Quantifying Missing Heritability at Known GWAS Loci

Recent work has shown that much of the missing heritability of complex traits can be resolved by estimates of heritability explained by all genotyped SNPs. However, it is currently unknown how much heritability is missing due to poor tagging or additional causal variants at known GWAS loci. Here, we use variance components to quantify the heritability explained by all SNPs at known GWAS loci in nine diseases from WTCCC1 and WTCCC2. After accounting for expectation, we observed all SNPs at known GWAS loci to explain more heritability than GWAS-associated SNPs on average (). For some diseases, this increase was individually significant: for Multiple Sclerosis (MS) () and for Crohn''s Disease (CD) (); all analyses of autoimmune diseases excluded the well-studied MHC region. Additionally, we found that GWAS loci from other related traits also explained significant heritability. The union of all autoimmune disease loci explained more MS heritability than known MS SNPs () and more CD heritability than known CD SNPs (), with an analogous increase for all autoimmune diseases analyzed. We also observed significant increases in an analysis of Rheumatoid Arthritis (RA) samples typed on ImmunoChip, with more heritability from all SNPs at GWAS loci () and more heritability from all autoimmune disease loci () compared to known RA SNPs (including those identified in this cohort). Our methods adjust for LD between SNPs, which can bias standard estimates of heritability from SNPs even if all causal variants are typed. By comparing adjusted estimates, we hypothesize that the genome-wide distribution of causal variants is enriched for low-frequency alleles, but that causal variants at known GWAS loci are skewed towards common alleles. These findings have important ramifications for fine-mapping study design and our understanding of complex disease architecture.     

Epigenetic Inheritance and the Missing Heritability Problem

Epigenetic phenomena, and in particular heritable epigenetic changes, or transgenerational effects, are the subject of much discussion in the current literature. This article presents a model of transgenerational epigenetic inheritance and explores the effect of epigenetic inheritance on the risk and recurrence risk of a complex disease. The model assumes that epigenetic modifications of the genome are gained and lost at specified rates and that each modification contributes multiplicatively to disease risk. The potentially high rate of loss of epigenetic modifications causes the probability of identity in state in close relatives to be smaller than is implied by their relatedness. As a consequence, the recurrence risk to close relatives is reduced. Although epigenetic modifications may contribute substantially to average risk, they will not contribute much to recurrence risk and heritability unless they persist on average for many generations. If they do persist for long times, they are equivalent to mutations and hence are likely to be in linkage disequilibrium with SNPs surveyed in genomewide association studies. Thus epigenetic modifications are a potential solution to the problem of missing causality of complex diseases but not to the problem of missing heritability. The model highlights the need for empirical estimates of the persistence times of heritable epialleles.THE modern definition of epigenetics is the study of heritable changes in gene expression that are not caused by changes in DNA sequence (Richards 2006; Bird 2007; Bossdorf et al. 2008). Epigenetic effects include methylation of the cytosine residue in DNA and the modification of chromatin proteins that package DNA (Youngson and Whitelaw 2008). Although this definition of epigenetics includes inheritance during both mitosis and meiosis, I am concerned in this article only with epigenetic changes that are transmitted to offspring, what has been called “transgenerational epigenetic inheritance” (Morgan and Whitelaw 2008; Youngson and Whitelaw 2008). The modern definition of epigenetics arose from the original definition of Waddington (1957; Holliday and Pugh 1975).The possibility of nongenetic inherited effects on phenotype has excited great interest among both evolutionary biologists and human geneticists because it provides an additional mechanism of inherited variability and one that is not detectable in genomic surveys of sequence variation. Inherited epigenetic changes have been proposed as an explanation for the “missing heritability,” meaning inherited causes of risk of complex genetic diseases that have not yet been identified in genomewide association studies (GWAS) (Maher 2008; McCarthy and Hirschhorn 2008). Inherited epigenetic changes that contribute to disease risk would not be detectable in GWAS but may contribute to average risk and to similarities among relatives.In this article, I present a simple model of the inheritance of epigenetic changes. The goal is to quantify the potential contribution they can make to average risk and recurrence risk. The model is developed in a standard population genetics framework and can be regarded as a generalization of previous multilocus models of complex diseases, particularly that of Risch (1990).I assume that epigenetic effects are caused by the presence or the absence of epigenetic modifications of specific chromosomal locations. Bird (2007), Haig (2007), Richards (2008), and others have emphasized that, although epigenetic changes differ in many ways from mutations, their transmission to offspring is the same as the transmission of mutations, except for the possibility that they might be spontaneously lost. If the gain and loss of epigenetic changes are controlled by a locus elsewhere in the genome, as modeled by Bjornsson et al. (2004), then the resulting phenotypic effects are attributable to variation at that locus (Richards 2006; Johannes et al. 2008). The epigenetic changes are simply the mechanism by which that locus affects phenotype. If, however, the appearance of an epigenetic change at a location in the genome is not attributable to any particular locus or loci, then the phenotypic effects of the presence or the absence of an epigenetic change are attributable to the genomic location itself. That is the case I am concerned with here.I begin by introducing the basic model of a randomly mating population and extend standard genetic theory to the case of epigenetic inheritance. Then I consider nonequilibrium populations in which environmental changes cause an increase in the rate of gain of epigenetic changes.     

Estimating Genetic Effects and Quantifying Missing Heritability Explained by Identified Rare-Variant Associations

Next-generation sequencing has led to many complex-trait rare-variant (RV) association studies. Although single-variant association analysis can be performed, it is grossly underpowered. Therefore, researchers have developed many RV association tests that aggregate multiple variant sites across a genetic region (e.g., gene), and test for the association between the trait and the aggregated genotype. After these aggregate tests detect an association, it is only possible to estimate the average genetic effect for a group of RVs. As a result of the "winner’s curse," such an estimate can be biased. Although for common variants one can obtain unbiased estimates of genetic parameters by analyzing a replication sample, for RVs it is desirable to obtain unbiased genetic estimates for the study where the association is identified. This is because there can be substantial heterogeneity of RV sites and frequencies even among closely related populations. In order to obtain an unbiased estimate for aggregated RV analysis, we developed bootstrap-sample-split algorithms to reduce the bias of the winner’s curse. The unbiased estimates are greatly important for understanding the population-specific contribution of RVs to the heritability of complex traits. We also demonstrate both theoretically and via simulations that for aggregate RV analysis the genetic variance for a gene or region will always be underestimated, sometimes substantially, because of the presence of noncausal variants or because of the presence of causal variants with effects of different magnitudes or directions. Therefore, even if RVs play a major role in the complex-trait etiologies, a portion of the heritability will remain missing, and the contribution of RVs to the complex-trait etiologies will be underestimated.     

The Nature of Genetic Variation for Complex Traits Revealed by GWAS and Regional Heritability Mapping Analyses

We use computer simulations to investigate the amount of genetic variation for complex traits that can be revealed by single-SNP genome-wide association studies (GWAS) or regional heritability mapping (RHM) analyses based on full genome sequence data or SNP chips. We model a large population subject to mutation, recombination, selection, and drift, assuming a pleiotropic model of mutations sampled from a bivariate distribution of effects of mutations on a quantitative trait and fitness. The pleiotropic model investigated, in contrast to previous models, implies that common mutations of large effect are responsible for most of the genetic variation for quantitative traits, except when the trait is fitness itself. We show that GWAS applied to the full sequence increases the number of QTL detected by as much as 50% compared to the number found with SNP chips but only modestly increases the amount of additive genetic variance explained. Even with full sequence data, the total amount of additive variance explained is generally below 50%. Using RHM on the full sequence data, a slightly larger number of QTL are detected than by GWAS if the same probability threshold is assumed, but these QTL explain a slightly smaller amount of genetic variance. Our results also suggest that most of the missing heritability is due to the inability to detect variants of moderate effect (∼0.03–0.3 phenotypic SDs) segregating at substantial frequencies. Very rare variants, which are more difficult to detect by GWAS, are expected to contribute little genetic variation, so their eventual detection is less relevant for resolving the missing heritability problem.     

A Multi-SNP Locus-Association Method Reveals a Substantial Fraction of the Missing Heritability

There are many known examples of multiple semi-independent associations at individual loci; such associations might arise either because of true allelic heterogeneity or because of imperfect tagging of an unobserved causal variant. This phenomenon is of great importance in monogenic traits but has not yet been systematically investigated and quantified in complex-trait genome-wide association studies (GWASs). Here, we describe a multi-SNP association method that estimates the effect of loci harboring multiple association signals by using GWAS summary statistics. Applying the method to a large anthropometric GWAS meta-analysis (from the Genetic Investigation of Anthropometric Traits consortium study), we show that for height, body mass index (BMI), and waist-to-hip ratio (WHR), 3%, 2%, and 1%, respectively, of additional phenotypic variance can be explained on top of the previously reported 10% (height), 1.5% (BMI), and 1% (WHR). The method also permitted a substantial increase (by up to 50%) in the number of loci that replicate in a discovery-validation design. Specifically, we identified 74 loci at which the multi-SNP, a linear combination of SNPs, explains significantly more variance than does the best individual SNP. A detailed analysis of multi-SNPs shows that most of the additional variability explained is derived from SNPs that are not in linkage disequilibrium with the lead SNP, suggesting a major contribution of allelic heterogeneity to the missing heritability.     

Dominance Genetic Variation Contributes Little to the Missing Heritability for Human Complex Traits

For human complex traits, non-additive genetic variation has been invoked to explain “missing heritability,” but its discovery is often neglected in genome-wide association studies. Here we propose a method of using SNP data to partition and estimate the proportion of phenotypic variance attributed to additive and dominance genetic variation at all SNPs (hSNP2 and δSNP2) in unrelated individuals based on an orthogonal model where the estimate of hSNP2 is independent of that of δSNP2. With this method, we analyzed 79 quantitative traits in 6,715 unrelated European Americans. The estimate of δSNP2 averaged across all the 79 quantitative traits was 0.03, approximately a fifth of that for additive variation (average hSNP2 = 0.15). There were a few traits that showed substantial estimates of δSNP2, none of which were replicated in a larger sample of 11,965 individuals. We further performed genome-wide association analyses of the 79 quantitative traits and detected SNPs with genome-wide significant dominance effects only at the ABO locus for factor VIII and von Willebrand factor. All these results suggest that dominance variation at common SNPs explains only a small fraction of phenotypic variation for human complex traits and contributes little to the missing narrow-sense heritability problem.     

Finding Missing Heritability in Less Significant Loci and Allelic Heterogeneity: Genetic Variation in Human Height

Genome-wide association studies (GWAS) have identified many common variants associated with complex traits in human populations. Thus far, most reported variants have relatively small effects and explain only a small proportion of phenotypic variance, leading to the issues of ‘missing’ heritability and its explanation. Using height as an example, we examined two possible sources of missing heritability: first, variants with smaller effects whose associations with height failed to reach genome-wide significance and second, allelic heterogeneity due to the effects of multiple variants at a single locus. Using a novel analytical approach we examined allelic heterogeneity of height-associated loci selected from SNPs of different significance levels based on the summary data of the GIANT (stage 1) studies. In a sample of 1,304 individuals collected from an island population of the Adriatic coast of Croatia, we assessed the extent of height variance explained by incorporating the effects of less significant height loci and multiple effective SNPs at the same loci. Our results indicate that approximately half of the 118 loci that achieved stringent genome-wide significance (p-value<5×10⁻⁸) showed evidence of allelic heterogeneity. Additionally, including less significant loci (i.e., p-value<5×10⁻⁴) and accounting for effects of allelic heterogeneity substantially improved the variance explained in height.     

Dominant Genetic Variation and Missing Heritability for Human Complex Traits: Insights from Twin versus Genome-wide Common SNP Models

In order to further illuminate the potential role of dominant genetic variation in the “missing heritability” debate, we investigated the additive (narrow-sense heritability, h²) and dominant (δ²) genetic variance for 18 human complex traits. Within the same study base (10,682 Swedish twins), we calculated and compared the estimates from classic twin-based structural equation model with SNP-based genomic-relatedness-matrix restricted maximum likelihood [GREML(d)] method. Contributions of δ² were evident for 14 traits in twin models (average δ²_twin = 0.25, range 0.14–0.49), two of which also displayed significant δ² in the GREMLd analyses (triglycerides δ²_SNP = 0.28 and waist circumference δ²_SNP = 0.19). On average, the proportion of h²_SNP/h²_twin was 70% for ADE-fitted traits (for which the best-fitting model included additive and dominant genetic and unique environmental components) and 31% for AE-fitted traits (for which the best-fitting model included additive genetic and unique environmental components). Independent evidence for contribution from shared environment, also in ADE-fitted traits, was obtained from self-reported within-pair contact frequency and age at separation. We conclude that despite the fact that additive genetics appear to constitute the bulk of genetic influences for most complex traits, dominant genetic variation might often be masked by shared environment in twin and family studies and might therefore have a more prominent role than what family-based estimates often suggest. The risk of erroneously attributing all inherited genetic influences (additive and dominant) to the h² in too-small twin studies might also lead to exaggerated “missing heritability” (the proportion of h² that remains unexplained by SNPs).     

Reducing GWAS Complexity

Genome-wide association studies (GWAS) have revealed numerous genomic 'hits' associated with complex phenotypes. In most cases these hits, along with surrogate genetic variation as measure by numerous single nucleotide polymorphisms (SNPs) that are in linkage disequilibrium, are not in coding genes making assignment of functionality or causality intractable. Here we propose that fine-mapping along with the matching of risk SNPs at chromatin biofeatures lessen this complexity by reducing the number of candidate functional/causal SNPs. For example, we show here that only on average 2 SNPs per prostate cancer risk locus are likely candidates for functionality/causality; we further propose that this manageable number should be taken forward in mechanistic studies. The candidate SNPs can be looked up for each prostate cancer risk region in 2 recent publications in 2015^1,2 Han Y, Hazelett DJ, Wiklund F, Schumacher FR, Stram DO, Berndt SI, Wang Z, Rand KA, Hoover RN, Machiela MJ, et al. Integration of Multiethnic Fine-mapping and Genomic Annotation to Prioritize Candidate Functional SNPs at Prostate Cancer Susceptibility Regions. Hum Mol Genet 2015; 24(19):5603–18. Amin Al Olama A, Dadaev T, Hazelett DJ, Li Q, Leongamornlert D, Saunders EJ, Stephens S, Cieza-Borrella C, Whitmore I, Benlloch Garcia S, et al. Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans. Hum Mol Genet 2015; 24(19):5589–602.  from our groups.     

GWAS 2011: Opportunities and challenges

The wide adoption of genome-wide association study (GWAS) has dramatically changed the landscape of the genetic studies of human diseases.Banking on the study design that employs large and multiple-independent samples,linkage disequilibrium (LD) -based systematic genome-wide interrogation,and vigorous statistical standard for declaring genetic association,GWAS has greatly advanced the genetic studies of human disease by successfully identifying over 4 thousands of genetic susceptibility SNPs or loci for 210 diseases/traits in human.Besides its unrivaled scientific achievements,GWAS has also transformed the communities of human genetics by stimulating unprecedented broad and large-scale collaboration and data sharing across different groups and countries,which has enabled some mega genetic studies where dozens or hundreds of thousands of samples were investigated through mete-analysis,providing enormous statistical power for discovering genetic variants,even the ones with very moderate effect on disease phenotype or physiological trait.While the success of GWAS has been widely recognized,there are many concerns on GWAS that are being passionately debated.In particular,there ate heated discussions on why only a limited proportion,quiet often a small proportion,of genetic heritability can be explained by the GWAS findings,and whether and how the GWAS findings have really advanced the biological investigation and understanding of disease mechanisms.Concerning these shortcomings of GWAS,many have genuinely questioned the perspective of translating the GWAS findings into clinical practice.To provide a platform to discuss the progresses and shortcomings of GWAS,a nature conference,titled “GWAS 2011:Opportunities and Challenges”,was organized through the collaboration between the Nature Genetics and Anhui Medical University,which was held in Hefei,China on May 19-21,2011.     

Mechanisms for Nonrecurrent Genomic Rearrangements Associated with CMT1A or HNPP: Rare CNVs as a Cause for Missing Heritability

Genomic rearrangements involving the peripheral myelin protein gene (PMP22) in human chromosome 17p12 are associated with neuropathy: duplications cause Charcot-Marie-Tooth disease type 1A (CMT1A), whereas deletions lead to hereditary neuropathy with liability to pressure palsies (HNPP). Our previous studies showed that >99% of these rearrangements are recurrent and mediated by nonallelic homologous recombination (NAHR). Rare copy number variations (CNVs) generated by nonrecurrent rearrangements also exist in 17p12, but their underlying mechanisms are not well understood. We investigated 21 subjects with rare CNVs associated with CMT1A or HNPP by oligonucleotide-based comparative genomic hybridization microarrays and breakpoint sequence analyses, and we identified 17 unique CNVs, including two genomic deletions, ten genomic duplications, two complex rearrangements, and three small exonic deletions. Each of these CNVs includes either the entire PMP22 gene, or exon(s) only, or ultraconserved potential regulatory sequences upstream of PMP22, further supporting the contention that PMP22 is the critical gene mediating the neuropathy phenotypes associated with 17p12 rearrangements. Breakpoint sequence analysis reveals that, different from the predominant NAHR mechanism in recurrent rearrangement, various molecular mechanisms, including nonhomologous end joining, Alu-Alu-mediated recombination, and replication-based mechanisms (e.g., FoSTeS and/or MMBIR), can generate nonrecurrent 17p12 rearrangements associated with neuropathy. We document a multitude of ways in which gene function can be altered by CNVs. Given the characteristics, including small size, structural complexity, and location outside of coding regions, of selected rare CNVs, their identification remains a challenge for genome analysis. Rare CNVs may potentially represent an important portion of “missing heritability” for human diseases.     

Five years of GWAS discovery

The past five years have seen many scientific and biological discoveries made through the experimental design of genome-wide association studies (GWASs). These studies were aimed at detecting variants at genomic loci that are associated with complex traits in the population and, in particular, at detecting associations between common single-nucleotide polymorphisms (SNPs) and common diseases such as heart disease, diabetes, auto-immune diseases, and psychiatric disorders. We start by giving a number of quotes from scientists and journalists about perceived problems with GWASs. We will then briefly give the history of GWASs and focus on the discoveries made through this experimental design, what those discoveries tell us and do not tell us about the genetics and biology of complex traits, and what immediate utility has come out of these studies. Rather than giving an exhaustive review of all reported findings for all diseases and other complex traits, we focus on the results for auto-immune diseases and metabolic diseases. We return to the perceived failure or disappointment about GWASs in the concluding section.     

Heritability: uses and abuses

This paper begins with a brief summary of the history of the development of ideas in the field of quantitative genetics. Next there is discussion of the controversy surrounding the contention that IQ tests validly estimate some highly heritable general intelligence factor. The validity of the reasoning supporting this contention is questioned. The theory of correlation between relatives has been of vast importance in plant and animal breeding because it is possible to design and carry out experiments to estimate variance components in expressions for covariances between relatives. However, data on humans is observational and individuals are not randomly assigned to environments, so that estimation of heritability from such data is not on the same firm foundation as it is in plant and animal breeding contexts. This revised version was published online in August 2006 with corrections to the Cover Date.