Brian L. Browning

The paper presents a large reference panel of 515,579 genomes to improve rare-variant imputation across underrepresented populations, enhancing accuracy for variants with frequencies as low as 0.002%.

FLARE2 improves local ancestry inference by using haplotype clustering, enabling accurate results even when reference panels are poorly matched.

This paper estimates gene conversion rates using multi-individual identity-by-descent inference and finds higher correlation with PRDM9 binding enrichment than crossover maps.

This study shows that adjusting for principal components in GWAS of admixed populations can lead to biased results and spurious associations due to collider bias.

This study estimates genotype error rates and uncalled deletion rates in whole genome sequence data using a new model applied to UK Biobank parent-offspring trios.

The paper introduces a method to efficiently infer multi-individual identity by descent and detect gene conversion in biobank-scale sequence data, finding over 9 million converted alleles in UK Biobank data.

FLARE infers local ancestry with high accuracy and efficiency, scaling to large datasets using advanced computational techniques.

The paper describes a pipeline for phasing 150,119 UK Biobank genomes, achieving a switch error rate of 0.00032 after filtering.

The paper estimates X chromosome effective population size using IBD segments and applies it to infer sex-specific demographic history from UK Biobank data.

The paper estimates genotype error rates in trio-based haplotype phasing and shows genotype errors inflate switch error rates in offspring.

Over 400 million variants, 97% below 1% frequency, emerge from deep whole-genome sequencing of 53,831 diverse TOPMed participants; TOPMed haplotypes enable imputation down to ~0.01% frequency.

The paper estimates IBD segment endpoints with uncertainty quantification and detects recent selection signals using UK Biobank data.

The paper introduces hap-IBD, a fast and accurate method for detecting IBD segments in large-scale genotype data, outperforming existing methods in speed and accuracy.

The paper introduces Beagle 5.0, a genotype imputation method that significantly reduces computational cost compared to existing methods while maintaining similar accuracy.