By a News Reporter-Staff News Editor at Life Science Weekly -- Data detailed on Life Science Research - Genetics have been presented. According to news reporting originating in Brisbane, Australia, by NewsRx journalists, research stated, “Genome-wide association studies (GWAS) have identified thousands of loci that are robustly associated with complex diseases. The use of linear mixed model (LMM) methodology for GWAS is becoming more prevalent due to its ability to control for population structure and cryptic relatedness and to increase power.”
The news reporters obtained a quote from the research from the University of Queensland, “The odds ratio (OR) is a common measure of the association of a disease with an exposure (e.g., a genetic variant) and is readably available from logistic regression. However, when the LMM is applied to all-or-none traits it provides estimates of genetic effects on the observed 0-1 scale, a different scale to that in logistic regression. This limits the comparability of results across studies, for example in a meta-analysis, and makes the interpretation of the magnitude of an effect from an LMM GWAS difficult. In this study, we derived transformations from the genetic effects estimated under the LMM to the OR that only rely on summary statistics. To test the proposed transformations, we used real genotypes from two large, publicly available data sets to simulate all-or-none phenotypes for a set of scenarios that differ in underlying model, disease prevalence, and heritability. Furthermore, we applied these transformations to GWAS summary statistics for type 2 diabetes generated from 108,042 individuals in the UK Biobank. In both simulation and real-data application, we observed very high concordance between the transformed OR from the LMM and either the simulated truth or estimates from logistic regression.”
According to the news reporters, the research concluded: “The transformations derived and validated in this study improve the comparability of results from prospective and already performed LMM GWAS on complex diseases by providing a reliable transformation to a common comparative scale for the genetic effects.”
For more information on this research see: Transformation of Summary Statistics from Linear Mixed Model Association on All-or-None Traits to Odds Ratio. Genetics , 2018;208(4):1397-1408. Genetics can be contacted at: Genetics Society America, 9650 Rockville Ave, Bethesda, MD 20814, USA. (Cell Press - www.cell.com; Genetics - http://www.cell.com/trends/genetics/home)
Our news correspondents report that additional information may be obtained by contacting L.R. Lloyd-Jones, University of Queensland, Inst Mol Biosci, Brisbane, Qld 4072, Australia. Additional authors for this research include M.R. Robinson, J. Yang and P.M. Visscher.
The direct object identifier (DOI) for that additional information is: https://doi.org/10.1534/genetics.117.300360. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.
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CITATION: (2018-05-01), New Findings Reported from University of Queensland Describe Advances in Genetics (Transformation of Summary Statistics from Linear Mixed Model Association on All-or-None Traits to Odds Ratio), Life Science Weekly, 2602, ISSN: 1552-2474, BUTTER® ID: 015575438
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