scholarly journals Impact of repeated measures and sample selection on genome-wide association studies of fasting glucose

2010 ◽  
Vol 34 (7) ◽  
pp. 665-673 ◽  
Author(s):  
Laura J. Rasmussen-Torvik ◽  
Alvaro Alonso ◽  
Man Li ◽  
Wen Kao ◽  
Anna Köttgen ◽  
...  
Keyword(s):  
Repeated Measures ◽  
Fasting Glucose ◽  
Association Studies ◽  
Sample Selection ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

Abstract P260: Large-scale Meta-analysis of Diverse Ancestry Genome-wide Association Studies to Identify Pharmacogenomic Interactions of Sulfonylureas and ECG Phenotypes

Circulation ◽  
2016 ◽  
Vol 133 (suppl_1) ◽  
Author(s):  
James S Floyd ◽  
Colleen Sitlani ◽  
Christy L Avery ◽  
Eric A Whitsel ◽  
Leslie Lange ◽  
...  
Keyword(s):  
Repeated Measures ◽  
Qt Interval ◽  
Association Studies ◽  
Meta Analysis ◽  
Small Sample ◽  
Genome Wide Association ◽  
European Ancestry ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Meta Analyses

Introduction: Sulfonylureas are a commonly-used class of diabetes medication that can prolong the QT-interval, which is a leading cause of drug withdrawals from the market given the possible risk of life-threatening arrhythmias. Previously, we conducted a meta-analysis of genome-wide association studies of sulfonylurea-genetic interactions on QT interval among 9 European-ancestry (EA) cohorts using cross-sectional data, with null results. To improve our power to identify novel drug-gene interactions, we have included repeated measures of medication use and QT interval and expanded our study to include several additional cohorts, including African-American (AA) and Hispanic-ancestry (HA) cohorts with a high prevalence of sulfonylurea use. To identify potentially differential effects on cardiac depolarization and repolarization, we have also added two phenotypes - the JT and QRS intervals, which together comprise the QT interval. Hypothesis: The use of repeated measures and expansion of our meta-analysis to include diverse ancestry populations will allow us to identify novel pharmacogenomic interactions for sulfonylureas on the ECG phenotypes QT, JT, and QRS. Methods: Cohorts with unrelated individuals used generalized estimating equations to estimate interactions; cohorts with related individuals used mixed effect models clustered on family. For each ECG phenotype (QT, JT, QRS), we conducted ancestry-specific (EA, AA, HA) inverse variance weighted meta-analyses using standard errors based on the t-distribution to correct for small sample inflation in the test statistic. Ancestry-specific summary estimates were combined using MANTRA, an analytic method that accounts for differences in local linkage disequilibrium between ethnic groups. Results: Our study included 65,997 participants from 21 cohorts, including 4,020 (6%) sulfonylurea users, a substantial increase from the 26,986 participants and 846 sulfonylureas users in the previous meta-analysis. Preliminary ancestry-specific meta-analyses have identified genome-wide significant associations (P < 5х10–8) for each ECG phenotype, and analyses with MANTRA are in progress. Conclusions: In the setting of the largest collection of pharmacogenomic studies to date, we used repeated measurements and leveraged diverse ancestry populations to identify new pharmacogenomic loci for ECG traits associated with cardiovascular risk.

scholarly journals Genome‐wide association studies for milk production traits in Valle del Belice sheep using repeated measures

2019 ◽  
Vol 50 (3) ◽  
pp. 311-314 ◽  
Author(s):  
A. M. Sutera ◽  
V. Riggio ◽  
S. Mastrangelo ◽  
R. Di Gerlando ◽  
M. T. Sardina ◽  
...  
Keyword(s):  
Milk Production ◽  
Repeated Measures ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Production Traits ◽  
Milk Production Traits ◽  
Genome Wide

scholarly journals Increasing the power of genome wide association studies in natural populations using repeated measures – evaluation and implementation

2016 ◽  
Vol 7 (7) ◽  
pp. 792-799 ◽  
Author(s):  
Lars Rönnegård ◽  
S. Eryn McFarlane ◽  
Arild Husby ◽  
Takeshi Kawakami ◽  
Hans Ellegren ◽  
...  
Keyword(s):  
Repeated Measures ◽  
Association Studies ◽  
Natural Populations ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

scholarly journals Integrating Genome-Wide Association and eQTLs Studies Identifies the Genes and Gene Sets Associated with Diabetes

2017 ◽  
Vol 2017 ◽  
pp. 1-4 ◽  
Author(s):  
Xiao Liang ◽  
Awen He ◽  
Wenyu Wang ◽  
Li Liu ◽  
Yanan Du ◽  
...  
Keyword(s):  
Fasting Glucose ◽  
Association Studies ◽  
Integrative Analysis ◽  
Gene Set Enrichment Analysis ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Gene Set ◽  
Gene Sets ◽  
Genome Wide ◽  
Summary Data

Aim. To identify novel candidate genes and gene sets for diabetes. Methods. We performed an integrative analysis of genome-wide association studies (GWAS) and expression quantitative trait loci (eQTLs) data for diabetes. Summary data was driven from a large-scale GWAS of diabetes, totally involving 58,070 individuals. eQTLs dataset included 923,021 cis-eQTL for 14,329 genes and 4,732 trans-eQTL for 2,612 genes. Integrative analysis of GWAS and eQTLs data was conducted by summary data-based Mendelian randomization (SMR). To identify the gene sets associated with diabetes, the SMR single gene analysis results were further subjected to gene set enrichment analysis (GSEA). A total of 13,311 annotated gene sets were analyzed in this study. Results. SMR analysis identified 6 genes significantly associated with fasting glucose, such as C11ORF10 (p value = 6.04 × 10−8), MRPL33 (p value = 1.24 × 10−7), and FADS1 (p value = 2.39 × 10−7). Gene set analysis identified HUANG_FOXA2_TARGETS_UP (false discovery rate = 0.047) associated with fasting glucose. Conclusion. Our study provides novel clues for clarifying the genetic mechanism of diabetes. This study also illustrated the good performance of SMR approach and extended it to gene set association analysis for complex diseases.

scholarly journals Genome wide association study in 3,173 outbred rats identifies multiple loci for body weight, adiposity, and fasting glucose

2018 ◽  
Author(s):  
Apurva S. Chitre ◽  
Oksana Polesskaya ◽  
Katie Holl ◽  
Jianjun Gao ◽  
Riyan Cheng ◽  
...  
Keyword(s):  
Body Weight ◽  
Candidate Genes ◽  
Family Relationships ◽  
Fasting Glucose ◽  
Association Studies ◽  
Genetic Correlations ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genetic Loci ◽  
Genome Wide

AbstractObjectiveObesity is influenced by genetic and environmental factors. Despite success of human genome wide association studies (GWAS), the specific genes that confer obesity remain largely unknown. The objective of this study was to use outbred rats to identify genetic loci underlying obesity and related morphometric and metabolic traits.MethodsWe measured obesity-relevant traits including body weight, body length, body mass index, fasting glucose, and retroperitoneal, epididymal, and parametrial fat pad weight in 3,173 male and female adult N/NIH heterogeneous stock (HS) rats across three institutions, providing data for the largest rat GWAS to date. Genetic loci were identified using a linear mixed model that accounted for the complex family relationships of the HS and covariate to account for differences among the three phenotyping centers.ResultsWe identified 32 independent loci, several of which contained only a single gene (e.g. Epha5, Nrg1 and Klhl14) or obvious candidate genes (Adcy3, Prlhr). There were strong phenotypic and genetic correlations among obesity-related traits, and extensive pleiotropy at individual loci.ConclusionsThese studies demonstrate utility of HS rats for investigating the genetics of obesity related traits across institutions and identify several candidate genes for future functional testing.

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