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

Abstract

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%.

Fig. 1. Distribution of genetic variants across the genome.

Common (allele frequency ≥ 0.5%) and rare (allele frequency < 0.5%) variant counts are shown above and below the x axis, respectively, within 1-Mb concatenated segments (see Methods). Segments are stratified by CADD functionality score, and sorted based on their number of rare variants according to the functionality category. There were 22 high CADD, 22 medium CADD and 34 low CADD coding segments, and 40 high CADD, 238 medium CADD and 2,381 low CADD noncoding segments. Noncoding regions of the genome with low CADD scores (<10, reflecting lower predicted function) have the largest levels of common and rare variation (noncoding plot region, dark and light blue, respectively), followed by low CADD coding regions (coding plot region, dark and light blue, respectively). Overall, the vast majority of human genomic variation comprises rare variation.

Fig. 2. Characteristics of singleton clustering patterns.

Parameter estimates for exponential mixture models of singleton density. Each point represents one of the four components in one of the 3,000 individuals in the sample, coloured according to the genetically inferred population of that individual. The rate parameters of each component are shown across the x axis, and the lambda parameters (that is, the proportion that the component contributes to the mixture) are shown on the y axis (on a log–log scale). Histograms show the distribution of the lambda and rate parameters for each component. AFR, African ancestry; EAS, East Asian ancestry; EUR, European ancestry.

Fig. 3. Retained non-reference ancestral sequences discovered from unmapped reads.

a, Length distribution of fully resolved ancestral sequences, coloured by overlap with GENCODE v.29 genic features. b, Percentage of non-reference (alternative) alleles compared with the percentage of non-reference sequence identified per individual, coloured by population group. c, Venn diagram showing the positional concordance with insertions identified using short-read data from two previous studies^,. The number of sequences specific to each study and that have not been partially resolved in the other studies is given between brackets.

Fig. 4. Ancestry, genetic diversity and rare-variant genetic relatedness across the TOPMed studies.

Each study label is shaded based on their population group. From the outside moving in each track represents: the unrelated sample size of each study used in these calculations, average admixture values, average number of heterozygous sites in each individual’s genome, average number of singleton variants in each individual’s genome and the average within-study rare-variant (RV) sharing comparisons. The links depict the 75th percentile of between-study rare-variant sharing comparisons. All between-study rare-variant sharing comparisons can be found in Supplementary Fig. 29. The sample size, average heterozygosity, number of singletons, within-cohort rare-variant sharing and admixture values by TOPMed study and population group can be found in Supplementary Table 13. Study name abbreviations are defined in Extended Data Tables 1, 2 and Supplementary Table 20.

Fig. 5. Relative increase in singletons and doubletons of the site frequency spectrum across McVicker’s B and the population size inferred from demographic inference using various sample sizes.

a, The relative increase in the singleton (left) and doubleton (right) bins of the site frequency spectrum for decreasing percentile bins of McVicker’s B compared with the highest percentile bin of B. The higher percentiles of B indicate weaker effects of selection at linked sites (SaLS). These relative increases are plotted for different sample sizes. b, Each point corresponds to the population size inferred in the last generation of an exponential growth model for Europeans. Demographic inference was conducted with different sample sizes for fourfold degenerate sites (n = 4,718,653 sites) and the highest 1% B sites (n = 10,977,437 sites). Error bars show 95% confidence intervals (see Supplementary Table 14 for parameter values). N_e, effective population size.

Extended Data Fig. 1. Principal components of the genotypic data from freeze 5 pooled across studies.

a, Three-dimensional plot of principal components (PC) 1, 2 and 3. b, Parallel coordinate plot colour-coded by categories defined according to race, ancestry and/or ethnic information provided by the study participants and/or by study investigators according to study inclusion criteria. Individuals with missing values for ancestry or ethnicity are excluded.

Extended Data Fig. 2. Distribution of genetic variants across the genome.

After filtering to focus on regions of the genome that are accessible through short-read sequencing, most contiguous 1-Mb segments show similar levels of common (5,141 ± 1,298 variants with MAF ≥ 0.5%) and rare variation (120,414 ± 19,862 variants with MAF < 0.5%). From top to bottom, panel 1 shows the levels of variation across the genome for common coding variants, panel 2 for rare coding variants, panel 3 for common noncoding variants and panel 4 for rare noncoding variants. Variation levels are represented by the Z-score (X-mean/s.d.) of the adjusted variant counts per 1-Mb contiguous segment for each variant category.

Extended Data Fig. 3. Characteristics of singleton clustering patterns.

a, Mutational spectra of singletons assigned to each of the four mixture components, separated by population. b, Density of mixture component 2 singletons in 1-Mb windows across the genome. Windows with mixture component 2 singleton counts above the 95th percentile (calculated genome-wide per population subsample) are classified as hotspots and are highlighted in green.

Extended Data Fig. 4. Estimates of recent effective population size by population group.

Each line represents the estimate from a single study, considering only individuals with an annotated population group. The included studies are the same as those in Supplementary Fig. 31. The Amish and Samoan results are individually identified due to their distinct recent population size trajectories. N_e, effective population size. The overlay view is shown in Supplementary Fig. 33.