Integrating Genetic and Transcriptomic Data to Identify Genes Underlying Obesity Risk Loci.

Genome-wide association studies (GWAS) have identified numerous body mass index (BMI) loci. However, most underlying mechanisms from risk locus to BMI remain unknown. Leveraging omics data through integrative analyses could provide more comprehensive views of biological pathways on BMI. We analyzed genotype and blood gene expression data in up to 5,619 samples from the Framingham Heart Study (FHS). Using 3,992 single nucleotide polymorphisms (SNPs) at 97 BMI loci and 20,692 transcripts within 1 Mb, we performed separate association analyses of transcript with BMI and SNP with transcript (PBMI and PSNP, respectively) and then a correlated meta-analysis between the full summary data sets (PMETA). We identified transcripts that met Bonferroni-corrected significance for each omic, were more significant in the correlated meta-analysis than each omic, and were at least nominally associated with BMI in FHS data. Among 308 significant SNP-transcript-BMI associations, we identified seven genes (NT5C2, GSTM3, SNAPC3, SPNS1, TMEM245, YPEL3, and ZNF646) in five association regions. Using an independent sample of blood gene expression data, we validated results for SNAPC3 and YPEL3. We tested for generalization of these associations in hypothalamus, nucleus accumbens, and liver and observed significant (PMETA<0.05 & PMETA

Epigenetic Age Mediates the Association of Life's Essential 8 With Cardiovascular Disease and Mortality.

BACKGROUND: Life's Essential 8 (LE8) is an enhanced metric for cardiovascular health. The interrelations among LE8, biomarkers of aging, and disease risks are unclear. METHODS AND RESULTS: LE8 score was calculated for 5682 Framingham Heart Study participants. We implemented 4 DNA methylation-based epigenetic age biomarkers, with older epigenetic age hypothesized to represent faster biological aging, and examined whether these biomarkers mediated the associations between the LE8 score and cardiovascular disease (CVD), CVD-specific mortality, and all-cause mortality. We found that a 1 SD increase in the LE8 score was associated with a 35% (95% CI, 27-41; P=1.8E-15) lower risk of incident CVD, a 36% (95% CI, 24-47; P=7E-7) lower risk of CVD-specific mortality, and a 29% (95% CI, 22-35; P=7E-15) lower risk of all-cause mortality. These associations were partly mediated by epigenetic age biomarkers, particularly the GrimAge and the DunedinPACE scores. The potential mediation effects by epigenetic age biomarkers tended to be more profound in participants with higher genetic risk for older epigenetic age, compared with those with lower genetic risk. For example, in participants with higher GrimAge polygenic scores (greater than median), the mean proportion of mediation was 39%, 39%, and 78% for the association of the LE8 score with incident CVD, CVD-specific mortality, and all-cause mortality, respectively. No significant mediation was observed in participants with lower GrimAge polygenic score. CONCLUSIONS: DNA methylation-based epigenetic age scores mediate the associations between the LE8 score and incident CVD, CVD-specific mortality, and all-cause mortality, particularly in individuals with higher genetic predisposition for older epigenetic age.

Key variants via the Alzheimer's Disease Sequencing Project whole genome sequence data.

INTRODUCTION: Genome-wide association studies (GWAS) have identified loci associated with Alzheimer's disease (AD) but did not identify specific causal genes or variants within those loci. Analysis of whole genome sequence (WGS) data, which interrogates the entire genome and captures rare variations, may identify causal variants within GWAS loci. METHODS: We performed single common variant association analysis and rare variant aggregate analyses in the pooled population (N cases = 2184, N controls = 2383) and targeted analyses in subpopulations using WGS data from the Alzheimer's Disease Sequencing Project (ADSP). The analyses were restricted to variants within 100 kb of 83 previously identified GWAS lead variants. RESULTS: Seventeen variants were significantly associated with AD within five genomic regions implicating the genes OARD1/NFYA/TREML1, JAZF1, FERMT2, and SLC24A4. KAT8 was implicated by both single variant and rare variant aggregate analyses. DISCUSSION: This study demonstrates the utility of leveraging WGS to gain insights into AD loci identified via GWAS.

Familial Hypercholesterolemia Variant and Cardiovascular Risk in Individuals With Elevated Cholesterol.

Importance: Familial hypercholesterolemia (FH) is a genetic disorder that often results in severely high low-density lipoprotein cholesterol (LDL-C) and high risk of premature coronary heart disease (CHD). However, the impact of FH variants on CHD risk among individuals with moderately elevated LDL-C is not well quantified. Objective: To assess CHD risk associated with FH variants among individuals with moderately (130-189 mg/dL) and severely (≥190 mg/dL) elevated LDL-C and to quantify excess CHD deaths attributable to FH variants in US adults. Design, Setting, and Participants: A total of 21 426 individuals without preexisting CHD from 6 US cohort studies (Atherosclerosis Risk in Communities study, Coronary Artery Risk Development in Young Adults study, Cardiovascular Health Study, Framingham Heart Study Offspring cohort, Jackson Heart Study, and Multi-Ethnic Study of Atherosclerosis) were included, 63 of whom had an FH variant. Data were collected from 1971 to 2018, and the median (IQR) follow-up was 18 (13-28) years. Data were analyzed from March to May 2023. Exposures: LDL-C, cumulative past LDL-C, FH variant status. Main Outcomes and Measures: Cox proportional hazards models estimated associations between FH variants and incident CHD. The Cardiovascular Disease Policy Model projected excess CHD deaths associated with FH variants in US adults. Results: Of the 21 426 individuals without preexisting CHD (mean [SD] age 52.1 [15.5] years; 12 041 [56.2%] female), an FH variant was found in 22 individuals with moderately elevated LDL-C (0.3%) and in 33 individuals with severely elevated LDL-C (2.5%). The adjusted hazard ratios for incident CHD comparing those with and without FH variants were 2.9 (95% CI, 1.4-6.0) and 2.6 (95% CI, 1.4-4.9) among individuals with moderately and severely elevated LDL-C, respectively. The association between FH variants and CHD was slightly attenuated when further adjusting for baseline LDL-C level, whereas the association was no longer statistically significant after adjusting for cumulative past LDL-C exposure. Among US adults 20 years and older with no history of CHD and LDL-C 130 mg/dL or higher, more than 417 000 carry an FH variant and were projected to experience more than 12 000 excess CHD deaths in those with moderately elevated LDL-C and 15 000 in those with severely elevated LDL-C compared with individuals without an FH variant. Conclusions and Relevance: In this pooled cohort study, the presence of FH variants was associated with a 2-fold higher CHD risk, even when LDL-C was only moderately elevated. The increased CHD risk appeared to be largely explained by the higher cumulative LDL-C exposure in individuals with an FH variant compared to those without. Further research is needed to assess the value of adding genetic testing to traditional phenotypic FH screening.

A statistical framework for powerful multi-trait rare variant analysis in large-scale whole-genome sequencing studies.

Large-scale whole-genome sequencing (WGS) studies have improved our understanding of the contributions of coding and noncoding rare variants to complex human traits. Leveraging association effect sizes across multiple traits in WGS rare variant association analysis can improve statistical power over single-trait analysis, and also detect pleiotropic genes and regions. Existing multi-trait methods have limited ability to perform rare variant analysis of large-scale WGS data. We propose MultiSTAAR, a statistical framework and computationally-scalable analytical pipeline for functionally-informed multi-trait rare variant analysis in large-scale WGS studies. MultiSTAAR accounts for relatedness, population structure and correlation among phenotypes by jointly analyzing multiple traits, and further empowers rare variant association analysis by incorporating multiple functional annotations. We applied MultiSTAAR to jointly analyze three lipid traits (low-density lipoprotein cholesterol, high-density lipoprotein cholesterol and triglycerides) in 61,861 multi-ethnic samples from the Trans-Omics for Precision Medicine (TOPMed) Program. We discovered new associations with lipid traits missed by single-trait analysis, including rare variants within an enhancer of NIPSNAP3A and an intergenic region on chromosome 1.

Multi-tissue epigenetic analysis identifies distinct associations underlying insulin resistance and Alzheimer's disease at CPT1A locus.

BACKGROUND: Insulin resistance (IR) is a major risk factor for Alzheimer's disease (AD) dementia. The mechanisms by which IR predisposes to AD are not well-understood. Epigenetic studies may help identify molecular signatures of IR associated with AD, thus improving our understanding of the biological and regulatory mechanisms linking IR and AD. METHODS: We conducted an epigenome-wide association study of IR, quantified using the homeostatic model assessment of IR (HOMA-IR) and adjusted for body mass index, in 3,167 participants from the Framingham Heart Study (FHS) without type 2 diabetes at the time of blood draw used for methylation measurement. We identified DNA methylation markers associated with IR at the genome-wide level accounting for multiple testing (P < 1.1 × 10-7) and evaluated their association with neurological traits in participants from the FHS (N = 3040) and the Religious Orders Study/Memory and Aging Project (ROSMAP, N = 707). DNA methylation profiles were measured in blood (FHS) or dorsolateral prefrontal cortex (ROSMAP) using the Illumina HumanMethylation450 BeadChip. Linear regressions (ROSMAP) or mixed-effects models accounting for familial relatedness (FHS) adjusted for age, sex, cohort, self-reported race, batch, and cell type proportions were used to assess associations between DNA methylation and neurological traits accounting for multiple testing. RESULTS: We confirmed the strong association of blood DNA methylation with IR at three loci (cg17901584-DHCR24, cg17058475-CPT1A, cg00574958-CPT1A, and cg06500161-ABCG1). In FHS, higher levels of blood DNA methylation at cg00574958 and cg17058475 were both associated with lower IR (P = 2.4 × 10-11 and P = 9.0 × 10-8), larger total brain volumes (P = 0.03 and P = 9.7 × 10-4), and smaller log lateral ventricular volumes (P = 0.07 and P = 0.03). In ROSMAP, higher levels of brain DNA methylation at the same two CPT1A markers were associated with greater risk of cognitive impairment (P = 0.005 and P = 0.02) and higher AD-related indices (CERAD score: P = 5 × 10-4 and 0.001; Braak stage: P = 0.004 and P = 0.01). CONCLUSIONS: Our results suggest potentially distinct epigenetic regulatory mechanisms between peripheral blood and dorsolateral prefrontal cortex tissues underlying IR and AD at CPT1A locus.

Key variants via Alzheimer's Disease Sequencing Project whole genome sequence data.

INTRODUCTION: Genome-wide association studies (GWAS) have identified loci associated with Alzheimer's disease (AD) but did not identify specific causal genes or variants within those loci. Analysis of whole genome sequence (WGS) data, which interrogates the entire genome and captures rare variations, may identify causal variants within GWAS loci. METHODS: We performed single common variant association analysis and rare variant aggregate analyses in the pooled population (N cases=2,184, N controls=2,383) and targeted analyses in sub-populations using WGS data from the Alzheimer's Disease Sequencing Project (ADSP). The analyses were restricted to variants within 100 kb of 83 previously identified GWAS lead variants. RESULTS: Seventeen variants were significantly associated with AD within five genomic regions implicating the genes OARD1/NFYA/TREML1, JAZF1, FERMT2, and SLC24A4. KAT8 was implicated by both single variant and rare variant aggregate analyses. DISCUSSION: This study demonstrates the utility of leveraging WGS to gain insights into AD loci identified via GWAS.

Association of Common and Rare Variants with Alzheimer's Disease in over 13,000 Diverse Individuals with Whole-Genome Sequencing from the Alzheimer's Disease Sequencing Project.

Alzheimer's Disease (AD) is a common disorder of the elderly that is both highly heritable and genetically heterogeneous. Here, we investigated the association between AD and both common variants and aggregates of rare coding and noncoding variants in 13,371 individuals of diverse ancestry with whole genome sequence (WGS) data. Pooled-population analyses identified genetic variants in or near APOE, BIN1, and LINC00320 significantly associated with AD (p < 5×10-8). Population-specific analyses identified a haplotype on chromosome 14 including PSEN1 associated with AD in Hispanics, further supported by aggregate testing of rare coding and noncoding variants in this region. Finally, we observed suggestive associations (p < 5×10-5) of aggregates of rare coding rare variants in ABCA7 among non-Hispanic Whites (p=5.4×10-6), and rare noncoding variants in the promoter of TOMM40 distinct of APOE in pooled-population analyses (p=7.2×10-8). Complementary pooled-population and population-specific analyses offered unique insights into the genetic architecture of AD.

Expression quantitative trait methylation analysis elucidates gene regulatory effects of DNA methylation: the Framingham Heart Study.

Expression quantitative trait methylation (eQTM) analysis identifies DNA CpG sites at which methylation is associated with gene expression. The present study describes an eQTM resource of CpG-transcript pairs derived from whole blood DNA methylation and RNA sequencing gene expression data in 2115 Framingham Heart Study participants. We identified 70,047 significant cis CpG-transcript pairs at p < 1E-7 where the top most significant eGenes (i.e., gene transcripts associated with a CpG) were enriched in biological pathways related to cell signaling, and for 1208 clinical traits (enrichment false discovery rate [FDR] ≤ 0.05). We also identified 246,667 significant trans CpG-transcript pairs at p < 1E-14 where the top most significant eGenes were enriched in biological pathways related to activation of the immune response, and for 1191 clinical traits (enrichment FDR ≤ 0.05). Independent and external replication of the top 1000 significant cis and trans CpG-transcript pairs was completed in the Women's Health Initiative and Jackson Heart Study cohorts. Using significant cis CpG-transcript pairs, we identified significant mediation of the association between CpG sites and cardiometabolic traits through gene expression and identified shared genetic regulation between CpGs and transcripts associated with cardiometabolic traits. In conclusion, we developed a robust and powerful resource of whole blood eQTM CpG-transcript pairs that can help inform future functional studies that seek to understand the molecular basis of disease.

Correlations between complex human phenotypes vary by genetic background, gender, and environment.

We develop a closed-form Haseman-Elston estimator for genetic and environmental correlation coefficients between complex phenotypes, which we term HEc, that is as precise as GCTA yet ∼20× faster. We estimate genetic and environmental correlations between over 7,000 phenotype pairs in subgroups from the Trans-Omics in Precision Medicine (TOPMed) program. We demonstrate substantial differences in both heritabilities and genetic correlations for multiple phenotypes and phenotype pairs between individuals of self-reported Black, Hispanic/Latino, and White backgrounds. We similarly observe differences in many of the genetic and environmental correlations between genders. To estimate the contribution of genetics to the observed phenotypic correlation, we introduce "fractional genetic correlation" as the fraction of phenotypic correlation explained by genetics. Finally, we quantify the enrichment of correlations between phenotypic domains, each of which is comprised of multiple phenotypes. Altogether, we demonstrate that the observed correlations between complex human phenotypes depend on the genetic background of the individuals, their gender, and their environment.

Elucidating the genetic architecture of DNA methylation to identify promising molecular mechanisms of disease.

DNA methylation commonly occurs at cytosine-phosphate-guanine sites (CpGs) that can serve as biomarkers for many diseases. We analyzed whole genome sequencing data to identify DNA methylation quantitative trait loci (mQTLs) in 4126 Framingham Heart Study participants. Our mQTL mapping identified 94,362,817 cis-mQTLvariant-CpG pairs (for 210,156 unique autosomal CpGs) at P < 1e-7 and 33,572,145 trans-mQTL variant-CpG pairs (for 213,606 unique autosomal CpGs) at P < 1e-14. Using cis-mQTL variants for 1258 CpGs associated with seven cardiovascular disease (CVD) risk factors, we found 104 unique CpGs that colocalized with at least one CVD trait. For example, cg11554650 (PPP1R18) colocalized with type 2 diabetes, and was driven by a single nucleotide polymorphism (rs2516396). We performed Mendelian randomization (MR) analysis and demonstrated 58 putatively causal relations of CVD risk factor-associated CpGs to one or more risk factors (e.g., cg05337441 [APOB] with LDL; MR P = 1.2e-99, and 17 causal associations with coronary artery disease (e.g. cg08129017 [SREBF1] with coronary artery disease; MR P = 5e-13). We also showed that three CpGs, e.g., cg14893161 (PM20D1), are putatively causally associated with COVID-19 severity. To assist in future analyses of the role of DNA methylation in disease pathogenesis, we have posted a comprehensive summary data set in the National Heart, Lung, and Blood Institute's BioData Catalyst.

Whole genome DNA and RNA sequencing of whole blood elucidates the genetic architecture of gene expression underlying a wide range of diseases.

To create a scientific resource of expression quantitative trail loci (eQTL), we conducted a genome-wide association study (GWAS) using genotypes obtained from whole genome sequencing (WGS) of DNA and gene expression levels from RNA sequencing (RNA-seq) of whole blood in 2622 participants in Framingham Heart Study. We identified 6,778,286 cis-eQTL variant-gene transcript (eGene) pairs at p < 5 × 10-8 (2,855,111 unique cis-eQTL variants and 15,982 unique eGenes) and 1,469,754 trans-eQTL variant-eGene pairs at p < 1e-12 (526,056 unique trans-eQTL variants and 7233 unique eGenes). In addition, 442,379 cis-eQTL variants were associated with expression of 1518 long non-protein coding RNAs (lncRNAs). Gene Ontology (GO) analyses revealed that the top GO terms for cis-eGenes are enriched for immune functions (FDR < 0.05). The cis-eQTL variants are enriched for SNPs reported to be associated with 815 traits in prior GWAS, including cardiovascular disease risk factors. As proof of concept, we used this eQTL resource in conjunction with genetic variants from public GWAS databases in causal inference testing (e.g., COVID-19 severity). After Bonferroni correction, Mendelian randomization analyses identified putative causal associations of 60 eGenes with systolic blood pressure, 13 genes with coronary artery disease, and seven genes with COVID-19 severity. This study created a comprehensive eQTL resource via BioData Catalyst that will be made available to the scientific community. This will advance understanding of the genetic architecture of gene expression underlying a wide range of diseases.

Whole Genome DNA and RNA Sequencing of Whole Blood Elucidates the Genetic Architecture of Gene Expression Underlying a Wide Range of Diseases.

To create a scientific resource of expression quantitative trail loci (eQTL), we conducted a genome-wide association study (GWAS) using genotypes obtained from whole genome sequencing (WGS) of DNA and gene expression levels from RNA sequencing (RNA-seq) of whole blood in 2622 participants in Framingham Heart Study. We identified 6,778,286 cis -eQTL variant-gene transcript (eGene) pairs at p < 5x10 - 8 (2,855,111 unique cis -eQTL variants and 15,982 unique eGenes) and 1,469,754 trans -eQTL variant-eGene pairs at p < 1e-12 (526,056 unique trans -eQTL variants and 7,233 unique eGenes). In addition, 442,379 cis -eQTL variants were associated with expression of 1518 long non-protein coding RNAs (lncRNAs). Gene Ontology (GO) analyses revealed that the top GO terms for cis- eGenes are enriched for immune functions (FDR < 0.05). The cis -eQTL variants are enriched for SNPs reported to be associated with 815 traits in prior GWAS, including cardiovascular disease risk factors. As proof of concept, we used this eQTL resource in conjunction with genetic variants from public GWAS databases in causal inference testing (e.g., COVID-19 severity). After Bonferroni correction, Mendelian randomization analyses identified putative causal associations of 60 eGenes with systolic blood pressure, 13 genes with coronary artery disease, and seven genes with COVID-19 severity. This study created a comprehensive eQTL resource via BioData Catalyst that will be made available to the scientific community. This will advance understanding of the genetic architecture of gene expression underlying a wide range of diseases.

Whole Genome DNA and RNA Sequencing of Whole Blood Elucidates the Genetic Architecture of Gene Expression Underlying a Wide Range of Diseases.

To create a scientific resource of expression quantitative trail loci (eQTL), we conducted a genome-wide association study (GWAS) using genotypes obtained from whole genome sequencing (WGS) of DNA and gene expression levels from RNA sequencing (RNA-seq) of whole blood in 2622 participants in Framingham Heart Study. We identified 6,778,286 cis -eQTL variant-gene transcript (eGene) pairs at p <5×10 -8 (2,855,111 unique cis -eQTL variants and 15,982 unique eGenes) and 1,469,754 trans -eQTL variant-eGene pairs at p <1e-12 (526,056 unique trans -eQTL variants and 7,233 unique eGenes). In addition, 442,379 cis -eQTL variants were associated with expression of 1518 long non-protein coding RNAs (lncRNAs). Gene Ontology (GO) analyses revealed that the top GO terms for cis- eGenes are enriched for immune functions (FDR <0.05). The cis -eQTL variants are enriched for SNPs reported to be associated with 815 traits in prior GWAS, including cardiovascular disease risk factors. As proof of concept, we used this eQTL resource in conjunction with genetic variants from public GWAS databases in causal inference testing (e.g., COVID-19 severity). After Bonferroni correction, Mendelian randomization analyses identified putative causal associations of 60 eGenes with systolic blood pressure, 13 genes with coronary artery disease, and seven genes with COVID-19 severity. This study created a comprehensive eQTL resource via BioData Catalyst that will be made available to the scientific community. This will advance understanding of the genetic architecture of gene expression underlying a wide range of diseases.

Genome-wide association meta-analysis identifies 48 risk variants and highlights the role of the stria vascularis in hearing loss.

Hearing loss is one of the top contributors to years lived with disability and is a risk factor for dementia. Molecular evidence on the cellular origins of hearing loss in humans is growing. Here, we performed a genome-wide association meta-analysis of clinically diagnosed and self-reported hearing impairment on 723,266 individuals and identified 48 significant loci, 10 of which are novel. A large proportion of associations comprised missense variants, half of which lie within known familial hearing loss loci. We used single-cell RNA-sequencing data from mouse cochlea and brain and mapped common-variant genomic results to spindle, root, and basal cells from the stria vascularis, a structure in the cochlea necessary for normal hearing. Our findings indicate the importance of the stria vascularis in the mechanism of hearing impairment, providing future paths for developing targets for therapeutic intervention in hearing loss.

Whole-Genome Sequencing Association Analyses of Stroke and Its Subtypes in Ancestrally Diverse Populations From Trans-Omics for Precision Medicine Project.

BACKGROUND AND PURPOSE: Stroke is the leading cause of death and long-term disability worldwide. Previous genome-wide association studies identified 51 loci associated with stroke (mostly ischemic) and its subtypes among predominantly European populations. Using whole-genome sequencing in ancestrally diverse populations from the Trans-Omics for Precision Medicine (TOPMed) Program, we aimed to identify novel variants, especially low-frequency or ancestry-specific variants, associated with all stroke, ischemic stroke and its subtypes (large artery, cardioembolic, and small vessel), and hemorrhagic stroke and its subtypes (intracerebral and subarachnoid). METHODS: Whole-genome sequencing data were available for 6833 stroke cases and 27 116 controls, including 22 315 European, 7877 Black, 2616 Hispanic/Latino, 850 Asian, 54 Native American, and 237 other ancestry participants. In TOPMed, we performed single variant association analysis examining 40 million common variants and aggregated association analysis focusing on rare variants. We also combined TOPMed European populations with over 28 000 additional European participants from the UK BioBank genome-wide array data through meta-analysis. RESULTS: In the single variant association analysis in TOPMed, we identified one novel locus 13q33 for large artery at whole-genome-wide significance (P<5.00×10-9) and 4 novel loci at genome-wide significance (P<5.00×10-8), all of which need confirmation in independent studies. Lead variants in all 5 loci are low-frequency but are more common in non-European populations. An aggregation of synonymous rare variants within the gene C6orf26 demonstrated suggestive evidence of association for hemorrhagic stroke (P<3.11×10-6). By meta-analyzing European ancestry samples in TOPMed and UK BioBank, we replicated several previously reported stroke loci including PITX2, HDAC9, ZFHX3, and LRCH1. CONCLUSIONS: We represent the first association analysis for stroke and its subtypes using whole-genome sequencing data from ancestrally diverse populations. While our findings suggest the potential benefits of combining whole-genome sequencing data with populations of diverse genetic backgrounds to identify possible low-frequency or ancestry-specific variants, they also highlight the need to increase genome coverage and sample sizes.

A System for Phenotype Harmonization in the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine (TOPMed) Program.

Genotype-phenotype association studies often combine phenotype data from multiple studies to increase statistical power. Harmonization of the data usually requires substantial effort due to heterogeneity in phenotype definitions, study design, data collection procedures, and data-set organization. Here we describe a centralized system for phenotype harmonization that includes input from phenotype domain and study experts, quality control, documentation, reproducible results, and data-sharing mechanisms. This system was developed for the National Heart, Lung, and Blood Institute's Trans-Omics for Precision Medicine (TOPMed) program, which is generating genomic and other -omics data for more than 80 studies with extensive phenotype data. To date, 63 phenotypes have been harmonized across thousands of participants (recruited in 1948-2012) from up to 17 studies per phenotype. Here we discuss challenges in this undertaking and how they were addressed. The harmonized phenotype data and associated documentation have been submitted to National Institutes of Health data repositories for controlled access by the scientific community. We also provide materials to facilitate future harmonization efforts by the community, which include 1) the software code used to generate the 63 harmonized phenotypes, enabling others to reproduce, modify, or extend these harmonizations to additional studies, and 2) the results of labeling thousands of phenotype variables with controlled vocabulary terms.

De novo mutations across 1,465 diverse genomes reveal mutational insights and reductions in the Amish founder population.

De novo mutations (DNMs), or mutations that appear in an individual despite not being seen in their parents, are an important source of genetic variation whose impact is relevant to studies of human evolution, genetics, and disease. Utilizing high-coverage whole-genome sequencing data as part of the Trans-Omics for Precision Medicine (TOPMed) Program, we called 93,325 single-nucleotide DNMs across 1,465 trios from an array of diverse human populations, and used them to directly estimate and analyze DNM counts, rates, and spectra. We find a significant positive correlation between local recombination rate and local DNM rate, and that DNM rate explains a substantial portion (8.98 to 34.92%, depending on the model) of the genome-wide variation in population-level genetic variation from 41K unrelated TOPMed samples. Genome-wide heterozygosity does correlate with DNM rate, but only explains <1% of variation. While we are underpowered to see small differences, we do not find significant differences in DNM rate between individuals of European, African, and Latino ancestry, nor across ancestrally distinct segments within admixed individuals. However, we did find significantly fewer DNMs in Amish individuals, even when compared with other Europeans, and even after accounting for parental age and sequencing center. Specifically, we found significant reductions in the number of C→A and T→C mutations in the Amish, which seem to underpin their overall reduction in DNMs. Finally, we calculated near-zero estimates of narrow sense heritability (h2), which suggest that variation in DNM rate is significantly shaped by nonadditive genetic effects and the environment.

Genome-wide association meta-analysis identifies five novel loci for age-related hearing impairment.

Previous research has shown that genes play a substantial role in determining a person's susceptibility to age-related hearing impairment. The existing studies on this subject have different results, which may be caused by difficulties in determining the phenotype or the limited number of participants involved. Here, we have gathered the largest sample to date (discovery n = 9,675; replication n = 10,963; validation n = 356,141), and examined phenotypes that represented low/mid and high frequency hearing loss on the pure tone audiogram. We identified 7 loci that were either replicated and/or validated, of which 5 loci are novel in hearing. Especially the ILDR1 gene is a high profile candidate, as it contains our top SNP, is a known hearing loss gene, has been linked to age-related hearing impairment before, and in addition is preferentially expressed within hair cells of the inner ear. By verifying all previously published SNPs, we can present a paper that combines all new and existing findings to date, giving a complete overview of the genetic architecture of age-related hearing impairment. This is of importance as age-related hearing impairment is highly prevalent in our ageing society and represents a large socio-economic burden.