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, 100 kb, 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 fall 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 binding 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 maps provide 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.

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 3.2×10−4(90%CI:[2.8×10−4,6.2×10−4]). We estimate that 77% of the genotype errors at these markers are attributable to uncalled deletions (90%CI:[73%,88%]).

We present a method for efficiently identifying clusters of identical -by -descent haplotypes in biobank-scale sequence data. Our multiindividual approach enables much more computationally efficient inference of identity by descent (IBD) than approaches that infer pairwise IBD segments and provides locus -specific IBD clusters rather than IBD segments. Our method's 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 2,900 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. Our IBD clustering method is implemented in the open -source ibdcluster software package.

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 Women’s 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.

Epistasis refers to changes in the effect on phenotype of a unit of genetic information, such as a single nucleotide polymorphism or a gene, dependent on the context of other genetic units. Such interactions are both biologically plausible and good candidates to explain observations which are not fully explained by an additive heritability model. However, the search for epistasis has so far largely failed to recover this missing heritability. We identify key challenges and propose that future works need to leverage idealized systems, known biology and even previously identified epistatic interactions, in order to guide the search for new interactions.

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.

The effective size of a population (N-e) in the recent past can be estimated through analysis of identity-by-descent (IBD) segments. Several methods have been developed for estimating N-e 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 N-e and X chromosome N-e to estimate the female and male effective population sizes. We demonstrate the accuracy of our autosome and X chromosome N-e estimation with simulated data. We find that the 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 N-e 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.

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 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 datasets with hundreds of thousands of sequenced individuals and to provide superior accuracy on large-scale data. FLARE is open source and available at https://github.com/browning-lab/flare.

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 × 10 −3 vs 5.79 × 10 −4 ) and that the average distance between phase errors is 64 megabases.

The Trans-Omics for Precision Medicine (TOPMed) programme seeks to elucidate the genetic architecture and biology of heart, lung, blood and sleep disorders, with the ultimate goal of improving diagnosis, treatment and prevention of these diseases. The initial phases of the programme focused on whole-genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here we describe the TOPMed goals and design as well as the available resources and early insights obtained from the sequence data. The resources include a variant browser, a genotype imputation server, and genomic and phenotypic data that are available through dbGaP (Database of Genotypes and Phenotypes) 1 . In the first 53,831 TOPMed samples, we detected more than 400 million single-nucleotide and insertion or deletion variants after alignment with the reference genome. Additional previously undescribed variants were detected through assembly of unmapped reads and customized analysis in highly variable loci. Among the more than 400 million detected variants, 97% have frequencies of less than 1% and 46% are singletons that are present in only one individual (53% among unrelated individuals). These rare variants provide insights into mutational processes and recent human evolutionary history. The extensive catalogue of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and noncoding sequence variants to phenotypic variation. Furthermore, combining TOPMed haplotypes with modern imputation methods improves the power and reach of genome-wide association studies to include variants down to a frequency of approximately 0.01%.

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 Women's Women's 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.

Haplotype phasing is the estimation of haplotypes from genotype data. We present a fast, accurate, and memory-efficient haplotype phasing method that scales to large-scale SNP array and sequence data. The method uses marker windowing and composite reference haplotypes to reduce memory usage and computation time. It incorporates a progressive phasing algorithm that identifies confidently phased heterozygotes in each iteration and fixes the phase of these heterozygotes in subsequent iterations. For data with many low-frequency variants, such as whole-genome sequence data, the method employs a two-stage phasing algorithm that phases high-frequency markers via progressive phasing in the first stage and phases low-frequency markers via genotype imputation in the second stage. This haplotype phasing method is implemented in the open-source Beagle 5.2 software package. We compare Beagle 5.2 and SHAPEIT 4.2.1 by using expanding subsets of 485,301 UK Biobank samples and 38,387 TOPMed samples. Both methods have very similar accuracy and computation time for UK Biobank SNP array data. However, for TOPMed sequence data, Beagle is more than 20 times faster than SHAPEIT, achieves similar accuracy, and scales to larger sample sizes.

MOTIVATION Estimation of pairwise kinship coefficients in large datasets is computationally challenging because the number of related individuals increases quadratically with sample size. RESULTS We present IBDkin, a software package written in C for estimating kinship coefficients from identity by descent (IBD) segments. We use IBDkin to estimate kinship coefficients for 7.95 billion pairs of individuals in the UK Biobank who share at least one detected IBD segment with length ≥ 4 cM. AVAILABILITY AND IMPLEMENTATION https://github.com/YingZhou001/IBDkin. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Background The BRCA1 c.3331_3334delCAAG founder mutation has been reported in hereditary breast and ovarian cancer families from multiple Hispanic groups. We aimed to evaluate BRCA1 c.3331_3334delCAAG haplotype diversity in cases of European, African, and Latin American ancestry. Methods BC mutation carrier cases from Colombia ( n = 32), Spain ( n = 13), Portugal ( n = 2), Chile ( n = 10), Africa ( n = 1), and Brazil ( n = 2) were genotyped with the genome-wide single nucleotide polymorphism (SNP) arrays to evaluate haplotype diversity around BRCA1 c.3331_3334delCAAG. Additional Portuguese ( n = 13) and Brazilian ( n = 18) BC mutation carriers were genotyped for 15 informative SNPs surrounding BRCA1 . Data were phased using SHAPEIT2, and identical by descent regions were determined using BEAGLE and GERMLINE. DMLE+ was used to date the mutation in Colombia and Iberia. Results The haplotype reconstruction revealed a shared 264.4-kb region among carriers from all six countries. The estimated mutation age was ~ 100 generations in Iberia and that it was introduced to South America early during the European colonization period. Conclusions Our results suggest that this mutation originated in Iberia and later introduced to Colombia and South America at the time of Spanish colonization during the early 1500s. We also found that the Colombian mutation carriers had higher European ancestry, at the BRCA1 gene harboring chromosome 17, than controls, which further supported the European origin of the mutation. Understanding founder mutations in diverse populations has implications in implementing cost-effective, ancestry-informed screening.

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, and it can be applied to large cohorts with whole-genome sequence or SNP array data. We find that our method's estimates of uncertainty are well calibrated for homogeneous samples. We quantify endpoint uncertainty for 77.7 billion IBD segments from 408,883 individuals of white British ancestry in the UK Biobank, and we 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. Eleven of the twelve 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.

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 and software, called IBDrecomb, for using segments of identity by descent to infer recombination rates. IBDrecomb can be applied to sequenced population cohorts to obtain high-resolution, population-specific recombination maps. In simulated admixed data, IBDrecomb obtains higher accuracy than admixture-based estimation of recombination rates. When applied to 2,500 simulated individuals, IBDrecomb obtains similar accuracy to a linkage-disequilibrium (LD)-based method applied to 96 individuals (the largest number for which computation is tractable). Compared to LD-based maps, our IBD-based maps have the advantage of estimating recombination rates in the recent past rather than the distant past. We used IBDrecomb to generate new recombination maps for European Americans and for African Americans from TOPMed sequence data from the Framingham Heart Study (1,626 unrelated individuals) and the Jackson Heart Study (2,046 unrelated individuals), and we compare them to LD-based, admixture-based, and family-based maps.

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.

The two primary methods for estimating the genome-wide mutation rate have been counting de novo mutations in parent-offspring trios and comparing sequence data between closely related species. With parent-offspring trio analysis it is difficult to control for genotype error, and resolution is limited because each trio provides information from only two meioses. Inter-species comparison is difficult to calibrate due to uncertainty in the number of meioses separating species, and it can be biased by selection and by changing mutation rates over time. An alternative class of approaches for estimating mutation rates that avoids these limitations is based on identity by descent (IBD) segments that arise from common ancestry within the past few thousand years. Existing IBD-based methods are limited to highly inbred samples, or lack robustness to genotype error and error in the estimated demographic history. We present an IBD-based method that uses sharing of IBD segments among sets of three individuals to estimate the mutation rate. Our method is applicable to accurately phased genotype data, such as parent-offspring trio data phased using Mendelian rules of inheritance. Unlike standard parent-offspring analysis, our method utilizes distant relationships and is robust to genotype error. We apply our method to data from 1,307 European-ancestry individuals in the Framingham Heart Study sequenced by the NHLBI TOPMed project. We obtain an estimate of 1.29 x 10(-8) mutations per base pair per meiosis with a 95% confidence interval of [1.02 x 10(-8),1.56 x 10(-8)].