Biology subjects

Browning, S.

Publications and source records attributed to Browning, S..

7 recordsLinked to original sources

bioRxiv2025

Identity-by-descent mapping using multi-individual IBD with genome-wide multiple testing adjustment

We present an identity-by-descent mapping approach to test the association between genome-wide loci and complex traits. Our method evaluates whether levels of genetic similarities at specific genomic locations, captured by local relatedness matrices derived from multi-individual IBD sharing, are associated with phenotypic variation in complex traits. In addition, we propose an approach to adjust for multiple testing in genome-wide IBD mapping scans based on the correlation structure between test statistics across the genome. Through simulation studies, we demonstrate that our test has a well-controlled genome-wide type-I error rate and superior power to detect rare and untyped variants compared to standard single-variant tests. We applied our method to systolic blood pressure data from White British individuals in the UK Biobank.

genetics↗

bioRxiv2024

Simultaneous estimation of genotype error and uncalled deletion rates in whole genome sequence data

Genotype data include errors that may influence conclusions reached by downstream statistical analyses. Previous studies have estimated genotype error rates from discrepancies in human pedigree data, such as Mendelian inconsistent genotypes or apparent phase violations. However, uncalled deletions, which generally have not been accounted for in these studies, can lead to biased error rate estimates. In this study, we propose a genotype error model that considers both genotype errors and uncalled deletions when calculating the likelihood of the observed genotypes in parent-offspring trios. Using simulations, we show that when there are uncalled deletions, our model produces genotype error rate estimates that are less biased than estimates from a model that does not account for these deletions. We applied our model to SNVs in 77 sequenced White British parent-offspring trios in the UK Biobank. We use the Akaike information criterion to show that our model fits the data better than a model that does not account for uncalled deletions. We estimate the genotype error rate at SNVs with minor allele frequency > 0.001 in these data to be 3.2 x 10-4 (90% CI: [2.8 x 10-4, 6.2 x 10-4]). We estimate that 77% of the genotype errors at these markers are attributable to uncalled deletions (90% CI: [73%, 88%]). Author summaryA genotype error occurs when the genotype identified through molecular analysis does not match the actual genotype of the individual being analyzed. Because genotype errors can influence downstream statistical results, previous studies have attempted to estimate the rate of genotype errors in a study sample. However, uncalled deletions, which generally have not been accounted for in these studies, can lead to biased error rate estimates. In this study, we formulate a model adjusting for uncalled deletions when estimating genotype error rates. We show that when uncalled deletions are present, this model results in less biased estimates of genotype error rates compared to a model that does not adjust for uncalled deletions. We apply this model to SNVs in 77 sequenced White British parent-offspring trios in the UK Biobank and estimate the genotype error rate and the proportion of genotype errors that are attributable to uncalled deletions at SNVs with minor allele frequency > 0.001.

genetics↗

bioRxiv2022

Estimating the genome-wide mutation rate from thousands of unrelated individuals

We provide a method for estimating the genome-wide mutation rate from sequence data on unrelated individuals by using segments of identity by descent (IBD). The length of an IBD segment indicates the time to shared ancestor of the segment, and mutations that have occurred since the shared ancestor result in discordances between the two IBD haplotypes. Previous methods for IBD-based estimation of mutation rate have required the use of family data in order to accurately phase the genotypes. This has limited the scope of application of IBD-based mutation rate estimation. Here, we develop an IBD-based method for mutation rate estimation from population data, and we apply it to whole genome sequence data on 4,166 European American individuals from the TOPMed Framingham Heart Study, 2,966 European American individuals from the TOPMed My Life Our Future study, and 1,586 African American individuals from the TOPMed Hypertension Genetic Epidemiology Network study. Although mutation rates may differ between populations due to genetic factors, demographic factors such as average parental age, and environmental exposures, our results are consistent with equal genome-wide average mutation rates across these three populations. Our overall estimate of the average genome-wide mutation rate per 108 base pairs per generation for single nucleotide variants is 1.24 (95% CI 1.18-1.33).

genetics↗

bioRxiv2021

AFA: Computationally efficient Ancestral Frequency estimation in Admixed populations: the Hispanic Community Health Study/Study of Latinos

We developed a computationally efficient method, Ancestral Frequency estimation in Admixed populations (AFA), to estimate the frequencies of bi-allelic variants in admixed populations with an unlimited number of ancestries. AFA uses maximum likelihood estimation by modeling the conditional probability of having an allele given proportions of genetic ancestries. It can be applied using either global or local proportions of genetic ancestries. Simulations mimicking admixture demonstrated the high accuracy of the method. We implemented the method on data from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), an admixed population with three predominant continental ancestries: Amerindian, European, and African. Comparison of the European and African estimated frequencies to the respective gnomAD frequencies demonstrated high correlations, with Pearson R2=0.97-0.99. We provide a genome-wide dataset of the estimated three ancestral allele frequencies in HCHS/SOL for all available variants with allele frequency between 5%-95% in at least one of the three ancestral populations.

genomics↗

bioRxiv2020

Probabilistic estimation of identity by descent segment endpoints and detection of recent selection

Most methods for fast detection of identity by descent (IBD) segments report identity by state segments without any quantification of the uncertainty in the endpoints and lengths of the IBD segments. We present a method for determining the posterior probability distribution of IBD segment endpoints. Our approach accounts for genotype errors, recent mutations, and gene conversions which disrupt DNA sequence identity within IBD segments. We find that our methods estimates of uncertainty are well calibrated for homogeneous samples. We quantify endpoint uncertainty for 7.7 billion IBD segments from 408,883 individuals of White British ancestry in the UK Biobank, and use these IBD segments to find regions showing evidence of recent natural selection. We show that many spurious selection signals are eliminated by the use of unbiased estimates of IBD segment endpoints and a pedigree-based genetic map. Nine of the top ten regions with the greatest evidence for recent selection in our scan have been identified as selected in previous analyses using different approaches. Our computationally efficient method for quantifying IBD segment endpoint uncertainty is implemented in the open source ibd-ends software package.

genetics↗

bioRxiv2019

A fast and simple method for detecting identity by descent segments in large-scale data

Segments of identity by descent (IBD) are used in many genetic analyses. We present a method for detecting identical-by-descent haplotype segments that is optimized for large-scale genotype data. Our method, called hap-IBD, combines a compressed representation of genotype data, the positional Burrows-Wheeler transform, and multi-threaded execution to produce very fast analysis times. An attractive feature of hap-IBD is its simplicity: the input parameters clearly and precisely define the IBD segments that are reported, so that program correctness can be confirmed by users. We evaluate hap-IBD and four state-of-the-art IBD segment detection methods (GERMLINE, iLASH, RaPID, and TRUFFLE) using UK Biobank chromosome 20 data and simulated sequence data. We show that hap-IBD detects IBD segments faster and more accurately than competing methods, and that hap-IBD is the only method that can rapidly and accurately detect short 2-4 cM IBD segments in the full UK Biobank data. Analysis of 485,346 UK Biobank samples using hap-IBD with 12 computational threads detects 231.5 billion autosomal IBD segments with length [≥]2 cM in 24.4 hours.

bioinformatics↗

bioRxiv2019

Population-specific recombination maps from segments of identity by descent

Recombination rates vary significantly across the genome, and estimates of recombination rates are needed for downstream analyses such as haplotype phasing and genotype imputation. Existing methods for recombination rate estimation are limited by insufficient amounts of informative genetic data or by high computational cost. We present a method for using segments of identity by descent to infer recombination rates. Our method can be applied to sequenced population cohorts to obtain high-resolution, population-specific recombination maps. We use our method to generate new recombination maps for European Americans and for African Americans from TOPMed sequence data from the Framingham Heart Study (1626 unrelated individuals) and the Jackson Heart Study (2046 unrelated individuals). We compare our maps to existing maps using the Pearson correlation between estimated recombination rates. In Europeans we use the deCODE map, which is based on a very large set of Icelandic family data (126,407 meioses), as a gold standard against which to compare other maps. Our European American map has higher accuracy at fine-scale resolution (1-10kb) than linkage disequilibrium maps from the HapMap and 1000 Genomes projects. Our African American map has much higher accuracy than an admixture-based map that is derived from a similar number individuals, and similar accuracy at fine scales (1-10kb) to an admixture-based map that is derived from 15 times as many individuals.

evolutionary biology↗