Biology subjects

Browning, B. L.

Publications and source records attributed to Browning, B. L..

13 recordsLinked to original sources

bioRxiv2025

FLARE2: local ancestry inference with poorly-matched reference panels

The original FLARE method provides computationally efficient and highly accurate local ancestry inference in cases where a closely-matched reference panel is available for each ancestry. In this work, we extend FLARE to incorporate a haplotype clustering algorithm that enables accurate local ancestry inference in scenarios where one or more ancestries do not have a closely-matched reference. This method retains the computational efficiency and accuracy of the original FLARE method while greatly extending its applicability. We apply the new method to data from the Mozabite population from the Human Genome Diversity Project. On the autosomes, we find that the Mozabite samples derive 67% of their ancestry from a population related to European and Middle Eastern populations, with the other 33% of their ancestry coming from a population related to West African populations, with an admixture time 48 generations ago. In contrast, on the X chromosome, we find that the individuals have 76% of their ancestry from a population related to European and Middle Eastern populations. Author summaryAdmixed individuals have ancestry from more than one ancestral population. At each point in the genome, the ancestry can be inferred using statistical techniques. These techniques make use of genetic data from reference populations that represent the ancestries. We recently developed the FLARE method that can accurately estimate ancestry and is computationally fast even for very large data sets. However, the original FLARE method required that each admixing ancestry has a close-related reference population. In many cases, it is not possible to obtain good reference data for each of the ancestries. The methods developed in this paper allow for local ancestry inference in these cases. We applied our method to inferring local ancestry in North African Mozabite individuals.

genetics↗

bioRxiv2025

Estimating gene conversion rates from population data using multi-individual identity by descent

In humans, homologous gene conversions occur at a higher rate than crossovers, however gene conversion tracts are small and often unobservable. As a result, estimating gene conversion rates is more difficult than estimating crossover rates. We present a method for multi-individual identity-by-descent (IBD) inference that allows for mismatches due to genotype error and gene conversion. We use the inferred IBD to detect alleles that have changed due to gene conversion in the recent past. We analyze data from the TOPMed and UK Biobank studies to estimate autosome-wide maps of gene conversion rates. For 10 kb, 100kb, and 1 Mb windows, the correlation between our TOPMed gene conversion map and the deCODE sex-averaged crossover map ranges from 0.56 to 0.67. We find that the strongest gene conversion hotspots typically die back to the baseline gene conversion rate within 1 kb. In 100 kb and 1 Mb windows, our estimated gene conversion map has higher correlation than the deCODE sex-averaged crossover map with PRDM9 binding enrichment (0.34 vs 0.29 for 100 kb windows and 0.52 vs 0.34 for 1 Mb windows), suggesting that the effect of PRDM9 is greater on gene conversion than on crossover recombination. Our TOPMed gene conversion maps are constructed from 55-fold more observed allele conversions than the recently published deCODE gene conversion maps. Our map provides sex-averaged estimates for 10 kb, 100 kb, and 1 Mb windows, whereas the deCODE gene conversion maps provide sex-specific estimates for 3 Mb windows.

bioinformatics↗

bioRxiv2024

Adjusting for principal components can induce spurious associations in genome-wide association studies in admixed populations

Principal component analysis (PCA) is widely used to control for population structure in genome-wide association studies (GWAS). Top principal components (PCs) typically reflect population structure, but challenges arise in deciding how many PCs are needed and ensuring that PCs do not capture other artifacts such as regions with atypical linkage disequilibrium (LD). In response to the latter, many groups suggest performing LD pruning or excluding known high LD regions prior to PCA. However, these suggestions are not universally implemented and the implications for GWAS are not fully understood, especially in the context of admixed populations. In this paper, we investigate the impact of pre-processing and the number of PCs included in GWAS models in African American samples from the Womens Womens Health Initiative SNP Health Association Resource and two Trans-Omics for Precision Medicine Whole Genome Sequencing Project contributing studies (Jackson Heart Study and Genetic Epidemiology of Chronic Obstructive Pulmonary Disease Study). In all three samples, we find the first PC is highly correlated with genome-wide ancestry whereas later PCs often capture local genomic features. The pattern of which, and how many, genetic variants are highly correlated with individual PCs differs from what has been observed in prior studies focused on European populations and leads to distinct downstream consequences: adjusting for such PCs yields biased effect size estimates and elevated rates of spurious associations due to the phenomenon of collider bias. Excluding high LD regions identified in previous studies does not resolve these issues. LD pruning proves more effective, but the optimal choice of thresholds varies across datasets. Altogether, our work highlights unique issues that arise when using PCA to control for ancestral heterogeneity in admixed populations and demonstrates the importance of careful pre-processing and diagnostics to ensure that PCs capturing multiple local genomic features are not included in GWAS models. Author SummaryPrincipal component analysis (PCA) is a widely used technique in human genetics research. One of its most frequent applications is in the context of genetic association studies, wherein researchers use PCA to infer, and then adjust for, the genetic ancestry of study participants. Although a powerful approach, prior work has shown that PCA sometimes captures other features or data quality issues, and pre-processing steps have been suggested to address these concerns. However, the utility and downstream implications of this recommended preprocessing are not fully understood, nor are these steps universally implemented. Moreover, the vast majority of prior work in this area was conducted in studies that exclusively included individuals of European ancestry. Here, we revisit this work in the context of admixed populations--populations with diverse, mixed ancestry that have been largely underrepresented in genetics research to date. We demonstrate the unique concerns that can arise in this context and illustrate the detrimental effects that including principal components in genetic association study models can have when not implemented carefully. Altogether, we hope our work serves as a reminder of the care that must be taken--including careful pre-processing, diagnostics, and modeling choices--when implementing PCA in admixed populations and beyond.

genetics↗

bioRxiv2023

Biobank-scale inference of multi-individual identity by descent and gene conversion

We present a method for efficiently identifying clusters of identical-by-descent haplotypes in biobank-scale sequence data. Our multi-individual approach enables much more efficient collection and storage of identity by descent (IBD) information than approaches that detect and store pairwise IBD segments. Our methods computation time, memory requirements, and output size scale linearly with the number of individuals in the dataset. We also present a method for using multi-individual IBD to detect alleles changed by gene conversion. Application of our methods to the autosomal sequence data for 125,361 White British individuals in the UK Biobank detects more than 9 million converted alleles. This is 2900 times more alleles changed by gene conversion than were detected in a previous analysis of familial data. We estimate that more than 250,000 sequenced probands and a much larger number of additional genomes from multi-generational family members would be required to find a similar number of alleles changed by gene conversion using a family-based approach.

genetics↗

bioRxiv2022

Statistical phasing of 150,119 sequenced genomes in the UK Biobank

The first release of UK Biobank whole genome sequence data contains 150,119 genomes. We present an open-source pipeline for filtering, phasing, and indexing these genomes on the cloud-based UK Biobank Research Analysis Platform. This pipeline makes it possible to apply haplotype-based methods to UK Biobank whole genome sequence data. The pipeline uses BCFtools for marker filtering, Beagle for genotype phasing, and tabix for VCF indexing. We used the pipeline to phase 406 million single nucleotide variants on chromosomes 1-22 and X at a cost of 2,309 British pounds. The maximum time required to process a chromosome was 2.6 days. In order to assess phase accuracy, we modified the pipeline to exclude trio parents. We observed a switch error rate of 0.0016 on chromosome 20 in the White British trio offspring. If we exclude markers with nonmajor allele frequency < 0.1% after phasing, this switch error rate decreases by 80% to 0.00032.

bioinformatics↗

bioRxiv2022

Fast, accurate local ancestry inference with FLARE

Local ancestry is the source ancestry at each point in the genome of an admixed individual. Inferred local ancestry is used for admixture mapping and population genetic analyses. We present FLARE (Fast Local Ancestry Estimation), a new method for local ancestry inference. FLARE achieves high accuracy through the use of an extended Li and Stephens model, and it achieves exceptional computational performance through incorporation of computational techniques developed for genotype imputation. Memory requirements are reduced through on-the-fly compression of reference haplotypes and stored checkpoints. Computation time is reduced through the use of composite reference haplotypes. These techniques allow FLARE to scale to data sets with hundreds of thousands of sequenced individuals and to provide superior accuracy on large-scale data. FLARE is freely available at https://github.com/browning-lab/flare.

genetics↗

bioRxiv2022

IBD-based estimation of X chromosome effective population size with application to sex-specific demographic history

The effective size of a population (Ne) in the recent past can be estimated through analysis of identity-by-descent (IBD) segments. Several methods have been developed for estimating Ne from autosomal IBD segments, but no such effort has been made with X chromosome IBD segments. In this work, we propose a method to estimate the X chromosome effective population size from X chromosome IBD segments. We show how to use the estimated autosome Ne and X chromosome Ne to estimate female and male effective population sizes. We demonstrate the accuracy of our autosome and X chromosome Ne estimation with simulated data. We find that estimated female and male effective population sizes generally reflect the simulated sex-specific effective population sizes across the past 100 generations, but that short-term differences between the estimated sex-specific Ne across tens of generations may not reliably indicate true sex-specific differences. We analyzed the effective size of populations represented by samples of sequenced UK White British and UK Indian individuals from the UK Biobank.

genetics↗

bioRxiv2022

Genotype error biases trio-based estimates of haplotype phase accuracy

Haplotypes can be estimated from unphased genotype data using statistical methods. When parent-offspring data are available for inferring true phase from Mendelian inheritance rules, the accuracy of statistical phasing is usually measured by the switch error rate, which is the proportion of pairs of consecutive heterozygotes that are incorrectly phased. We present a method for estimating the genotype error rate from parent-offspring trios and a method for estimating the bias in the observed switch error rate that is caused by genotype error. We apply these methods to 485,301 genotyped UK Biobank samples that include 899 White British trios and to 38,387 sequenced TOPMed samples that include 217 African Caribbean trios and 669 European American trios. We show that genotype error inflates the observed switch error rate and that the relative bias increases with sample size. For the UK Biobank White British trios, we estimate that the observed switch error rate in the trio offspring is 2.4 times larger than the true switch error rate (1.41 x 10-3 vs 5.79 x 10-4) and that the average distance between phase errors is 64 megabases.

bioinformatics↗

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↗

bioRxiv2019

Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program

Summary paragraphThe Trans-Omics for Precision Medicine (TOPMed) program seeks to elucidate the genetic architecture and disease biology of heart, lung, blood, and sleep disorders, with the ultimate goal of improving diagnosis, treatment, and prevention. The initial phases of the program focus on whole genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here, we describe TOPMed goals and design as well as resources and early insights from the sequence data. The resources include a variant browser, a genotype imputation panel, and sharing of genomic and phenotypic data via dbGaP. In 53,581 TOPMed samples, >400 million single-nucleotide and insertion/deletion variants were detected by alignment with the reference genome. Additional novel variants are detectable through assembly of unmapped reads and customized analysis in highly variable loci. Among the >400 million variants detected, 97% have frequency <1% and 46% are singletons. These rare variants provide insights into mutational processes and recent human evolutionary history. The nearly complete catalog of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and non-coding sequence variants to phenotypic variation. Furthermore, combining TOPMed haplotypes with modern imputation methods improves the power and extends the reach of nearly all genome-wide association studies to include variants down to ~0.01% in frequency.

genomics↗

bioRxiv2018

A one penny imputed genome from next generation reference panels

Genotype imputation is commonly performed in genome-wide association studies because it greatly increases the number of markers that can be tested for association with a trait. In general, one should perform genotype imputation using the largest reference panel that is available because the number of accurately imputed variants increases with reference panel size. However, one impediment to using larger reference panels is the increased computational cost of imputation. We present a new genotype imputation method, Beagle 5.0, which greatly reduces the computational cost of imputation from large reference panels. We compare Beagle 5.0 with Beagle 4.1, Impute4, Minimac3, and Minimac4 using 1000 Genomes Project data, Haplotype Reference Consortium data, and simulated data for 10k, 100k, 1M, and 10M reference samples. All methods produce nearly identical accuracy, but Beagle 5.0 has the lowest computation time and the best scaling of computation time with increasing reference panel size. For 10k, 100k, 1M, and 10M reference samples and 1000 phased target samples, Beagle 5.0s computation time is 3x (10k), 12x (100k), 43x (1M), and 533x (10M) faster than the fastest alternative method. Cost data from the Amazon Elastic Compute Cloud show that Beagle 5.0 can perform genome-wide imputation from 10M reference samples into 1000 phased target samples at a cost of less than one US cent per sample.\n\nBeagle 5.0 is freely available from https://faculty.washington.edu/browning/beagle/beagle.html.

bioinformatics↗