Dec 1, 2015

Accurate genetic profiling of anthropometric traits using a big data approach

BioRxiv : the Preprint Server for Biology
Oriol Canela-XandriAlbert Tenesa

Abstract

Genome-wide association studies (GWAS) promised to translate their findings into clinically beneficial improvements of patient management by tailoring disease management to the individual through the prediction of disease risk. However, the ability to translate genetic findings from GWAS into predictive tools that are of clinical utility and which may inform clinical practice has, so far, been encouraging but limited. Here we propose to use a more powerful statistical approach that enables the prediction of multiple medically relevant phenotypes without the costs associated with developing a genetic test for each of them. As a proof of principle, we used a common panel of 319,038 SNPs to train the prediction models in 114,264 unrelated White-British for height and four obesity related traits (body mass index, basal metabolic rate, body fat percentage, and waist-to-hip ratio). We obtained prediction accuracies that ranged between 46% and 75% of the maximum achievable given their explained heritable component. This represents an improvement of up to 75% over the phenotypic variance explained by the predictors developed through large collaborations, which used more than twice as many training samples. Across-population predictions...Continue Reading

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Mentioned in this Paper

Genome-Wide Association Study
Genome
Genomic Profile
Genetic Screening Method
Genetic Markers
Participant
Obesity
Body Fat
Body Mass Index Procedure
Genomics

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