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The challenge of eradicating leukemia in patients with acute myelogenous leukemia (AML) after initial cytoreduction has motivated modern efforts to combine synergistic active modalities including immunotherapy. Recently, the ETCTN/CTEP 10026 study tested the combination of the DNA methyltransferase inhibitor decitabine together with the immune checkpoint inhibitor ipilimumab for AML/myelodysplastic syndrome (MDS) either after allogeneic hematopoietic stem cell transplantation (HSCT) or in the HSCT-naïve setting. Integrative transcriptome-based analysis of 304 961 individual marrow-infiltrating cells for 18 of 48 subjects treated on study revealed the strong association of response with a high baseline ratio of T to AML cells. Clinical responses were predominantly driven by decitabine-induced cytoreduction. Evidence of immune activation was only apparent after ipilimumab exposure, which altered CD4+ T-cell gene expression, in line with ongoing T-cell differentiation and increased frequency of marrow-infiltrating regulatory T cells. For post-HSCT samples, relapse could be attributed to insufficient clearing of malignant clones in progenitor cell populations. In contrast to AML/MDS bone marrow, the transcriptomes of leukemia cutis samples from patients with durable remission after ipilimumab monotherapy showed evidence of increased infiltration with antigen-experienced resident memory T cells and higher expression of CTLA-4 and FOXP3. Altogether, activity of combined decitabine and ipilimumab is impacted by cellular expression states within the microenvironmental niche of leukemic cells. The inadequate elimination of leukemic progenitors mandates urgent development of novel approaches for targeting these cell populations to generate long-lasting responses. This trial was registered at www.clinicaltrials.gov as #NCT02890329.

We explored the role of genetic ancestry in shaping the genetic architecture of whole blood gene expression using whole genome and RNA sequencing data from 2,733 African American and Hispanic/Latino children. We find that heritability of gene expression significantly increases with greater proportion of genome-wide African ancestry and decreases with higher levels of Indigenous American ancestry. Fine-mapping of expression quantitative trait loci (eQTLs) in individuals with predominantly African or Indigenous American ancestry revealed ancestry-specific eQTLs in over 30% of heritable genes. We leveraged our data to train genetically derived transcriptome prediction models, which identified significantly more associated genes than GTEx or MESA models when applied to 28 traits from a multi-ancestry population. Our findings underscore the importance of increasing representation from ancestrally diverse populations in genomic studies to enable new discoveries and ensure their equitable translation., {"references":["Kachuri L, Mak ACY, Hu D et al. (2022) Gene expression in African Americans and Latinos reveals ancestry-specific patterns of genetic architecture. bioRxiv. doi:10.1101/2021.08.19.456901"]}

Despite the growing number of genome-wide association studies (GWASs), it remains unclear to what extent gene-by-gene and gene-by-environment interactions influence complex traits in humans. The magnitude of genetic interactions in complex traits has been difficult to quantify because GWASs are generally underpowered to detect individual interactions of small effect. Here, we develop a method to test for genetic interactions that aggregates information across all trait-associated loci. Specifically, we test whether SNPs in regions of European ancestry shared between European American and admixed African American individuals have the same causal effect sizes. We hypothesize that in African Americans, the presence of genetic interactions will drive the causal effect sizes of SNPs in regions of European ancestry to be more similar to those of SNPs in regions of African ancestry. We apply our method to two traits: gene expression in 296 African Americans and 482 European Americans in the Multi-Ethnic Study of Atherosclerosis (MESA) and low-density lipoprotein cholesterol (LDL-C) in 74K African Americans and 296K European Americans in the Million Veteran Program (MVP). We find significant evidence for genetic interactions in our analysis of gene expression; for LDL-C, we observe a similar point estimate, although this is not significant, most likely due to lower statistical power. These results suggest that gene-by-gene or gene-by-environment interactions modify the effect sizes of causal variants in human complex traits.

Background: Plasma proteins are critical mediators of cardiovascular processes and are the targets of many drugs. Previous efforts to characterize the genetic architecture of the plasma proteome have been limited by a focus on individuals of European descent and leveraged genotyping arrays and imputation. Here we describe whole genome sequence analysis of the plasma proteome in individuals with greater African ancestry, increasing our power to identify novel genetic determinants. Methods: Proteomic profiling of 1301 proteins was performed in 1852 Black adults from the Jackson Heart Study using aptamer-based proteomics (SomaScan). Whole genome sequencing association analysis was ascertained for all variants with minor allele count ≥5. Results were validated using an alternative, antibody-based, proteomic platform (Olink) as well as replicated in the Multi-Ethnic Study of Atherosclerosis and the HERITAGE Family Study (Health, Risk Factors, Exercise Training and Genetics). Results: We identify 569 genetic associations between 479 proteins and 438 unique genetic regions at a Bonferroni-adjusted significance level of 3.8×10 -11 . These associations include 114 novel locus-protein relationships and an additional 217 novel sentinel variant-protein relationships. Novel cardiovascular findings include new protein associations at the APOE gene locus including ZAP70 (sentinel single nucleotide polymorphism [SNP] rs7412-T, β=0.61±0.05, P =3.27×10 -30 ) and MMP-3 (β=-0.60±0.05, P =1.67×10 -32 ), as well as a completely novel pleiotropic locus at the HPX gene, associated with 9 proteins. Further, the associations suggest new mechanisms of genetically mediated cardiovascular disease linked to African ancestry; we identify a novel association between variants linked to APOL1-associated chronic kidney and heart disease and the protein CKAP2 (rs73885319-G, β=0.34±0.04, P =1.34×10 -17 ) as well as an association between ATTR amyloidosis and RBP4 levels in community-dwelling individuals without heart failure. Conclusions: Taken together, these results provide evidence for the functional importance of variants in non-European populations, and suggest new biological mechanisms for ancestry-specific determinants of lipids, coagulation, and myocardial function.

Supplementary Figure 1. Mutation rates by cohort. Supplementary Figure 2. ERCC2 mutations. Supplementary Figure 3. ERCC2 mutation status and overall survival. Supplementary Figure 4. ERCC2 mutations occur in highly conserved regions.

Significantly altered genes in clinical subsets. MutSigCV ranked lists for significantly mutated genes in the non-responders only (n = 25), the responders only (n = 25), and the ERCC2WT responders only (n = 16).

All alterations in responders and non-responders. All somatic mutations and short insertion/deletions are listed, with additional annotations regarding genomic coordinates, protein change, allelic fractions, and validation status (if available).

Summary of clinically informative (TARGET) genes.

Supplementary Methods, Figures S1 - S20. Figure S1. Branched evolution leads to tissue-sampling bias in primary tumor samples. Figure S2. Results of ABSOLUTE on samples from patient 418. Figure S3. 2D Bayesian clustering analysis of point-mutation CCF distributions in case 418. Figure S4. Bayesian clustering of private point-mutation CCF distributions in all sequenced tissue samples from case 418. Figure S5. Genetic alterations supporting phylogeny construction in case 418. Figure S6. Phylogenetic tree for case 418 Figure S7. Evolutionary relationships between primary tumor-samples and brain metastasis samples Figure S8. Detection of homozygous deletion in CDKN2A in the brain metastasis of case 24. Figure S9. Amplification of FGFR1 and MYC detected in the brain metastasis of case 331. Figure S10. Additional alterations under investigation for association with various targeted therapies. Figure S11. Power for paired-detection of somatic mutations. Figure S12. Amplification of CCNE1 detected in the brain metastasis of case 314. Figure S13. Amplification of EGFR detected in the brain metastasis of case 314. Figure S14. Amplification of MYC detected in the brain metastasis of case 308. Figure S15. Amplification of MYC detected in the brain metastasis of case 138. Figure S16. Amplifications of CDK6 and MET detected in the brain metastasis of case 138. Figure S17. Amplifications of CCNE1 and AKT2 detected in the brain metastasis of case 138. Figure S18. Amplification of EGFR detected in a regional lymph node from case 296. Figure S19. Power for somatic mutation detection in 86 matched primary-tumor and brain-metastasis samples. Figure S20. Calling of amplifications in primary-tumor samples and paired brain metastases.

Damaging scores for alterations. This table shows the damaging scores (Methods) for nonsynonymous alterations (missense, nonsense, splice site, frame shift) studied in this cohort. The damaging score was derived as follows: missense mutations were scored using the Polyphen2 score(41) for the amino acid substitution. Nonsense mutations, splice site mutations, and short insertion/deletions were automatically assigned a damaging score of 1. "Unavailable" damaging scores are those that are missense mutations without a PolyPhen2 score.

Supplementary File 1. Power for detection of somatic point mutations in coding exons of TARGET genes. The description for the plots in Supplementary File 1 can be found in the Legend of Supplementary Figure S1.

Supplemental Table 1. Overall cohort characteristics of AAPC whole exome sequencing cohort (n=102) Supplemental Table 2. Clinical and pathological characteristics of AAPC WES discovery cohort (n=102) Supplemental Table 3. Comparison of numbers of tumors by Gleason grade between the TCGA and AAPC cohorts Supplemental Table 4. Germline pathogenic variants in genes in the DNA repair pathway and list of 20 DNA repair genes assessed in targeted analysis Supplemental Table 5. Alteration file of somatic variants detected in the AAPC WES discovery cohort (n=102) Supplemental Table 6. Focal deletion and amplification peaks and genes located within peaks by GISTIC2.0 analysis of AAPC WES cohort Supplemental Table 7. ERF deletions and mutations in prostate tumors of the Weill Cornell Precision Medicine cohort Supplemental Table 8. Clinical and pathological characteristics of AAPC targeted sequencing extension cohort Supplemental Table 9. List of genes and intronic regions targeted with hybrid capture bait set used for AAPC extension cohort Supplemental Table 10. Alteration file of somatic variants detected in the AAPC extension cohort (n=90) using the targeted sequencing panel Supplemental Table 11. ERF mutation frequencies across published prostate cancer cohorts Supplemental Table 12. Germline ERF variant detected in the AAPC WES cohort Supplemental Table 13. Mutations in cytochrome P450 genes in the AAPC exome discovery cohort Supplemental Table 14. ERF oligonucleotides used for RNA in situ hybridization Supplemental Table 15. Gene sets shERF_VCAP_UP_LnCAP_UP_overlap_UP and shERF_vs_CNTRL.LHSAR_UP Supplemental Table 16. Tumor purity estimates for AAPC exome discovery cohort

Supplemental Methods

Treatment information and sequencing metrics for all patients The treatment information and raw sequencing metrics, including mean target coverage, for each case is listed here. In addition, the mutation rate data referred to in the Results and represented in Supplementary Figure 1 is listed here by synonymous rate, non-synonymous rate, and total number of mutations per patient. *Cases that were included in the TCGA effort (UC-0296 and UC-0234). (dd = dose dense, GC = gemcitabine/cisplatin, MVAC = methotrexate, vinblastine, doxorubicin, cisplatin). Unavailable cases for Fluidigm Validation (due to inadequate remaining DNA or technical failure) are also denoted (n = 15).

Supplementary File 3. Detailed plots of the data used for phylogenetic inference in each case. The description for the plots in Supplementary File 3 can be found in the Legend of Supplementary Figure S13-S17.

Selective alteration enrichment. This table shows the results of applying Fisher's Exact Test to a tabulated set of predicted damaging somatically altered genes in responders compared to non-responders. A Benjamini-Hochberg/FDR q-value is reported for genes with the minimum number of events necessary to be considered for a theoretically significant p-value of {less than or equal to} 0.01 (n = 6). The results are sorted by p-value (lowest to highest).

Supplemental Figure Legends

Supplementary File 2. Detailed plots of SCNA calls in TARGET genes. The description for the plots in Supplementary File 2 can be found in the Legend of Supplementary Figure S3.

Supplementary Information from Integrative Transcriptome Analysis Reveals Common Molecular Subclasses of Human Hepatocellular Carcinoma

Ever larger Structural Variant (SV) catalogs highlighting the diversity within and between populations help researchers better understand the links between SVs and disease. The identification of SVs from DNA sequence data is non-trivial and requires a balance between comprehensiveness and precision. Here we present a catalog of 355,667 SVs (59.34% novel) across autosomes and the X chromosome (50bp+) from 138,134 individuals in the diverse TOPMed consortium. We describe our methodologies for SV inference resulting in high variant quality and >90% allele concordance compared to long-read de-novo assemblies of well-characterized control samples. We demonstrate utility through significant associations between SVs and important various cardio-metabolic and hematologic traits. We have identified 690 SV hotspots and deserts and those that potentially impact the regulation of medically relevant genes. This catalog characterizes SVs across multiple populations and will serve as a valuable tool to understand the impact of SV on disease development and progression.

BACKGROUND/OBJECTIVES: Obesity, defined as excessive fat accumulation that represents a health risk, is increasing in adults and children, reaching global epidemic proportions. Body mass index (BMI) correlates with body fat and future health risk, yet differs in prediction by fat distribution, across populations and by age. Nonetheless, few genetic studies of BMI have been conducted in ancestrally diverse populations. Gene expression association with BMI was assessed in the Multi-Ethnic Study of Atherosclerosis (MESA) in four self-identified race and ethnicity (SIRE) groups to identify genes associated with obesity. SUBJECTS/METHODS: RNA-sequencing was performed on 1096 MESA participants (37.8% white, 24.3% Hispanic, 28.4% African American, and 9.5% Chinese American) and linear models were used to assess the association of expression from each gene for its effect on BMI, adjusting for age, sex, sequencing center, study site, five expression and four genetic principal components in each self-identified race group. Sample-size-weighted meta-analysis was performed to identify genes with BMI-associated expression across ancestry groups. RESULTS: Within individual SIRE groups, there were zero to three genes whose expression is significantly (p < 1.97 × 10(−6)) associated with BMI. Across all groups, 45 genes were identified by meta-analysis whose expression was significantly associated with BMI, explaining 29.7% of BMI variation. The 45 genes are expressed in a variety of tissues and cell types and are enriched for obesity-related processes including erythrocyte function, oxygen binding and transport, and JAK-STAT signaling. CONCLUSIONS: We have identified genes whose expression is significantly associated with obesity in a multi-ethnic cohort. We have identified novel genes associated with BMI as well as confirmed previously identified genes from earlier genetic analyses. These novel genes and their biological pathways represent new targets for understanding the biology of obesity as well as new therapeutic intervention to reduce obesity and improve global public health.

Genomic medicine holds great promise for improving health care, but integrating searchable and actionable genetic data into electronic health records (EHRs) remains a challenge. Here we describe Neptune, a system for managing the interaction between a clinical laboratory and an EHR system during the clinical reporting process. We developed Neptune and applied it to two clinical sequencing projects that required report customization, variant reanalysis, and EHR integration. Neptune has been applied for the generation and delivery of over 15,000 clinical genomic reports. This work spans two clinical tests based on targeted gene panels that contain 68 and 153 genes respectively. These projects demanded customizable clinical reports that contained a variety of genetic data types including single-nucleotide variants (SNVs), copy-number variants (CNVs), pharmacogenomics, and polygenic risk scores. Two variant reanalysis activities were also supported, highlighting this important workflow. Methods are needed for delivering structured genetic data to EHRs. This need extends beyond developing data formats to providing infrastructure that manages the reporting process itself. Neptune was successfully applied on two high-throughput clinical sequencing projects to build and deliver clinical reports to EHR systems. The software is open source and available at https://gitlab.com/bcm-hgsc/neptune .

During July 2021, 469 cases of COVID-19 associated with multiple summer events and large public gatherings in a town in Barnstable County, Massachusetts, were identified among Massachusetts residents; vaccination coverage among eligible Massachusetts residents was 69%. Approximately three quarters (346; 74%) of cases occurred in fully vaccinated persons (those who had completed a 2-dose course of mRNA vaccine [Pfizer-BioNTech or Moderna] or had received a single dose of Janssen [Johnson & Johnson] vaccine ≥14 days before exposure). Genomic sequencing of specimens from 133 patients identified the B.1.617.2 (Delta) variant of SARS-CoV-2, the virus that causes COVID-19, in 119 (89%) and the Delta AY.3 sublineage in one (1%). Overall, 274 (79%) vaccinated patients with breakthrough infection were symptomatic. Among five COVID-19 patients who were hospitalized, four were fully vaccinated; no deaths were reported. Real-time reverse transcription-polymerase chain reaction (RT-PCR) cycle threshold (Ct) values in specimens from 127 vaccinated persons with breakthrough cases were similar to those from 84 persons who were unvaccinated, not fully vaccinated, or whose vaccination status was unknown (median = 22.77 and 21.54, respectively). The Delta variant of SARS-CoV-2 is highly transmissible (1); vaccination is the most important strategy to prevent severe illness and death. On July 27, CDC recommended that all persons, including those who are fully vaccinated, should wear masks in indoor public settings in areas where COVID-19 transmission is high or substantial.* Findings from this investigation suggest that even jurisdictions without substantial or high COVID-19 transmission might consider expanding prevention strategies, including masking in indoor public settings regardless of vaccination status, given the potential risk of infection during attendance at large public gatherings that include travelers from many areas with differing levels of transmission.

Background: A key goal of precision medicine is to disaggregate common, complex diseases into discrete molecular subtypes. Rare coding variants in the low-density lipoprotein receptor gene ( LDLR ) are identified in 1% to 2% of coronary artery disease (CAD) patients, defining a molecular subtype with risk driven by hypercholesterolemia. Methods: To search for additional subtypes, we compared the frequency of rare, predicted loss-of-function and damaging missense variants aggregated within a given gene in 41 081 CAD cases versus 217 115 controls. Results: Rare variants in LDLR were most strongly associated with CAD, present in 1% of cases and associated with 4.4-fold increased CAD risk. A second subtype was characterized by variants in endothelial nitric oxide synthase gene ( NOS3 ), a key enzyme regulating vascular tone, endothelial function, and platelet aggregation. A rare predicted loss-of-function or damaging missense variants in NOS3 was present in 0.6% of cases and associated with 2.42-fold increased risk of CAD (95% CI, 1.80–3.26; P =5.50×10 −9 ). These variants were associated with higher systolic blood pressure (+3.25 mm Hg; [95% CI, 1.86–4.65]; P =5.00×10 −6 ) and increased risk of hypertension (adjusted odds ratio 1.31; [95% CI, 1.14–1.51]; P =2.00×10 −4 ) but not circulating cholesterol concentrations, suggesting that, beyond lipid pathways, nitric oxide synthesis is a key nonlipid driver of CAD risk. Conclusions: Beyond LDLR , we identified an additional nonlipid molecular subtype of CAD characterized by rare variants in the NOS3 gene.

Background Unbiased assessment of the risks associated with acquisition of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is critical to informing mitigation efforts during pandemics. The objective of our study was to understand the risk factors for acquiring coronavirus disease 2019 (COVID-19) in a large prospective cohort of adult residents in a large US metropolitan area. Methods We designed a fully remote longitudinal cohort study involving monthly at-home SARS-CoV-2 polymerase chain reaction (PCR) and serology self-testing and monthly surveys. Results Between October 2020 and January 2021, we enrolled 10 289 adults reflective of the Boston metropolitan area census data. At study entry, 567 (5.5%) participants had evidence of current or prior SARS-CoV-2 infection. This increased to 13.4% by June 15, 2021. Compared with Whites, Black non-Hispanic participants had a 2.2-fold greater risk of acquiring COVID-19 (hazard ratio [HR], 2.19; 95% CI, 1.91–2.50; P < .001), and Hispanics had a 1.5-fold greater risk (HR, 1.52; 95% CI, 1.32–1.71; P < .016). Individuals aged 18–29, those who worked outside the home, and those living with other adults and children were at an increased risk. Individuals in the second and third lowest disadvantaged neighborhood communities were associated with an increased risk of acquiring COVID-19. Individuals with medical risk factors for severe disease were at a decreased risk of SARS-CoV-2 acquisition. Conclusions These results demonstrate that race/ethnicity and socioeconomic status are the biggest determinants of acquisition of infection. This disparity is significantly underestimated if based on PCR data alone, as noted by the discrepancy in serology vs PCR detection for non-White participants, and points to persistent disparity in access to testing. Medical conditions and advanced age, which increase the risk for severity of SARS-CoV-2 disease, were associated with a lower risk of COVID-19 acquisition, suggesting the importance of behavior modifications. These findings highlight the need for mitigation programs that overcome challenges of structural racism in current and future pandemics.

Each human genome has tens of thousands of rare genetic variants; however, identifying impactful rare variants remains a major challenge. We demonstrate how use of personal multi-omics can enable identification of impactful rare variants by using the Multi-Ethnic Study of Atherosclerosis (MESA) which included several hundred individuals with whole genome sequencing, transcriptomes, methylomes, and proteomes collected across two time points, ten years apart. We evaluated each multi-omic phenotype’s ability to separately and jointly inform functional rare variation. By combining expression and protein data, we observed rare stop variants 62x and rare frameshift variants 216x as frequently as controls, compared to 13x to 27x for expression or protein effects alone. We developed a Bayesian hierarchical model to prioritize specific rare variants underlying multi-omic signals across the regulatory cascade. With this approach, we identified rare variants that exhibited large effect sizes on multiple complex traits including height, schizophrenia, and Alzheimer’s disease.

Summary Whole-genome sequencing (WGS), a powerful tool for detecting novel coding and non-coding disease-causing variants, has largely been applied to clinical diagnosis of inherited disorders. Here we leveraged WGS data in up to 62,653 ethnically diverse participants from the NHLBI Trans-Omics for Precision Medicine (TOPMed) program and assessed statistical association of variants with seven red blood cell (RBC) quantitative traits. We discovered 14 single variant-RBC trait associations at 12 genomic loci, which have not been reported previously. Several of the RBC trait-variant associations (RPN1, ELL2, MIDN, HBB, HBA1, PIEZO1, and G6PD) were replicated in independent GWAS datasets imputed to the TOPMed reference panel. Most of these discovered variants are rare/low frequency, and several are observed disproportionately among non-European Ancestry (African, Hispanic/Latino, or East Asian) populations. We identified a 3 bp indel p.Lys2169del (g.88717175_88717177TCT[4]) (common only in the Ashkenazi Jewish population) of PIEZO1, a gene responsible for the Mendelian red cell disorder hereditary xerocytosis (MIM: 194380 ), associated with higher mean corpuscular hemoglobin concentration (MCHC). In stepwise conditional analysis and in gene-based rare variant aggregated association analysis, we identified several of the variants in HBB, HBA1, TMPRSS6, and G6PD that represent the carrier state for known coding, promoter, or splice site loss-of-function variants that cause inherited RBC disorders. Finally, we applied base and nuclease editing to demonstrate that the sentinel variant rs112097551 (nearest gene RPN1) acts through a cis-regulatory element that exerts long-range control of the gene RUVBL1 which is essential for hematopoiesis. Together, these results demonstrate the utility of WGS in ethnically diverse population-based samples and gene editing for expanding knowledge of the genetic architecture of quantitative hematologic traits and suggest a continuum between complex trait and Mendelian red cell disorders.

Relapsed myeloid disease after allogeneic stem cell transplantation (HSCT) remains largely incurable. We previously demonstrated the potent activity of immune checkpoint blockade in this clinical setting with ipilimumab or nivolumab. To define the molecular and cellular pathways by which CTLA-4 blockade with ipilimumab can reinvigorate an effective graft-versus-leukemia (GVL) response, we integrated transcriptomic analysis of leukemic biopsies with immunophenotypic profiling of matched peripheral blood samples collected from patients treated with ipilimumab following HSCT on the Experimental Therapeutics Clinical Trials Network 9204 trial. Response to ipilimumab was associated with transcriptomic evidence of increased local CD8+ T-cell infiltration and activation. Systemically, ipilimumab decreased naïve and increased memory T-cell populations and increased expression of markers of T-cell activation and costimulation such as PD-1, HLA-DR, and ICOS, irrespective of response. However, responding patients were characterized by higher turnover of T-cell receptor sequences in peripheral blood and showed increased expression of proinflammatory chemokines in plasma that was further amplified by ipilimumab. Altogether, these data highlight the compositional T-cell shifts and inflammatory pathways induced by ipilimumab both locally and systemically that associate with successful GVL outcomes. This trial was registered at www.clinicaltrials.gov as #NCT01822509.

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.

Background: Premature menopause is an independent risk factor for cardiovascular disease in women, but mechanisms underlying this association remain unclear. Clonal hematopoiesis of indeterminate potential (CHIP), the age-related expansion of hematopoietic cells with leukemogenic mutations without detectable malignancy, is associated with accelerated atherosclerosis. Whether premature menopause is associated with CHIP is unknown. Methods: We included postmenopausal women from the UK Biobank (n=11 495) aged 40 to 70 years with whole exome sequences and from the Women’s Health Initiative (n=8111) aged 50 to 79 years with whole genome sequences. Premature menopause was defined as natural or surgical menopause occurring before age 40 years. Co–primary outcomes were the presence of any CHIP and CHIP with variant allele frequency >0.1. Logistic regression tested the association of premature menopause with CHIP, adjusted for age, race, the first 10 principal components of ancestry, smoking, diabetes, and hormone therapy use. Secondary analyses considered natural versus surgical premature menopause and gene-specific CHIP subtypes. Multivariable-adjusted Cox models tested the association between CHIP and incident coronary artery disease. Results: The sample included 19 606 women, including 418 (2.1%) with natural premature menopause and 887 (4.5%) with surgical premature menopause. Across cohorts, CHIP prevalence in postmenopausal women with versus without a history of premature menopause was 8.8% versus 5.5% ( P P =0.004; CHIP with variant allele frequency >0.1: odds ratio, 1.40 [95% CI, 1.10–1.79]; P =0.007). Associations were larger for natural premature menopause (all CHIP: odds ratio, 1.73 [95% CI, 1.23–2.44]; P =0.001; CHIP with variant allele frequency >0.1: odds ratio, 1.91 [95% CI, 1.30–2.80]; P DNMT3A CHIP was significantly associated with premature menopause. Among postmenopausal middle-aged women, CHIP was independently associated with incident coronary artery disease (hazard ratio associated with all CHIP: 1.36 [95% CI, 1.07–1.73]; P =0.012; hazard ratio associated with CHIP with variant allele frequency >0.1: 1.48 [95% CI, 1.13–1.94]; P =0.005). Conclusions: Premature menopause, especially natural premature menopause, is independently associated with CHIP among postmenopausal women. Natural premature menopause may serve as a risk signal for predilection to develop CHIP and CHIP-associated cardiovascular disease.