Improving Genetic Prediction by Leveraging Genetic Correlations Among Human Diseases and Traits

Genomic prediction has the potential to contribute to precision medicine. However, to date, the utility of such predictors is limited due to low accuracy for most traits. Here theory and simulation study are used to demonstrate that widespread pleiotropy among phenotypes can be utilised to improve genomic risk prediction. We show how a genetic predictor can be created as a weighted index that combines published genome-wide association study (GWAS) summary statistics across many different traits. We apply this framework to predict risk of schizophrenia and bipolar disorder in the Psychiatric Genomics consortium data, finding substantial heterogeneity in prediction accuracy increases across cohorts. For six additional phenotypes in the UK Biobank data, we find increases in prediction accuracy ranging from 0.7 for height to 47 for type 2 diabetes, when using a multi-trait predictor that combines published summary statistics from multiple traits, as compared to a predictor based only on one trait. © 2018 The Author(s)

Age at first birth in women is genetically associated with increased risk of schizophrenia

Prof. Paunio on PGC:n jäsenPrevious studies have shown an increased risk for mental health problems in children born to both younger and older parents compared to children of average-aged parents. We previously used a novel design to reveal a latent mechanism of genetic association between schizophrenia and age at first birth in women (AFB). Here, we use independent data from the UK Biobank (N = 38,892) to replicate the finding of an association between predicted genetic risk of schizophrenia and AFB in women, and to estimate the genetic correlation between schizophrenia and AFB in women stratified into younger and older groups. We find evidence for an association between predicted genetic risk of schizophrenia and AFB in women (P-value = 1.12E-05), and we show genetic heterogeneity between younger and older AFB groups (P-value = 3.45E-03). The genetic correlation between schizophrenia and AFB in the younger AFB group is -0.16 (SE = 0.04) while that between schizophrenia and AFB in the older AFB group is 0.14 (SE = 0.08). Our results suggest that early, and perhaps also late, age at first birth in women is associated with increased genetic risk for schizophrenia in the UK Biobank sample. These findings contribute new insights into factors contributing to the complex bio-social risk architecture underpinning the association between parental age and offspring mental health.Peer reviewe

A comparison of columnar-to-equiaxed transition prediction methods using simulation of the growing columnar front

International audienceIn this article, the columnar-to-equiaxed transition (CET) in directionally solidified castings is investigated. Three CET prediction methods from the literature that use a simulation of the growing columnar front are compared to the experimental results, for a range of Al-Si alloys: Al-3 wt pct Si, Al-7 wt pct Si, and Al-11 wt pct Si. The three CET prediction methods are the constrained-to-unconstrained criterion, the critical cooling rate criterion, and the equiaxed index criterion. These methods are termed indirect methods, because no information is required for modeling the equiaxed nucleation and growth; only the columnar solidification is modeled. A two-dimensional (2-D) front-tracking model of columnar growth is used to compare each criterion applied to each alloy. The constrained-to-unconstrained criterion and a peak equiaxed index criterion agree well with each other and some agreement is found with the experimental findings. For the critical cooling rate criterion, a minimum value for the cooling rate (between 0.07 and 0.11 K/s) is found to occur close to the CET position. However, this range of values differs from those cited in the literature (0.15 to 0.16 K/s), leading to a considerable difference in the prediction of the CET positions. A reason for this discrepancy is suggested, based on the fundamental differences in the modeling approaches. © The Minerals, Metals & Materials Society and ASM International 2009