Genetics of eating disorders in the genome-wide era

Abstract

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.

Figure 1. Timeline of key advances in eating disorder genetics.

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; h² = narrow-sense heritability; NNAT = neuronatin (protein coding gene), SNP = single nucleotide polymorphism.

Table 1.

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

Genetic Epidemiology		Twin-based heritability estimates are: 16–74% for AN, 28–83% for BN, and 39–45% for BED Evidence for shared genetic risk between eating disorders, OCD, MDD, and alcohol and substance use disorders ~60% of the genetic risk for AN and BN may be shared
Molecular Genetics	GWAS	GWAS of AN identified 8 genome-wide significant loci and implicated 133 genes SNP-based heritability is estimated to range from 11–17%
	Genetic correlations	AN has positive genetic correlations with MDD, anxiety disorders, OCD, neuroticism, SCZ, HDL cholesterol, educational attainment, and physical activity, and negative genetic correlations with fat mass, fat-free mass, BMI, obesity, type 2 diabetes, fasting insulin, insulin resistance, leptin, and subjective well-being
	Genetic risk scores	Individuals with the highest genetic risk scores (top decile) for AN have four times greater odds of developing AN Genetic risk scores for OCD, SCZ, and bipolar disorder show significant associations with eating disorder diagnosis
	Cross-disorder GWAS	109 genetic loci were shown to be pleiotropic, with the 18q21.2 region showing association with schizophrenia, bipolar disorder, MDD, ADHD, autism spectrum disorder, OCD, and Tourette’s syndrome AN, OCD, and Tourette’s syndrome cluster together genetically
	Mendelian randomization	AN risk alleles may causally affect BMI (i.e., lower), and low BMI risk alleles may causally affect AN (i.e., increase risk) Higher adiponectin may cause eating behavior disinhibition
	Rare and structural variants	Copy number variants: 13q12 deletion and 15q11.2 micro-duplication in AN Whole-exome sequencing: NNAT, ESRRA, and HDAC4 variants in AN, linked to the estrogen system Whole-genome sequencing: TTC22, MRPS9, DNAJC30, HEPACAM2, USP20, ESF1, and CDK5RAP1 variants in AN
	Gene expression	AN gene expression is associated with subcortical feeding and reward circuits Evidence of enrichment of gene expression in hippocampal neurons linked to feeding and reward CPA3 and GATA2 expression in AN is positively associated with leptin, a hormone linking nutritional status and the immune response TACR1 shows expression in induced pluripotent stem cells from AN patients; the gene was previously associated with anxiety, bipolar disorder, and ADHD
	Epigenetic and gene-environment mechanisms	Epigenome-wide association studies: TNXB hypermethylation has been replicated in multiple studies Gene-environment (G × E) interaction studies: Candidate gene-environment interaction study results have failed to replicate. Novel approaches using genetic risk scores to capture ‘G’ are in development.
Clinical Implications		Improved understanding for patients and clinicians on the role genomics plays in eating disorder risk Speed up the search for novel, effective treatments Develop the next generation of interventions
Future Directions		Increase GWAS sample sizes to facilitate discovery Expand phenotypes beyond AN Use of functional genomics analyses to understand the mechanisms involved

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 (h²_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 (h²_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 h²_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.

Figure 2. Genome-wide significant loci for AN.

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_gs 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).

Figure 3. Genetic correlations (r_g) between anorexia nervosa and other phenotypes (Watson et al., 2019).

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_gs 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

GWAS

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.