Poster #S142 GENETIC ASSOCIATION STUDIES OF SCHIZOPHRENIA RISK GENES WITH COGNITIVE AND NEUROIMAGING TRAITS IN THE GENUS CONSORTIUM COLLECTION

2014 ◽  
Vol 153 ◽  
pp. S140
Author(s):  
Tracey Petryshen ◽  
Gabriella Blokland
2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Margot Gunning ◽  
Paul Pavlidis

AbstractDiscovering genes involved in complex human genetic disorders is a major challenge. Many have suggested that machine learning (ML) algorithms using gene networks can be used to supplement traditional genetic association-based approaches to predict or prioritize disease genes. However, questions have been raised about the utility of ML methods for this type of task due to biases within the data, and poor real-world performance. Using autism spectrum disorder (ASD) as a test case, we sought to investigate the question: can machine learning aid in the discovery of disease genes? We collected 13 published ASD gene prioritization studies and evaluated their performance using known and novel high-confidence ASD genes. We also investigated their biases towards generic gene annotations, like number of association publications. We found that ML methods which do not incorporate genetics information have limited utility for prioritization of ASD risk genes. These studies perform at a comparable level to generic measures of likelihood for the involvement of genes in any condition, and do not out-perform genetic association studies. Future efforts to discover disease genes should be focused on developing and validating statistical models for genetic association, specifically for association between rare variants and disease, rather than developing complex machine learning methods using complex heterogeneous biological data with unknown reliability.


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

2008 ◽  
Vol 9 (1) ◽  
Author(s):  
Veronica Latini ◽  
Gabriella Sole ◽  
Laurent Varesi ◽  
Giuseppe Vona ◽  
Maria Serafina Ristaldi

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