Biology subjects

Sriram Sankararaman

Publications and source records attributed to Sriram Sankararaman.

5 recordsLinked to original sources

bioRxiv2016

Widespread allelic heterogeneity in complex traits

Recent successes in genome-wide association studies (GWASs) make it possible to address important questions about the genetic architecture of complex traits, such as allele frequency and effect size. One lesser-known aspect of complex traits is the extent of allelic heterogeneity (AH) arising from multiple causal variants at a locus. We developed a computational method to infer the probability of AH and applied it to three GWAS and four expression quantitative trait loci (eQTL) datasets. We identified a total of 4152 loci with strong evidence of AH. The proportion of all loci with identified AH is 4-23% in eQTLs, 35% in GWAS of High-Density Lipoprotein (HDL), and 23% in schizophrenia. For eQTLs, we observed a strong correlation between sample size and the proportion of loci with AH (R2=0.85, P = 2.2e-16), indicating that statistical power prevents identification of AH in other loci. Understanding the extent of AH may guide the development of new methods for fine mapping and association mapping of complex traits.

Genetics↗

bioRxiv2016

Colocalization of GWAS and eQTL Signals Detects Target Genes

The vast majority of genome-wide association studies (GWAS) risk loci fall in non-coding regions of the genome. One possible hypothesis is that these GWAS risk loci alter the individuals disease risk through their effect on gene expression in different tissues. In order to understand the mechanisms driving a GWAS risk locus, it is helpful to determine which gene is affected in specific tissue types. For example, the relevant gene and tissue may play a role in the disease mechanism if the same variant responsible for a GWAS locus also affects gene expression. Identifying whether or not the same variant is causal in both GWAS and eQTL studies is challenging due to the uncertainty induced by linkage disequilibrium (LD) and the fact that some loci harbor multiple causal variants. However, current methods that address this problem assume that each locus contains a single causal variant. In this paper, we present a new method, eCAVIAR, that is capable of accounting for LD while computing the quantity we refer to as the colocalization posterior probability (CLPP). The CLPP is the probability that the same variant is responsible for both the GWAS and eQTL signal. eCAVIAR has several key advantages. First, our method can account for more than one causal variant in any loci. Second, it can leverage summary statistics without accessing the individual genotype data. We use both simulated and real datasets to demonstrate the utility of our method. Utilizing publicly available eQTL data on 45 different tissues, we demonstrate that computing CLPP can prioritize likely relevant tissues and target genes for a set of Glucose and Insulin-related traits loci. eCAVIAR is available at http://genetics.cs.ucla.edu/caviar/

Genetics↗

bioRxiv2015

Dating ancient human samples using the recombination clock

The study of human evolution has been revolutionized by inferences from ancient DNA analyses. Key to these is the reliable estimation of the age of ancient specimens. The current best practice is radiocarbon dating, which relies on characterizing the decay of radioactive carbon isotope (14C), and is applicable for dating up to 50,000-year-old samples. Here, we introduce a new genetic method that uses recombination clock for dating. The key idea is that an ancient genome has evolved less than the genomes of extant individuals. Thus, given a molecular clock provided by the steady accumulation of recombination events, one can infer the age of the ancient genome based on the number of missing years of evolution. To implement this idea, we take advantage of the shared history of Neanderthal gene flow into non-Africans that occurred around 50,000 years ago. Using the Neanderthal ancestry decay patterns, we estimate the Neanderthal admixture time for both ancient and extant samples. The difference in these admixture dates then provides an estimate of the age of the ancient genome. We show that our method provides reliable results in simulations. We apply our method to date five ancient Eurasian genomes with radiocarbon dates ranging between 12,000 to 45,000 years and recover consistent age estimates. Our method provides a complementary approach for dating ancient human samples and is applicable to ancient non-African genomes with Neanderthal ancestry. Extensions of this methodology that use older shared events may be able to date ancient genomes that fall beyond the radiocarbon frontier.\n\nSignificanceWe introduce a new genetic method for dating ancient human samples that uses the recombination clock. The main idea relies on the insight that an ancient genome lacks several thousand years of evolution compared to genomes of living individuals. To infer the age of ancient genomes, we take advantage of the shared history of Neanderthal gene flow into non-Africans that occurred around 50,000 years ago. By characterizing the dates of Neanderthal gene flow in ancient and extant genomes and quantifying the difference in these dates, we estimate the age of the ancient specimen. Our method is applicable for dating ancient samples more recent than the Neanderthal mixture event, so on par with radiocarbon dating, providing a complementary approach for dating.

Evolutionary Biology↗

bioRxiv2015

Leveraging distant relatedness to quantify human mutation and gene conversion rates

The rate at which human genomes mutate is a central biological parameter that has many implications for our ability to understand demographic and evolutionary phenomena. We present a method for inferring mutation and gene conversion rates using the number of sequence differences observed in identical-by-descent (IBD) segments together with a reconstructed model of recent population size history. This approach is robust to, and can quantify, the presence of substantial genotyping error, as validated in coalescent simulations. We applied the method to 498 trio-phased Dutch individuals from the Genome of the Netherlands (GoNL) project, sequenced at an average depth of 13x. We infer a point mutation rate of 1.66 {+/-} 0.04 x 10-8 per base per generation, and a rate of 1.26 {+/-} 0.06 x 10-9 for < 20 bp indels. Our estimated average genome-wide mutation rate is higher than most pedigree-based estimates reported thus far, but lower than estimates obtained using substitution rates across primates. By quantifying how estimates vary as a function of allele frequency, we infer the probability that a site is involved in non-crossover gene conversion as 5.99 {+/-} 0.69 x 10-6, consistent with recent reports. We find that recombination does not have observable mutagenic effects after gene conversion is accounted for, and that local gene conversion rates reflect recombination rates. We detect a strong enrichment for recent deleterious variation among mismatching variants found within IBD regions, and observe summary statistics of local IBD sharing to closely match previously proposed metrics of background selection, but find no significant effects of selection on our estimates of mutation rate. We detect no evidence for strong variation of mutation rates in a number of genomic annotations obtained from several recent studies.

Genetics↗

bioRxiv2015

Calibrating the Human Mutation Rate via Ancestral Recombination Density in Diploid Genomes

The human mutation rate is an essential parameter for studying the evolution of our species, interpreting present-day genetic variation, and understanding the incidence of genetic disease. Nevertheless, our current estimates of the rate are uncertain. Most notably, recent approaches based on counting de novo mutations in family pedigrees have yielded significantly smaller values than classical methods based on sequence divergence. Here, we propose a new method that uses the fine-scale human recombination map to calibrate the rate of accumulation of mutations. By comparing local heterozygosity levels in diploid genomes to the genetic distance scale over which these levels change, we are able to estimate a long-term mutation rate averaged over hundreds or thousands of generations. We infer a rate of 1.61 {+/-} 0.13 x 10-8 mutations per base per generation, which falls in between phylogenetic and pedigree-based estimates, and we suggest possible mechanisms to reconcile our estimate with previous studies. Our results support intermediate-age divergences among human populations and between humans and other great apes.

Genetics↗