Mutation spectrum and polygenic score in German patients with familial hypercholesterolemia

Autosomal-dominant familial hypercholesterolemia (FH) is characterized by increased plasma concentrations of low-density lipoprotein cholesterol (LDL-C) and a substantial risk to develop cardiovascular disease. Causative mutations in three major genes are known: the LDL receptor gene (LDLR), the apolipoprotein B gene (APOB) and the proprotein convertase subtilisin/kexin 9 gene (PCSK9). We clinically characterized 336 patients suspected to have FH and screened them for disease causing mutations in LDLR, APOB, and PCSK9. We genotyped six single nucleotide polymorphisms (SNPs) to calculate a polygenic risk score for the patients and 1985 controls. The 117 patients had a causative variant in one of the analyzed genes. Most variants were found in the LDLR gene (84.9%) with 11 novel mutations. The mean polygenic risk score was significantly higher in FH mutation negative subjects than in FH mutation positive patients (P < .05) and healthy controls (P < .001), whereas the score of the two latter groups did not differ significantly. However, the score explained only about 3% of the baseline LDL-C variance. We verified the previously described clinical and genetic variability of FH for German hypercholesterolemic patients. Evaluation of a six-SNP polygenic score recently proposed for clinical use suggests that it is not a reliable tool to classify hypercholesterolemic patients

Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals

We conduct a genome-wide association study (GWAS) of educational attainment (EA) in a sample of ~3 million individuals and identify 3,952 approximately uncorrelated genome-wide-significant single-nucleotide polymorphisms (SNPs). A genome-wide polygenic predictor, or polygenic index (PGI), explains 12-16% of EA variance and contributes to risk prediction for ten diseases. Direct effects (i.e., controlling for parental PGIs) explain roughly half the PGI's magnitude of association with EA and other phenotypes. The correlation between mate-pair PGIs is far too large to be consistent with phenotypic assortment alone, implying additional assortment on PGI-associated factors. In an additional GWAS of dominance deviations from the additive model, we identify no genome-wide-significant SNPs, and a separate X-chromosome additive GWAS identifies 57

Genome-wide association study identifies 74 loci associated with educational attainment

Educational attainment is strongly influenced by social and other environmental factors, but genetic factors are estimated to account for at least 20% of the variation across individuals1. Here we report the results of a genome-wide association study (GWAS) for educational attainment that extends our earlier discovery sample1,2 of 101,069 individuals to 293,723 individuals, and a replication study in an independent sample of 111,349 individuals from the UK Biobank. We identify 74 genome-wide significant loci associated with the number of years of schooling completed. Single-nucleotide polymorphisms associated with educational attainment are disproportionately found in genomic regions regulating gene expression in the fetal brain. Candidate genes are preferentially expressed in neural tissue, especially during the prenatal period, and enriched for biological pathways involved in neural development. Our findings demonstrate that, even for a behavioural phenotype that is mostly environmentally determined, a well-powered GWAS identifies replicable associated genetic variants that suggest biologically relevant pathways. Because educational attainment is measured in large numbers of individuals, it will continue to be useful as a proxy phenotype in efforts to characterize the genetic influences of related phenotypes, including cognition and neuropsychiatric diseases

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts