Biology subjects

Tahmasbi, R.

Publications and source records attributed to Tahmasbi, R..

3 recordsLinked to original sources

bioRxiv2017

Testing the moderation of quantitative gene by environment interactions in unrelated individuals

The environment can moderate the effect of genes - a phenomenon called gene-environment (GxE) interaction. There are two broad types of GxE modeled in human behavior - qualitative GxE, where the effects of individual genetic variants differ depending on some environmental moderator, and quantitative GxE, where the additive genetic variance changes as a function of an environmental moderator. Tests of both qualitative and quantitative GxE have traditionally relied on comparing the covariances between twins and close relatives, but recently there has been interest in testing such models on unrelated individuals measured on genomewide data. However, to date, there has been no ability to test quantitative GxE effects in unrelated individuals using genomewide data because standard software cannot solve nonlinear constraints. Here, we introduce a maximum likelihood approach with parallel constrained optimization to fit such models. We use simulation to estimate the accuracy, power, and type I error rates of our method and to gauge its computational performance, and then apply this method to IQ data measured on 40,172 individuals with whole-genome SNP data from the UK Biobank. We found that the additive genetic variation of IQ tagged by SNPs increases as socioeconomic status (SES) decreases, opposite the direction found by several twin studies conducted in the U.S. on adolescents, but consistent with several studies from Europe and Australia on adults.

bioinformatics↗

bioRxiv2017

Narrow-sense heritability estimation of complex traits using identity-by-descent information.

Heritability is a fundamental parameter in genetics. Traditional estimates based on family or twin studies can be biased due to shared environmental or non-additive genetic variance. Alternatively, those based on genotyped or imputed variants typically underestimate narrow-sense heritability contributed by rare or otherwise poorly-tagged causal variants. Identical-by-descent (IBD) segments of the genome share all variants between pairs of chromosomes except new mutations that have arisen since the last common ancestor. Therefore, relating phenotypic similarity to degree of IBD sharing among classically unrelated individuals is an appealing approach to estimating the near full additive genetic variance while avoiding biases that can occur when modeling close relatives. We applied an IBD-based approach (GREML-IBD) to estimate heritability in unrelated individuals using phenotypic simulation with thousands of whole genome sequences across a range of stratification, polygenicity levels, and the minor allele frequencies of causal variants (CVs). IBD-based heritability estimates were unbiased when using unrelated individuals, even for traits with extremely rare CVs, but stratification led to strong biases in IBD-based heritability estimates with poor precision. We used data on two traits in ~120,000 people from the UK Biobank to demonstrate that, depending on the trait and possible confounding environmental effects, GREML-IBD can be applied successfully to very large genetic datasets to infer the contribution of very rare variants lost using other methods. However, we observed apparent biases in this real data that were not predicted from our simulation, suggesting that more work may be required to understand factors that influence IBD-based estimates.

genetics↗

bioRxiv2017

Comparison of methods that use whole genome data to estimate the heritability and genetic architecture of complex traits.

Heritability, h2, is a foundational concept in genetics, critical to understanding the genetic basis of complex traits. Recently-developed methods that estimate heritability from genotyped SNPs, h2 SNP, explain substantially more genetic variance than genome-wide significant loci, but less than classical estimates from twins and families. However, h2SNP estimates have yet to be comprehensively compared under a range of genetic architectures, making it difficult to draw conclusions from sometimes conflicting published estimates. Here, we used thousands of real whole genome sequences to simulate realistic phenotypes under a variety of genetic architectures, including those from very rare causal variants. We compared the performance of ten methods across different types of genotypic data (commercial SNP array positions, whole genome sequence variants, and imputed variants) and under differing causal variant frequencies, levels of stratification, and relatedness thresholds. These results provide guidance in interpreting past results and choosing optimal approaches for future studies. We then chose two methods (GREML-MS and GREML-LDMS) that best estimated overall h2SNP and the causal variant frequency spectra to six phenotypes in the UK Biobank using imputed genome-wide variants. Our results suggest that as imputation reference panels become larger and more diverse, estimates of the frequency distribution of causal variants will become increasingly unbiased and the vast majority of trait narrow-sense heritability will be accounted for.

genetics↗