Finding the sources of missing heritability in a yeast cross

Bloom, Joshua S.; Ehrenreich, Ian M.; Loo, Wesley T.; Lite, Thúy-Lan Võ; Kruglyak, Leonid

doi:10.1038/nature11867

Cited by 444 publications

(718 citation statements)

References 49 publications

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“…Our results complement one such study that recovered the chromosomal determinants accounting for almost all of the additive portion of heritability of several traits by dramatically expanding study sizes (4). In this previous study, potentially confounding nonchromosomal effects were mitigated by standardizing on a single mitochondrial background and by using only dsRNA virus-free strains (Supporting Information, Note 2, Fig.…”

Section: Discussionsupporting

confidence: 69%

Interactions between chromosomal and nonchromosomal elements reveal missing heritability

Edwards

Symbor-Nagrabska

Dollard

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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The measurement of any nonchromosomal genetic contribution to the heritability of a trait is often confounded by the inability to control both the chromosomal and nonchromosomal information in a population. We have designed a unique system in yeast where we can control both sources of information so that the phenotype of a single chromosomal polymorphism can be measured in the presence of different cytoplasmic elements. With this system, we have shown that both the source of the mitochondrial genome and the presence or absence of a dsRNA virus influence the phenotype of chromosomal variants that affect the growth of yeast. Moreover, by considering this nonchromosomal information that is passed from parent to offspring and by allowing chromosomal and nonchromosomal information to exhibit nonadditive interactions, we are able to account for much of the heritability of growth traits. Taken together, our results highlight the importance of including all sources of heritable information in genetic studies and suggest a possible avenue of attack for finding additional missing heritability.A fundamental problem in genetics is unraveling the link between genotype and phenotype. Ascertaining the heritability of a trait is a key step toward harnessing the predictive capacity of genetic information for human disease risk assessment and therapy (1). Knowledge of all of the elements contributing to heritability would facilitate the establishment of a causal relationship between the information that is passed down from generation to generation and the resulting phenotype. Genomewide association studies (GWASs) have successfully identified many human polymorphisms that are associated with traits such as height, eye color, or susceptibility to common diseases, but these variants typically explain only a small proportion of the observed heritability of a trait (2, 3).A number of explanations for missing heritability have been suggested (2), including the existence of many weak variants with effects too small to achieve statistical significance (4), interactions between variants that cannot be identified with current studies (5), rare variants that were not identified by GWAS, and epigenetic effects (6-8). The contribution of nonchromosomal information to the missing heritability is rarely considered, despite the fact that there is a long history documenting the effect in many organisms of diverse cytoplasmic elements on phenotype. Recent work on a mouse model of Crohn disease supports a combinatorial model of complex disease traits in which the pathology requires the interaction between a specific mutation in the mouse and a specific strain of virus (9). Another recent study showed strong effects on the plant metabolome stemming from variation in mitochondrial and chloroplast genomes (10). In humans, the importance of nonchromosomal information has been supported by targeted analyses, but these studies have not analyzed its impact on heritability in a well-controlled context (11-13). Such nonchromosomal interactions might help e...

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Section: Discussionsupporting

confidence: 69%

Interactions between chromosomal and nonchromosomal elements reveal missing heritability

Edwards

Symbor-Nagrabska

Dollard

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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“…Despite the challenges in estimation, thousands of QTL have been found and many findings of interaction effects reported in crosses of divergent lines of chicken (Pettersson et al 2011), yeast (Bloom et al 2013), and Drosophila (Huang et al 2012. Further, there is recent evidence of detection of epistatic loci for levels of gene expression in human populations (Hemani et al 2014;Brown et al 2014).Nevertheless the variation contributed by these epistatic loci detected in segregating populations is generally found to be small (Huang et al 2012;Brown et al 2014;Hemani et al 2014), which fits with the theoretical predictions (Hill et al 2008) that only at high heterozygosity levels do loci contribute much epistatic variance; Bloom et al (2013) note that the epistatic variance they found would have been much lower had they not been using an F1-based population.The genetic variation accounted for by significant SNPs in genome-wide association studies (GWAS) in humans for both metric traits such as height and for multifactorial disease traits has typically been substantially less than the estimates of the additive genetic variation found in conventional pedigreebased quantitative genetic analyses. For example, the top 150 loci identified by SNPs account for only 10% of the variance in human height (Lango-Allen et al 2010).…”

supporting

confidence: 72%

Influence of Gene Interaction on Complex Trait Variation with Multilocus Models

2014

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Although research effort is being expended into determining the importance of epistasis and epistatic variance for complex traits, there is considerable controversy about their importance. Here we undertake an analysis for quantitative traits utilizing a range of multilocus quantitative genetic models and gene frequency distributions, focusing on the potential magnitude of the epistatic variance. All the epistatic terms involving a particular locus appear in its average effect, with the number of two-locus interaction terms increasing in proportion to the square of the number of loci and that of third order as the cube and so on. Hence multilocus epistasis makes substantial contributions to the additive variance and does not, per se, lead to large increases in the nonadditive part of the genotypic variance. Even though this proportion can be high where epistasis is antagonistic to direct effects, it reduces with multiple loci. As the magnitude of the epistatic variance depends critically on the heterozygosity, for models where frequencies are widely dispersed, such as for selectively neutral mutations, contributions of epistatic variance are always small. Epistasis may be important in understanding the genetic architecture, for example, of function or human disease, but that does not imply that loci exhibiting it will contribute much genetic variance. Overall we conclude that theoretical predictions and experimental observations of low amounts of epistatic variance in outbred populations are concordant. It is not a likely source of missing heritability, for example, or major influence on predictions of rates of evolution. EPISTATIC variance in quantitative traits arises from the interaction effects or epistasis between segregating genes at two or more loci that affect these complex traits. Such gene interaction is a common phenomenon because many factors have, for example, a regulatory role in a hierarchical system (Phillips 2008). The statistical theory of quantitative genetics following Fisher (1918) is based on a partition between average effects across loci, which contribute to the additive genetic variance, and to interactions within loci and between loci, which contribute to the dominance and epistatic variance, respectively (Cockerham 1954;Kempthorne 1954). The magnitudes of these components of the genotypic variance each depend on the frequencies, the effects, and the interactions among the contributing genes (see also Falconer and Mackay 1996;Lynch and Walsh 1998). The actual causal genetic factors are usually not known, but many quantitative genetic analyses, including selection on metric traits, have been applied successfully without such knowledge.Among quantitative geneticists, interest in epistasis continues, both to understand the genetic architecture and as a potential way to improve the genomic predictions of disease and quantitative traits, utilizing some of the unexplained parts of the genetic variation (e.g., Carlborg and Haley 2004;Nelson et al. 2013;Mackay 2014). Despite the obviou...

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“…Significance was determined using a Wilcoxon rank sum test. *P < 0.01; **P < 0.001; ***P < 0.0001. effects across environments (65), and the interactions between or within loci (70).…”

Section: Discussionmentioning

confidence: 99%

Genetic architecture of nonadditive inheritance inArabidopsis thalianahybrids

Seymour

Chae

Grimm

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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The ubiquity of nonparental hybrid phenotypes, such as hybrid vigor and hybrid inferiority, has interested biologists for over a century and is of considerable agricultural importance. Although examples of both phenomena have been subject to intense investigation, no general model for the molecular basis of nonadditive genetic variance has emerged, and prediction of hybrid phenotypes from parental information continues to be a challenge. Here we explore the genetics of hybrid phenotype in 435 Arabidopsis thaliana individuals derived from intercrosses of 30 parents in a half diallel mating scheme. We find that nonadditive genetic effects are a major component of genetic variation in this population and that the genetic basis of hybrid phenotype can be mapped using genome-wide association (GWA) techniques. Significant loci together can explain as much as 20% of phenotypic variation in the surveyed population and include examples that have both classical dominant and overdominant effects. One candidate region inherited dominantly in the half diallel contains the gene for the MADS-box transcription factor AGAMOUS-LIKE 50 (AGL50), which we show directly to alter flowering time in the predicted manner. Our study not only illustrates the promise of GWA approaches to dissect the genetic architecture underpinning hybrid performance but also demonstrates the contribution of classical dominance to genetic variance.T he often observed phenotypic superiority of progeny relative to their parents, or heterosis, is a universal phenomenon and of great importance to plant agriculture. The earliest description of heterosis (also known as hybrid vigor or superiority) dates to Darwin's studies of cross-fertilization in plants. He noticed that intercrossing distantly related individuals gave rise to larger, more vigorous progeny (1). Four decades later, George Shull coined the term "heterosis" (2) for this phenomenon, which he and Edward East had independently described for hybrids of inbred maize in 1908 (3, 4). Heterosis has long been of interest to evolutionary biologists as a potential explanation for the ubiquity of cross-fertilization in plants and animals, but it is also a central component of agricultural breeding programs. The combination of hybrid seed technology and inbred line improvement has driven an unprecedented improvement in maize yield over the past century (5). Despite the economic importance of heterosis and its intensive investigation in a wide spectrum of species, prediction of hybrid performance from parental information remains a major challenge (6).In the terms of quantitative genetics, hybrid vigor (and its opposite, inferiority) describes a deviation of progeny from the phenotypic mean of the parents. This means that heterosis cannot be explained by the addition of the effects of contributing alleles (7). Nonadditive genetic variance can result from a nonlinear phenotypic effect of alleles at one locus, as in the case of dominant/recessive allele pairs in classical genetics, or from epistatic interactions ...

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Finding the sources of missing heritability in a yeast cross

Cited by 444 publications

References 49 publications

Interactions between chromosomal and nonchromosomal elements reveal missing heritability

Interactions between chromosomal and nonchromosomal elements reveal missing heritability

Influence of Gene Interaction on Complex Trait Variation with Multilocus Models

Genetic architecture of nonadditive inheritance inArabidopsis thalianahybrids

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