@article{OriolCanela-Xandri2016,
title = {Improved Genetic Profiling of Anthropometric Traits Using a Big Data Approach},
author = {Oriol Canela-Xandri , Konrad Rawlik , John A. Woolliams, Albert Tenesa },
url = {http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0166755},
year = {2016},
date = {2016-12-15},
journal = {Plos One},
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, the use of which has traditionally been limited due to computational requirements and lack of sufficiently large individual level genotyped cohorts, but which improve the prediction of multiple medically relevant phenotypes using the same panel of SNPs. As a proof of principle, we used a shared panel of 319,038 common SNPs with MAF > 0.05 to train the prediction models in 114,264 unrelated White-British individuals 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 the captured heritable component. For height, this represents an improvement in prediction accuracy of up to 68% (184% more phenotypic variance explained) over SNPs reported to be robustly associated with height in a previous GWAS meta-analysis of similar size. Across-population predictions in White non-British individuals were similar to those in White-British whilst those in Asian and Black individuals were informative but less accurate. We estimate that the genotyping of circa 500,000 unrelated individuals will yield predictions between 66% and 82% of the SNP-heritability captured by common variants in our array. Prediction accuracies did not improve when including rarer SNPs or when fitting multiple traits jointly in multivariate models.},
keywords = {8447, anthropometric traits, Genetic profiling},
pubstate = {published},
tppubtype = {article}
}

@article{Tenesa2016,
title = {Genetic determination of height-mediated mate choice},
author = { Albert Tenesa, Konrad Rawlik, Pau Navarro and Oriol Canela-Xandri },
url = {http://www.genomebiology.com/2016/16/1/269},
year = {2016},
date = {2016-01-19},
journal = {Genome Biology},
abstract = {
Background

Numerous studies have reported positive correlations among couples for height. This suggests that humans find individuals of similar height attractive. However, the answer to whether the choice of a mate with a similar phenotype is genetically or environmentally determined has been elusive.
Results

Here we provide an estimate of the genetic contribution to height choice in mates in 13,068 genotyped couples. Using a mixed linear model we show that 4.1 % of the variation in the mate height choice is determined by a person’s own genotype, as expected in a model where one’s height determines the choice of mate height. Furthermore, the genotype of an individual predicts their partners’ height in an independent dataset of 15,437 individuals with 13 % accuracy, which is 64 % of the theoretical maximum achievable with a heritability of 0.041. Theoretical predictions suggest that approximately 5 % of the heritability of height is due to the positive covariance between allelic effects at different loci, which is caused by assortative mating. Hence, the coupling of alleles with similar effects could substantially contribute to the missing heritability of height.
Conclusions

These estimates provide new insight into the mechanisms that govern mate choice in humans and warrant the search for the genetic causes of choice of mate height. They have important methodological implications and contribute to the missing heritability debate.
},
keywords = {8447, height, mate},
pubstate = {published},
tppubtype = {article}
}