Publications by authors named "Kenneth M Rice"

85 Publications

The genomics of heart failure: design and rationale of the HERMES consortium.

ESC Heart Fail 2021 Sep 3. Epub 2021 Sep 3.

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

Aims: The HERMES (HEart failure Molecular Epidemiology for Therapeutic targetS) consortium aims to identify the genomic and molecular basis of heart failure.

Methods And Results: The consortium currently includes 51 studies from 11 countries, including 68 157 heart failure cases and 949 888 controls, with data on heart failure events and prognosis. All studies collected biological samples and performed genome-wide genotyping of common genetic variants. The enrolment of subjects into participating studies ranged from 1948 to the present day, and the median follow-up following heart failure diagnosis ranged from 2 to 116 months. Forty-nine of 51 individual studies enrolled participants of both sexes; in these studies, participants with heart failure were predominantly male (34-90%). The mean age at diagnosis or ascertainment across all studies ranged from 54 to 84 years. Based on the aggregate sample, we estimated 80% power to genetic variant associations with risk of heart failure with an odds ratio of ≥1.10 for common variants (allele frequency ≥ 0.05) and ≥1.20 for low-frequency variants (allele frequency 0.01-0.05) at P < 5 × 10 under an additive genetic model.

Conclusions: HERMES is a global collaboration aiming to (i) identify the genetic determinants of heart failure; (ii) generate insights into the causal pathways leading to heart failure and enable genetic approaches to target prioritization; and (iii) develop genomic tools for disease stratification and risk prediction.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/ehf2.13517DOI Listing
September 2021

BinomiRare: A robust test for association of a rare genetic variant with a binary outcome for mixed models and any case-control proportion.

HGG Adv 2021 Jul 12;2(3). Epub 2021 Jun 12.

Framingham Heart Study, Framingham, MA, USA.

Whole-genome sequencing (WGS) and whole-exome sequencing studies have become increasingly available and are being used to identify rare genetic variants associated with health and disease outcomes. Investigators routinely use mixed models to account for genetic relatedness or other clustering variables (e.g., family or household) when testing genetic associations. However, no existing tests of the association of a rare variant with a binary outcome in the presence of correlated data control the type 1 error where there are (1) few individuals harboring the rare allele, (2) a small proportion of cases relative to controls, and (3) covariates to adjust for. Here, we address all three issues in developing a framework for testing rare variant association with a binary trait in individuals harboring at least one risk allele. In this framework, we estimate outcome probabilities under the null hypothesis and then use them, within the individuals with at least one risk allele, to test variant associations. We extend the BinomiRare test, which was previously proposed for independent observations, and develop the Conway-Maxwell-Poisson (CMP) test and study their properties in simulations. We show that the BinomiRare test always controls the type 1 error, while the CMP test sometimes does not. We then use the BinomiRare test to test the association of rare genetic variants in target genes with small-vessel disease (SVD) stroke, short sleep, and venous thromboembolism (VTE), in whole-genome sequence data from the Trans-Omics for Precision Medicine (TOPMed) program.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.xhgg.2021.100040DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8321319PMC
July 2021

Variant-specific inflation factors for assessing population stratification at the phenotypic variance level.

Nat Commun 2021 06 9;12(1):3506. Epub 2021 Jun 9.

Department of Biostatistics, University of Washington, Seattle, WA, USA.

In modern Whole Genome Sequencing (WGS) epidemiological studies, participant-level data from multiple studies are often pooled and results are obtained from a single analysis. We consider the impact of differential phenotype variances by study, which we term 'variance stratification'. Unaccounted for, variance stratification can lead to both decreased statistical power, and increased false positives rates, depending on how allele frequencies, sample sizes, and phenotypic variances vary across the studies that are pooled. We develop a procedure to compute variant-specific inflation factors, and show how it can be used for diagnosis of genetic association analyses on pooled individual level data from multiple studies. We describe a WGS-appropriate analysis approach, implemented in freely-available software, which allows study-specific variances and thereby improves performance in practice. We illustrate the variance stratification problem, its solutions, and the proposed diagnostic procedure, in simulations and in data from the Trans-Omics for Precision Medicine Whole Genome Sequencing Program (TOPMed), used in association tests for hemoglobin concentrations and BMI.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41467-021-23655-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8190158PMC
June 2021

The Multi-Ethnic Study of Atherosclerosis individual response to vitamin D trial: Building a randomized clinical trial into an observational cohort study.

Contemp Clin Trials 2021 04 12;103:106318. Epub 2021 Feb 12.

Division of Nephrology and Kidney Research Institute, Department of Medicine, University of Washington, Seattle, WA, United States of America.

The INdividual response to VITamin D (INVITe) trial was a randomized, placebo-controlled, parallel group trial of vitamin D supplementation (2000 IU daily) designed to determine clinical and genetic characteristics that modify the response to vitamin D supplementation. To enhance internal and external validity and reduce cost, the INVITe trial was nested within the Multi-Ethnic Study of Atherosclerosis (MESA), an ongoing prospective observational cohort study. The INVITe trial enrolled a community-based population of 666 racially and ethnically diverse participants from January 2017 to April 2019. This represents 30% of 2210 MESA participants approached for screening, and 96% of those found to be eligible. Barriers to enrollment included delayed initiation of the trial relative to scheduled MESA study visits, a lower number of available MESA participants than expected, and a high prevalence (18%) of high-dose vitamin D supplementation (>1000 IU daily, an exclusion criterion). The final study visit was attended by 611 participants (92%), and median adherence was 98%. Our experience suggests that integration of a randomized trial into an existing observational cohort study may leverage strengths of the source population and enhance enrollment, retention, and adherence, although with limited enrollment capacity. The INVITe trial will use rigorously-collected data to advance understanding of individual determinants of vitamin D response.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.cct.2021.106318DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8089051PMC
April 2021

Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program.

Nature 2021 02 10;590(7845):290-299. Epub 2021 Feb 10.

The Broad Institute of MIT and Harvard, Cambridge, MA, USA.

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). 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%.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41586-021-03205-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7875770PMC
February 2021

Genetic loci associated with prevalent and incident myocardial infarction and coronary heart disease in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium.

PLoS One 2020 13;15(11):e0230035. Epub 2020 Nov 13.

The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, California, United States of America.

Background: Genome-wide association studies have identified multiple genomic loci associated with coronary artery disease, but most are common variants in non-coding regions that provide limited information on causal genes and etiology of the disease. To overcome the limited scope that common variants provide, we focused our investigation on low-frequency and rare sequence variations primarily residing in coding regions of the genome.

Methods And Results: Using samples of individuals of European ancestry from ten cohorts within the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium, both cross-sectional and prospective analyses were conducted to examine associations between genetic variants and myocardial infarction (MI), coronary heart disease (CHD), and all-cause mortality following these events. For prevalent events, a total of 27,349 participants of European ancestry, including 1831 prevalent MI cases and 2518 prevalent CHD cases were used. For incident cases, a total of 55,736 participants of European ancestry were included (3,031 incident MI cases and 5,425 incident CHD cases). There were 1,860 all-cause deaths among the 3,751 MI and CHD cases from six cohorts that contributed to the analysis of all-cause mortality. Single variant and gene-based analyses were performed separately in each cohort and then meta-analyzed for each outcome. A low-frequency intronic variant (rs988583) in PLCL1 was significantly associated with prevalent MI (OR = 1.80, 95% confidence interval: 1.43, 2.27; P = 7.12 × 10-7). We conducted gene-based burden tests for genes with a cumulative minor allele count (cMAC) ≥ 5 and variants with minor allele frequency (MAF) < 5%. TMPRSS5 and LDLRAD1 were significantly associated with prevalent MI and CHD, respectively, and RC3H2 and ANGPTL4 were significantly associated with incident MI and CHD, respectively. No loci were significantly associated with all-cause mortality following a MI or CHD event.

Conclusion: This study identified one known locus (ANGPTL4) and four new loci (PLCL1, RC3H2, TMPRSS5, and LDLRAD1) associated with cardiovascular disease risk that warrant further investigation.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0230035PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7665790PMC
December 2020

Meta-analysis uncovers genome-wide significant variants for rapid kidney function decline.

Kidney Int 2021 04 31;99(4):926-939. Epub 2020 Oct 31.

Division of Nephrology, University of Washington, Seattle, Washington, USA; Kidney Research Institute, University of Washington, Seattle, Washington, USA.

Rapid decline of glomerular filtration rate estimated from creatinine (eGFRcrea) is associated with severe clinical endpoints. In contrast to cross-sectionally assessed eGFRcrea, the genetic basis for rapid eGFRcrea decline is largely unknown. To help define this, we meta-analyzed 42 genome-wide association studies from the Chronic Kidney Diseases Genetics Consortium and United Kingdom Biobank to identify genetic loci for rapid eGFRcrea decline. Two definitions of eGFRcrea decline were used: 3 mL/min/1.73m/year or more ("Rapid3"; encompassing 34,874 cases, 107,090 controls) and eGFRcrea decline 25% or more and eGFRcrea under 60 mL/min/1.73m at follow-up among those with eGFRcrea 60 mL/min/1.73m or more at baseline ("CKDi25"; encompassing 19,901 cases, 175,244 controls). Seven independent variants were identified across six loci for Rapid3 and/or CKDi25: consisting of five variants at four loci with genome-wide significance (near UMOD-PDILT (2), PRKAG2, WDR72, OR2S2) and two variants among 265 known eGFRcrea variants (near GATM, LARP4B). All these loci were novel for Rapid3 and/or CKDi25 and our bioinformatic follow-up prioritized variants and genes underneath these loci. The OR2S2 locus is novel for any eGFRcrea trait including interesting candidates. For the five genome-wide significant lead variants, we found supporting effects for annual change in blood urea nitrogen or cystatin-based eGFR, but not for GATM or LARP4B. Individuals at high compared to those at low genetic risk (8-14 vs. 0-5 adverse alleles) had a 1.20-fold increased risk of acute kidney injury (95% confidence interval 1.08-1.33). Thus, our identified loci for rapid kidney function decline may help prioritize therapeutic targets and identify mechanisms and individuals at risk for sustained deterioration of kidney function.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.kint.2020.09.030DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8010357PMC
April 2021

Type 2 and interferon inflammation regulate SARS-CoV-2 entry factor expression in the airway epithelium.

Nat Commun 2020 10 12;11(1):5139. Epub 2020 Oct 12.

Center for Genes, Environment, and Health, National Jewish Health, Denver, CO, USA.

Coronavirus disease 2019 (COVID-19) is caused by SARS-CoV-2, an emerging virus that utilizes host proteins ACE2 and TMPRSS2 as entry factors. Understanding the factors affecting the pattern and levels of expression of these genes is important for deeper understanding of SARS-CoV-2 tropism and pathogenesis. Here we explore the role of genetics and co-expression networks in regulating these genes in the airway, through the analysis of nasal airway transcriptome data from 695 children. We identify expression quantitative trait loci for both ACE2 and TMPRSS2, that vary in frequency across world populations. We find TMPRSS2 is part of a mucus secretory network, highly upregulated by type 2 (T2) inflammation through the action of interleukin-13, and that the interferon response to respiratory viruses highly upregulates ACE2 expression. IL-13 and virus infection mediated effects on ACE2 expression were also observed at the protein level in the airway epithelium. Finally, we define airway responses to common coronavirus infections in children, finding that these infections generate host responses similar to other viral species, including upregulation of IL6 and ACE2. Our results reveal possible mechanisms influencing SARS-CoV-2 infectivity and COVID-19 clinical outcomes.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41467-020-18781-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7550582PMC
October 2020

Dynamic incorporation of multiple in silico functional annotations empowers rare variant association analysis of large whole-genome sequencing studies at scale.

Nat Genet 2020 09 24;52(9):969-983. Epub 2020 Aug 24.

Department of Data Sciences, Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health, Boston, MA, USA.

Large-scale whole-genome sequencing studies have enabled the analysis of rare variants (RVs) associated with complex phenotypes. Commonly used RV association tests have limited scope to leverage variant functions. We propose STAAR (variant-set test for association using annotation information), a scalable and powerful RV association test method that effectively incorporates both variant categories and multiple complementary annotations using a dynamic weighting scheme. For the latter, we introduce 'annotation principal components', multidimensional summaries of in silico variant annotations. STAAR accounts for population structure and relatedness and is scalable for analyzing very large cohort and biobank whole-genome sequencing studies of continuous and dichotomous traits. We applied STAAR to identify RVs associated with four lipid traits in 12,316 discovery and 17,822 replication samples from the Trans-Omics for Precision Medicine Program. We discovered and replicated new RV associations, including disruptive missense RVs of NPC1L1 and an intergenic region near APOC1P1 associated with low-density lipoprotein cholesterol.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41588-020-0676-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7483769PMC
September 2020

Type 2 and interferon inflammation strongly regulate SARS-CoV-2 related gene expression in the airway epithelium.

bioRxiv 2020 Apr 10. Epub 2020 Apr 10.

Center for Genes, Environment, and Health, National Jewish Health, Denver, CO, 80206 USA.

Coronavirus disease 2019 (COVID-19) outcomes vary from asymptomatic infection to death. This disparity may reflect different airway levels of the SARS-CoV-2 receptor, ACE2, and the spike protein activator, TMPRSS2. Here we explore the role of genetics and co-expression networks in regulating these genes in the airway, through the analysis of nasal airway transcriptome data from 695 children. We identify expression quantitative trait loci (eQTL) for both and , that vary in frequency across world populations. Importantly, we find is part of a mucus secretory network, highly upregulated by T2 inflammation through the action of interleukin-13, and that interferon response to respiratory viruses highly upregulates expression. Finally, we define airway responses to coronavirus infections in children, finding that these infections upregulate while also stimulating a more pronounced cytotoxic immune response relative to other respiratory viruses. Our results reveal mechanisms likely influencing SARS-CoV-2 infectivity and COVID-19 clinical outcomes.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1101/2020.04.09.034454DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7239056PMC
April 2020

Incorporating sampling weights into robust estimation of Cox proportional hazards regression model, with illustration in the Multi-Ethnic Study of Atherosclerosis.

BMC Med Res Methodol 2020 03 14;20(1):62. Epub 2020 Mar 14.

Department of Epidemiolgy, University of Washington, Seattle, WA, USA.

Background: Cox proportional hazards regression models are used to evaluate associations between exposures of interest and time-to-event outcomes in observational data. When exposures are measured on only a sample of participants, as they are in a case-cohort design, the sampling weights must be incorporated into the regression model to obtain unbiased estimating equations.

Methods: Robust Cox methods have been developed to better estimate associations when there are influential outliers in the exposure of interest, but these robust methods do not incorporate sampling weights. In this paper, we extend these robust methods, which already incorporate influence weights, so that they also accommodate sampling weights.

Results: Simulations illustrate that in the presence of influential outliers, the association estimate from the weighted robust method is closer to the true value than the estimate from traditional weighted Cox regression. As expected, in the absence of outliers, the use of robust methods yields a small loss of efficiency. Using data from a case-cohort study that is nested within the Multi-Ethnic Study of Atherosclerosis (MESA) longitudinal cohort study, we illustrate differences between traditional and robust weighted Cox association estimates for the relationships between immune cell traits and risk of stroke.

Conclusions: Robust weighted Cox regression methods are a new tool to analyze time-to-event data with sampling, e.g. case-cohort data, when exposures of interest contain outliers.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12874-020-00945-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7071747PMC
March 2020

Genome-wide association and Mendelian randomisation analysis provide insights into the pathogenesis of heart failure.

Nat Commun 2020 01 9;11(1):163. Epub 2020 Jan 9.

Department of Biostatistics, University of Liverpool, Liverpool, UK.

Heart failure (HF) is a leading cause of morbidity and mortality worldwide. A small proportion of HF cases are attributable to monogenic cardiomyopathies and existing genome-wide association studies (GWAS) have yielded only limited insights, leaving the observed heritability of HF largely unexplained. We report results from a GWAS meta-analysis of HF comprising 47,309 cases and 930,014 controls. Twelve independent variants at 11 genomic loci are associated with HF, all of which demonstrate one or more associations with coronary artery disease (CAD), atrial fibrillation, or reduced left ventricular function, suggesting shared genetic aetiology. Functional analysis of non-CAD-associated loci implicate genes involved in cardiac development (MYOZ1, SYNPO2L), protein homoeostasis (BAG3), and cellular senescence (CDKN1A). Mendelian randomisation analysis supports causal roles for several HF risk factors, and demonstrates CAD-independent effects for atrial fibrillation, body mass index, and hypertension. These findings extend our knowledge of the pathways underlying HF and may inform new therapeutic strategies.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41467-019-13690-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6952380PMC
January 2020

Target genes, variants, tissues and transcriptional pathways influencing human serum urate levels.

Nat Genet 2019 10 2;51(10):1459-1474. Epub 2019 Oct 2.

Department of Neurobiology, Care Sciences and Society, Division of Family Medicine and Primary Care, Karolinska Institutet, Stockholm, Sweden.

Elevated serum urate levels cause gout and correlate with cardiometabolic diseases via poorly understood mechanisms. We performed a trans-ancestry genome-wide association study of serum urate in 457,690 individuals, identifying 183 loci (147 previously unknown) that improve the prediction of gout in an independent cohort of 334,880 individuals. Serum urate showed significant genetic correlations with many cardiometabolic traits, with genetic causality analyses supporting a substantial role for pleiotropy. Enrichment analysis, fine-mapping of urate-associated loci and colocalization with gene expression in 47 tissues implicated the kidney and liver as the main target organs and prioritized potentially causal genes and variants, including the transcriptional master regulators in the liver and kidney, HNF1A and HNF4A. Experimental validation showed that HNF4A transactivated the promoter of ABCG2, encoding a major urate transporter, in kidney cells, and that HNF4A p.Thr139Ile is a functional variant. Transcriptional coregulation within and across organs may be a general mechanism underlying the observed pleiotropy between urate and cardiometabolic traits.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41588-019-0504-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6858555PMC
October 2019

Genome-wide association meta-analyses and fine-mapping elucidate pathways influencing albuminuria.

Nat Commun 2019 09 11;10(1):4130. Epub 2019 Sep 11.

Department of Medicine, Division of Nephrology and Hypertension, University of Utah, Salt Lake City, UT, USA.

Increased levels of the urinary albumin-to-creatinine ratio (UACR) are associated with higher risk of kidney disease progression and cardiovascular events, but underlying mechanisms are incompletely understood. Here, we conduct trans-ethnic (n = 564,257) and European-ancestry specific meta-analyses of genome-wide association studies of UACR, including ancestry- and diabetes-specific analyses, and identify 68 UACR-associated loci. Genetic correlation analyses and risk score associations in an independent electronic medical records database (n = 192,868) reveal connections with proteinuria, hyperlipidemia, gout, and hypertension. Fine-mapping and trans-Omics analyses with gene expression in 47 tissues and plasma protein levels implicate genes potentially operating through differential expression in kidney (including TGFB1, MUC1, PRKCI, and OAF), and allow coupling of UACR associations to altered plasma OAF concentrations. Knockdown of OAF and PRKCI orthologs in Drosophila nephrocytes reduces albumin endocytosis. Silencing fly PRKCI further impairs slit diaphragm formation. These results generate a priority list of genes and pathways for translational research to reduce albuminuria.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41467-019-11576-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6739370PMC
September 2019

Genetic association testing using the GENESIS R/Bioconductor package.

Bioinformatics 2019 12;35(24):5346-5348

Department of Biostatistics, University of Washington, Seattle, WA, USA.

Summary: The Genomic Data Storage (GDS) format provides efficient storage and retrieval of genotypes measured by microarrays and sequencing. We developed GENESIS to perform various single- and aggregate-variant association tests using genotype data stored in GDS format. GENESIS implements highly flexible mixed models, allowing for different link functions, multiple variance components and phenotypic heteroskedasticity. GENESIS integrates cohesively with other R/Bioconductor packages to build a complete genomic analysis workflow entirely within the R environment.

Availability And Implementation: https://bioconductor.org/packages/GENESIS; vignettes included.

Supplementary Information: Supplementary data are available at Bioinformatics online.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/bioinformatics/btz567DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7904076PMC
December 2019

Pharmacogenomics of statin-related myopathy: Meta-analysis of rare variants from whole-exome sequencing.

PLoS One 2019 26;14(6):e0218115. Epub 2019 Jun 26.

Clinical Lipidology and Rare Lipid Disorders Unit, Department of Medicine, Université de Montréal Community Gene Medicine Center, Lipid Clinic Chicoutimi Hospital and ECOGENE-21 Clinical and Translational Research Center, Chicoutimi, Quebec, Canada.

Aims: Statin-related myopathy (SRM), which includes rhabdomyolysis, is an uncommon but important adverse drug reaction because the number of people prescribed statins world-wide is large. Previous association studies of common genetic variants have had limited success in identifying a genetic basis for this adverse drug reaction. We conducted a multi-site whole-exome sequencing study to investigate whether rare coding variants confer an increased risk of SRM.

Methods And Results: SRM 3-5 cases (N = 505) and statin treatment-tolerant controls (N = 2047) were recruited from multiple sites in North America and Europe. SRM 3-5 was defined as symptoms consistent with muscle injury and an elevated creatine phosphokinase level >4 times upper limit of normal without another likely cause of muscle injury. Whole-exome sequencing and variant calling was coordinated from two analysis centres, and results of single-variant and gene-based burden tests were meta-analysed. No genome-wide significant associations were identified. Given the large number of cases, we had 80% power to identify a variant with minor allele frequency of 0.01 that increases the risk of SRM 6-fold at genome-wide significance.

Conclusions: In this large whole-exome sequencing study of severe statin-related muscle injury conducted to date, we did not find evidence that rare coding variants are responsible for this adverse drug reaction. Larger sample sizes would be required to identify rare variants with small effects, but it is unclear whether such findings would be clinically actionable.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0218115PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6594672PMC
February 2020

A catalog of genetic loci associated with kidney function from analyses of a million individuals.

Nat Genet 2019 06 31;51(6):957-972. Epub 2019 May 31.

Diabetes and Cardiovascular Disease-Genetic Epidemiology, Department of Clincial Sciences in Malmö, Lund University, Malmö, Sweden.

Chronic kidney disease (CKD) is responsible for a public health burden with multi-systemic complications. Through trans-ancestry meta-analysis of genome-wide association studies of estimated glomerular filtration rate (eGFR) and independent replication (n = 1,046,070), we identified 264 associated loci (166 new). Of these, 147 were likely to be relevant for kidney function on the basis of associations with the alternative kidney function marker blood urea nitrogen (n = 416,178). Pathway and enrichment analyses, including mouse models with renal phenotypes, support the kidney as the main target organ. A genetic risk score for lower eGFR was associated with clinically diagnosed CKD in 452,264 independent individuals. Colocalization analyses of associations with eGFR among 783,978 European-ancestry individuals and gene expression across 46 human tissues, including tubulo-interstitial and glomerular kidney compartments, identified 17 genes differentially expressed in kidney. Fine-mapping highlighted missense driver variants in 11 genes and kidney-specific regulatory variants. These results provide a comprehensive priority list of molecular targets for translational research.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41588-019-0407-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6698888PMC
June 2019

A fully adjusted two-stage procedure for rank-normalization in genetic association studies.

Genet Epidemiol 2019 04 17;43(3):263-275. Epub 2019 Jan 17.

Department of Biostatistics, University of Washington, Seattle, Washington.

When testing genotype-phenotype associations using linear regression, departure of the trait distribution from normality can impact both Type I error rate control and statistical power, with worse consequences for rarer variants. Because genotypes are expected to have small effects (if any) investigators now routinely use a two-stage method, in which they first regress the trait on covariates, obtain residuals, rank-normalize them, and then use the rank-normalized residuals in association analysis with the genotypes. Potential confounding signals are assumed to be removed at the first stage, so in practice, no further adjustment is done in the second stage. Here, we show that this widely used approach can lead to tests with undesirable statistical properties, due to both combination of a mis-specified mean-variance relationship and remaining covariate associations between the rank-normalized residuals and genotypes. We demonstrate these properties theoretically, and also in applications to genome-wide and whole-genome sequencing association studies. We further propose and evaluate an alternative fully adjusted two-stage approach that adjusts for covariates both when residuals are obtained and in the subsequent association test. This method can reduce excess Type I errors and improve statistical power.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/gepi.22188DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6416071PMC
April 2019

Efficient Variant Set Mixed Model Association Tests for Continuous and Binary Traits in Large-Scale Whole-Genome Sequencing Studies.

Am J Hum Genet 2019 02 10;104(2):260-274. Epub 2019 Jan 10.

Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.

With advances in whole-genome sequencing (WGS) technology, more advanced statistical methods for testing genetic association with rare variants are being developed. Methods in which variants are grouped for analysis are also known as variant-set, gene-based, and aggregate unit tests. The burden test and sequence kernel association test (SKAT) are two widely used variant-set tests, which were originally developed for samples of unrelated individuals and later have been extended to family data with known pedigree structures. However, computationally efficient and powerful variant-set tests are needed to make analyses tractable in large-scale WGS studies with complex study samples. In this paper, we propose the variant-set mixed model association tests (SMMAT) for continuous and binary traits using the generalized linear mixed model framework. These tests can be applied to large-scale WGS studies involving samples with population structure and relatedness, such as in the National Heart, Lung, and Blood Institute's Trans-Omics for Precision Medicine (TOPMed) program. SMMATs share the same null model for different variant sets, and a virtue of this null model, which includes covariates only, is that it needs to be fit only once for all tests in each genome-wide analysis. Simulation studies show that all the proposed SMMATs correctly control type I error rates for both continuous and binary traits in the presence of population structure and relatedness. We also illustrate our tests in a real data example of analysis of plasma fibrinogen levels in the TOPMed program (n = 23,763), using the Analysis Commons, a cloud-based computing platform.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.ajhg.2018.12.012DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6372261PMC
February 2019

Exome-chip meta-analysis identifies novel loci associated with cardiac conduction, including ADAMTS6.

Genome Biol 2018 07 17;19(1):87. Epub 2018 Jul 17.

Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK.

Background: Genome-wide association studies conducted on QRS duration, an electrocardiographic measurement associated with heart failure and sudden cardiac death, have led to novel biological insights into cardiac function. However, the variants identified fall predominantly in non-coding regions and their underlying mechanisms remain unclear.

Results: Here, we identify putative functional coding variation associated with changes in the QRS interval duration by combining Illumina HumanExome BeadChip genotype data from 77,898 participants of European ancestry and 7695 of African descent in our discovery cohort, followed by replication in 111,874 individuals of European ancestry from the UK Biobank and deCODE cohorts. We identify ten novel loci, seven within coding regions, including ADAMTS6, significantly associated with QRS duration in gene-based analyses. ADAMTS6 encodes a secreted metalloprotease of currently unknown function. In vitro validation analysis shows that the QRS-associated variants lead to impaired ADAMTS6 secretion and loss-of function analysis in mice demonstrates a previously unappreciated role for ADAMTS6 in connexin 43 gap junction expression, which is essential for myocardial conduction.

Conclusions: Our approach identifies novel coding and non-coding variants underlying ventricular depolarization and provides a possible mechanism for the ADAMTS6-associated conduction changes.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s13059-018-1457-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6048820PMC
July 2018

Exome Chip Analysis Identifies Low-Frequency and Rare Variants in MRPL38 for White Matter Hyperintensities on Brain Magnetic Resonance Imaging.

Stroke 2018 08;49(8):1812-1819

Department of Biochemistry (D.W.B., N.D.P.), Wake Forest School of Medicine, Winston-Salem, NC.

Background and Purpose- White matter hyperintensities (WMH) on brain magnetic resonance imaging are typical signs of cerebral small vessel disease and may indicate various preclinical, age-related neurological disorders, such as stroke. Though WMH are highly heritable, known common variants explain a small proportion of the WMH variance. The contribution of low-frequency/rare coding variants to WMH burden has not been explored. Methods- In the discovery sample we recruited 20 719 stroke/dementia-free adults from 13 population-based cohort studies within the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium, among which 17 790 were of European ancestry and 2929 of African ancestry. We genotyped these participants at ≈250 000 mostly exonic variants with Illumina HumanExome BeadChip arrays. We performed ethnicity-specific linear regression on rank-normalized WMH in each study separately, which were then combined in meta-analyses to test for association with single variants and genes aggregating the effects of putatively functional low-frequency/rare variants. We then sought replication of the top findings in 1192 adults (European ancestry) with whole exome/genome sequencing data from 2 independent studies. Results- At 17q25, we confirmed the association of multiple common variants in TRIM65, FBF1, and ACOX1 ( P<6×10). We also identified a novel association with 2 low-frequency nonsynonymous variants in MRPL38 (lead, rs34136221; P=4.5×10) partially independent of known common signal ( P=1.4×10). We further identified a locus at 2q33 containing common variants in NBEAL1, CARF, and WDR12 (lead, rs2351524; P=1.9×10). Although our novel findings were not replicated because of limited power and possible differences in study design, meta-analysis of the discovery and replication samples yielded stronger association for the 2 low-frequency MRPL38 variants ( P=2.8×10). Conclusions- Both common and low-frequency/rare functional variants influence WMH. Larger replication and experimental follow-up are essential to confirm our findings and uncover the biological causal mechanisms of age-related WMH.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1161/STROKEAHA.118.020689DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6202149PMC
August 2018

Common and Rare Coding Genetic Variation Underlying the Electrocardiographic PR Interval.

Circ Genom Precis Med 2018 05;11(5):e002037

Section of Computational Biomedicine (H.L.) and Section of Cardiovascular Medicine (E.J.B.), Department of Medicine, Boston University School of Medicine, MA. National Heart Lung and Blood Institute's and Boston University's Framingham Heart Study, MA (H.L., E.J.B.). Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, University of Utrecht, The Netherlands (J.v.S., F.W.A.). Icelandic Heart Association, Kopavogur (A.V.S., V.G.). Faculty of Medicine, University of Iceland, Reykjavik (A.V.S., V.G.). Predoctoral Training Program in Human Genetics, McKusick-Nathans Institute of Genetic Medicine (N.A.B.) and McKusick-Nathans Institute of Genetic Medicine (D.E.A.), Johns Hopkins University School of Medicine, Baltimore, MD. William Harvey Research Institute (H.R.W., P.B.M.) and NIHR Barts Cardiovascular Research Unit (H.R.W., P.B.M.), Barts and The London School of Medicine and Dentistry, Queen Mary University of London, United Kingdom. Cardiovascular Health Research Unit, Department of Medicine (J.A.B., J.C.B., C.M.S.), Department of Biostatistics (K.M.R.), Cardiovascular Health Research Unit, Division of Cardiology, Departments of Medicine and Epidemiology (N.S.), Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology and Health Services (B.M.P.), and Cardiovascular Health Research Unit, Department of Epidemiology (S.R.H.), University of Washington, Seattle. Center for Human Genetic Research (F. Radmanesh, J.R.) and Cardiovascular Research Center (P.L.H., L.-C.W., H.S.J., W.H., A.H., N.R.T., P.T.E., S.A.L.), Massachusetts General Hospital, Boston. Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA (L.-C.W., P.T.E., S.A.L.). Department of Cardiovascular Sciences, University of Leicester, United Kingdom (L.H., C.P.N., N.J.S.). NIHR Leicester Cardiovascular Biomedical Research Unit, Glenfield Hospital, United Kingdom (L.H., C.P.N., N.J.S.). The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences (N.G., J.B.-J., O. Pedersen, T.H.), Laboratory of Experimental Cardiology (J.K.K.), and Department of Clinical Medicine, Faculty of Health and Medical Sciences (A.L.), University of Copenhagen, Denmark. Department of Medicine I, University Hospital Munich, Ludwig Maximilian's University Munich, Germany (M.M.-N., M.F.S., S.K.). Chair of Genetic Epidemiology, IBE, Faculty of Medicine, LMU Munich, Germany (K.S.). DZHK (German Cardiovascular Research Centre), Partner Site: Munich Heart Alliance, Germany (M.M.-N., M.F.S., A.P., T.M., S.K.). Institute of Genetic Epidemiology (M.M.-N., K.S.), Institute of Epidemiology II (A.P., M.W.), Research Unit of Molecular Epidemiology (M.W.), and Institute of Human Genetics (T.M.), Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany. Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine (T.B., J.M., C.H.) and Usher Institute of Population Health Sciences and Informatics (I.R.), University of Edinburgh, United Kingdom. University of Groningen, University Medical Center Groningen, Department of Cardiology, The Netherlands (N.V., R.A.d.B., P.v.d.M., P.v.d.H.). Institute for Translational Genomics and Population Sciences and Department of Pediatrics, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA (H.J.L., Y.-D.I.C., J.Y., X.G., K.D.T., J.I.R.). Department of Clinical Epidemiology (R.L.-G., D.O.M.-K.) and Department of Cardiology (S.T., J.W.J.), Leiden University Medical Center, The Netherlands. Department of Medical Informatics (M.E.v.d.B.), Human Genomics Facility (F. Rivadeneira), Human Genotyping Facility (A.U.), and Department of Epidemiology (M.E., B.H. Stricker), Erasmus MC, University Medical Center Rotterdam, The Netherlands. Interfaculty Institute for Genetics and Functional Genomics, University Medicine and Ernst-Moritz-Arndt-University Greifswald, Germany (S.W., G.H., U.V.). DZHK (German Cardiovascular Research Centre), Partner Site Greifswald, Germany (S.W., H.V., S.B.F., U.V., M.D.). Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA (J.H., C.K.). Department of Clinical Chemistry, Fimlab Laboratories and Faculty of Medicine and Life Sciences (L.-P.L., T.L.) and Department of Clinical Physiology, Tampere University Hospital and Faculty of Medicine and Life Sciences (M.K.), University of Tampere, Finland. Department of Data Science (H.M.) and Physiology and Biophysics (J.G.W.), University of Mississippi Medical Center, Jackson. Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Intramural Research Program, National Institutes of Health, Bethesda, MD (T.B.H., L.J.L.). Division of Nephrology and Hypertension, Internal Medicine, School of Medicine, University of Utah, Salt Lake City (M.L.). Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA (A.A.). Epidemiological Cardiology Research Center (EPICARE), Wake Forest School of Medicine, Winston Salem, NC (E.Z.S.). Medical Research Institute (J.M.C.) and Division of Population Health Sciences (B.H. Smith), Ninewells Hospital and Medical School, University of Dundee, United Kingdom. Department of Medical Informatics (J.A.K.) and Genetic Epidemiology Unit, Department of Epidemiology (C.M.v.D.), Erasmus MC, Rotterdam, The Netherlands. TCM Clinical Basis Institute, Zhejiang Chinese Medicine University, Hangzhou, China (Z.X., C.W.). Division of Cardiology, Department of Medicine, UPMC Heart and Vascular Institute, University of Pittsburgh, PA (J.W.M.). German Center for Diabetes Research, Neuherberg, Germany (A.P.). Institute of Human Genetics, Technische Universität München, Germany (T.M.). Research Centre for Prevention and Health, Capital Region of Denmark, Copenhagen (A.L.). Department of Clinical Experimental Research, Rigshospitalet, Denmark (A.L.). British Heart Foundation Glasgow Cardiovascular Research Centre, Institute of Cardiovascular and Medical Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, Scotland (S.P.). Institute for Community Medicine (H.V.) and Department of Internal Medicine B (S.B.F., M.D.), University Medicine Greifswald, Germany. Department of Twin Research and Genetic Epidemiology, King's College London, United Kingdom (M.M., T.D.S.). Julius Center for Health Sciences and Primary Care, University Medical Center, Utrecht, The Netherlands (M.L.B.). Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, CA (M.P.). Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, and Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Finland (O.T.R.). Kaiser Permanente Washington Health Research Institute, Kaiser Foundation Health Plan of Washington, Seattle (B.M.P., S.R.H.). Faculty of Medicine, University of Split, Croatia (O. Polasek). Cardiogenetics Lab, Genetics and Molecular Cell Sciences Research Centre, Cardiovascular and Cell Sciences Institute, St George's, University of London, Cranmer Terrace, United Kingdom (B.P.P., Y.J.). Durrer Center for Cardiovascular Research, Netherlands Heart Institute, Utrecht, The Netherlands (F.W.A.). Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, United Kingdom (F.W.A.). Farr Institute of Health Informatics Research and Institute of Health Informatics, University College London, London, United Kingdom; CARIM School for Cardiovascular Diseases, Maastricht Centre for Systems Biology (MaCSBio) and Department of Biochemistry, Maastricht University, The Netherlands (A.I.).

Background: Electrical conduction from the cardiac sinoatrial node to the ventricles is critical for normal heart function. Genome-wide association studies have identified more than a dozen common genetic loci that are associated with PR interval. However, it is unclear whether rare and low-frequency variants also contribute to PR interval heritability.

Methods: We performed large-scale meta-analyses of the PR interval that included 83 367 participants of European ancestry and 9436 of African ancestry. We examined both common and rare variants associated with the PR interval.

Results: We identified 31 genetic loci that were significantly associated with PR interval after Bonferroni correction (<1.2×10), including 11 novel loci that have not been reported previously. Many of these loci are involved in heart morphogenesis. In gene-based analysis, we found that multiple rare variants at (=5.9×10) and (=1.1×10) were associated with PR interval. locus also was implicated in the common variant analysis, whereas was a novel locus.

Conclusions: We identified common variants at 11 novel loci and rare variants within 2 gene regions that were significantly associated with PR interval. Our findings provide novel insights to the current understanding of atrioventricular conduction, which is critical for cardiac activity and an important determinant of health.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1161/CIRCGEN.117.002037DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5951629PMC
May 2018

Addressing the estimation of standard errors in fixed effects meta-analysis.

Stat Med 2018 05 25;37(11):1788-1809. Epub 2018 Mar 25.

Department of Biostatistics, University of Washington, Seattle, WA, USA.

Standard methods for fixed effects meta-analysis assume that standard errors for study-specific estimates are known, not estimated. While the impact of this simplifying assumption has been shown in a few special cases, its general impact is not well understood, nor are general-purpose tools available for inference under more realistic assumptions. In this paper, we aim to elucidate the impact of using estimated standard errors in fixed effects meta-analysis, showing why it does not go away in large samples and quantifying how badly miscalibrated standard inference will be if it is ignored. We also show the important role of a particular measure of heterogeneity in this miscalibration. These developments lead to confidence intervals for fixed effects meta-analysis with improved performance for both location and scale parameters.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/sim.7625DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6001579PMC
May 2018

Selecting Shrinkage Parameters for Effect Estimation: The Multi-Ethnic Study of Atherosclerosis.

Am J Epidemiol 2018 02;187(2):358-365

Department of Biostatistics, School of Public Health, University of Washington, Seattle, Washington.

We present a method for improving estimation in linear regression models in samples of moderate size, using shrinkage techniques. Our work connects the theory of causal inference, which describes how variable adjustment should be performed with large samples, with shrinkage estimators such as ridge regression and the least absolute shrinkage and selection operator (LASSO), which can perform better in sample sizes seen in epidemiologic practice. Shrinkage methods reduce mean squared error by trading off some amount of bias for a reduction in variance. However, when inference is the goal, there are no standard methods for choosing the penalty "tuning" parameters that govern these tradeoffs. We propose selecting the penalty parameters for these shrinkage estimators by minimizing bias and variance in future similar data sets drawn from the posterior predictive distribution. Our method provides both the point estimate of interest and corresponding standard error estimates. Through simulations, we demonstrate that it can achieve better mean squared error than using cross-validation for penalty parameter selection. We apply our method to a cross-sectional analysis of the association between smoking and carotid intima-media thickness in the Multi-Ethnic Study of Atherosclerosis (multiple US locations, 2000-2002) and compare it with similar analyses of these data.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/aje/kwx225DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5859997PMC
February 2018

Large-scale genome-wide analysis identifies genetic variants associated with cardiac structure and function.

J Clin Invest 2017 May 10;127(5):1798-1812. Epub 2017 Apr 10.

Background: Understanding the genetic architecture of cardiac structure and function may help to prevent and treat heart disease. This investigation sought to identify common genetic variations associated with inter-individual variability in cardiac structure and function.

Methods: A GWAS meta-analysis of echocardiographic traits was performed, including 46,533 individuals from 30 studies (EchoGen consortium). The analysis included 16 traits of left ventricular (LV) structure, and systolic and diastolic function.

Results: The discovery analysis included 21 cohorts for structural and systolic function traits (n = 32,212) and 17 cohorts for diastolic function traits (n = 21,852). Replication was performed in 5 cohorts (n = 14,321) and 6 cohorts (n = 16,308), respectively. Besides 5 previously reported loci, the combined meta-analysis identified 10 additional genome-wide significant SNPs: rs12541595 near MTSS1 and rs10774625 in ATXN2 for LV end-diastolic internal dimension; rs806322 near KCNRG, rs4765663 in CACNA1C, rs6702619 near PALMD, rs7127129 in TMEM16A, rs11207426 near FGGY, rs17608766 in GOSR2, and rs17696696 in CFDP1 for aortic root diameter; and rs12440869 in IQCH for Doppler transmitral A-wave peak velocity. Findings were in part validated in other cohorts and in GWAS of related disease traits. The genetic loci showed associations with putative signaling pathways, and with gene expression in whole blood, monocytes, and myocardial tissue.

Conclusion: The additional genetic loci identified in this large meta-analysis of cardiac structure and function provide insights into the underlying genetic architecture of cardiac structure and warrant follow-up in future functional studies.

Funding: For detailed information per study, see Acknowledgments.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1172/JCI84840DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5409098PMC
May 2017

A genome-wide interaction analysis of tricyclic/tetracyclic antidepressants and RR and QT intervals: a pharmacogenomics study from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium.

J Med Genet 2017 05 30;54(5):313-323. Epub 2016 Dec 30.

Laboratory of Epidemiology, Demography, and Biometry, National Institute on Aging, Bethesda, Maryland, USA.

Background: Increased heart rate and a prolonged QT interval are important risk factors for cardiovascular morbidity and mortality, and can be influenced by the use of various medications, including tricyclic/tetracyclic antidepressants (TCAs). We aim to identify genetic loci that modify the association between TCA use and RR and QT intervals.

Methods And Results: We conducted race/ethnic-specific genome-wide interaction analyses (with HapMap phase II imputed reference panel imputation) of TCAs and resting RR and QT intervals in cohorts of European (n=45 706; n=1417 TCA users), African (n=10 235; n=296 TCA users) and Hispanic/Latino (n=13 808; n=147 TCA users) ancestry, adjusted for clinical covariates. Among the populations of European ancestry, two genome-wide significant loci were identified for RR interval: rs6737205 in (β=56.3, p=3.9e) and rs9830388 in (β=25.2, p=1.7e). In Hispanic/Latino cohorts, rs2291477 in significantly modified the association between TCAs and QT intervals (β=9.3, p=2.55e). In the meta-analyses of the other ethnicities, these loci either were excluded from the meta-analyses (as part of quality control), or their effects did not reach the level of nominal statistical significance (p>0.05). No new variants were identified in these ethnicities. No additional loci were identified after inverse-variance-weighted meta-analysis of the three ancestries.

Conclusions: Among Europeans, TCA interactions with variants in and were identified in relation to RR intervals. Among Hispanic/Latinos, variants in modified the relation between TCAs and QT intervals. Future studies are required to confirm our results.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1136/jmedgenet-2016-104112DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5406254PMC
May 2017

Genome-wide Trans-ethnic Meta-analysis Identifies Seven Genetic Loci Influencing Erythrocyte Traits and a Role for RBPMS in Erythropoiesis.

Am J Hum Genet 2017 Jan 22;100(1):51-63. Epub 2016 Dec 22.

Icahn Institute for Multiscale Biology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.

Genome-wide association studies (GWASs) have identified loci for erythrocyte traits in primarily European ancestry populations. We conducted GWAS meta-analyses of six erythrocyte traits in 71,638 individuals from European, East Asian, and African ancestries using a Bayesian approach to account for heterogeneity in allelic effects and variation in the structure of linkage disequilibrium between ethnicities. We identified seven loci for erythrocyte traits including a locus (RBPMS/GTF2E2) associated with mean corpuscular hemoglobin and mean corpuscular volume. Statistical fine-mapping at this locus pointed to RBPMS at this locus and excluded nearby GTF2E2. Using zebrafish morpholino to evaluate loss of function, we observed a strong in vivo erythropoietic effect for RBPMS but not for GTF2E2, supporting the statistical fine-mapping at this locus and demonstrating that RBPMS is a regulator of erythropoiesis. Our findings show the utility of trans-ethnic GWASs for discovery and characterization of genetic loci influencing hematologic traits.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.ajhg.2016.11.016DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5223059PMC
January 2017

KLB is associated with alcohol drinking, and its gene product β-Klotho is necessary for FGF21 regulation of alcohol preference.

Proc Natl Acad Sci U S A 2016 12 28;113(50):14372-14377. Epub 2016 Nov 28.

Medical Research Council Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, United Kingdom.

Excessive alcohol consumption is a major public health problem worldwide. Although drinking habits are known to be inherited, few genes have been identified that are robustly linked to alcohol drinking. We conducted a genome-wide association metaanalysis and replication study among >105,000 individuals of European ancestry and identified β-Klotho (KLB) as a locus associated with alcohol consumption (rs11940694; P = 9.2 × 10). β-Klotho is an obligate coreceptor for the hormone FGF21, which is secreted from the liver and implicated in macronutrient preference in humans. We show that brain-specific β-Klotho KO mice have an increased alcohol preference and that FGF21 inhibits alcohol drinking by acting on the brain. These data suggest that a liver-brain endocrine axis may play an important role in the regulation of alcohol drinking behavior and provide a unique pharmacologic target for reducing alcohol consumption.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1073/pnas.1611243113DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5167198PMC
December 2016

Rare Functional Variant in TM2D3 is Associated with Late-Onset Alzheimer's Disease.

PLoS Genet 2016 Oct 20;12(10):e1006327. Epub 2016 Oct 20.

Generation Scotland, Centre for Genomic and Experimental Medicine, University of Edinburgh, Edinburgh, United Kingdom.

We performed an exome-wide association analysis in 1393 late-onset Alzheimer's disease (LOAD) cases and 8141 controls from the CHARGE consortium. We found that a rare variant (P155L) in TM2D3 was enriched in Icelanders (~0.5% versus <0.05% in other European populations). In 433 LOAD cases and 3903 controls from the Icelandic AGES sub-study, P155L was associated with increased risk and earlier onset of LOAD [odds ratio (95% CI) = 7.5 (3.5-15.9), p = 6.6x10-9]. Mutation in the Drosophila TM2D3 homolog, almondex, causes a phenotype similar to loss of Notch/Presenilin signaling. Human TM2D3 is capable of rescuing these phenotypes, but this activity is abolished by P155L, establishing it as a functionally damaging allele. Our results establish a rare TM2D3 variant in association with LOAD susceptibility, and together with prior work suggests possible links to the β-amyloid cascade.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1371/journal.pgen.1006327DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5072721PMC
October 2016
-->