Browse
Submit Data
Databases
API
Help

Dataset Information

13 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Quantifying the contribution of dominance deviation effects to complex trait variation in biobank-scale data.

ABSTRACT: The proportion of variation in complex traits that can be attributed to non-additive genetic effects has been a topic of intense debate. The availability of biobank-scale datasets of genotype and trait data from unrelated individuals opens up the possibility of obtaining precise estimates of the contribution of non-additive genetic effects. We present an efficient method to estimate the variation in a complex trait that can be attributed to additive (additive heritability) and dominance deviation (dominance heritability) effects across all genotyped SNPs in a large collection of unrelated individuals. Over a wide range of genetic architectures, our method yields unbiased estimates of additive and dominance heritability. We applied our method, in turn, to array genotypes as well as imputed genotypes (at common SNPs with minor allele frequency [MAF] > 1%) and 50 quantitative traits measured in 291,273 unrelated white British individuals in the UK Biobank. Averaged across these 50 traits, we find that additive heritability on array SNPs is 21.86% while dominance heritability is 0.13% (about 0.48% of the additive heritability) with qualitatively similar results for imputed genotypes. We find no statistically significant evidence for dominance heritability (p<0.05/50 accounting for the number of traits tested) and estimate that dominance heritability is unlikely to exceed 1% for the traits analyzed. Our analyses indicate a limited contribution of dominance heritability to complex trait variation.

SUBMITTER: Pazokitoroudi A

PROVIDER: S-EPMC8206203 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Quantifying selection acting on a complex trait using allele frequency time series data.

Project description:When selection is acting on a large genetically diverse population, beneficial alleles increase in frequency. This fact can be used to map quantitative trait loci by sequencing the pooled DNA from the population at consecutive time points and observing allele frequency changes. Here, we present a population genetic method to analyze time series data of allele frequencies from such an experiment. Beginning with a range of proposed evolutionary scenarios, the method measures the consistency of each with the observed frequency changes. Evolutionary theory is utilized to formulate equations of motion for the allele frequencies, following which likelihoods for having observed the sequencing data under each scenario are derived. Comparison of these likelihoods gives an insight into the prevailing dynamics of the system under study. We illustrate the method by quantifying selective effects from an experiment, in which two phenotypically different yeast strains were first crossed and then propagated under heat stress (Parts L, Cubillos FA, Warringer J, et al. [14 co-authors]. 2011. Revealing the genetic structure of a trait by sequencing a population under selection. Genome Res). From these data, we discover that about 6% of polymorphic sites evolve nonneutrally under heat stress conditions, either because of their linkage to beneficial (driver) alleles or because they are drivers themselves. We further identify 44 genomic regions containing one or more candidate driver alleles, quantify their apparent selective advantage, obtain estimates of recombination rates within the regions, and show that the dynamics of the drivers display a strong signature of selection going beyond additive models. Our approach is applicable to study adaptation in a range of systems under different evolutionary pressures.

| S-EPMC3731369 | biostudies-literature

Haplotype estimation for biobank-scale data sets.

Project description:The UK Biobank (UKB) has recently released genotypes on 152,328 individuals together with extensive phenotypic and lifestyle information. We present a new phasing method, SHAPEIT3, that can handle such biobank-scale data sets and results in switch error rates as low as ?0.3%. The method exhibits O(NlogN) scaling with sample size N, enabling fast and accurate phasing of even larger cohorts.

| S-EPMC4926957 | biostudies-other

Quantifying physiological trait variation with automated hyperspectral imaging in rice

Project description: Not available

| S-EPMC10548215 | biostudies-literature

Quantifying the contribution of recessive coding variation to developmental disorders.

Project description:We estimated the genome-wide contribution of recessive coding variation in 6040 families from the Deciphering Developmental Disorders study. The proportion of cases attributable to recessive coding variants was 3.6% in patients of European ancestry, compared with 50% explained by de novo coding mutations. It was higher (31%) in patients with Pakistani ancestry, owing to elevated autozygosity. Half of this recessive burden is attributable to known genes. We identified two genes not previously associated with recessive developmental disorders, KDM5B and EIF3F, and functionally validated them with mouse and cellular models. Our results suggest that recessive coding variants account for a small fraction of currently undiagnosed nonconsanguineous individuals, and that the role of noncoding variants, incomplete penetrance, and polygenic mechanisms need further exploration.

| S-EPMC6726470 | biostudies-literature

Quantifying the contribution of individual variation in timing to delay-discounting.

Project description:Delay-discounting studies in neuroscience, psychology, and economics have been mostly focused on concepts of self-control, reward evaluation, and discounting. Another important relationship to consider is the link between intertemporal choice and time perception. We presented 50 college students with timing tasks on the range of seconds to minutes and intertemporal-choice tasks on both the time-scale of seconds and of days. We hypothesized that individual differences in time perception would influence decisions about short experienced delays but not long delays. While we found some evidence that individual differences in internal clock speed account for some unexplained variance between choices across time-horizons, overall our findings suggest a nominal contribution of the altered sense of time in intertemporal choice.

| S-EPMC8443764 | biostudies-literature

Inferring population structure in biobank-scale genomic data.

Project description:Inferring the structure of human populations from genetic variation data is a key task in population and medical genomic studies. Although a number of methods for population structure inference have been proposed, current methods are impractical to run on biobank-scale genomic datasets containing millions of individuals and genetic variants. We introduce SCOPE, a method for population structure inference that is orders of magnitude faster than existing methods while achieving comparable accuracy. SCOPE infers population structure in about a day on a dataset containing one million individuals and variants as well as on the UK Biobank dataset containing 488,363 individuals and 569,346 variants. Furthermore, SCOPE can leverage allele frequencies from previous studies to improve the interpretability of population structure estimates.

| S-EPMC9069078 | biostudies-literature

Incomplete dominance of deleterious alleles contributes substantially to trait variation and heterosis in maize.

Project description:Deleterious alleles have long been proposed to play an important role in patterning phenotypic variation and are central to commonly held ideas explaining the hybrid vigor observed in the offspring of a cross between two inbred parents. We test these ideas using evolutionary measures of sequence conservation to ask whether incorporating information about putatively deleterious alleles can inform genomic selection (GS) models and improve phenotypic prediction. We measured a number of agronomic traits in both the inbred parents and hybrids of an elite maize partial diallel population and re-sequenced the parents of the population. Inbred elite maize lines vary for more than 350,000 putatively deleterious sites, but show a lower burden of such sites than a comparable set of traditional landraces. Our modeling reveals widespread evidence for incomplete dominance at these loci, and supports theoretical models that more damaging variants are usually more recessive. We identify haplotype blocks using an identity-by-decent (IBD) analysis and perform genomic prediction analyses in which we weigh blocks on the basis of complementation for segregating putatively deleterious variants. Cross-validation results show that incorporating sequence conservation in genomic selection improves prediction accuracy for grain yield and other fitness-related traits as well as heterosis for those traits. Our results provide empirical support for an important role for incomplete dominance of deleterious alleles in explaining heterosis and demonstrate the utility of incorporating functional annotation in phenotypic prediction and plant breeding.

| S-EPMC5633198 | biostudies-literature

Quantifying the heritability of glioma using genome-wide complex trait analysis.

Project description:Genome-wide association studies (GWAS) have successfully identified a number of common single-nucleotide polymorphisms (SNPs) influencing glioma risk. While these SNPs only explain a small proportion of the genetic risk it is unclear how much is left to be detected by other, yet to be identified, common SNPs. Therefore, we applied Genome-Wide Complex Trait Analysis (GCTA) to three GWAS datasets totalling 3,373 cases and 4,571 controls and performed a meta-analysis to estimate the heritability of glioma. Our results identify heritability estimates of 25% (95% CI: 20-31%, P?=?1.15?×?10(-17)) for all forms of glioma - 26% (95% CI: 17-35%, P?=?1.05?×?10(-8)) for glioblastoma multiforme (GBM) and 25% (95% CI: 17-32%, P?=?1.26?×?10(-10)) for non-GBM tumors. This is a substantial increase from the genetic variance identified by the currently identified GWAS risk loci (~6% of common heritability), indicating that most of the heritable risk attributable to common genetic variants remains to be identified.

| S-EPMC4667278 | biostudies-literature

Fast estimation of genetic correlation for biobank-scale data.

Project description:Genetic correlation is an important parameter in efforts to understand the relationships among complex traits. Current methods that analyze individual genotype data for estimating genetic correlation are challenging to scale to large datasets. Methods that analyze summary data, while being computationally efficient, tend to yield estimates of genetic correlation with reduced precision. We propose SCORE (scalable genetic correlation estimator), a randomized method of moments estimator of genetic correlation that is both scalable and accurate. SCORE obtains more precise estimates of genetic correlations relative to summary-statistic methods that can be applied at scale; it achieves a 44% reduction in standard error relative to LD-score regression (LDSC) and a 20% reduction relative to high-definition likelihood (HDL) (averaged over all simulations). The efficiency of SCORE enables computation of genetic correlations on the UK Biobank dataset, consisting of ≈300 K individuals and ≈500 K SNPs, in a few h (orders of magnitude faster than methods that analyze individual data, such as GCTA). Across 780 pairs of traits in 291,273 unrelated white British individuals in the UK Biobank, SCORE identifies significant genetic correlation between 200 additional pairs of traits over LDSC (beyond the 245 pairs identified by both).

| S-EPMC8764132 | biostudies-literature

Correction: Incomplete dominance of deleterious alleles contributes substantially to trait variation and heterosis in maize.

Project description:[This corrects the article DOI: 10.1371/journal.pgen.1007019.].

| S-EPMC8478179 | biostudies-literature