Genetic Analysis Workshop 19: methods and strategies for analyzing human sequence and gene expression data in extended families and unrelated individuals

Engelman, Corinne D.; Greenwood, Celia M.T.; Bailey, Julia N.; Cantor, Rita M.; Kent, Jack W.; König, Inke R.; Bermejo, Justo Lorenzo; Melton, Phillip E.; Santorico, Stephanie A.; Schillert, Arne; Wijsman, Ellen M.; MacCluer, Jean W.; Almasy, Laura

doi:10.1186/s12919-016-0007-z

Cited by 7 publications

(8 citation statements)

References 15 publications

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“…GAW19 data consist of multiple phenotypes including systolic and diastolic blood pressures (SBP and DBP) for each subject (Engelman et al, ). These two phenotypes are correlated (sample correlation coefficient = 0.549) and may have some common underlying genetic mechanism (Schillert & Konigorski, ).…”

Section: Application To Gaw19 Blood Pressure Datamentioning

confidence: 99%

“…We find that bivariate LBL has power higher or similar to that of univariate LBL in most scenarios. Finally, we apply the methods to two data sets—exome sequence data from Genetic Analysis Workshop (GAW) 19 (Engelman et al, ) and GWAS data on lung cancer (Database of Genotypes & Phenotypes, ). In the GAW data, we analyze haplotype blocks in several genes for testing association with systolic and diastolic blood pressures jointly.…”

Section: Introductionmentioning

confidence: 99%

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Bivariate logistic Bayesian LASSO for detecting rare haplotype association with two correlated phenotypes

Yuan

Biswas

2019

Genetic Epidemiology

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In genetic association studies, joint modeling of related traits/phenotypes can utilize the correlation between them and thereby provide more power and uncover additional information about genetic etiology. Moreover, detecting rare genetic variants are of current scientific interest as a key to missing heritability.Logistic Bayesian LASSO (LBL) has been proposed recently to detect rare haplotype variants using case-control data, that is, a single binary phenotype. As there is currently no haplotype association method that can handle multiple binary phenotypes, we extend LBL to fill this gap. We develop a bivariate model by using a latent variable to induce correlation between the two outcomes. We carry out extensive simulations to investigate the bivariate LBL and compare with the univariate LBL. The bivariate LBL performs better or similar to the univariate LBL in most settings. It has the highest gain in power when a haplotype is associated with both traits and it affects at least one trait in a direction opposite to the direction of the correlation between the traits. We analyze two data sets-Genetic Analysis Workshop 19 sequence data on systolic and diastolic blood pressures and a genome-wide association data set on lung cancer and smoking and detect several associated rare haplotypes.

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Section: Application To Gaw19 Blood Pressure Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bivariate logistic Bayesian LASSO for detecting rare haplotype association with two correlated phenotypes

Yuan

Biswas

2019

Genetic Epidemiology

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“…Genetic Analysis Workshops (GAWs) are designed to evaluate the performance of different statistical methods applied on high density genotype. Among them, GAW13 [27], GAW16 [28], GAW18 [29] and GAW19 [30] have focused on analysis of longitudinal datasets. GAW19 [30], the focus of our work, is based on data from San Antonio Family Heart Study (SAFHS), conducted to investigate the genetics of cardiovascular disease in Mexican-Americans.…”

Section: Resultsmentioning

confidence: 99%

Longitudinal linear combination test for gene set analysis

et al. 2019

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BackgroundAlthough microarray studies have greatly contributed to recent genetic advances, lack of replication has been a continuing concern in this area. Complex study designs have the potential to address this concern, though they remain undervalued by investigators due to the lack of proper analysis methods. The primary challenge in the analysis of complex microarray study data is handling the correlation structure within data while also dealing with the combination of large number of genetic measurements and small number of subjects that are ubiquitous even in standard microarray studies. Motivated by the lack of available methods for analysis of repeatedly measured phenotypic or transcriptomic data, herein we develop a longitudinal linear combination test (LLCT).ResultsLLCT is a two-step method to analyze multiple longitudinal phenotypes when there is high dimensionality in response and/or explanatory variables. Alternating between calculating within-subjects and between-subjects variations in two steps, LLCT examines if the maximum possible correlation between a linear combination of the time trends and a linear combination of the predictors given by the gene expressions is statistically significant. A generalization of this method can handle family-based study designs when the subjects are not independent. This method is also applicable to time-course microarray, with the ability to identify gene sets that exhibit significantly different expression patterns over time. Based on the results from a simulation study, LLCT outperformed its alternative: pathway analysis via regression. LLCT was shown to be very powerful in the analysis of large gene sets even when the sample size is small.ConclusionsThis self-contained pathway analysis method is applicable to a wide range of longitudinal genomics, proteomics, metabolomics (OMICS) data, allows adjusting for potentially time-dependent covariates and works well with unbalanced and incomplete data. An important potential application of this method could be time-course linkage of OMICS, an attractive possibility for future genetic researchers.Availability: R package of LLCT is available at: https://github.com/its-likeli-jeff/LLCT

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“…We applied the HomMega‐KS, HomMega‐O, GenHetMega‐KS, GenHetMega‐O, Mega‐BT, MF, and MZ tests to sequencing data from GAW19 to identify variants associated with hypertension. GAW19 comprised two studies: (a) A longitudinal study with 20 large Mexican‐American families and (b) a relatively large cross‐sectional case–control study with 1,943 unrelated Mexican‐American individuals (Blangero et al, ; Engelman et al, ). A more detailed description of data from GAW19 can be found in Blangero et al ().…”

Section: Methodsmentioning

confidence: 99%

General retrospective mega‐analysis framework for rare variant association tests

Chien

Chiu

2018

Genetic Epidemiology

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Here, we describe a retrospective mega-analysis framework for gene- or region-based multimarker rare variant association tests. Our proposed mega-analysis association tests allow investigators to combine longitudinal and cross-sectional family- and/or population-based studies. This framework can be applied to a continuous, categorical, or survival trait. In addition to autosomal variants, the tests can be applied to conduct mega-analyses on X-chromosome variants. Tests were built on study-specific region- or gene-level quasiscore statistics and, therefore, do not require estimates of effects of individual rare variants. We used the generalized estimating equation approach to account for complex multiple correlation structures between family members, repeated measurements, and genetic markers. While accounting for multilevel correlations and heterogeneity across studies, the test statistics were computationally efficient and feasible for large-scale sequencing studies. The retrospective aspect of association tests helps alleviate bias due to phenotype-related sampling and type I errors due to misspecification of phenotypic distribution. We evaluated our developed mega-analysis methods through comprehensive simulations with varying sample sizes, covariates, population stratification structures, and study designs across multiple studies. To illustrate application of the proposed framework, we conducted a mega-association analysis combining a longitudinal family study and a cross-sectional case-control study from Genetic Analysis Workshop 19.

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Genetic Analysis Workshop 19: methods and strategies for analyzing human sequence and gene expression data in extended families and unrelated individuals

Cited by 7 publications

References 15 publications

Bivariate logistic Bayesian LASSO for detecting rare haplotype association with two correlated phenotypes

Bivariate logistic Bayesian LASSO for detecting rare haplotype association with two correlated phenotypes

Longitudinal linear combination test for gene set analysis

General retrospective mega‐analysis framework for rare variant association tests

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