Discovering genes involved in disease and the mystery of missing heritability

Eskin, Eleazar

doi:10.1145/2817827

Cited by 22 publications

(28 citation statements)

References 56 publications

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“…In particular, denote B −vg as the matrix B g without the effect sizes of the variants that belong to the same segment as v. U vg can be estimated as U vg = B −vg B −vg (after proper scaling applied to B −vg ). This computation is similar to how one would compute a kinship matrix using the genotype matrix (Eskin, 2015). In this scheme, we also hope that the variants in strong LD with v are removed, so that there are fewer vectors in B −vg that resembles b vg when computing U vg .…”

Section: Recov: Random Effects (Re) Model With a Covariance (Cov) Termmentioning

confidence: 98%

“…where ∈ R m is the sampling errors ∼ N(0, σ 2 I), and β vgt ∈ R is the true effect size of the variant v on g in tissue t (Darnell et al, 2012;Eskin, 2015;Hormozdiari et al, 2015). The estimate b vgt of the true value β vgt can be computed using the basic least squares equation b vgt = arg min βvgt ||q − β vgt s|| 2 2 .…”

Section: Eqtl Study In One Tissuementioning

confidence: 99%

“…then compute the p-value p vgt = p-value(b vgt ) (Abraham and Ledolter, 2006;Eskin, 2015). If p vgt is less than some significance level , then we reject H 0 : β vgt = 0, and conclude that v is an eQTL of g in tissue t. When many variants are tested, multiple testing correction is needed due to linkage disequilibrium (LD); for example, one can apply Bonferroni correction by using the threshold α/|V | where α is the significance level for the whole family of tests.…”

Section: Eqtl Study In One Tissuementioning

confidence: 99%

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Applying meta-analysis to Genotype-Tissue Expression data from multiple tissues to identify eQTLs and increase the number of eGenes

Duong

2017

Preprint

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During the last decade, with the advent of inexpensive microarray and RNA-seq technologies, there have been many expression quantitative trait loci (eQTL) studies for identifying genetic variants called eQTLs that regulate gene expression. Discovering eQTLs has been increasingly important as they may elucidate the functional consequence of non-coding variants identified from genome-wide association studies. Recently, several eQTL studies such as the Genotype-Tissue Expression (GTEx) consortium have made a great effort to obtain gene expression from multiple tissues. One advantage of these multi-tissue eQTL datasets is that they may allow one to identify more eQTLs by combining information across multiple tissues. Although a few methods have been proposed for multi-tissue eQTL studies, they are often computationally intensive and may not achieve optimal power because they do not consider a biological insight that a genetic variant regulates gene expression similarly in related tissues. In this paper, we propose an efficient meta-analysis approach for identifying eQTLs from large multi-tissue eQTL datasets. We name our method RECOV because it uses a random effects (RE) meta-analysis with an explicit covariance (COV) term to model the correlation of effect that eQTLs have across tissues. Our approach is faster than the previous approaches and properly controls the false-positive rate. We apply our approach to the real multi-tissue eQTL dataset from GTEx that contains 44 tissues, and show that our approach detects more eQTLs and eGenes than previous approaches.

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Section: Recov: Random Effects (Re) Model With a Covariance (Cov) Termmentioning

confidence: 98%

Section: Eqtl Study In One Tissuementioning

confidence: 99%

Section: Eqtl Study In One Tissuementioning

confidence: 99%

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Applying meta-analysis to Genotype-Tissue Expression data from multiple tissues to identify eQTLs and increase the number of eGenes

Duong

2017

Preprint

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“…One of the main arguments trying to explain the missing heritability of phenotypes is that the effects of the different variants on the phenotype are not additive [40,11,12]. Nonlinear interactions between these variants, known as epistatic effects, could be hiding the heritability of phenotypes.…”

Section: Figmentioning

confidence: 99%

Phenotype Heritability in holobionts: An Evolutionary Model

Huitzil

Sandoval-Motta

Frank

et al. 2020

Preprint

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Many complex diseases are expressed with high incidence only in certain populations. Genealogy studies determine that these diseases are inherited with a high probability. However, genetic studies have been unable to identify the genomic signatures responsible for such heritability, as identifying the genetic variants that make a population prone to a given disease is not enough to explain its high occurrence within the population. This gap is known as the missing heritability problem. We know that the microbiota plays a very important role in determining many important phenotypic characteristics of its host, in particular, the complex diseases for which the missing heritability occurs. Therefore, when computing the heritability of a phenotype it is important to consider not only the genetic variation in the host but also in its microbiota. Here we test this hypothesis by studying an evolutionary model based on gene regulatory networks. Our results show that the holobiont (the host plus its microbiota) is capable of generating a much larger variability than the host alone, greatly reducing the missing heritability of the phenotype. This result strongly suggests that a considerably large part of the missing heritability can be attributed to the microbiome.

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“…As shown in Table 1, only mixed models adequately correct for population structure in this sample. Mixed models became important in human GWAS analysis because the estimates of and can be used to estimate the heritability of the trait which suggested that common variants explain a large proportion of the variance of complex traits than previously thought (Purcell et al 2009;Yang et al 2010;Eskin 2015).…”

Section: Population Structure and Mixed Models In Human Association Smentioning

confidence: 99%

Review: Population Structure in Genetic Studies: Confounding Factors and Mixed Models

Martin

Eskin

2016

Preprint

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A genome-wide association study (GWAS) seeks to identify genetic variants that contribute to the development and progression of a specific disease. Over the past 10 years, new approaches using mixed models have emerged to mitigate the deleterious effects of population structure and relatedness in association studies. However, developing GWAS techniques to effectively test for association while correcting for population structure is a computational and statistical challenge. Our review motivates the problem of population structure in association studies using laboratory mouse strains and how it can cause false positives associations. We then motivate mixed models in the context of unmodeled factors.

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Discovering genes involved in disease and the mystery of missing heritability

Cited by 22 publications

References 56 publications

Applying meta-analysis to Genotype-Tissue Expression data from multiple tissues to identify eQTLs and increase the number of eGenes

Applying meta-analysis to Genotype-Tissue Expression data from multiple tissues to identify eQTLs and increase the number of eGenes

Phenotype Heritability in holobionts: An Evolutionary Model

Review: Population Structure in Genetic Studies: Confounding Factors and Mixed Models

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