Polygenic scores (PGS) summarize the genetic contribution of a person's genotype to a disease or phenotype. They can be used to group participants into different risk categories for diseases, and are also used as covariates in epidemiological analyses. A number of possible ways of calculating PGS have been proposed, and recently there is much interest in methods that incorporate information available in published summary statistics. As there is no inherent information on linkage disequilibrium (LD) in summary statistics, a pertinent question is how we can use LD information available elsewhere to supplement such analyses. To answer this question, we propose a method for constructing PGS using summary statistics and a reference panel in a penalized regression framework, which we call lassosum. We also propose a general method for choosing the value of the tuning parameter in the absence of validation data. In our simulations, we showed that pseudovalidation often resulted in prediction accuracy that is comparable to using a dataset with validation phenotype and was clearly superior to the conservative option of setting the tuning parameter of lassosum to its lowest value. We also showed that lassosum achieved better prediction accuracy than simple clumping and P-value thresholding in almost all scenarios. It was also substantially faster and more accurate than the recently proposed LDpred.

Blood lipids are important risk factors for coronary artery disease (CAD). Here we perform an exome-wide association study by genotyping 12,685 Chinese, using a custom Illumina HumanExome BeadChip, to identify additional loci influencing lipid levels. Single-variant association analysis on 65,671 single nucleotide polymorphisms reveals 19 loci associated with lipids at exome-wide significance (P<2.69 × 10−7), including three Asian-specific coding variants in known genes (CETP p.Asp459Gly, PCSK9 p.Arg93Cys and LDLR p.Arg257Trp). Furthermore, missense variants at two novel loci—PNPLA3 p.Ile148Met and PKD1L3 p.Thr429Ser—also influence levels of triglycerides and low-density lipoprotein cholesterol, respectively. Another novel gene, TEAD2, is found to be associated with high-density lipoprotein cholesterol through gene-based association analysis. Most of these newly identified coding variants show suggestive association (P<0.05) with CAD. These findings demonstrate that exome-wide genotyping on samples of non-European ancestry can identify additional population-specific possible causal variants, shedding light on novel lipid biology and CAD.

The genetic and environmental contributions to the variation and longitudinal stability in childhood aggressive behavior were assessed in two large twin cohorts, the Netherlands Twin Register (NTR), and the Twins Early Development Study (TEDS; United Kingdom). In NTR, maternal ratings on aggression from the Child Behavior Checklist (CBCL) were available for 10,765 twin pairs at age 7, for 8,557 twin pairs at age 9/10, and for 7,176 twin pairs at age 12. In TEDS, parental ratings of conduct disorder from the Strength and Difficulty Questionnaire (SDQ) were available for 6,897 twin pairs at age 7, for 3,028 twin pairs at age 9 and for 5,716 twin pairs at age 12. In both studies, stability and heritability of aggressive behavioral problems was high. Heritability was on average somewhat, but significantly, lower in TEDS (around 60%) than in NTR (between 50% and 80%) and sex differences were slightly larger in the NTR sample. In both studies, the influence of shared environment was similar: in boys shared environment explained around 20% of the variation in aggression across all ages while in girls its influence was absent around age 7 and only came into play at later ages. Longitudinal genetic correlations were the main reason for stability of aggressive behavior. Individual differences in CBCL-Aggressive Behavior and SDQ-Conduct disorder throughout childhood are driven by a comparable but significantly different genetic architecture.