Biology subjects

Kemper, K. E.

Publications and source records attributed to Kemper, K. E..

4 recordsLinked to original sources

bioRxiv2018

Imprint of Assortative Mating on the Human Genome

Non-random mate-choice with respect to complex traits is widely observed in humans, but whether this reflects true phenotypic assortment, environment (social homogamy) or convergence after choosing a partner is not known. Understanding the causes of mate choice is important, because assortative mating (AM) if based upon heritable traits, has genetic and evolutionary consequences. AM is predicted under Fishers classical theory1 to induce a signature in the genome at trait-associated loci that can be detected and quantified. Here, we develop and apply a method to quantify AM on a specific trait by estimating the correlation ({theta}) between genetic predictors of the trait from SNPs on odd versus even chromosomes. We show by theory and simulation that the effect of AM can be distinguished from population stratification. We applied this approach to 32 complex traits and diseases using SNP data from [~]400,000 unrelated individuals of European ancestry. We found significant evidence of AM for height ({theta}=3.2%) and educational attainment ({theta}=2.7%), both consistent with theoretical predictions. Overall, our results imply that AM involves multiple traits, affects the genomic architecture of loci that are associated with these traits and that the consequence of mate choice can be detected from a random sample of genomes.

genetics↗

bioRxiv2018

Comparison of Genotypic and Phenotypic Correlations: Cheveruds Conjecture in Humans

Accurate estimation of genetic correlation requires large sample sizes and access to genetically informative data, which are not always available. Accordingly, phenotypic correlations are often assumed to reflect genotypic correlations in evolutionary biology. Cheveruds conjecture asserts that the use of phenotypic correlations as proxies for genetic correlations is appropriate. Empirical evidence of the conjecture has been found across plant and animal species, with results suggesting that there is indeed a robust relationship between the two. Here, we investigate the conjecture in human populations, an analysis made possible by recent developments in availability of human genomic data and computing resources. A sample of 108,035 British European individuals from the UK Biobank was split equally into discovery and replication datasets. 17 traits were selected based on sample size, distribution and heritability. Genetic correlations were calculated using linkage disequilibrium score regression applied to the genome-wide association summary statistics of pairs of traits, and compared within and across datasets. Strong and significant correlations were found for the between-dataset comparison, suggesting that the genetic correlations from one independent sample were able to predict the phenotypic correlations from another independent sample within the same population. Designating the selected traits as morphological or non-morphological indicated little difference in correlation. The results of this study support the existence of a relationship between genetic and phenotypic correlations in humans. This finding is of specific interest in anthropological studies, which use measured phenotypic correlations to make inferences about the genetics of ancient human populations.

genetics↗

bioRxiv2018

Novel susceptibility loci and genetic regulation mechanisms for type 2 diabetes

We conducted a meta-analysis of genome-wide association studies (GWAS) with [~]16 million genotyped/imputed genetic variants in 62,892 type 2 diabetes (T2D) cases and 596,424 controls of European ancestry. We identified 139 common and 4 rare (minor allele frequency < 0.01) variants associated with T2D, 42 of which (39 common and 3 rare variants) were independent of the known variants. Integration of the gene expression data from blood (n = 14,115 and 2,765) and other T2D-relevant tissues (n = up to 385) with the GWAS results identified 33 putative functional genes for T2D, three of which were targeted by approved drugs. A further integration of DNA methylation (n = 1,980) and epigenomic annotations data highlighted three putative T2D genes (CAMK1D, TP53INP1 and ATP5G1) with plausible regulatory mechanisms whereby a genetic variant exerts an effect on T2D through epigenetic regulation of gene expression. We further found evidence that the T2D-associated loci have been under purifying selection.

genetics↗

bioRxiv2018

Meta-analysis of genome-wide association studies for height and body mass index in ~700,000 individuals of European ancestry

Genome-wide association studies (GWAS) stand as powerful experimental designs for identifying DNA variants associated with complex traits and diseases. In the past decade, both the number of such studies and their sample sizes have increased dramatically. Recent GWAS of height and body mass index (BMI) in [~]250,000 European participants have led to the discovery of [~]700 and [~]100 nearly independent SNPs associated with these traits, respectively. Here we combine summary statistics from those two studies with GWAS of height and BMI performed in [~]450,000 UK Biobank participants of European ancestry. Overall, our combined GWAS meta-analysis reaches N[~]700,000 individuals and substantially increases the number of GWAS signals associated with these traits. We identified 3,290 and 716 near-independent SNPs associated with height and BMI, respectively (at a revised genome-wide significance threshold of p<1 x 10-8), including 1,185 height-associated SNPs and 554 BMI-associated SNPs located within loci not previously identified by these two GWAS. The genome-wide significant SNPs explain [~]24.6% of the variance of height and [~]5% of the variance of BMI in an independent sample from the Health and Retirement Study (HRS). Correlations between polygenic scores based upon these SNPs with actual height and BMI in HRS participants were 0.44 and 0.20, respectively. From analyses of integrating GWAS and eQTL data by Summary-data based Mendelian Randomization (SMR), we identified an enrichment of eQTLs amongst lead height and BMI signals, prioritisting 684 and 134 genes, respectively. Our study demonstrates that, as previously predicted, increasing GWAS sample sizes continues to deliver, by discovery of new loci, increasing prediction accuracy and providing additional data to achieve deeper insight into complex trait biology. All summary statistics are made available for follow up studies.

genetics↗