High coverage whole genome sequencing of the expanded 1000 Genomes Project cohort including 602 trios

Abstract

The 1000 Genomes Project (1kGP), launched in 2008, is the largest fully open resource of whole genome sequencing (WGS) data consented for public distribution of raw sequence data without access or use restrictions. The final (phase 3) 2015 release of 1kGP included 2,504 unrelated samples from 26 populations, representing five continental regions of the world and was based on a combination of technologies including low coverage WGS (mean depth 7.4X), high coverage whole exome sequencing (mean depth 65.7X), and microarray genotyping. Here, we present a new, high coverage WGS resource encompassing the original 2,504 1kGP samples, as well as an additional 698 related samples that result in 602 complete trios in the 1kGP cohort. We sequenced this expanded 1kGP cohort of 3,202 samples to a targeted depth of 30X using Illumina NovaSeq 6000 instruments. We performed SNV/INDEL calling against the GRCh38 reference using GATK HaplotypeCaller, and generated a comprehensive set of SVs by integrating multiple analytic methods through a sophisticated machine learning model, upgrading the 1kGP dataset to current state-of-the-art standards. Using this strategy, we defined over 111 million SNVs, 14 million INDELs, and ~170 thousand SVs across the entire cohort of 3,202 samples with estimated false discovery rate (FDR) of 0.3%, 1.0%, and 1.8%, respectively. By comparison to the low-coverage phase 3 callset, we observed substantial improvements in variant discovery and estimated FDR that were facilitated by high coverage re-sequencing and expansion of the cohort. Specifically, we called 7% more SNVs, 59% more INDELs, and 170% more SVs per genome than the phase 3 callset. Moreover, we leveraged the presence of families in the cohort to achieve superior haplotype phasing accuracy and we demonstrate improvements that the high coverage panel brings especially for INDEL imputation. We make all the data generated as part of this project publicly available and we envision this updated version of the 1kGP callset to become the new de facto public resource for the worldwide scientific community working on genomics and genetics.

Nonparametric coalescent inference of mutation spectrum history and demography

“…We next inferred the histories of human populations from large publicly available resequencing data. We computed a -SFS for each of the 26 human populations from five continental ancestries sequenced in the 1KG ( 27 ) using an unphased variant call set (mapped to human genome assembly GRCh38 [hg38]) from the recent high-coverage (30×) resequencing data of 1KG samples from the New York Genome Center ( 28 ). Our bioinformatic pipeline for computing the -SFS for each 1KG population is detailed in Materials and Methods .…”

“…The recovered MuSH is a rich object that illuminates dimensions of population history and addresses biological questions about the evolution of the mutation process. After validating with data simulated under known histories, we use mushi to independently infer histories for each of the 26 populations (from 5 superpopulations defined by continental ancestry) from the 1000 Genomes Project (1KG) Consortium ( 27 ) using recent high-coverage sequencing data ( 28 ). We demonstrate that mushi is a powerful tool for demographic inference that has several advantages over existing demographic inference methods and then go on to describe the illuminated features of human MuSH.…”

de novo variant calling identifies cancer mutation profiles in the 1000 Genomes Project

“…A total of 602 trios from the 1000 Genomes Project were sequenced at the New York Genome Center as described previously (2). The aligned data files (crams) are located at http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/1000G_2504_high_coverage/1000G_ 2504_high_coverage.sequence.index and http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/1000G_2504_high_coverage/1000G_ 698_related_high_coverage.sequence.index .…”

“…The 1000 Genomes Project is a data resource for the study of genetic variation that includes individuals from diverse genetic ancestries (1,2). In this study, we present the first ever assessment of new mutations, termed de novo variants (DNVs) that are only found in children and not their parents, represented in this collection.…”

Jasmine: Population-scale structural variant comparison and analysis

“…We call variants with our pipeline from publicly available long-read data for 31 samples, and generate a panel of long-read SV calls which can be used for screening further samples. Finally, we genotype this SV panel in 444 high coverage short-read samples from the 1000 Genomes Project (Byrska-Bishop et al 2021) and discover thousands of novel SV associations with gene expression. Many of these SVs have CAVIAR posterior probabilities of causality that exceed those of previously reported SNPs, indicating likely functional relevance.…”