The mystery of missing heritability: Genetic interactions create phantom heritability
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Human genetics has been haunted by the mystery of "missing heritability" of common traits. Although studies have discovered >1,200 variants associated with common diseases and traits, these variants typically appear to explain only a minority of the heritability. The proportion of heritability explained by a set of variants is the ratio of (i) the heritability due to these variants (numerator), estimated directly from their observed effects, to (ii) the total heritability (denominator), inferred indirectly from population data. The prevailing view has been that the explanation for missing heritability lies in the numerator--that is, in as-yet undiscovered variants. While many variants surely remain to be found, we show here that a substantial portion of missing heritability could arise from overestimation of the denominator, creating "phantom heritability." Specifically, (i) estimates of total heritability implicitly assume the trait involves no genetic interactions (epistasis) among loci; (ii) this assumption is not justified, because models with interactions are also consistent with observable data; and (iii) under such models, the total heritability may be much smaller and thus the proportion of heritability explained much larger. For example, 80% of the currently missing heritability for Crohn's disease could be due to genetic interactions, if the disease involves interaction among three pathways. In short, missing heritability need not directly correspond to missing variants, because current estimates of total heritability may be significantly inflated by genetic interactions. Finally, we describe a method for estimating heritability from isolated populations that is not inflated by genetic interactions.Keywords:
Missing heritability problem
Trait
Epistasis
Genetic correlation
To discuss the basis of 'missing heritability', which has emerged as an enigma in the post-genome-wide association studies (GWAS) era.Alleles identified through GWAS account for a relatively small fraction of heritability of the complex phenotypes. Accordingly, a significant part of heritability of the complex traits remains unaccounted for ('missing heritability'). Recent findings offer several explanations, including overestimation of heritability of the complex traits and underestimation of the effects of alleles identified through GWAS. In addition, yet-to-be identified common as well as rare alleles might in part explain the 'missing heritability'. Moreover, gene-gene (epistasis) and gene-environmental interactions might explain another fraction of heritability of complex traits. Moreover, transgenerational epigenetic changes, regulated in part by microRNAs, might also contribute to the 'missing heritability'.The new findings suggest a multifarious nature of the 'missing heritability'. The findings de-emphasize the focus on delineating the basis of 'missing heritability' and shift the focus to elucidation of the molecular mechanisms by which genomic and genetic factors govern the pathogenesis of the complex phenotypes.
Missing heritability problem
Genome-wide Association Study
Epistasis
Genetic Association
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Presented here is a simple method for cross-validated genome-wide association studies (cvGWAS). Focusing on phenotype prediction, the method is able to reveal a significant amount of missing heritability by properly selecting a small number of loci with implicit predictive ability. The results provide new insights into the missing heritability problem and the underlying genetic architecture of complex traits.
Missing heritability problem
Genetic architecture
Genome-wide Association Study
Genetic Association
Association (psychology)
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Missing heritability problem
Genome-wide Association Study
Concordance
Trait
Genetic Association
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Heritability estimates obtained from genome-wide association studies (GWAS) are much lower than those of traditional quantitative methods. This phenomenon has been called the “missing heritability problem.” By analyzing and comparing GWAS and traditional quantitative methods, we first show that the estimates obtained from the latter involve some terms other than additive genetic variance, while the estimates from the former do not. Second, GWAS, when used to estimate heritability, do not take into account additive epigenetic factors transmitted across generations, while traditional quantitative methods do. Given these two points we show that the missing heritability problem can largely be dissolved.
Missing heritability problem
Genome-wide Association Study
Genetic Association
Association (psychology)
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Presented here is a simple method for cross-validated genome-wide association studies (cvGWAS). Focusing on phenotype prediction, the method is able to reveal a significant amount of missing heritability by properly selecting a small number of loci with implicit predictive ability. The results provide new insights into the missing heritability problem and the underlying genetic architecture of complex traits.
Missing heritability problem
Genetic architecture
Genetic Association
Genome-wide Association Study
Cite
Citations (0)
Human genetics has been haunted by the mystery of "missing heritability" of common traits. Although studies have discovered >1,200 variants associated with common diseases and traits, these variants typically appear to explain only a minority of the heritability. The proportion of heritability explained by a set of variants is the ratio of (i) the heritability due to these variants (numerator), estimated directly from their observed effects, to (ii) the total heritability (denominator), inferred indirectly from population data. The prevailing view has been that the explanation for missing heritability lies in the numerator--that is, in as-yet undiscovered variants. While many variants surely remain to be found, we show here that a substantial portion of missing heritability could arise from overestimation of the denominator, creating "phantom heritability." Specifically, (i) estimates of total heritability implicitly assume the trait involves no genetic interactions (epistasis) among loci; (ii) this assumption is not justified, because models with interactions are also consistent with observable data; and (iii) under such models, the total heritability may be much smaller and thus the proportion of heritability explained much larger. For example, 80% of the currently missing heritability for Crohn's disease could be due to genetic interactions, if the disease involves interaction among three pathways. In short, missing heritability need not directly correspond to missing variants, because current estimates of total heritability may be significantly inflated by genetic interactions. Finally, we describe a method for estimating heritability from isolated populations that is not inflated by genetic interactions.
Missing heritability problem
Trait
Epistasis
Genetic correlation
Cite
Citations (1,527)
There are four major hypotheses (H1, H2, H3, and H4) as to the source of missing heritability. We propose that estimates obtained from GWAS underestimate heritability by not taking into account non-DNA (epigenetic) sources of heritability. Taking those factors into account (H4) should result in increased heritability estimates.
Missing heritability problem
Genome-wide Association Study
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Missing heritability problem
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Quantitative Genetics
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ABSTRACT Thousands of genes responsible for many diseases and other common traits in humans have been detected by Genome Wide Association Studies (GWAS) in the last decade. However, candidate causal variants found so far usually explain only a small fraction of the heritability estimated by family data, the rest remaining ‘missing’. The most common explanation for this observation is that the missing heritability corresponds to variants, either rare or common, with very small effect, which pass undetected due to a lack of statistical power. We carried out a meta-analysis using data from the NHGRI-EBI GWAS Catalog in order to explore the distribution of gene effects for a set of 59 complex traits, to investigate the factors related to new gene discovery and to quantify their contributions to heritability. With the data obtained we were able to predict the expected distribution of gene effects for 16 traits and diseases including cancer and immune disorders, the missing number of genes yet to be discovered, and the additive contribution of common variants to heritability. Our results indicate that, for most traits, the additive contribution of hundreds or thousands of genes is able to explain the familiar heritability. However, for some traits, the predicted heritability is lower than the familiar one, so that part of the missing heritability cannot be explained by the contribution of common variants with additive effects, and other phenomena ( e.g . dominance or epistasis) should be invoked. AUTHOR SUMMARY The heritability of biological traits refers to the fraction of the phenotypic ( i.e. visible or diagnosable) variability that is explained by the underlying genetic variability. Despite the efforts made to find the genes responsible for complex traits and diseases, most of the heritability explained by the variants found explains only a small fraction of that measured by population and family studies. The remaining ‘missing’ heritability is usually assumed to be due to the additive effect of undetected variants. Here we carried out a meta-analysis using records from a publicly available database of genome-wide studies on humans. With these data, we were able to investigate and make inferences on the nature of heritability and the factors associated to new gene discovery. Our results indicate that increasing population sample size, as well as its diversity, enhances the discovery of new genes, but these have lower and lower effects, contributing little to heritability. We were also able to predict the distribution of gene effects for many traits and the number of variants needed to fully explain the heritability. For some traits, the additive effects of single variants yet to be discovered cannot explain the heritability, suggesting that other sources of variation are involved.
Missing heritability problem
Epistasis
Genome-wide Association Study
Genetic Association
Candidate gene
Genetic architecture
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Abstract Objective Thousands of Genome-Wide Association Studies (GWAS) have been carried out to pinpoint genetic variants associated with complex disease. However, the proportion of phenotypic variance which can be explained by the identified genetic variants is relatively low. Thus, it is desirable to propose new computational models to explain the “missing heritability” problem. Results Here, we propose the additive epistatic interaction model, consisting of widespread pure epistatic interactions whose effects are additive and can be summarized by a genetic risk score. Based on a simulated genotype dataset, the additive epistatic interaction model well depicted genetic risks and hereditary patterns of complex diseases. When applied to real genotypic datasets, the additive epistatic interaction model showed potential for accurately classifying human populations from the 1000 Genomes Project, and individuals with and without diabetes from the UK Biobank database. Moreover, the model’s genetic risk score can be replaced by a deep learning model which is more resistant to noises. We suggest that the additive epistatic interaction model might help to explain the “missing heritability” problem. Source code is publicly available at https://github.com/wyp1125/additive_epistasis.
Epistasis
Missing heritability problem
Genome-wide Association Study
Genetic architecture
genetic model
Genetic Association
Additive genetic effects
Interaction
Additive model
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