient-Caregiver-Education/Fact-Sheets/Neurofibromatosis-Fact-Sheet. Accessed Feb. 21, 2024.
• Korf BR, et al. Neurofibromatosis type 1 (NF1): Pathogenesis, clinical features, and diagnosis. https://www.uptodate.com/contents/search. Accessed Feb. 21, 2024.
• Saleh M, et al. Neurofibromatosis type 1 system-based manifestations and treatments: A review. Neurological Sciences. 2023; doi:10.1007/s10072-023-06680-5.
• Neurofibromatosis. American Association of Neurological Surgeons. https://www.aans.org/en/Patients/Neurosurgical-Conditions-and-Treatments/Neurofibromatosis. Accessed Feb. 21, 2024.
• Neurofibromatosis. Merck Manual Professional Version. https://www.merckmanuals.com/professional/pediatrics/neurocutaneous-syndromes/neurofibromatosis. Accessed Feb. 21, 2024.
• Jankovic J, et al., eds. Neurocutaneous syndromes. In: Bradley and Daroff's Neurology in Clinical Practice. 8th ed. Elsevier; 2022. https://www.clinicalkey.com. Accessed Feb. 21, 2024.
• Armstrong AE, et al. Treatment decisions and the use of the MEK inhibitors for children with neurofibromatosis type 1-related plexiform neurofibromas. BMC Cancer. 2023; doi:10.1186/s12885-023-10996-y.
• Zitelli BJ, et al., eds. Neurology. In: Zitelli and Davis' Atlas of Pediatric Physical Diagnoses. 8th ed. Elsevier; 2023. https://www.clinicalkey.com. Accessed Feb. 21, 2024.
• Kellerman RD, et al. Neurofibromatosis (type 1). In: Conn's Current Therapy 2024. Elsevier; 2024. https://www.clinicalkey.com. Accessed Feb. 21, 2024.
• Babovic-Vuksanovic D (expert opinion). Mayo Clinic. March 26, 2024.
• Tamura R. Current understanding of neurofibromatosis type 1, 2 and schwannomatosis. International Journal of Molecular Sciences. 2021; doi:10.3390/ijms22115850.
• Legius E, et al. Revised diagnostic criteria for neurofibromatosis type 1 and Legius syndrome: An international consensus recommendation. Genetics in Medicine. 2021; doi:10.1038/s41436-021-01170-5.
• Find a doctor. Children's Tumor Foundation. https://www.ctf.org/understanding-nf/find-a-doctor/. Accessed Feb. 26, 2024.
• Ami TR. Allscripts EPSi. Mayo Clinic. April 18, 2024.

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Genetics of eating disorders in the genome-wide era

Hunna j. watson.

1 Department of Psychiatry, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

2 Division of Paediatrics, School of Medicine, The University of Western Australia, Perth, Australia

3 School of Psychology, Curtin University, Perth, Australia

Alish B. Palmos

4 Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom

Avina Hunjan

5 National Institute for Health Research (NIHR) Biomedical Research Centre for Mental Health, South London and Maudsley National Health Service (NHS) Trust, London, United Kingdom

Jessica H Baker

Zeynep yilmaz.

6 National Centre for Register-based Research, Aarhus BSS, Aarhus University, Aarhus, Denmark

7 Department of Genetics, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

Helena L. Davies

Associated data.

Enabled by advances in high throughput genomic sequencing and an unprecedented level of global data sharing, molecular genetic research is beginning to unlock the biological basis of eating disorders. This invited review provides an overview of genetic discoveries in eating disorders in the genome-wide era. To date, five genome-wide association studies (GWAS) on eating disorders have been conducted—all on anorexia nervosa (AN). For AN, several risk loci have been detected, and ~11–17% of the heritability has been accounted for by common genetic variants. There is extensive genetic overlap between AN and psychological traits, especially obsessive-compulsive disorder, and intriguingly, with metabolic phenotypes even after adjusting for BMI risk variants. Further, genetic risk variants predisposing to lower BMI may be causal risk factors for AN. Causal genes and biological pathways of eating disorders have yet to be elucidated and will require greater sample sizes and statistical power, and functional follow-up studies. Several studies are underway to recruit individuals with bulimia nervosa and binge-eating disorder to enable further genome-wide studies. Data collections and research labs focused on the genetics of eating disorders have joined together in a global effort with the Psychiatric Genomics Consortium. In sum, molecular genetics research in the genome-wide era is improving knowledge about the biology behind the established heritability of eating disorders. This has the potential to offer new hope for understanding eating disorder etiology and for overcoming the therapeutic challenges that confront the eating disorder field.

Introduction

Cross-disciplinary efforts spanning the behavioral sciences, medicine, and genomics are furthering progress toward unlocking the biological basis of eating disorders. These efforts to develop insights into pathophysiology are intended to encompass major translational areas for the personalized care of patients, including screening, risk assessment, and treatment. Genes and the biological pathways underpinning eating disorders could provide novel targets for the development of safe, effective treatments and improve diagnostic nosology and classification of these disorders. This review provides an overview of genetic discoveries in eating disorders from human genomics research in the genome-wide era. Human genomics research in psychiatry has accelerated significantly in the past decade due to advances in high-throughput genomic sequencing and large-scale genomic data sharing and collaboration. A timeline of key achievements in the genetics of eating disorders is shown in Figure 1 . Table 1 overviews the topics covered in this paper and synthesizes the current state of knowledge.

Timeline outlining the history of our understanding of the genetics of eating disorders. Boxes with a heavy outline indicate the dates different types of studies were first undertaken within the eating disorders field. Boxes with a faded outline represent landmark achievements in genetics more broadly. The timeline is not drawn to scale and contains only a small portion of genetic studies in the field. References can be found in the Supplementary Material . AN = anorexia nervosa; BED = binge-eating disorder; BN = bulimia nervosa; CNV = copy-number variation; GWAS = genome-wide association study; h 2 = narrow-sense heritability; NNAT = neuronatin (protein coding gene), SNP = single nucleotide polymorphism.

Summary of the state of knowledge on the genetics of eating disorders

ADHD = attention-deficit/hyperactivity disorder, AN = anorexia nervosa, BED = binge-eating disorder, BMI = body mass index, BN = bulimia nervosa, GWAS = genome-wide association study, MDD = major depressive disorder, OCD = obsessive-compulsive disorder, SCZ = schizophrenia. See the manuscript text for citations.

Eating Disorders

Eating disorders are mental health conditions characterized by disordered eating behaviors and carry serious physical and mental health morbidity, and elevated mortality ( Arcelus et al., 2011 ). The Diagnostic and Statistical Manual of Mental Disorders , fifth edition (DSM-5), defines the most widely recognized eating disorder, anorexia nervosa (AN), as maintenance of a significantly low body weight through restrictive eating behavior, acute fear of gaining weight, and body image disturbance ( American Psychiatric Association, 2013 ). AN is subdivided into two subtypes: restrictive and binge-eating/purging. Binge eating is a key feature of two other DSM-5 defined eating disorders: bulimia nervosa (BN) and binge-eating disorder (BED) ( American Psychiatric Association, 2013 ). BN is accompanied by recurrent compensatory behaviors such as self-induced vomiting (i.e., a purging behavior) and/or fasting (i.e., a non-purging behavior), whereas BED is defined by binge eating in the absence of such behaviors. Unlike with AN, there are no weight criteria for BN and BED. Persons with BN are typically of normal weight and overweight or obese for BED. Whilst AN and BN have been recognized in the DSM since 1980 ( American Psychiatric Association, 1980 ), BED was only formally recognized as of the DSM-5 in 2013 ( American Psychiatric Association, 2013 ). Lifetime prevalence estimates for AN, BN, and BED range from 0.9–1.4%, 0.5–1.5%, and 1.2–3.5% for females and 0–0.3%, 0.1–0.5%, and 0.3–2.0% in males, respectively ( Hudson et al., 2007 ; Preti et al., 2009 ; Udo & Grilo, 2018 ). The DSM-5 defines additional eating and feeding disorders; however, this review will focus on AN, BN and BED, due to a current lack of genetic and epidemiological research on other eating disorders.

Genetic Epidemiology

Twin studies were among the first lines of evidence to suggest a genetic component for eating disorders. They revealed substantial heritability ( h 2 twin ), which ranges from 16–74% for AN ( Klump et al., 2001 ; Kortegaard et al., 2001 ; Wade et al., 2000 ; Walters & Kendler, 1995 ), 28–83% for BN ( Bulik et al., 1998 ; Bulik et al., 2000 ; Kendler et al., 1995 ), and 39–45% for BED ( Javaras et al., 2008 ; Mitchell et al., 2010 ). The estimates vary, partly, based on whether studies use threshold or relaxed DSM criteria. For example, the heritability of AN is higher when the definition of AN is broadened to include subsyndromal cases ( Dellava et al., 2011 ).

Twin and sibling studies are also a valuable tool to assess the genetic overlap between phenotypes, and have shown that roughly 60% of the genetic effects of AN and BN may be shared ( Bulik et al., 2010 ; Yao et al., in press). Additionally, twin studies have suggested shared genetic risk between eating disorders and alcohol and substance use disorders ( Baker et al., 2010 ; Munn-Chernoff & Baker, 2016 ), obsessive-compulsive disorder (OCD) ( Cederlof et al., 2015 ), and major depressive disorder (MDD) ( Wade et al., 2000 ).

Molecular Genetics

Twin studies do not illuminate the biological and molecular mechanisms involved in risk for which molecular genetics is instrumental. Genetics research in eating disorders has transitioned to the genome-wide era, further, genome-wide association studies (GWAS) have become the dominant approach to identify genetic risk variants associated with complex traits and disorders. GWAS findings can facilitate gene and biological pathway discovery, polygenic risk prediction, and can illuminate causal risk factors and cross-disorder relationships. In this section, we focus predominantly on molecular genetic discoveries for AN, since such genetic studies of BN and BED are limited.

Genome-wide association studies

To date, there have been several eating disorder GWASs, all on AN ( Boraska et al., 2014 ; Duncan et al., 2017 ; Nakabayashi et al., 2009 ; Wang et al., 2011 ; Watson et al., 2019 ). Early GWAS for AN have been subject to important criticisms. With the benefit of hindsight, sample sizes have been too small and underpowered. The first GWAS (320 cases, 341 controls) identified 10 associated microsatellite markers, two of which remained associated in fine-mapping analysis (331 cases, 872 controls). However, the study used DNA pooling which is prone to errors, included only 23,000 markers, and did not safeguard against false positives from multiple testing or population stratification ( Nakabayashi et al., 2009 ). Neither Wang et al.’s (2011) study among 1,033 cases and 3,733 controls or the Genetics Consortium of Anorexia Nervosa and Wellcome Trust Case Control Consortium 3 GWAS among 2,907 cases and 14,860 controls ( Boraska et al., 2014 ) identified any single-nucleotide polymorphisms (SNPs) of genome-wide significance, but this is typical of small sample sizes.

The Psychiatric Genomics Consortium’s (PGC) first GWAS for AN, amongst 3,495 cases and 10,982 controls, identified one genome-wide significant locus on chromosome 12 (rs4622308) that also tags genes implicated in type 1 diabetes and other autoimmune diseases ( Duncan et al., 2017 ). SNP-based heritability ( h 2 snp ) was 20% (s.e. = 2%), indicating that common genetic variants accounted for a large proportion of the twin-based heritability of AN (i.e., at least 27%).

The second PGC GWAS with 16,992 AN cases and 55,525 controls identified eight genome-wide significant loci ( Watson et al., 2019 ) ( Figure 2 ). The locus in the first PGC GWAS did not replicate in the second ( P = 7.02 × 10 −5 ). The risk effect was stronger in the first (i.e., C-allele: OR = 1.20, s.e. = 0.03 vs OR = 1.06, s.e. = 0.01) and showed between-cohort heterogeneity in the second GWAS. Note, we expect that if the effect is in the more modest realm observed in the second GWAS, which is likely given it was initially large, or if the source of heterogeneity is accounted for, the locus will re-emerge in subsequent higher-powered data freezes. Gene-wise association tests, eQTL analyses, chromatin interaction analyses, and base pair coordinates implicated 133 genes. The h 2 snp ranged from 11–17% (s.e. = 1%). The odds ratios (ORs) of SNPs with the largest effects were in the same realm (OR ~1.08–1.17) as observed for psychiatric disorders with more advanced molecular genetic research (OR ~ 1.05–1.15), such as schizophrenia ( Smoller, 2019 ). The results of these GWAS confirm that AN is highly polygenic and suggest that as sample sizes continue to grow, the field will discover more novel risk variants associated with eating disorders.

A Manhattan plot depicting eight genome-wide significant loci associated with AN in the second Psychiatric Genomics Consortium genome-wide association study of AN ( Watson et al., 2019 ). In single-gene loci, the gene is annotated. The genome-wide significance threshold ( P < 5 × 10 −8 ) is represented by the horizontal line.

SNP-based genetic correlations

SNP-based genetic correlations ( r g ) provide insight into the overlapping genetics of traits and give further clues to their biological basis. The second PGC GWAS estimated SNP- r g of AN with 447 phenotypes. Statistically significant results fell into six categories: psychiatric, personality, educational attainment, physical activity, metabolic, and anthropometric ( Figure 3 ) ( Watson et al., 2019 ). AN had significant positive SNP- r g with other psychiatric disorders, corresponding with many of the comorbidities observed in clinical and epidemiological studies ( Hudson et al., 2007 ), and with physical activity, which is compelling since compulsive exercise can be a clinical feature of AN. There were negative genetic correlations with anthropometric and metabolic traits, such as body mass index (BMI), leptin, and fasting insulin. The authors investigated whether the SNP- r g between AN and such traits was confounded by low body weight being a diagnostic criterion of AN, by exploring whether SNP- r g s remained significant after removing variance in AN genetic risk accounted for by BMI risk variants. There were modest and statistically non-significant SNP- r g attenuations with metabolic, glycemic, and anthropometric traits, suggesting that AN may be driven, at least in part, by metabolic mechanisms ( Watson et al., 2019 ). This biological clue could explain why those with AN are able to sustain extremely low weights and long-term caloric restriction in the face of strong evolutionary and metabolic forces in the human population toward body fat retention. In a separate study examining the genetic architecture of substance-use-related traits with phenotype sample sizes ranging from 2,400 to 537,000, a significant positive genetic correlation was found for AN with cannabis initiation (SNP- r g = 0.23) and alcohol use disorder (SNP- r g = 0.18), but the latter was no longer significant after co-varying for MDD loci (Munn-Chernoff et al., in press). Additionally, while a positive genetic correlation was observed with cannabis initiation and AN with binge eating (SNP- r g = 0.27), significant negative genetic correlations emerged for smoking phenotypes and AN without binge eating (SNP- r g = −0.19 to −0.23), providing evidence for potential differences in the genetic architecture of AN subtypes (Munn-Chernoff et al., in press).

Error bars show the standard error of the r g . Correlations with 447 phenotypes were tested. Only significant correlations surpassing a Bonferroni-corrected P value threshold (P < 1.11 × 10 –4 ) are shown. Complete results are in Supplementary Table 10 of Watson et al. 2019 .

Genetic risk scores

Similar to other psychiatric disorders, genetic risk score (GRS) analyses on GWAS results have suggested that thousands of genetic variants are associated with AN disease risk. GRS, also known as polygenic risk score, is the sum of risk alleles weighted by their effect sizes from GWAS (for a primer, see Wray et al., in press). GRSs for eating disorders are in an early stage of use, given they rely on large, well-powered GWAS. In the second PGC GWAS of AN with 16,992 cases and 55,525 controls, individuals in the highest decile of AN GRS had more than four times the odds of lifetime AN than those in the lowest decile ( Watson et al., 2019 ). GRS for psychiatric traits (such as OCD, schizophrenia, and bipolar disorder) and anthropometric traits have shown significant associations with eating disorder diagnosis and symptom phenotypes in population-based samples ( Abdulkadir et al., 2020 ; Nagata et al., 2019 ; Solmi et al., 2019 ). Many uses for GRS in eating disorder research are on the horizon. GRS can be used to investigate premorbid developmental trajectory, endophenotypes, and course of illness, such as disease severity, relapse, and treatment response. Other possible uses include shedding light on diagnostic nosology and classification, modeling gene-environment etiology, and evaluating whether GWAS findings generalize to multi-ethnic populations for which GWAS may not yet be available. Larger GWAS and more powerful GRS will improve prediction accuracy and advance scientific discovery.

Cross-disorder GWAS

Mental health conditions are highly comorbid with one another, and at times differentiation of a diagnosis based on symptoms may be complex, suggesting shared risk. Cross-disorder GWAS have begun to investigate common genetic pathways in etiology ( Cross-Disorder Group of the Psychiatric Genomics Consortium, 2013 ; Cross-Disorder Phenotype Group of the Psychiatric GWAS Consortium et al., 2009 ). One such effort that included AN and seven other disorders (schizophrenia, bipolar disorder, MDD, attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder, OCD, and Tourette syndrome) in 232,964 cases and 494,162 controls identified 109 pleiotropic loci, with the 18q21.2 region showing pleiotropic association with all eight disorders ( Cross-Disorder Group of the Psychiatric Genomics Consortium, 2019 ). AN, OCD, and—to a smaller extent—Tourette syndrome clustered together at a genetic level ( Cross-Disorder Group of the Psychiatric Genomics Consortium, 2019 ). A cross-disorder GWAS of AN and OCD (combined sample of 3,495 AN cases, 2,688 OCD cases, and 18,013 controls) found that the SNP- r g s of the AN-OCD cross-disorder phenotype resembled the SNP- r g patterns of both disorders, but the GWAS did not detect any genome-wide significant variants ( Yilmaz et al., 2020 ). When the unique contributions by AN and OCD were examined, the metabolic and anthropometric correlations observed were driven by AN and not OCD. A gene-set enrichment analysis using AN and OCD GWAS summary data (among 16,992 AN cases and 55,525 controls and 2,688 OCD cases and 7,037 controls) also revealed an overlap in many common features of brain regions and developmental stages for AN and OCD in mRNA expression profile, but not as much in DNA-level transcriptome-wide association studies ( Cheng et al., 2020 ), suggesting a role for future mRNA sequencing efforts alongside GWAS to better understand the biological nature of the shared risk between AN and OCD.

Mendelian randomization

Although follow-up analyses using GWAS data (such as genetic correlations and GRSs) have been instrumental in identifying risk factors associated with eating disorders, they do not provide evidence on causality. Mendelian randomization analysis uses linkage-disequilibrium independent genome-wide significant SNPs identified in GWAS as instrumental variables for a given exposure, and measures the degree to which the exposure is causally associated with the outcome ( Davey Smith & Ebrahim, 2003 ). Since genotypes are transmitted randomly from parents to offspring during meiosis, the genotype distribution should be unrelated to confounding factors that are often present in observational studies ( Burgess et al., 2020 ; Teumer, 2018 ). For this reason, Mendelian randomization is often referred to as a natural randomized controlled trial. In the second PGC GWAS of AN, a Mendelian randomization analysis revealed a causal bi-directional relationship between AN and BMI, whereby genetic risk variants for AN led to lower BMI and genetic variants for lower BMI led to an increased risk of lifetime AN ( Watson et al., 2019 ). This finding is complemented by findings that lower BMI predicts the onset of AN ( Stice et al., 2017 ; Yilmaz et al., 2019 ). In general population samples, Mendelian randomization results point to a positive causal association between higher BMI and eating disorder behaviors and symptoms ( Reed et al., 2017 ). Similarly, epidemiological results link higher BMI to the onset of body dissatisfaction and disordered eating in the general population ( Stice et al., 2002 ).

Adiponectin is a fat-derived hormone that plays a key role in energy homeostasis and appetite regulation ( Coll et al., 2007 ; Steinberg & Kemp, 2007 ). Altered adiponectin levels have been observed in patients with AN and BN. Awofala and colleagues (2019) performed a Mendelian randomization study using GWAS summary data for adiponectin (29,347 samples) and eating behavior disinhibition (898 samples) and found that higher blood adiponectin was causally associated with eating behavior disinhibition. These findings support previous studies which showed an association between effect allele carriers in the adiponectin gene and increased frequency of overeating, which may consequently lead to symptoms of eating disorders ( Rohde et al., 2015 ). These studies point towards innate biological drivers that may lead toward symptoms of eating disorders.

Mendelian randomization for eating disorder research is in its infancy, since the strength of genetic instruments in Mendelian randomization is determined by well-powered GWAS ( Zhu et al., 2018 ). Recent Mendelian randomization approaches allow for nonlinear-associations, which may significantly advance the application of Mendelian randomization in eating disorder research in the future, particularly with respect to anthropometric risk factors ( Burgess et al., 2014 ).

Rare and structural variants

Genetic studies have started to explore the role of rare and structural variants in eating disorders. Regarding studies of copy number variants (CNVs), Wang et al. (2011) examined 2,158 AN cases and 15,458 controls and found no evidence that cases had a significantly higher burden of CNVs. However, they identified a recurrent 13q12 deletion (1.5 Mb) in two cases, and CNVs disrupting the CNTN6 / CNTN4 region in several cases. Similarly, in a sample of 1,983 cases with AN, Yilmaz et al. (2017) found another AN case with a deletion in the 13q12 region. The authors also observed two instances of CNVs with at least 50% reciprocal overlap with regions associated with psychiatric and neurodevelopmental disorders ( Yilmaz et al., 2017 ). Alongside these well-established neuropsychiatric CNVs, instances of rare and large CNVs in AN cases were also observed ( Yilmaz et al., 2017 ). In addition, mixed results have been found for microduplications at 15q11.2 ( Chang et al., 2019 ; Wang et al., 2011 ; Yilmaz et al., 2017 ).

Whole-exome and whole-genome analyses have also provided evidence for an enrichment of rare variants in AN ( Bienvenu et al., 2019 ; Cui et al., 2013 ; Iacobellis & Barbaro, 2019 ; Lombardi et al., 2019 ; Lutter et al., 2017 ). A whole‐exome analysis in two independent families with males with AN found variants in the neuronatin ( NNAT ) gene in both probands: one nonsense variant (p.Trp33*) and one rare variant in the 5′UTR ( Lombardi et al., 2019 ). Eleven additional NNAT variants were found in a follow-up cohort of eight male and 144 females with AN. Another study combined exome sequencing, whole‐genome sequencing, and linkage analysis to examine two families with recurrence of AN ( Cui et al., 2013 ). In the first pedigree, they found a missense variant co‐segregating with the affected family members in the ESRRA (estrogen-related receptor alpha), and a potentially damaging mutation in the HDAC4 (histone deacetylase 4) in the second pedigree ( Cui et al., 2013 ). These genes are linked to the estrogen system.

Whole-genome sequencing analyses in eating disorders are less common. Bergen et al. (2019) performed a whole-genome sequencing analysis in six individuals—two maternally-linked cousins with severe AN and their parents—and found, of the approximately 5.3 million variants per individual analyzed, that 494,712 variants were shared identical-by-descent by the cousin pair based on maternally derived haplotypes ( Bergen et al., 2019 ). They identified novel variants in seven genes: TTC22 , MRPS9 , DNAJC30 , HEPACAM2 , USP20 , ESF1 , and CDK5RAP1 . These findings suggest that there may be utility in whole-genome sequencing of families with affected individuals to detect rare variants that may influence AN ( Bergen et al., 2019 ).

Despite strong evidence for the heritable polygenic risk of AN, rare variant contributions of large effect have not yet been identified. Early studies show promise and larger-scale studies with well-matched control groups and replication studies will be necessary for illuminating whether rare and structural variants contribute to eating disorders.

Gene expression

Gene expression offers insight into the genes and molecular mechanisms that influence phenotypes. Note, that whereas GWASs identify inherited genetic variants associated with disease risk and epigenetic studies investigate changes to the physical structure of DNA, gene expression studies measure messenger RNA expression levels in any given tissue, thus capturing the degree to which a gene is being expressed. Howard et al. (2020) investigated brain regions enriched for gene expression to understand the molecular neuroanatomy of AN. The authors combined the gene lists from two common variant, a rare variant, and a stem-cell study ( Duncan et al., 2017 ; Lutter et al., 2017 ; Negraes et al., 2017 ; Watson et al., 2019 ), and used genetic and transcriptomic resources spanning human fetal and adult and mouse gene expression data. Genes associated with AN resided in subcortical feeding and reward circuits; and furthermore, they implicated microglia genes and genes responding to fasting in mice hypothalami (i.e., RPS26 and DALRD3 ). Likewise, the PGCs recent GWAS of AN (2019) found an enrichment of gene expression in CNS brain tissues and striatal and hippocampus neurons linked to feeding and reward.

Another set of studies applied transcriptome expression profiling to assess gene expression changes in six individuals with AN before and after inpatient weight restoration. Among the top 20 genes, was down-regulation of genes encoding for a cholesterol side-chain cleavage enzyme (CYPP450scc) and up-regulation of genes related to protein secretion, protein signaling, defense response to bacterial regulation, and olfactory receptor regulation ( Kim et al., 2013 ). Of the top differentially expressed genes, CPA3 and GATA2 expression were positively associated with levels of leptin, a hormone linked to nutritional status and the immune response ( Baker et al., 2019 ). This aligns with studies suggesting a genetic overlap between AN, autoimmune disease, and metabolic function ( Baker et al., 2019 ).

In a study with seven females with AN and four healthy controls, Negraes and colleagues (2017) modelled AN using induced pluripotent stem cells, with their transcriptomic analyses revealing a novel gene, TACR1 , that may contribute to AN pathophysiology. The TACR1 gene encodes the tachykinin (or neurokinin) 1 receptor which is involved in a range of biological processes, interacts with several neurotransmitters, and has previously been associated with anxiety disorders, bipolar disorder, and ADHD, suggesting a novel system that might contribute to AN symptoms ( Schank, 2014 ; Sharp et al., 2014 ). Although several studies on gene expression in eating disorders exist, there are not many and most have small samples, limiting the conclusions that can be drawn ( Kim et al., 2013 ).

Epigenetic and gene-environment mechanisms

Epigenetics refers to chemical modifications to DNA and chromatin proteins that control gene expression but do not change the underlying base-pair sequence of the DNA ( Ryan et al., 2018 ). Epigenetic changes are typically measured via global methylation (amount of methylated cytosine compared to total cytosine), via a candidate gene approach, or more recently, via epigenome-wide association studies (EWAS), which have gained popularity and are carried out in a similar way to GWAS. Global methylation and candidate gene methylation study results in eating disorders, specifically AN, have been mixed, with largely inconsistent findings and opposite effects ( Booij et al., 2015 ; Frieling et al., 2007 ; Hübel et al., 2019 ; Saffrey et al., 2014 ; Tremolizzo et al., 2014 ). EWAS using a hypothesis-free driven method have however identified multiple differentially methylated sites associated with AN ( Booij et al., 2015 ; Kesselmeier et al., 2018 ; Ramoz et al., 2017 ). Samples in these studies have ranged from 29 to 47 AN cases and 15 to 147 controls. TNXB hypermethylation has been replicated, although the significance level was nominal and therefore a false positive finding cannot be ruled out ( Kesselmeier et al., 2018 ). TNXB plays a role in maintaining muscles, joints, organs and skin and regulates the production of collagen. Future studies would need to replicate this finding using larger sample sizes, but these early findings could indicate epigenetic changes in people with eating disorders. Notably, future studies need to be well-designed in order to disentangle epigenetic differences in eating disorder patients by disorder type, tissue type, cell type, and take into account large numbers of environmental factors such as diet, binge eating and purging behaviors, and medication ( Horvath & Raj, 2018 ; Kubota et al., 2012 ; Moore et al., 2013 ).

Gene-environment interaction studies in eating disorders have predominantly focused on candidate genes relating to behavior, emotion, and the stress response, such as serotonin and glucocorticoid genes. However, candidate gene studies are subject to false-positive results. Another avenue to study gene-environment interaction is via the use of GRS to capture ‘G’. Recent studies have begun modelling GRS by environment interactions in various psychiatric disorders with some interesting findings ( Mullins et al., 2016 ). However, this methodology is in its infancy and has not yet been applied to eating disorders due to a lack of well-powered GWAS needed to calculate GRS. In the future, we are likely to see great advancements in this area of study.

Clinical Implications

Integrating genomics into clinical practice.

Translatability is a key goal for genomics research in eating disorders. Through continued research with larger sample sizes, the era in which personal genomic information—in combination with other known risk factors—occupies a potential role in forecasting eating disorder risk, outcome, and clinical decision-making will emerge. The most immediate benefit of current genomics research is an improved scientific understanding of the role genomics plays in eating disorder risk. For instance, genomics can be integrated into clinical settings in the context of psychoeducation. As part of standard care, providers give patients and families psychoeducation about eating disorders, which ought to include up-to-date information about heritability and genetic risk. Communicating information to patients and families is complex for it can arouse emotions such as guilt and fear (i.e., “passing on ‘bad’ genes) and unhelpful cognitions, such as reduced self-efficacy and fatalism, the belief that little can be done to reduce risk. There are few empirical studies on genetic counseling for eating disorders, or mental health conditions in general, to date, but guidance for clinicians is available in a synthesis from related literature (see Bulik et al., 2019 ). When communicating such information with patients, it will be important for clinicians to recognize the possibility of such unhelpful cognitions and beliefs and be able to orient the patient to the meaning of genetic findings in the context of the totality of what is known about the risk for eating disorders. As genetics is just one, non-deterministic piece of the puzzle, it is essential that clinicians convey this. Evidence-based, user-friendly resources for treatment providers, patients, and families, continually updated as genomics research evolves, will help patients benefit from cutting-edge research ( Bulik et al., 2019 ). In the future when personalized genetic testing may become available, clinicians may want to collaborate with genetic counselors with expertise in communicating test results.

Bridging the therapeutic impasse

Genomic research progress is taking place contextually in a field with a long-standing clinical plateau in therapeutics, similar to other mental health disorders. The hope is that insights gained through genomics will break through the therapeutic impasse and speed up the search for novel, effective treatments.

Leading, empirically-supported treatments for eating disorders are a half-century old and their efficacy is limited: for example, cognitive-behavioral therapy (CBT), developed first for BN in the early 1980s ( Fairburn, 1981 ) was informed by depression theory and treatment pioneered in the 1960s ( Beck, 2019 ); interpersonal psychotherapy treatment (IPT) was first applied to BN in clinical trials in the 1990s ( Fairburn et al., 1991 ); family-based treatment (FBT), also known as the Maudsley approach and the most recent addition to the therapeutic repertoire, was developed at the Maudsley Hospital in London in the 1970s ( Lock & Le Grange, 2013 ); and pharmacological agents to treat BN and BED, most notably selective serotonin reuptake inhibitors, were an extension of serendipitous discoveries related to depression from the 1950s (an exception is a recent treatment advance for BED in the form of stimulant, lisdexamfetamine: Hudson et al., 2017 ). The long-term success rates of these treatments are around 25% to 65% ( Agras, 1997 ; Hilbert et al., 2012 ; Lock & Le Grange, 2019 ), with many individuals having chronic, partially recovered, or relapsing courses of illness, and no empirically-supported treatment has yet been established for AN in adults.

Understanding the biological pathways involved and potential drug target genes may be fruitful for developing the next generation of interventions. Schizophrenia GWAS results, for example, are associated with antipsychotic drugs, which are already used in treatment, and selective calcium channel blockers and antiepileptics, therapeutic classes that present repurposing opportunities ( Gaspar & Breen, 2017 ). Similarly, it is hoped that genomics findings will provide leads for novel eating disorder treatments. For example, pharmacological agents that address metabolic processes may represent pharmacotherapeutic targets for AN. Drug targets based on GWAS findings are more likely to be successful in phase II and III clinical trials and to make it to market ( King et al., 2019 ; Nelson et al., 2015 ).

Future Directions

The immediate priority is to increase the statistical power of analyses by increasing sample sizes, a pursuit underway within the Eating Disorders Workgroup of the PGC for AN and other eating disorders. Twin and SNP-heritability estimates imply that with increased sample size it is a matter of time before more risk loci are identified, for example, as has occurred for schizophrenia from 7 loci with ~18k cases ( Schizophrenia Psychiatric Genome-Wide Association Study (GWAS) Consortium, 2011 ) to 108 loci with ~37k cases ( Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014 ), and for MDD from 0 loci with ~9k cases ( Ripke et al., 2013 ), 44 loci with ~135k cases ( Wray et al., 2018 ), and 102 loci with ~246k cases ( Howard et al., 2019 ). The genetic architecture of complex traits and diseases means that individual risk loci account for only a very small fraction of the heritability, but they may tag causal genes and cooperatively make contributions to disease pathways. Further, statistical power will facilitate systems analyses that interpret the data in the context of how gene and biological pathways influence the phenotype, and other types of analyses discussed throughout this paper.

Expansion of eating disorder phenotypes

Similarly, expansion of eating disorder phenotypes to eating disorders beyond AN is needed. This includes gaining an understanding of how phenotype measurement—from gold standard (i.e., yielded through clinical interview) to other means (i.e., register diagnoses, electronic health record data, questionnaire-based algorithms, self-report diagnosis history)—affects genomic findings. Interestingly, the MDD field found that similar GWAS-related results were yielded under detailed interviewer-based versus self-reported depression diagnosis and that continuous measures of non-pathological depressive symptoms yielded substantial genetic correlations with MDD ( McIntosh et al., 2019 ). This tapped more samples globally for use and accelerated statistical power and discovery.

Functional genomics

As GWAS and rare and structural variant studies correlate genetic variants with eating disorder phenotypes, functional genomic analyses will be needed to convert these insights into an understanding of the underlying biological mechanisms. These analyses are important for identifying the causal variant tagged by the genome-wide significant SNP, the biological function of the causal variant, and the gene/s involved in its association. Many computational (i.e., statistical fine mapping, eQTL analysis, TWAS, gene-set enrichment analysis) and molecular biology (i.e., RNA-seq datasets, ChIP-seq studies, Hi-C analysis, chromatin accessibility assays, knock-out animal models) approaches are being used for functional follow-up. A challenge is that many significant SNP signals in mental health disorders, including AN, are falling in non-protein coding regions.

Conclusions

Genomics is a rapidly-evolving research area in eating disorders. Eating disorders aggregate in families, are moderately heritable, and (at least for AN so far) some of the risk is attributed to genetic variants commonly found in the population ( Duncan et al., 2017 ; Watson et al., 2019 ). Genome-wide era work is beginning to unravel the genetic architecture of AN. Several loci and over 130 genes have shown associations with AN, and genetic overlap between AN and psychiatric (especially OCD), personality and behavioral, physical activity, cognitive, metabolic, and anthropometric traits has been revealed. Now, efforts are needed to elaborate on the functional context of these genes. Genomic approaches such as Mendelian randomization are helping to identify causal risk factors and have so far highlighted the importance of metabolic traits such as BMI, and adiponectin, in AN. Further research is needed to disentangle metabolic genomic factors from low weight in AN. A peek over the horizon into clinical management suggests that patient screening, care, and outcomes may improve from advances in molecular genetics. Genomic discovery depends on very large sample sizes and large-scale collaborations. In the next few years, the Eating Disorders Working Group of the PGC and large-scale studies such as the Eating Disorders Genetics Initiative (EDGI) and Binge Eating Genetics INitiative (BEGIN) ( Bulik et al., 2020 ) will be important to watch for advances in progress.

Supplementary Material

Funding statement:.

AH and AP are supported by the National Institute for Health Research (NIHR) Biomedical Research Centre at the South London and Maudsley National Health Service (NHS) Foundation Trust. HD is supported by an Economic and Social Research Council studentship. HW is supported by the National Institute of Mental Health (NIMH) (U01MH109528, R01MH120170). JB is supported by the NIMH (K01MH106675). ZY is supported by the NIMH (K01MH109782; R01MH105500; R01MH120170) and a Brain and Behavior Research Foundation NARSAD Young Investigator Award (grant # 28799). The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.

Conflicts of interest: All authors declare that they have no conflicts of interest.

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