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Background and Hypothesis: Endophenotypes can help to bridge the gap between psychosis and its genetic predispositions, but their underlying mechanisms remain largely unknown. This study aims to identify biological mechanisms that are relevant to the endophenotypes for psychosis, by partitioning polygenic risk scores into specific gene sets and testing their associations with endophenotypes., Study Design: We computed polygenic risk scores for schizophrenia and bipolar disorder restricted to brain-related gene sets retrieved from public databases and previous publications. Three hundred and seventy-eight gene-set-specific polygenic risk scores were generated for 4506 participants. Seven endophenotypes were also measured in the sample. Linear mixed-effects models were fitted to test associations between each endophenotype and each gene-set-specific polygenic risk score., Study Results: After correction for multiple testing, we found that a reduced P300 amplitude was associated with a higher schizophrenia polygenic risk score of the forebrain regionalization gene set (mean difference per SD increase in the polygenic risk score: -1.15 µV; 95% CI: -1.70 to -0.59 µV; P = 6 × 10-5). The schizophrenia polygenic risk score of forebrain regionalization also explained more variance of the P300 amplitude (R2 = 0.032) than other polygenic risk scores, including the genome-wide polygenic risk scores., Conclusions: Our finding on reduced P300 amplitudes suggests that certain genetic variants alter early brain development thereby increasing schizophrenia risk years later. Gene-set-specific polygenic risk scores are a useful tool to elucidate biological mechanisms of psychosis and endophenotypes, offering leads for experimental validation in cellular and animal models., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center.)

Dravet syndrome is an archetypal rare severe epilepsy, considered 'monogenic', typically caused by loss-of-function SCN1A variants. Despite a recognizable core phenotype, its marked phenotypic heterogeneity is incompletely explained by differences in the causal SCN1A variant or clinical factors. In 34 adults with SCN1A-related Dravet syndrome, we show additional genomic variation beyond SCN1A contributes to phenotype and its diversity, with an excess of rare variants in epilepsy-related genes as a set and examples of blended phenotypes, including one individual with an ultra-rare DEPDC5 variant and focal cortical dysplasia. The polygenic risk score for intelligence was lower, and for longevity, higher, in Dravet syndrome than in epilepsy controls. The causal, major-effect, SCN1A variant may need to act against a broadly compromised genomic background to generate the full Dravet syndrome phenotype, whilst genomic resilience may help to ameliorate the risk of premature mortality in adult Dravet syndrome survivors., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Guarantors of Brain.)

Background: Genome-wide association studies suggest that the combined effects of breast cancer (BC)-associated single nucleotide polymorphisms (SNPs) can improve BC risk stratification using polygenic risk scores (PRSs). The performance of PRSs in genome-wide association studies-independent clinical cohorts is poorly studied in individuals carrying mutations in moderately penetrant BC predisposition genes such as CHEK2., Methods: A total of 760 female CHEK2 mutation carriers were included; 561 women were affected with BC, of whom 74 developed metachronous contralateral BC (mCBC). For PRS calculations, 2 SNP sets covering 77 (SNP set 1, developed for BC risk stratification in women unselected for their BRCA1/2 germline mutation status) and 88 (SNP set 2, developed for BC risk stratification in female BRCA1/2 mutation carriers) BC-associated SNPs were used. All statistical tests were 2-sided., Results: Both SNP sets provided concordant PRS results at the individual level (r = 0.91, P < 2.20 × 10-16). Weighted cohort Cox regression analyses revealed statistically significant associations of PRSs with the risk for first BC. For SNP set 1, a hazard ratio of 1.71 per SD of the PRS was observed (95% confidence interval = 1.36 to 2.15, P = 3.87 × 10-6). PRSs identify a subgroup of CHEK2 mutation carriers with a predicted lifetime risk for first BC that exceeds the surveillance thresholds defined by international guidelines. Association of PRS with mCBC was examined via Cox regression analysis (SNP set 1 hazard ratio = 1.23, 95% confidence interval = 0.86 to 1.78, P = .26)., Conclusions: PRSs may be used to personalize risk-adapted preventive measures for women with CHEK2 mutations. Larger studies are required to assess the role of PRSs in mCBC predisposition., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Motivation: Very low-depth sequencing has been proposed as a cost-effective approach to capture low-frequency and rare variation in complex trait association studies. However, a full characterization of the genotype quality and association power for very low-depth sequencing designs is still lacking., Results: We perform cohort-wide whole-genome sequencing (WGS) at low depth in 1239 individuals (990 at 1× depth and 249 at 4× depth) from an isolated population, and establish a robust pipeline for calling and imputing very low-depth WGS genotypes from standard bioinformatics tools. Using genotyping chip, whole-exome sequencing (75× depth) and high-depth (22×) WGS data in the same samples, we examine in detail the sensitivity of this approach, and show that imputed 1× WGS recapitulates 95.2% of variants found by imputed GWAS with an average minor allele concordance of 97% for common and low-frequency variants. In our study, 1× further allowed the discovery of 140 844 true low-frequency variants with 73% genotype concordance when compared to high-depth WGS data. Finally, using association results for 57 quantitative traits, we show that very low-depth WGS is an efficient alternative to imputed GWAS chip designs, allowing the discovery of up to twice as many true association signals than the classical imputed GWAS design., Availability and Implementation: The HELIC genotype and WGS datasets have been deposited to the European Genome-phenome Archive (https://www.ebi.ac.uk/ega/home): EGAD00010000518; EGAD00010000522; EGAD00010000610; EGAD00001001636, EGAD00001001637. The peakplotter software is available at https://github.com/wtsi-team144/peakplotter, the transformPhenotype app can be downloaded at https://github.com/wtsi-team144/transformPhenotype., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2018. Published by Oxford University Press.)