scholarly journals Kernel Approach for Modeling Interaction Effects in Genetic Association Studies of Complex Quantitative Traits

2015 ◽  
Vol 39 (5) ◽  
pp. 366-375 ◽  
Author(s):  
K. Alaine Broadaway ◽  
Richard Duncan ◽  
Karen N. Conneely ◽  
Lynn M. Almli ◽  
Bekh Bradley ◽  
...  
Keyword(s):  
Genetic Association ◽  
Quantitative Traits ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Interaction Effects ◽  
Kernel Approach

scholarly journals Retrospective analysis of main and interaction effects in genetic association studies of human complex traits

BMC Genetics ◽  
2007 ◽  
Vol 8 (1) ◽  
Author(s):  
Qihua Tan ◽  
Lene Christiansen ◽  
Charlotte Brasch-Andersen ◽  
Jing Hua Zhao ◽  
Shuxia Li ◽  
...  
Keyword(s):  
Retrospective Analysis ◽  
Genetic Association ◽  
Complex Traits ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Interaction Effects ◽  
Human Complex

A Perspective on Interaction Tests in Genetic Association Studies

2015 ◽  
Author(s):  
Hugues Aschard
Keyword(s):  
Genetic Association ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Genetic Effect ◽  
Interaction Effects ◽  
Effect Estimate ◽  
Environment Interaction ◽  
Interaction Patterns ◽  
New Perspective ◽  
Estimation Of Variance

The identification of gene-gene and gene-environment interaction in human traits and diseases is an active area of research that generates high expectation, and most often lead to high disappointment. This is partly explained by a misunderstanding of some of the inherent characteristics of interaction effects. Here, I untangle several theoretical aspects of standard regression-based interaction tests in genetic association studies. In particular, I discuss variables coding scheme, interpretation of effect estimate, power, and estimation of variance explained in regard of various hypothetical interaction patterns. I show first that the simplest biological interaction models—in which the magnitude of a genetic effect depends on a common exposure—are among the most difficult to identify. Then, I demonstrate the demerits of the current strategy to evaluate the contribution of interaction effects to the variance of quantitative outcomes and argue for the use of new approaches to overcome these issues. Finally I explore the advantages and limitations of multivariate models when testing for interaction between multiple SNPs and/or multiple exposures, using either a joint test, or a test of interaction based on risk score. Theoretical and simulated examples presented along the manuscript demonstrate that the application of these methods can provide a new perspective on the role of interaction in multifactorial traits.

scholarly journals A robust distribution-free test for genetic association studies of quantitative traits

2015 ◽  
Vol 14 (5) ◽  
Author(s):  
Julia Kozlitina ◽  
William R. Schucany
Keyword(s):  
Genetic Marker ◽  
Genetic Association ◽  
Quantitative Traits ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Distribution Free

AbstractIn association studies of quantitative traits, the association of each genetic marker with the trait of interest is typically tested using the

scholarly journals An ensemble learning approach jointly modeling main and interaction effects in genetic association studies

2008 ◽  
Vol 32 (4) ◽  
pp. 285-300 ◽  
Author(s):  
Zhaogong Zhang ◽  
Shuanglin Zhang ◽  
Man-Yu Wong ◽  
Nicholas J. Wareham ◽  
Qiuying Sha
Keyword(s):  
Genetic Association ◽  
Ensemble Learning ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Interaction Effects ◽  
Learning Approach

scholarly journals Fine scale human genetic structure in three regions of Cameroon reveals episodic diversifying selection

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kevin K. Esoh ◽  
Tobias O. Apinjoh ◽  
Steven G. Nyanjom ◽  
Ambroise Wonkam ◽  
Emile R. Chimusa ◽  
...  
Keyword(s):  
Genetic Structure ◽  
Ethnic Groups ◽  
Genetic Association ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Genomic Region ◽  
Sub Saharan Africa ◽  
Genome Wide Association Studies ◽  
Fine Scale ◽  
Sub Saharan

AbstractInferences from genetic association studies rely largely on the definition and description of the underlying populations that highlight their genetic similarities and differences. The clustering of human populations into subgroups (population structure) can significantly confound disease associations. This study investigated the fine-scale genetic structure within Cameroon that may underlie disparities observed with Cameroonian ethnicities in malaria genome-wide association studies in sub-Saharan Africa. Genotype data of 1073 individuals from three regions and three ethnic groups in Cameroon were analyzed using measures of genetic proximity to ascertain fine-scale genetic structure. Model-based clustering revealed distinct ancestral proportions among the Bantu, Semi-Bantu and Foulbe ethnic groups, while haplotype-based coancestry estimation revealed possible longstanding and ongoing sympatric differentiation among individuals of the Foulbe ethnic group, and their Bantu and Semi-Bantu counterparts. A genome scan found strong selection signatures in the HLA gene region, confirming longstanding knowledge of natural selection on this genomic region in African populations following immense disease pressure. Signatures of selection were also observed in the HBB gene cluster, a genomic region known to be under strong balancing selection in sub-Saharan Africa due to its co-evolution with malaria. This study further supports the role of evolution in shaping genomes of Cameroonian populations and reveals fine-scale hierarchical structure among and within Cameroonian ethnicities that may impact genetic association studies in the country.

scholarly journals Evaluation of genome-wide power of genetic association studies based on empirical data from the HapMap project

2007 ◽  
Vol 16 (20) ◽  
pp. 2494-2505 ◽  
Author(s):  
Yasuhito Nannya ◽  
Kenjiro Taura ◽  
Mineo Kurokawa ◽  
Shigeru Chiba ◽  
Seishi Ogawa
Keyword(s):  
Genetic Association ◽  
Empirical Data ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Hapmap Project ◽  
Genome Wide
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