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Background: The MHC class I chain-related protein A (MICA) and protein B (MICB) participate in tumor immunosurveillance and may be important in colorectal cancer, but have not been examined in colorectal cancer development. Methods: sMICA and sMICB blood levels were measured by SomaScan in Visit 2 (1990–92, baseline) and Visit 3 (1993–95) samples in cancer-free participants in the Atherosclerosis Risk in Communities Study. We selected rs1051792, rs1063635, rs2516448, rs3763288, rs1131896, rs2596542, and rs2395029 that were located in or in the vicinity of MICA or MICB and were associated with cancer or autoimmune diseases in published studies. SNPs were genotyped by the Affymetrix Genome-Wide Human SNP Array. We applied linear and Cox proportional hazards regressions to examine the associations of preselected SNPs with sMICA and sMICB levels and colorectal cancer risk (236 colorectal cancers, 8,609 participants) and of sMICA and sMICB levels with colorectal cancer risk (312 colorectal cancers, 10,834 participants). In genetic analyses, estimates adjusted for ancestry markers were meta-analyzed. Results: Rs1051792-A, rs1063635-A, rs2516448-C, rs3763288-A, rs2596542-T, and rs2395029-G were significantly associated with decreased sMICA levels. Rs2395029-G, in the vicinity of MICA and MICB, was also associated with increased sMICB levels. Rs2596542-T was significantly associated with decreased colorectal cancer risk. Lower sMICA levels were associated with lower colorectal cancer risk in males (HR = 0.68; 95% confidence interval, 0.49–0.96) but not in females (Pinteraction = 0.08). Conclusions: Rs2596542-T associated with lower sMICA levels was associated with decreased colorectal cancer risk. Lower sMICA levels were associated with lower colorectal cancer risk in males. Impact: These findings support an importance of immunosurveillance in colorectal cancer.

Background: Genome-wide association studies have reported 23 gene loci related to abdominal aortic aneurysm (AAA)—a potentially lethal condition characterized by a weakened dilated vessel wall. This study aimed to identify proteomic signatures and pathways related to these risk loci to better characterize AAA genetic susceptibility. Methods: Plasma concentrations of 4870 proteins were determined using a DNA aptamer-based array. Linear regression analysis estimated the associations between the 23 risk alleles and plasma protein levels with adjustments for potential confounders in a race-stratified analysis of 1671 Black and 7241 White participants. Significant proteins were then evaluated for their prediction of clinical AAA (454 AAA events in 11 064 individuals), and those significantly associated with AAA were further interrogated using Mendelian randomization analysis. Results: Risk variants proximal to PSRC1-CELSR2-SORT1 , PCIF1-ZNF335-MMP9, RP11-136O12.2/TRIB1 , ZNF259/APOA5, IL6R , PCSK9 , LPA , and APOE were associated with 118 plasma proteins in Whites and 59 were replicated in Black participants. Novel associations with clinical AAA incidence were observed for kit ligand (HR, 0.59 [95% CI, 0.42–0.82] for top versus first quintiles) and neogenin (HR, 0.64 [95% CI, 0.46–0.88]) over a median 21.2-year follow-up; neogenin was also associated with ultrasound-detected asymptomatic AAA (N=4295; 57 asymptomatic AAA cases). Mendelian randomization inverse variance weighted estimates suggested that AAA risk is promoted by lower levels of kit ligand (OR per SD=0.67; P =1.4×10 −5 ) and neogenin (OR per SD=0.50; P =0.03). Conclusions: Low levels of neogenin and kit ligand may be novel risk factors for AAA development in potentially causal pathways. These findings provide insights and potential targets to reduce AAA susceptibility.

Background: Venous thromboembolism (VTE) is a life-threatening vascular event with environmental and genetic determinants. Recent VTE genome-wide association studies (GWAS) meta-analyses involved nearly 30 000 VTE cases and identified up to 40 genetic loci associated with VTE risk, including loci not previously suspected to play a role in hemostasis. The aim of our research was to expand discovery of new genetic loci associated with VTE by using cross-ancestry genomic resources. Methods: We present new cross-ancestry meta-analyzed GWAS results involving up to 81 669 VTE cases from 30 studies, with replication of novel loci in independent populations and loci characterization through in silico genomic interrogations. Results: In our genetic discovery effort that included 55 330 participants with VTE (47 822 European, 6320 African, and 1188 Hispanic ancestry), we identified 48 novel associations, of which 34 were replicated after correction for multiple testing. In our combined discovery-replication analysis (81 669 VTE participants) and ancestry-stratified meta-analyses (European, African, and Hispanic), we identified another 44 novel associations, which are new candidate VTE-associated loci requiring replication. In total, across all GWAS meta-analyses, we identified 135 independent genomic loci significantly associated with VTE risk. A genetic risk score of the significantly associated loci in Europeans identified a 6-fold increase in risk for those in the top 1% of scores compared with those with average scores. We also identified 31 novel transcript associations in transcriptome-wide association studies and 8 novel candidate genes with protein quantitative-trait locus Mendelian randomization analyses. In silico interrogations of hemostasis and hematology traits and a large phenome-wide association analysis of the 135 GWAS loci provided insights to biological pathways contributing to VTE, with some loci contributing to VTE through well-characterized coagulation pathways and others providing new data on the role of hematology traits, particularly platelet function. Many of the replicated loci are outside of known or currently hypothesized pathways to thrombosis. Conclusions: Our cross-ancestry GWAS meta-analyses identified new loci associated with VTE. These findings highlight new pathways to thrombosis and provide novel molecules that may be useful in the development of improved antithrombosis treatments.

The structural equation model was used to empirically analyze the influence of the use of age-appropriate APP on the subjective well-being of the younger elderly. The results show that the attitudes of the younger elderly towards the use of age-appropriate apps are significantly affected by perceived usefulness and perceived ease of use, and perceived usefulness has a significant effect on perceived ease of use. The willingness of the younger elderly to use apps for aging is significantly affected by behavioral attitude, subjective norms, and perceived behavioral control. The behavior of using age-appropriate APP has a significant impact on the subjective well-being of the young elderly. The use of age-appropriate apps by the younger elderly is a positive and selective behavior, which is conducive to the younger elderly to actively adapt to and accept new technologies and improve the life satisfaction of the younger elderly.

Plasma levels of fibrinogen, coagulation factors VII and VIII and von Willebrand factor (vWF) are four intermediate phenotypes that are heritable and have been associated with the risk of clinical thrombotic events. To identify rare and low-frequency variants associated with these hemostatic factors, we conducted whole-exome sequencing in 10 860 individuals of European ancestry (EA) and 3529 African Americans (AAs) from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium and the National Heart, Lung and Blood Institute’s Exome Sequencing Project. Gene-based tests demonstrated significant associations with rare variation (minor allele frequency

Supplemental Table 7 shows the association of sMICA and sMICB levels (continuous variables) and pre-selected SNPsa with risk of CRC stratified by family history of CRC.

Supplemental Table 2 shows the associations between pre-selected SNPs and Visit 2 sMICA and sMICB levels (continuous variables) stratified by sex, race, or diabetes status.

Supplemental Table 4 shows the associations of Visit 2 sMICA and sMICB levels (in quartiles) with CRC risk.

Supplemental Table 3 shows the associations between pre-selected SNPs and CRC risk stratified by sex, race, or diabetes status.

Supplemental Table 8 shows the associations of pre-selected SNPs with Visit 2 sMICA and sMICB levels and CRC risk without adjustment for ancestry markers.

Background:The MHC class I chain-related protein A (MICA) and protein B (MICB) participate in tumor immunosurveillance and may be important in colorectal cancer, but have not been examined in colorectal cancer development.Methods:sMICA and sMICB blood levels were measured by SomaScan in Visit 2 (1990–92, baseline) and Visit 3 (1993–95) samples in cancer-free participants in the Atherosclerosis Risk in Communities Study. We selected rs1051792, rs1063635, rs2516448, rs3763288, rs1131896, rs2596542, and rs2395029 that were located in or in the vicinity of MICA or MICB and were associated with cancer or autoimmune diseases in published studies. SNPs were genotyped by the Affymetrix Genome-Wide Human SNP Array. We applied linear and Cox proportional hazards regressions to examine the associations of preselected SNPs with sMICA and sMICB levels and colorectal cancer risk (236 colorectal cancers, 8,609 participants) and of sMICA and sMICB levels with colorectal cancer risk (312 colorectal cancers, 10,834 participants). In genetic analyses, estimates adjusted for ancestry markers were meta-analyzed.Results:Rs1051792-A, rs1063635-A, rs2516448-C, rs3763288-A, rs2596542-T, and rs2395029-G were significantly associated with decreased sMICA levels. Rs2395029-G, in the vicinity of MICA and MICB, was also associated with increased sMICB levels. Rs2596542-T was significantly associated with decreased colorectal cancer risk. Lower sMICA levels were associated with lower colorectal cancer risk in males (HR = 0.68; 95% confidence interval, 0.49–0.96) but not in females (Pinteraction = 0.08).Conclusions:Rs2596542-T associated with lower sMICA levels was associated with decreased colorectal cancer risk. Lower sMICA levels were associated with lower colorectal cancer risk in males.Impact:These findings support an importance of immunosurveillance in colorectal cancer.

Supplemental Table 1 shows the results from previous studies that found significant associations between the pre-selected SNPs and risk of cancers or autoimmune diseases.

Supplemental Table 9 shows the associations between pre-selected SNPs and mean of Visit 2 and Visit 3 sMICA and sMICB levels (continuous variables).

Supplemental Table 6 shows the associations of sMICA and sMICB levels (continuous variables) and pre-selected SNPsb with risk of CRC and subtypes of CRC.

Supplemental table 5 shows the associations between pre-selected SNPs and risk of CRC with and without adjustment for sMICA and sMICB levels.

Background Genome-wide association studies have identified six genetic variants associated with severe COVID-19, yet the mechanisms through which they may affect disease remains unclear. We investigated proteomic signatures related to COVID-19 risk variants rs657152 (ABO), rs10735079 (OAS1/OAS2/OAS3), rs2109069 (DPP9), rs74956615 (TYK2), rs2236757 (IFNAR2) and rs11385942 (SLC6A20/LZTFL1/CCR9/FYCO1/CXCR6/XCR1) as well as their corresponding downstream pathways that may promote severe COVID-19 in risk allele carriers and their potential relevancies to other infection outcomes. Methods A DNA aptamer-based array measured 4870 plasma proteins among 11 471 participants. Linear regression estimated associations between the COVID-19 risk variants and proteins with correction for multiple comparisons, and canonical pathway analysis was conducted. Cox regression assessed associations between proteins identified in the main analysis and risk of incident hospitalized respiratory infections (2570 events) over a 20.7-year follow-up. Results The ABO variant rs657152 was associated with 84 proteins in 7241 white participants with 24 replicated in 1671 Black participants. The TYK2 variant rs74956615 was associated with ICAM-1 and -5 in white participants with ICAM-5 replicated in Black participants. Of the 84 proteins identified in the main analysis, seven were significantly associated with incident hospitalized respiratory infections including Ephrin type-A receptor 4 (hazard ratio (HR): 0.87; P = 2.3 × 10−11) and von Willebrand factor type A (HR: 1.17; P = 1.6x10−13). Conclusions Novel proteomics signatures and pathways for COVID-19-related risk variants TYK2 and ABO were identified. A subset of these proteins predicted greater risk of incident hospitalized pneumonia and respiratory infections. Further studies to examine these proteins in COVID-19 patients are warranted.

Introduction: Leukocyte telomere length (LTL) is an important aging biomarker implicated in the pathogenesis of age-related conditions. We conducted a large-scale proteomics study to characterize the proteomic signatures of LTL and its genetic determinants measured by polygenic risk score (PRS). Hypothesis: We hypothesize that LTL and LTL PRS are associated with blood proteomic signatures linked to aging-related conditions. Methods: We used TelSeq to estimate LTL based on whole genome sequencing (WGS) data measured in samples collected in ARIC visits 1, 2, and 3 (i.e., the baseline for this study). TelSeq counted reads containing at least 12 TTAGGG repeats. This count was then normalized to the number of reads with GC content between 48% and 52%. Only samples with read lengths of 150 or 151 were included in subsequent analyses, and LTL estimates were inverse normalized within read length group and WGS group before being merged. We derived a PRS for LTL based on previously published genome-wide association study (GWAS) summary statistics for 150 significant autosomal SNPs and ARIC GWAS data. We then analyzed LTL or LTL PRS as a predictor and plasma levels of proteins as outcomes, measured at ARIC visit 2 (for the LTL PRS analysis) and visit 3 (for the LTL analysis) using SOMAscan v4 (~5,000 proteins). Race-specific linear regressions were performed to evaluate the association between LTL or LTL PRS and levels of each protein adjusting for potential confounders at baseline. We used a Bonferroni correction to account for multiple testing. The analysis in Whites served as a discovery (p≤1x10 -5 ) and that in Blacks as a replication. Results: The final sample sizes were as follows: LTL PRS analysis (n=7,587 Whites and 2,094 Blacks) and LTL analysis (n=5,014 Whites and 884 Blacks). In Whites, LTL PRS was significantly associated with five proteins (p≤1x10 -5 ) that also showed the same direction of association in Blacks: THPO, GP1Bα, PEAR1, KDR, and GP5, with the association for KDR reaching nominal significance in Blacks (p=0.02). For LTL as the predictor, GP1Bα, GDF15 and TXNDC5 were significant in Whites, with consistent direction of associations in Blacks (p>0.05). These proteins are linked to hemostasis (all), endothelial proliferation (KDR), heart disease and cancer (GDF15), and cancer and rheumatoid arthritis (TXNDC5) in the literature. Conclusion: This large-scale proteomic analysis reveals blood proteomic signatures for LTL PRS and LTL, which will likely improve the understanding of biological pathways and clinical conditions implicated by LTL.

Introduction: The number of copies of the mitochondrial genome, termed mitochondrial DNA copy number (MTCN), is a proxy for mitochondrial function and varies by cell. Critically, lower MTCN has been associated with aging and chronic diseases such as dementia and cancer. We conducted a large-scale proteomics analysis of leukocyte MTCN (L-MTCN) to elucidate pathways modulated by L-MTCN that may be involved in diseases of aging. Hypothesis: We hypothesize that L-MTCN is significantly associated to plasma proteins that have been implicated in biological pathways of L-MTCN and related diseases. Methods: Based on whole genome sequencing (WGS) data measured in samples collected in ARIC visits 1, 2, and 3 (i.e., baseline for the current study), L-MTCN was calculated as (2 * coverage of the mitochondrial genome) / coverage of the autosome, using the median coverage of 1000 base pair bins overlapping the mitochondrial genome (16 total) and the median coverage of ~142,000 higher quality autosomal bins. Only samples with read lengths of 150 or 151 were included in subsequent analyses, and L-MTCN estimates were inverse normalized within read length group and WGS group before being merged together. This normalized L-MTCN was then analyzed as a predictor for an association with 4,870 plasma proteins measured by SOMAscan v4 in ARIC visit 3 in race-specific generalized linear models, adjusting for the following covariates at baseline: WGS sample visit, age, gender, field center, smoking status, and a measure of kidney function. Bonferroni correction was applied to account for the number of proteins tested in 5,007 White participants (p≤1x10 -5 ). Associations were tested for replication in 1,178 Black participants and considered significant if protein estimates had the same direction of association as Whites at p-value ≤ 2.2x10 -4 . Results: Among White participants, 230 proteins were significantly associated with L-MTCN. Of these proteins S100A9, GP1Bα, EGFR, THBS2, and FCGR3B were significantly replicated among Black participants. Strongest associations with L-MTCN were seen with GP1Bα (Estimate: 0.121) and S100A9 (Estimate: -0.191). Biologically S100A9 is a calgranulin and together with S100A8 forms calprotectin, which have roles mediating inflammatory processes. GP1Bα helps mediate platelet activation and forms the glycoprotein Ib-IX-V complex which acts as a platelet surface receptor for von Willebrand factor facilitating platelet adhesion. Conclusion: This large proteomics analysis identified 230 plasma proteins significantly associated with L-MTCN. Some of the proteins have been connected to inflammatory diseases, cardiovascular disease, and cancer in the literature. These results provide insight into proteins involved in biological pathways related to L-MTCN.

ObjectiveThrombosis is a major complication after cardiac surgery in children with congenital heart disease (CHD). The mechanisms underlying thrombosis development remain poorly understood. We aimed to identify novel circulating metabolites before cardiac surgery that are associated with the development of thrombosis after surgery in children with CHD.Approach and resultsAll blood samples were drawn right before surgical incision and after the induction of anesthesia, and plasma was separated immediately under 4 °C. Untargeted metabolomic data were measured by Metabolon in plasma from children (age range: 0 days-18 years) with CHD undergoing cardiac surgery. The primary outcome was thrombosis within 30 days of surgery or before discharge. Associations of individual metabolites with thrombosis were assessed with logistic regression with false discovery rate (FDR) correction for multiple comparison and adjustment for clinical characteristics; elastic net regression was used to select a prediction model. Out of 1,115 metabolites measured in samples from 203 children, 776 met the quality control criteria. In total, 26 children (12.8%) developed thrombosis. Among the 776 metabolites, 195 were significantly associated with thrombosis (FDR q-value < 0.05). The top three metabolites showing the strongest associations with thrombosis were eicosapentaenoate, steroid monosulfate C19H28O6S, and formiminoglutamate (FDR = 0.01 for all). Pathway analysis showed that the pathways of nicotinate and nicotinamide metabolism (P = 0.0007) and glycerophospholipid metabolism (P = 0.002) were enriched and had significant impacts on the development of thrombosis. In elastic net regression analysis, the area under the receiver operating-characteristic curve of a prediction model for thrombosis was 0.94 in the training sample (70% of the total sample) and 0.84 in the testing sample (the remaining 30%).ConclusionWe have identified promising novel metabolites and metabolic pathways associated with thrombosis. Future studies are warranted to confirm the findings and examine the mechanistic pathways to thrombosis.

Background The plasma proteome can be quantified using different types of highly multiplexed technologies, including aptamer-based and proximity-extension immunoassay methods. There has been limited characterization of how these protein measurements correlate across platforms and with absolute measures from targeted immunoassays. Methods We assessed the comparability of (a) highly multiplexed aptamer-based (SomaScan v4; Somalogic) and proximity-extension immunoassay (OLINK Proseek® v5003; Olink) methods in 427 Atherosclerosis Risk in Communities (ARIC) Study participants (Visit 5, 2011–2013), and (b) 18 of the SomaScan protein measurements against targeted immunoassays in 110 participants (55 cardiovascular disease cases, 55 controls). We calculated Spearman correlations (r) between the different measurements and compared associations with case-control status. Results There were 417 protein comparisons (366 unique proteins) between the SomaScan and Olink platforms. The average correlation was r = 0.46 (range: −0.21 to 0.97; 79 [19%] with r ≥ 0.8). For the comparison of SomaScan and targeted immunoassays, 6 of 18 assays (growth differentiation factor 15 [GDF15], interleukin-1 receptor-like 1 [ST2], interstitial collagenase [MMP1], adiponectin, leptin, and resistin) had good correlations (r ≥ 0.8), 2 had modest correlations (0.5 ≤ r < 0.8; osteopontin and interleukin-6 [IL6]), and 10 were poorly correlated (r < 0.5; metalloproteinase inhibitor 1 [TIMP1], stromelysin-1 [MMP3], matrilysin [MMP7], C-C motif chemokine 2 [MCP1], interleukin-10 [IL10], vascular cell adhesion protein 1 [VCAM1], intercellular adhesion molecule 1 [ICAM1], interleukin-18 [IL18], tumor necrosis factor [TNFα], and visfatin) overall. Correlations for SomaScan and targeted immunoassays were similar according to case status. Conclusions There is variation in the quantitative measurements for many proteins across aptamer-based and proximity-extension immunoassays (approximately 1/2 showing good or modest correlation and approximately 1/2 poor correlation) and also for correlations of these highly multiplexed technologies with targeted immunoassays. Design and interpretation of protein quantification studies should be informed by the variation across measurement techniques for each protein.

Plasma proteomic profiling may aid in the discovery of novel biomarkers upstream of the development of atrial fibrillation (AF). We used data from the Atherosclerosis Risk in Communities study to examine the relation between large-scale proteomics and incident AF in a cohort of older-aged adults in the United States. We quantified 4,877 plasma proteins in Atherosclerosis Risk in Communities participants at visit 5 (2011–2013) using an aptamer-based proteomic profiling platform. We used Cox proportional hazards models to assess the association between protein levels and incident AF, and explored relation of selected protein biomarkers using annotated pathway analysis. Our study included 4,668 AF-free participants (mean age 75 ± 5 years; 59% female; 20% Black race) with proteomic measures. A total of 585 participants developed AF over a mean follow-up of 5.7 ± 1.7 years. After adjustment for clinical factors associated with AF, N-terminal pro-B-type natriuretic peptide (NT-proBNP) was associated with the risk of incident AF (hazard ratio, 1.82; 95% CI, 1.68 to 1.98; p, 2.91 × 10−45 per doubling of NT-proBNP). In addition, 36 other proteins were also significantly associated with incident AF after Bonferroni correction. We further adjusted for medication use and estimated glomerular filtration rate and found 17 proteins, including angiopoietin-2 and transgelin, that remained significantly associated with incident AF. Pathway analyses implicated the inhibition of matrix metalloproteases as the top canonical pathway in AF pathogenesis. In conclusion, using a large-scale proteomic platform, we identified both novel and established proteins associated with incident AF and explored mechanistic pathways of AF development.

Common genetic variants explain less variation in complex phenotypes than inferred from family-based studies, and there is a debate on the source of this 'missing heritability'. We investigated the contribution of rare genetic variants to tobacco use with whole-genome sequences from up to 26,257 unrelated individuals of European ancestries and 11,743 individuals of African ancestries. Across four smoking traits, single-nucleotide-polymorphism-based heritability ([Formula: see text]) was estimated from 0.13 to 0.28 (s.e., 0.10-0.13) in European ancestries, with 35-74% of it attributable to rare variants with minor allele frequencies between 0.01% and 1%. These heritability estimates are 1.5-4 times higher than past estimates based on common variants alone and accounted for 60% to 100% of our pedigree-based estimates of narrow-sense heritability ([Formula: see text], 0.18-0.34). In the African ancestry samples, [Formula: see text] was estimated from 0.03 to 0.33 (s.e., 0.09-0.14) across the four smoking traits. These results suggest that rare variants are important contributors to the heritability of smoking.

Objective: Leukocytes contribute to the development of abdominal aortic aneurysm (AAA). We evaluated whether associations of differential leukocyte counts with AAA persist after accounting for traditional risk factors of AAA. Approach and Results: Among 11 217 adults from the Atherosclerosis Risk in Communities Study, we evaluated associations of differential leukocyte counts at baseline (1987–1989) with incident AAAs over a median follow-up of 22.5 years, using Cox proportional hazards regression. Each differential leukocyte count was categorized into 5 groups—below normal, tertiles within the normal range, and above normal, with the first tertile serving as the referent. We identified 377 incident AAAs through 2011, using hospital discharge diagnoses, linked Medicare records, or death certificates. At baseline, higher neutrophil, monocyte, and eosinophil counts were associated with higher risk of AAA, independent of smoking, other differential leukocyte counts, and other traditional risk factors. The association with incident AAA was the strongest for above normal neutrophil count, with an adjusted hazard ratio (95% CI) of 2.17 (1.29–3.64). Below normal neutrophil, lymphocyte, eosinophil and basophil counts were associated with higher risk of AAA with adjusted hazard ratio (95% CI) between 1.86 (1.04–3.35) and 1.62 (1.10–2.39). Conclusions: Higher neutrophil, monocyte, and eosinophil counts in midlife are associated with higher risk of AAA, even after accounting for traditional risk factors such as smoking, obesity, and atherosclerosis. This suggests the need to identify nontraditional risk factors and treatment strategies to mitigate the residual risk of AAA conferred by midlife inflammation. Whether immunosuppression is associated with higher risk of AAA needs further investigation.

Background Circulating white blood cell and platelet traits are clinically linked to various disease outcomes and differ across individuals and ancestry groups. Genetic factors play an important role in determining these traits and many loci have been identified. However, most of these findings were identified in populations of European ancestry (EA), with African Americans (AA), Hispanics/Latinos (HL), and other races/ethnicities being severely underrepresented. Results We performed ancestry-combined and ancestry-specific genome-wide association studies (GWAS) for white blood cell and platelet traits in the ancestrally diverse Population Architecture using Genomics and Epidemiology (PAGE) Study, including 16,201 AA, 21,347 HL, and 27,236 EA participants. We identified six novel findings at suggestive significance (P < 5E-8), which need confirmation, and independent signals at six previously established regions at genome-wide significance (P < 2E-9). We confirmed multiple previously reported genome-wide significant variants in the single variant association analysis and multiple genes using PrediXcan. Evaluation of loci reported from a Euro-centric GWAS indicated attenuation of effect estimates in AA and HL compared to EA populations. Conclusions Our results highlighted the potential to identify ancestry-specific and ancestry-agnostic variants in participants with diverse backgrounds and advocate for continued efforts in improving inclusion of racially/ethnically diverse populations in genetic association studies for complex traits.

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%., The goals, resources and design of the NHLBI Trans-Omics for Precision Medicine (TOPMed) programme are described, and analyses of rare variants detected in the first 53,831 samples provide insights into mutational processes and recent human evolutionary history.