Polygenic risk scores have shown great promise in predicting complex disease risk and will become more accurate as training sample sizes increase. The standard approach for calculating risk scores involves linkage disequilibrium (LD)-based marker pruning and applying a p value threshold to association statistics, but this discards information and can reduce predictive accuracy. We introduce LDpred, a method that infers the posterior mean effect size of each marker by using a prior on effect sizes and LD information from an external reference panel. Theory and simulations show that LDpred outperforms the approach of pruning followed by thresholding, particularly at large sample sizes. Accordingly, predicted R(2) increased from 20.1% to 25.3% in a large schizophrenia dataset and from 9.8% to 12.0% in a large multiple sclerosis dataset. A similar relative improvement in accuracy was observed for three additional large disease datasets and for non-European schizophrenia samples. The advantage of LDpred over existing methods will grow as sample sizes increase.

Breast cancer is the most common cancer among women. Common variants at 27 loci have been identified as associated with susceptibility to breast cancer, and these account for ~9% of the familial risk of the disease. We report here a meta-analysis of 9 genome-wide association studies, including 10,052 breast cancer cases and 12,575 controls of European ancestry, from which we selected 29,807 SNPs for further genotyping. These SNPs were genotyped in 45,290 cases and 41,880 controls of European ancestry from 41 studies in the Breast Cancer Association Consortium (BCAC). The SNPs were genotyped as part of a collaborative genotyping experiment involving four consortia (Collaborative Oncological Gene-environment Study, COGS) and used a custom Illumina iSelect genotyping array, iCOGS, comprising more than 200,000 SNPs. We identified SNPs at 41 new breast cancer susceptibility loci at genome-wide significance (P < 5 × 10−8). Further analyses suggest that more than 1,000 additional loci are involved in breast cancer susceptibility.

Smoking is a risk factor for most of the diseases leading in mortality1. We conducted genome-wide association (GWA) meta-analyses of smoking data within the ENGAGE consortium to search for common alleles associating with the number of cigarettes smoked per day (CPD) in smokers (N=31,266) and smoking initiation (N=46,481). We tested selected SNPs in a second stage (N=45,691 smokers), and assessed some in a third sample (N=9,040). Variants in three genomic regions associated with CPD (P< 5·10−8), including previously identified SNPs at 15q25 represented by rs1051730-A (0.80 CPD,P=2.4·10−69), and SNPs at 19q13 and 8p11, represented by rs4105144-C (0.39 CPD, P=2.2·10−12) and rs6474412-T (0.29 CPD,P= 1.4·10−8), respectively. Among the genes at the two novel loci, are genes encoding nicotine-metabolizing enzymes (CYP2A6 and CYP2B6), and nicotinic acetylcholine receptor subunits (CHRNB3 and CHRNA6) highlighted in previous studies of nicotine dependence2-3. Nominal associations with lung cancer were observed at both 8p11 (rs6474412-T,OR=1.09,P=0.04) and 19q13 (rs4105144-C,OR=1.12,P=0.0006).

Smoking is a leading global cause of disease and mortality1. We performed a genomewide meta-analytic association study of smoking-related behavioral traits in a total sample of 41,150 individuals drawn from 20 disease, population, and control cohorts. Our analysis confirmed an effect on smoking quantity (SQ) at a locus on 15q25 (P=9.45e-19) that includes three genes encoding neuronal nicotinic acetylcholine receptor subunits (CHRNA5, CHRNA3, CHRNB4). We used data from the 1000 Genomes project to investigate the region using imputation, which allowed analysis of virtually all common variants in the region and offered a five-fold increase in coverage over the HapMap. This increased the spectrum of potentially causal single nucleotide polymorphisms (SNPs), which included a novel SNP that showed the highest significance, rs55853698, located within the promoter region of CHRNA5. Conditional analysis also identified a secondary locus (rs6495308) in CHRNA3.

Liability to alcohol dependence (AD) is heritable, but little is known about its complex polygenic architecture or its genetic relationship with other disorders. To discover loci associated with AD and characterize the relationship between AD and other psychiatric and behavioral outcomes, we carried out the largest GWAS to date of DSM-IV diagnosed AD. Genome-wide data on 14,904 individuals with AD and 37,944 controls from 28 case/control and family-based studies were meta-analyzed, stratified by genetic ancestry (European, N = 46,568; African; N = 6,280). Independent, genome-wide significant effects of different ADH1B variants were identified in European (rs1229984; p = 9.8E-13) and African ancestries (rs2066702; p = 2.2E-9). Significant genetic correlations were observed with 17 phenotypes, including schizophrenia, ADHD, depression, and use of cigarettes and cannabis. The genetic underpinnings of AD only partially overlap with those for alcohol consumption, underscoring the genetic distinction between pathological and non-pathological drinking behaviors.