Biology subjects

Browning, S. R.

Publications and source records attributed to Browning, S. R..

14 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↗

bioRxiv2025

Multiple-testing corrections in selection scans using identity-by-descent segments

Failing to correct for multiple testing in selection scans can lead to false discoveries of recent genetic adaptations. The scanning statistics in selection studies are often too complicated to theoretically derive a genome-wide significance level or empirically validate control of the family-wise error rate (FWER). By modeling the autocorrelation of identity-by-descent (IBD) rates, we propose a computationally efficient method to determine genome-wide significance levels in an IBD-based scan for recent positive selection. In whole genome simulations, we show that our method has approximate control of the FWER and can adapt to the spacing of tests along the genome. We also show that these scans can have more than fifty percent power to reject the null model in hard sweeps with a selection coefficient s >= 0.01 and a sweeping allele frequency between twenty-five and seventy-five percent. A few human genes and gene complexes have statistically significant excesses of IBD segments in thousands of samples of African, European, and South Asian ancestry groups from the Trans-Omics for Precision Medicine project and the United Kingdom Biobank. Among the significant loci, many signals of recent selection are shared across ancestry groups. One shared selection signal at a skeletal cell development gene is extremely strong in African ancestry samples. HighlightsO_LIWe propose a method to address multiple testing when scanning along the genome for excess identity-by-descent rates. C_LIO_LIIn whole genome simulations, we calculate that the family-wise error rates of our method are close to the desired family-wise significance level. C_LIO_LIWe perform six selection scans in two consortium datasets covering different ancestry groups and reference genome builds. C_LIO_LIFor a genomic region on chromosome 16, we report extremely high identity-by-descent rates in African ancestry groups and replication in European and South Asian ancestry groups. C_LI

genetics↗

bioRxiv2024

Mean gene conversion tract length in humans estimated to be 459 bp from UK Biobank sequence data

Non-crossover gene conversion is a type of meiotic recombination characterized by the non-reciprocal transfer of genetic material between homologous chromosomes. Gene conversions are thought to occur within relatively short tracts of DNA. However, the number of observable gene conversion tracts per study has so far been limited by the use of pedigree or sperm-typing data to detect gene conversion events. In this study, we propose a statistical method to model the length distribution of gene conversion tracts in humans, using nearly one million gene conversion tracts detected from the UK Biobank whole autosome data. To handle the large number of tracts, we designed a computationally efficient inferential framework. Our method further accounts for regional variation in marker density and heterozygosity across the genome, which can influence the observed length of gene conversion tracts. We allow for multiple candidate tract length distributions and select the best fitting distribution using the Akaike Information Criterion (AIC). Applying our method, we estimate that most tracts have a mean of 16.9 bp (95% CI: [16.4, 17.0]), and only a very small proportion of tracts have a much larger mean of 724.7 bp (95% CI: [720.1, 728.7]). We further estimate the proportion of gene conversion tracts with the larger mean to be 0.00525 (95% CI: [0.005, 0.00525]). After stratifying by crossover-hotspot overlap, we infer that tracts whose midpoints lie within crossover hotspots are, on average, longer than the remaining tracts.

genetics↗

bioRxiv2024

Fast simulation of identity-by-descent segments

The worst-case runtime complexity to simulate haplotype segments identical by descent (IBD) is quadratic in sample size. We propose two main techniques to reduce the compute time, both of which are motivated by coalescent and recombination processes. We provide mathematical results that explain why our algorithm should outperform a naive implementation with high probability. In our experiments, we observe average compute times to simulate detectable IBD segments around a locus that scale approximately linearly in sample size and take a couple of seconds for sample sizes that are less than ten thousand diploid individuals. In contrast, we find that existing methods to simulate IBD segments take minutes to hours for sample sizes exceeding a few thousand diploid individuals. When using IBD segments to study recent positive selection around a locus, our efficient simulation algorithm makes feasible statistical inferences, e.g., parametric bootstrapping in analyses of large biobanks, that would be otherwise intractable.

genetics↗

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

Modeling recent positive selection in Americans of European ancestry

Recent positive selection can result in an excess of long identity-by-descent (IBD) haplotype segments. The statistical methods that we propose here address three major objectives in studying selective sweeps: scanning for regions of interest, identifying possible sweeping alleles, and estimating a selection coefficient s. First, we implement a selection scan to locate regions of excess IBD rate. Second, we develop a statistic to rank alleles that are in strong linkage disequilibrium with a putative sweeping allele. We aggregate these scores to estimate the allele frequency of the sweeping allele, even if it is not genotyped. Third, we propose an estimator for the selection coefficient and quantify uncertainty using the parametric bootstrap. Comparing against state-of-the-art methods in extensive simulations, we show that our methods are better at identifying sweeping alleles that are at low frequency and at estimating S when S [≥] 0.015. We apply these methods to study positive selection in European ancestry samples from the TOPMed project. We analyze eight loci where the IBD rate is more than four standard deviations above the population median. The IBD rate at LCT is thirty-five standard deviations above the population median, and our estimates of its selection coefficient imply strong selection within the past two hundred generations. Overall, we present robust and accurate approaches to study very recent adaptive evolution without knowing the identity of the causal allele or using time series data.

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↗

bioRxiv2019

De novo mutations across 1,465 diverse genomes reveal novel mutational insights and reductions in the Amish founder population.

de novo Mutations (DNMs), or mutations that appear in an individual despite not being seen in their parents, are an important source of genetic variation whose impact is relevant to studies of human evolution, genetics, and disease. Utilizing high-coverage whole genome sequencing data as part of the Trans-Omics for Precision Medicine (TOPMed) program, we directly estimate and analyze DNM counts, rates, and spectra from 1,465 trios across an array of diverse human populations. Using the resulting call set of 86,865 single nucleotide DNMs, we find a significant positive correlation between local recombination rate and local DNM rate, which together can explain up to 35.5% of the genome-wide variation in population level rare genetic variation from 41K unrelated TOPMed samples. While genome-wide heterozygosity does correlate weakly with DNM count, we do not find significant differences in DNM rate between individuals of European, African, and Latino ancestry, nor across ancestrally distinct segments within admixed individuals. However, interestingly, we do find significantly fewer DNMs in Amish individuals compared with other Europeans, even after accounting for parental age and sequencing center. Specifically, we find significant reductions in the number of T[->]C mutations in the Amish, which seems to underpin their overall reduction in DNMs. Finally, we calculate near-zero estimates of narrow sense heritability (h2), which suggest that variation in DNM rate is significantly shaped by non-additive genetic effects and/or the environment, and that a less mutagenic environment may be responsible for the reduced DNM rate in the Amish. SignificanceHere we provide one of the largest and most diverse human de novo mutation (DNM) call sets to date, and use it to quantify the genome-wide relationship between local mutation rate and population-level rare genetic variation. While we demonstrate that the human single nucleotide mutation rate is similar across numerous human ancestries and populations, we also discover a reduced mutation rate in the Amish founder population, which shows that mutation rates can shift rapidly. Finally, we find that variation in mutation rates is not heritable, which suggests that the environment may influence mutation rates more significantly than previously realized.

genetics↗

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↗