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

Abstract

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.

Identification of CFTR variants in Latino patients with cystic fibrosis from the Dominican Republic and Puerto Rico

“…Details on DNA sample handling, quality control, library construction, clustering and sequencing, read processing, and sequence data quality control are previously described. 10 Variant calls were obtained from TOPMed data freeze 8 variant call format files. The term "variant" is used in place of "mutation" or "polymorphism".…”

“…Other possible FILTER values include centromere (variant overlaps with centromeric region), SVM (variant failed SVM filter), and duplicate discordances (variant with high mendelian or duplicate genotype discordance [3/5% or more]). 10 Phased genotypes from TOPMed data freeze 8 were used to determine whether two variants are in cis or trans (see section below). 17…”

Population genetic simulation study of power in association testing across genetic architectures and study designs

“…Although the gold standard for capturing rare variation remains deep whole-genome sequencing (WGS), the $1000 per genome cost still means performing WGS on any sizeable group of individuals remains prohibitively expensive for all but the largest consortia. As more diverse reference panels become available (for example, TOPMed; Taliun et al, 2019), imputation in non-European and admixed populations will also improve, particularly for rare variants. With larger WGS reference panels like the Haplotype Reference Consortium (HRC; McCarthy et al, 2016), large numbers of genotyped samples can be imputed to gain some insight into rare variation.…”

“…Genotyping arrays make acquiring genetic data for a large number of individuals significantly less expensive, but lack coverage of rare variation. Imputation accuracy falls off at lower minor allele frequencies (MAF), but the use of large WGS reference panels reduces the threshold of acceptable imputation quality (r 2 > 0.3) to~0.004-0.006% (Taliun et al, 2019) in European and African populations. With such large reference panels, imputation accuracy of genetic variation down to MAF ≅ 0.1% is near perfect in European individuals (Quick et al, 2019).…”

“…With such large reference panels, imputation accuracy of genetic variation down to MAF ≅ 0.1% is near perfect in European individuals (Quick et al, 2019). As more diverse reference panels become available (for example, TOPMed; Taliun et al, 2019), imputation in non-European and admixed populations will also improve, particularly for rare variants. Capturing these rare variants using genotyping arrays and imputation is more cost-effective and can lead to many more individuals in a study.…”