Manichaikul, Ani

Abstract Rationale In addition to rare genetic variants and the MUC5B locus, common genetic variants contribute to idiopathic pulmonary fibrosis (IPF) risk. The predictive power of common variants outside the MUC5B locus for IPF and interstitial lung abnormalities (ILAs) is unknown. Objectives We tested the predictive value of IPF polygenic risk scores (PRSs) with and without the MUC5B region on IPF, ILA, and ILA progression. Methods We developed PRSs that included (PRS-M5B) and excluded (PRS-NO-M5B) the MUC5B region (500-kb window around rs35705950-T) using an IPF genome-wide association study. We assessed PRS associations with area under the receiver operating characteristic curve (AUC) metrics for IPF, ILA, and ILA progression. Measurements and Main Results We included 14,650 participants (1,970 IPF; 1,068 ILA) from six multi-ancestry population-based and case–control cohorts. In cases excluded from genome-wide association study, the PRS-M5B (odds ratio [OR] per SD of the score, 3.1; P = 7.1 × 10−95) and PRS-NO-M5B (OR per SD, 2.8; P = 2.5 × 10−87) were associated with IPF. Participants in the top PRS-NO-M5B quintile had ∼sevenfold odds for IPF compared with those in the first quintile. A clinical model predicted IPF (AUC, 0.61); rs35705950-T and PRS-NO-M5B demonstrated higher AUCs (0.73 and 0.7, respectively), and adding both genetic predictors to a clinical model yielded the highest performance (AUC, 0.81). The PRS-NO-M5B was associated with ILA (OR, 1.25) and ILA progression (OR, 1.16) in European ancestry participants. Conclusions A common genetic variant risk score complements the MUC5B variant to identify individuals at high risk of interstitial lung abnormalities and pulmonary fibrosis.

Nononcogenic somatic mutations are thought to be uncommon and inconsequential. To test this, we analyzed 43,693 National Heart, Lung and Blood Institute Trans-Omics for Precision Medicine blood whole genomes from 37 cohorts and identified 7131 non-missense somatic mutations that are recurrently mutated in at least 50 individuals. These recurrent non-missense somatic mutations (RNMSMs) are not clearly explained by other clonal phenomena such as clonal hematopoiesis. RNMSM prevalence increased with age, with an average 50-year-old having 27 RNMSMs. Inherited germline variation associated with RNMSM acquisition. These variants were found in genes involved in adaptive immune function, proinflammatory cytokine production, and lymphoid lineage commitment. In addition, the presence of eight specific RNMSMs associated with blood cell traits at effect sizes comparable to Mendelian genetic mutations. Overall, we found that somatic mutations in blood are an unexpectedly common phenomenon with ancestry-specific determinants and human health consequences.

Abstract Privacy protection is a core principle of genomic but not proteomic research. We identified independent single nucleotide polymorphism (SNP) quantitative trait loci (pQTL) from COPDGene and Jackson Heart Study (JHS), calculated continuous protein level genotype probabilities, and then applied a naïve Bayesian approach to match proteomes to genomes for 2,812 independent subjects from COPDGene, JHS, SubPopulations and InteRmediate Outcome Measures In COPD Study (SPIROMICS) and Multi-Ethnic Study of Atherosclerosis (MESA). We were able to correctly match 90%-95% of proteomes to their correct genome and for 95%-99% we could match the proteome to the 1% most likely genome. The accuracy of matching in subjects with African ancestry was lower (~ 60%) unless training included diverse subjects. With larger profiling (SomaScan 5K) in the Atherosclerosis Risk Communities (ARIC) correct identification was > 99% even in mixed ancestry populations. When serial proteomes are available, the matching algorithm can be used to identify and correct mislabeled samples. This work also demonstrates the importance of including diverse populations in omics research and that large proteomic datasets (> 1,000 proteins) can be accurately linked to a specific genome through pQTL knowledge and should not be considered unidentifiable.

Abstract Background Across complex traits, common variants explain only a modest amount of variance, with SNP-heritability consistently below heritability estimates from close relatives. Here, we examined the contribution of rare variant to tobacco use risk in up to 26,000 individuals of European ancestry in the Trans-Omics for Precision Medicine (TOPMed) program with whole genome sequence (WGS;~30X coverage).Method We grouped about 35 million genetic variants by their minor allele frequencies (MAF) and linkage disequilibrium (LD) and estimated SNP-heritability for age of smoking initiation (N = 14,747), cigarettes smoked per day (N = 15,425), smoking cessation (N = 17,871) and initiation (N = 26,340) using linear mixed model. Rare variant population structure is detected and adjusted for by permutation procedure. We estimated an upper bound for narrow-sense heritability for tobacco use using available pedigrees consisting of close relatives in TOPMed.Results Rare variants with MAF 0.1–0.01%, mostly from non-protein altering region, accounted for 26% of variation in age of initiation and 15% for cessation. Follow-up analysis indicated that about one-third of these rare variants contribtion is potentially confounded with rare variants structure even after adjusting for principal components. After further conservative adjustment of population structure, we estimated SNP-based heritability to be 0.21 (SE = 0.08) for age of initiation, 0.15 (0.06) for cigarettes per day, 0.21 (0.09) for cessation, and 0.24 (0.07) for initiation, 1.8–4.5 times higher than previous SNP-based estimates. Our pedigree-based upper-bound for SNP-based heritability ranged from 0.18–0.35.Conclusion The substantial contribution of rare variants for several smoking phenotypes sheds light on the missing heritability and genetic etiology of tobacco use. It also informs fine-mapping strategies since the majority of the rare variant contribution was located in non-coding regulatory regions.

Abstract Traditional Hardy–Weinberg equilibrium (HWE) tests (the χ2 test and the exact test) have long been used as a metric for evaluating genotype quality, as technical artifacts leading to incorrect genotype calls often can be identified as deviations from HWE. However, in data sets composed of individuals from diverse ancestries, HWE can be violated even without genotyping error, complicating the use of HWE testing to assess genotype data quality. In this manuscript, we present the Robust Unified Test for HWE (RUTH) to test for HWE while accounting for population structure and genotype uncertainty, and to evaluate the impact of population heterogeneity and genotype uncertainty on the standard HWE tests and alternative methods using simulated and real sequence data sets. Our results demonstrate that ignoring population structure or genotype uncertainty in HWE tests can inflate false-positive rates by many orders of magnitude. Our evaluations demonstrate different tradeoffs between false positives and statistical power across the methods, with RUTH consistently among the best across all evaluations. RUTH is implemented as a practical and scalable software tool to rapidly perform HWE tests across millions of markers and hundreds of thousands of individuals while supporting standard VCF/BCF formats. RUTH is publicly available at https://www.github.com/statgen/ruth.