Publications by authors named "Joshua S Weinstock"

6 Publications

  • Page 1 of 1

Healthy Lifestyle and Clonal Hematopoiesis of Indeterminate Potential: Results From the Women's Health Initiative.

J Am Heart Assoc 2021 02 23;10(5):e018789. Epub 2021 Feb 23.

Division of Preventive Medicine Department of Medicine Brigham and Women's Hospital Harvard Medical School Boston MA.

Background Presence of clonal hematopoiesis of indeterminate potential (CHIP) is associated with a higher risk of atherosclerotic cardiovascular disease, cancer, and mortality. The relationship between a healthy lifestyle and CHIP is unknown. Methods and Results This analysis included 8709 postmenopausal women (mean age, 66.5 years) enrolled in the WHI (Women's Health Initiative), free of cancer or cardiovascular disease, with deep-coverage whole genome sequencing data available. Information on lifestyle factors (body mass index, smoking, physical activity, and diet quality) was obtained, and a healthy lifestyle score was created on the basis of healthy criteria met (0 point [least healthy] to 4 points [most healthy]). CHIP was derived on the basis of a prespecified list of leukemogenic driver mutations. The prevalence of CHIP was 8.6%. A higher healthy lifestyle score was not associated with CHIP (multivariable-adjusted odds ratio [OR] [95% CI], 0.99 [0.80-1.23] and 1.13 [0.93-1.37]) for the upper (3 or 4 points) and middle category (2 points), respectively, versus referent (0 or 1 point). Across score components, a normal and overweight body mass index compared with obese was significantly associated with a lower odds for CHIP (OR, 0.71 [95% CI, 0.57-0.88] and 0.83 [95% CI, 0.68-1.01], respectively; -trend 0.0015). Having never smoked compared with being a current smoker tended to be associated with lower odds for CHIP. Conclusions A healthy lifestyle, based on a composite score, was not related to CHIP among postmenopausal women. However, across individual lifestyle factors, having a normal body mass index was strongly associated with a lower prevalence of CHIP. These findings support the idea that certain healthy lifestyle factors are associated with a lower frequency of CHIP.
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http://dx.doi.org/10.1161/JAHA.120.018789DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8174283PMC
February 2021

LabWAS: Novel findings and study design recommendations from a meta-analysis of clinical labs in two independent biobanks.

PLoS Genet 2020 11 11;16(11):e1009077. Epub 2020 Nov 11.

Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, United States of America.

Phenotypes extracted from Electronic Health Records (EHRs) are increasingly prevalent in genetic studies. EHRs contain hundreds of distinct clinical laboratory test results, providing a trove of health data beyond diagnoses. Such lab data is complex and lacks a ubiquitous coding scheme, making it more challenging than diagnosis data. Here we describe the first large-scale cross-health system genome-wide association study (GWAS) of EHR-based quantitative laboratory-derived phenotypes. We meta-analyzed 70 lab traits matched between the BioVU cohort from the Vanderbilt University Health System and the Michigan Genomics Initiative (MGI) cohort from Michigan Medicine. We show high replication of known association for these traits, validating EHR-based measurements as high-quality phenotypes for genetic analysis. Notably, our analysis provides the first replication for 699 previous GWAS associations across 46 different traits. We discovered 31 novel associations at genome-wide significance for 22 distinct traits, including the first reported associations for two lab-based traits. We replicated 22 of these novel associations in an independent tranche of BioVU samples. The summary statistics for all association tests are freely available to benefit other researchers. Finally, we performed mirrored analyses in BioVU and MGI to assess competing analytic practices for EHR lab traits. We find that using the mean of all available lab measurements provides a robust summary value, but alternate summarizations can improve power in certain circumstances. This study provides a proof-of-principle for cross health system GWAS and is a framework for future studies of quantitative EHR lab traits.
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http://dx.doi.org/10.1371/journal.pgen.1009077DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7682892PMC
November 2020

Inherited causes of clonal haematopoiesis in 97,691 whole genomes.

Nature 2020 10 14;586(7831):763-768. Epub 2020 Oct 14.

Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA.

Age is the dominant risk factor for most chronic human diseases, but the mechanisms through which ageing confers this risk are largely unknown. The age-related acquisition of somatic mutations that lead to clonal expansion in regenerating haematopoietic stem cell populations has recently been associated with both haematological cancer and coronary heart disease-this phenomenon is termed clonal haematopoiesis of indeterminate potential (CHIP). Simultaneous analyses of germline and somatic whole-genome sequences provide the opportunity to identify root causes of CHIP. Here we analyse high-coverage whole-genome sequences from 97,691 participants of diverse ancestries in the National Heart, Lung, and Blood Institute Trans-omics for Precision Medicine (TOPMed) programme, and identify 4,229 individuals with CHIP. We identify associations with blood cell, lipid and inflammatory traits that are specific to different CHIP driver genes. Association of a genome-wide set of germline genetic variants enabled the identification of three genetic loci associated with CHIP status, including one locus at TET2 that was specific to individuals of African ancestry. In silico-informed in vitro evaluation of the TET2 germline locus enabled the identification of a causal variant that disrupts a TET2 distal enhancer, resulting in increased self-renewal of haematopoietic stem cells. Overall, we observe that germline genetic variation shapes haematopoietic stem cell function, leading to CHIP through mechanisms that are specific to clonal haematopoiesis as well as shared mechanisms that lead to somatic mutations across tissues.
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http://dx.doi.org/10.1038/s41586-020-2819-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7944936PMC
October 2020

Estimation of DNA contamination and its sources in genotyped samples.

Genet Epidemiol 2019 12 26;43(8):980-995. Epub 2019 Aug 26.

Department of Biostatistics, Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, Michigan.

Array genotyping is a cost-effective and widely used tool that enables assessment of up to millions of genetic markers in hundreds of thousands of individuals. Genotyping array data are typically highly accurate but sensitive to mixing of DNA samples from multiple individuals before or during genotyping. Contaminated samples can lead to genotyping errors and consequently cause false positive signals or reduce power of association analyses. Here, we propose a new method to identify contaminated samples and the sources of contamination within a genotyping batch. Through analysis of array intensity and genotype data from intentionally mixed samples and 22,366 samples of the Michigan Genomics Initiative, an ongoing biobank-based study, we show that our method can reliably estimate contamination. We also show that identifying sources of contamination can implicate problematic sample processing steps and guide process improvements. Compared to existing methods, our approach can estimate the proportion of contaminating DNA more accurately, eliminate the need for external databases of allele frequencies, and provide contamination estimates that are more robust to the ancestral origin of the contaminating sample.
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http://dx.doi.org/10.1002/gepi.22257DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6829038PMC
December 2019

Meta-MultiSKAT: Multiple phenotype meta-analysis for region-based association test.

Genet Epidemiol 2019 10 21;43(7):800-814. Epub 2019 Aug 21.

Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan.

The power of genetic association analyses can be increased by jointly meta-analyzing multiple correlated phenotypes. Here, we develop a meta-analysis framework, Meta-MultiSKAT, that uses summary statistics to test for association between multiple continuous phenotypes and variants in a region of interest. Our approach models the heterogeneity of effects between studies through a kernel matrix and performs a variance component test for association. Using a genotype kernel, our approach can test for rare-variants and the combined effects of both common and rare-variants. To achieve robust power, within Meta-MultiSKAT, we developed fast and accurate omnibus tests combining different models of genetic effects, functional genomic annotations, multiple correlated phenotypes, and heterogeneity across studies. In addition, Meta-MultiSKAT accommodates situations where studies do not share exactly the same set of phenotypes or have differing correlation patterns among the phenotypes. Simulation studies confirm that Meta-MultiSKAT can maintain the type-I error rate at the exome-wide level of 2.5 × 10 . Further simulations under different models of association show that Meta-MultiSKAT can improve the power of detection from 23% to 38% on average over single phenotype-based meta-analysis approaches. We demonstrate the utility and improved power of Meta-MultiSKAT in the meta-analyses of four white blood cell subtype traits from the Michigan Genomics Initiative (MGI) and SardiNIA studies.
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http://dx.doi.org/10.1002/gepi.22248DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7006736PMC
October 2019
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