Effects of allele-specific open chromatin: Genetic variants in noncoding regions of the genome may underlie the development of disease. However, we are just beginning to tease apart the function of such variants associated with neuropsychiatric disease. Using five types of neural progenitor cells derived from 20 human induced pluripotent stem cell lines, Zhang et al. looked at allele-specific open chromatin (ASoC) variants. Many ASoC variants overlapped with genomic elements, such as transcription factor binding sites, and loci identified in genome-wide association studies for neurological traits. From the experimental and computational analyses, they identified single-nucleotide polymorphisms and illuminate how one schizophrenia-associated variant affects neurodevelopment. Science , this issue p. 561

Recent heritability analyses have indicated that genome-wide association studies (GWAS) have the potential to improve genetic risk prediction for complex diseases based on polygenic risk score (PRS), a simple modelling technique that can be implemented using summary-level data from the discovery samples. We herein propose modifications to improve the performance of PRS. We introduce threshold-dependent winner’s-curse adjustments for marginal association coefficients that are used to weight the single-nucleotide polymorphisms (SNPs) in PRS. Further, as a way to incorporate external functional/annotation knowledge that could identify subsets of SNPs highly enriched for associations, we propose variable thresholds for SNPs selection. We applied our methods to GWAS summary-level data of 14 complex diseases. Across all diseases, a simple winner’s curse correction uniformly led to enhancement of performance of the models, whereas incorporation of functional SNPs was beneficial only for selected diseases. Compared to the standard PRS algorithm, the proposed methods in combination led to notable gain in efficiency (25–50% increase in the prediction R²) for 5 of 14 diseases. As an example, for GWAS of type 2 diabetes, winner’s curse correction improved prediction R² from 2.29% based on the standard PRS to 3.10% (P = 0.0017) and incorporating functional annotation data further improved R² to 3.53% (P = 2×10⁻⁵). Our simulation studies illustrate why differential treatment of certain categories of functional SNPs, even when shown to be highly enriched for GWAS-heritability, does not lead to proportionate improvement in genetic risk-prediction because of non-uniform linkage disequilibrium structure. Large GWAS have identified tens or even hundreds of common SNPs significantly associated with individual complex diseases; however, these SNPs typically explain a small proportion of phenotypic variance. Recently, heritability analyses based on GWAS data suggest that common SNPs have the potential to explain substantially larger fraction of phenotypic variance and to improve the genetic risk prediction. Because of the polygenic nature, improving genetic risk prediction for complex diseases typically requires substantially increasing the sample size in the discovery set. Thus, it is crucial to develop more efficient algorithms using existing GWAS summary data. In this article, we extend the polygenic risk score (PRS) method by adjusting the marginal effect size of SNPs for winner’s curse and by incorporating external functional annotation data. Theoretical analysis and simulation studies show that the performance improvement depends on the genetic architecture of the trait, sample size of the discovery sample set and the degree of enrichment of association for SNPs annotated as “high-prior” and the linkage disequilibrium patterns of these SNPs. We applied our method to the summary data of 14 GWAS. Our method achieved 25–50% gain in efficiency (measured in the prediction R²) for 5 of 14 diseases compared to the standard PRS.

Lung adenocarcinoma is the most common type of lung cancer. Known risk variants explain only a small fraction of lung adenocarcinoma heritability. Here, we conducted a two-stage genome-wide association study of lung adenocarcinoma of East Asian ancestry (21,658 cases and 150,676 controls; 54.5% never-smokers) and identified 12 novel susceptibility variants, bringing the total number to 28 at 25 independent loci. Transcriptome-wide association analyses together with colocalization studies using a Taiwanese lung expression quantitative trait loci dataset (n = 115) identified novel candidate genes, including FADS1 at 11q12 and ELF5 at 11p13. In a multi-ancestry meta-analysis of East Asian and European studies, four loci were identified at 2p11, 4q32, 16q23, and 18q12. At the same time, most of our findings in East Asian populations showed no evidence of association in European populations. In our studies drawn from East Asian populations, a polygenic risk score based on the 25 loci had a stronger association in never-smokers vs. individuals with a history of smoking (P_interaction = 0.0058). These findings provide new insights into the etiology of lung adenocarcinoma in individuals from East Asian populations, which could be important in developing translational applications.

Three recent genome-wide association studies of testicular germ cell tumors have uncovered predisposition alleles in or near several genes, including KITLG, BAK1, SPRY4, TERT, ATF7IP, and DMRT1. The calculated per-allele odds ratio for variants in the region of KITLG is the highest reported for any malignancy so far. These findings are in agreement with epidemiological data indicating that testicular cancer has a higher heritability than most other cancers. Here, we discuss the question of whether the newly identified risk polymorphisms can be used to guide patient care.