Chances are, you're going to marry someone a lot like you. Similar intelligence, similar height, similar body weight. Several environmental factors may cause couples to resemble one another: Those from similar backgrounds share comparable upbringings and diets, people from the same geographic area may both exhibit that region's dominant physical attributes, and a shared life (such as experiencing the same hardships, income, healthcare, etc.) can be reflected in physical appearance. But it's also possible that people directly choose their mates based on visible, physical characteristics. This study investigated whether couples share regions of the genome that underlie a range of different characteristics, which tests whether mate choice is shaped by accidents of the environment, culture, or by actual preferences for genetically-based traits. To do this, we predicted an individual's height and body mass index (BMI) from genetic markers and compared this to their partner s actual height and BMI in more than 24,000 couples of European ancestry. We found a strong statistical correlation between people's genetic markers for height and the actual height of their partner. We also found a statistically significant, but weaker, correlation between people s genes for BMI and actual BMI in partners. This implies that people had actively chosen partners with similar genes to themselves. We also tested for assortative mating in other traits, such as years of education, in 7780 couples in the UK Biobank. We found concordance among partners in genetic markers previously linked to years of education, and found a remarkably high correlation. This doesn't mean that people choose mates based on actual years of education, but it likely implies that they select for similar interests, which are associated with level of education. These findings suggest that mate choice affects the genomic architecture of traits in humans.
The limits of predicting complex traits and diseases from genetic data
Results from genome-wide association studies (GWAS) have proven valuable for understanding the genetic architecture of complex traits and are potentially valuable for predicting disease risk. As GWAS sample sizes grow the prediction accuracy will increase and may eventually yield clinically actionable predictions, for example by stratifying individuals on risk. One limitation for making accurate disease risk prediction is the experimental sample size. We aim to quantify the limits of predicting disease risk for an individual by developing sophisticated statistical methods and applying them to quantitative traits in the large UK Biobank sample. Understanding of the limitations of predicting an individual?s risk of disease using genetic data is of great importance for disease prevention, and meets the UK Biobank?s stated purposes. Gaining accurate genetic risk predictors through the development of robust and powerful statistical methods, together with a large discovery sample (e.g. UK Biobank data), is critical for use in disease screening programs to stratify the population, which is expected to reduce the financial burden of the health system for the whole society. Through a focus on quantitative phenotypes, we will develop new approaches applicable to predicting disease risk. The genetic marker data will be used to estimate genome-wide relationships, which we will then correlate with phenotype. This analysis will simultaneously quantify how much of the observed individual differences in phenotype is due to genetic factors, and how accurate a genetic predictor can be. The accuracy of prediction will then be tested. We focus on well-characterised quantitative phenotypes of height, body mass index, blood pressure, osteoporosis, and metabolism. To have maximum power to predict risk of disease, we require access to the full cohort, because one of the main limiting factors of prediction is sample size. Our analyses will thus require individual-level imputed genotype and phenotype data. We request a wide range of phenotypes because prediction accuracy is sensitive to the underlying genetic architecture and we wish to quantify the limits of prediction across multiple diseases.
|Lead investigator:||Professor Peter Visscher|
|Lead institution:||University of Queensland|
7 related Returns
|Return ID||App ID||Description||Archive Date|
|3677||12514||A resource-efficient tool for mixed model association analysis of large-scale data||27 Jul 2021|
|3084||12514||Causal associations between risk factors and common diseases inferred from GWAS summary data||16 Dec 2020|
|3452||12514||Genome-wide association study of medication-use and associated disease in the UK Biobank||25 May 2021|
|3621||12514||Improved polygenic prediction by Bayesian multiple regression on summary statistics||2 Jul 2021|
|3074||12514||Meta-analysis of genome-wide association studies for height and body mass index in ~700000 individuals of European ancestry||16 Dec 2020|
|3060||12514||Misestimation of heritability and prediction accuracy of male-pattern baldness||14 Dec 2020|
|3041||12514||Transformation of Summary Statistics from Linear Mixed Model Association on All-or-None Traits to Odds Ratio||8 Dec 2020|
|1823||Genetic evidence of assortative mating in humans||Robinson et al.||2017||Nature Human Behaviour 2017|