Genetic association studies of complex traits: design and analysis issues

Author(s):  
Christopher Newton-Cheh ◽  
Joel N. Hirschhorn
BMC Genetics ◽  
2007 ◽  
Vol 8 (1) ◽  
Author(s):  
Qihua Tan ◽  
Lene Christiansen ◽  
Charlotte Brasch-Andersen ◽  
Jing Hua Zhao ◽  
Shuxia Li ◽  
...  

2020 ◽  
Vol 117 (32) ◽  
pp. 18924-18933
Author(s):  
Daniel J. M. Crouch ◽  
Walter F. Bodmer

The reconciliation between Mendelian inheritance of discrete traits and the genetically based correlation between relatives for quantitative traits was Fisher’s infinitesimal model of a large number of genetic variants, each with very small effects, whose causal effects could not be individually identified. The development of genome-wide genetic association studies (GWAS) raised the hope that it would be possible to identify single polymorphic variants with identifiable functional effects on complex traits. It soon became clear that, with larger and larger GWAS on more and more complex traits, most of the significant associations had such small effects, that identifying their individual functional effects was essentially hopeless. Polygenic risk scores that provide an overall estimate of the genetic propensity to a trait at the individual level have been developed using GWAS data. These provide useful identification of groups of individuals with substantially increased risks, which can lead to recommendations of medical treatments or behavioral modifications to reduce risks. However, each such claim will require extensive investigation to justify its practical application. The challenge now is to use limited genetic association studies to find individually identifiable variants of significant functional effect that can help to understand the molecular basis of complex diseases and traits, and so lead to improved disease prevention and treatment. This can best be achieved by 1) the study of rare variants, often chosen by careful candidate assessment, and 2) the careful choice of phenotypes, often extremes of a quantitative variable, or traits with relatively high heritability.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kevin K. Esoh ◽  
Tobias O. Apinjoh ◽  
Steven G. Nyanjom ◽  
Ambroise Wonkam ◽  
Emile R. Chimusa ◽  
...  

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.


2007 ◽  
Vol 16 (20) ◽  
pp. 2494-2505 ◽  
Author(s):  
Yasuhito Nannya ◽  
Kenjiro Taura ◽  
Mineo Kurokawa ◽  
Shigeru Chiba ◽  
Seishi Ogawa

2018 ◽  
Vol 65 (2) ◽  
pp. 241-250 ◽  
Author(s):  
Maciej Michał Kowalik ◽  
Romuald Lango ◽  
Piotr Siondalski ◽  
Magdalena Chmara ◽  
Maciej Brzeziński ◽  
...  

There is increasing evidence that genetic variability influence patients’ early morbidity after cardiac surgery performed using cardiopulmonary bypass (CPB). The use of mortality as an outcome measure in cardiac surgical genetic association studies is rare. We publish the 30-day and 5-year survival analyses with focus on pre-, intra-, postoperative variables, biochemical parameters, and genetic variants in the INFLACOR (INFlAmmation in Cardiac OpeRations) cohort.In a series of prospectively recruited 518 adult Polish Caucasians who underwent cardiac surgery in which CPB was used, the clinical data, biochemical parameters, IL-6, soluble ICAM-1, TNFa, soluble E-selectin, and 10 single nucleotide polymorphisms were evaluated for their associations with 30-day and 5-year mortality.The 30-day mortality was associated with: pre-operative prothrombin international normalized ratio, intra-operative blood lactate, postoperative serum creatine phosphokinase, and acute kidney injury requiring renal replacement therapy (AKI-RRT) in logistic regression. Factors that determined the 5-year survival included: pre-operative NYHA class, history of peripheral artery disease and severe chronic obstructive pulmonary disease, intra-operative blood transfusion; and postoperative peripheral hypothermia, myocardial infarction, infection, and AKI-RRT in Cox regression. The serum levels of IL-6 and ICAM-1 measured three hours after operation were associated with 30-day and 5-year mortality, respectively. The ICAM1 rs5498 was associated with 30-day and 5-year survival with borderline significance.Different risk factors determined the early (30-day) and late (5-year) survival after adult cardiac surgery in which cardiopulmonary bypass was used. Future genetic association studies in cardiac surgical patients should adjust for the identified chronic and acute postoperative risk factors.


2000 ◽  
Vol 107 (2) ◽  
pp. 197-197 ◽  
Author(s):  
Michael Krawczak ◽  
Stefan Boehringer ◽  
Jörg T. Epplen

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