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Variation in the rate at which humans age may be rooted in early life events acting through genomic regions that are influenced by such events and subsequently are related to health phenotypes in later life. The parent-of-origin-effect (POE)-regulated methylome includes regions either enriched for genetically controlled imprinting effects (the typical type of POE) or atypical POE introduced by environmental effects associated with parents. This part of the methylome is heavily influenced by early life events, making it a potential route connecting early environmental exposures, the epigenome and the rate of aging. Here, we aim to test the association of POE-influenced methylation of CpG dinucleotides (POE-CpG sites) with early and later environmental exposures and subsequently with health-related phenotypes and adult aging phenotypes. We do this by performing phenome-wide association analyses of the POE-influenced methylome using a large family-based population cohort (GS:SFHS, Ndiscovery=5,087, Nreplication=4,450). At the single CpG level, 92 associations of POE-CpGs with phenotypic variation were identified and replicated. Most of the associations were contributed by POE-CpGs belonging to the atypical class and the most strongly enriched associations were with aging (DNAmTL acceleration), intelligence and parental (maternal) smoking exposure phenotypes. We further found that a proportion of the atypical-POE-CpGs formed co-methylation networks (modules) which are associated with these phenotypes, with one of the aging-associated modules displaying increased internal module connectivity (strength of methylation correlation across constituent CpGs) with age. Atypical POE-CpGs also displayed high levels of methylation heterogeneity and epigenetic drift (i.e. information loss with age) and a strong correlation with CpGs contained within epigenetic clocks. These results identified associations between the atypical-POE-influenced methylome and aging and provided new evidence for the “early development of origin” hypothesis for aging in humans.

IMPORTANCEClinical epidemiological studies have found high rates of comorbidity between suicide attempts (SA) and opioid use disorder (OUD). However, the patterns of correlation and causation between them are still not clear due to psychiatric confounding.OBJECTIVETo investigate the pairwise associations and interrogate the potential bidirectional relationship between OUD and SA using genetically based methods.DESIGN, SETTING, AND PARTICIPANTSWe utilized raw phenotypes and genotypes from UK Biobank, and summary statistics from Million Veteran Program, Psychiatric Genomic Consortium, iPSYCH, and International Suicide Genetics Consortium. Statistical genetics tools were used to perform epidemiological association, genetic correlation, polygenic risk score prediction, and Mendelian randomizations (MR). Analyses were conducted to examine the OUD-SA relationship with and without controlling for psychiatric disease status (e.g., major depressive disorder [MDD]).MAIN OUTCOMES AND MEASURESOUD and SA with or without major psychiatric disorders (schizophrenia, bipolar disorder, major depressive disorder, and alcohol use disorder).RESULTSStrong correlations between OUD and SA were observed at both phenotypic level (overall samples [OR=2.94,P=1.59 ×10−14]; non-psychiatric subgroup [OR=2.15,P=1.07 ×10−3]) and genetic level (r2=0.4 and 0.5 with or without conditioning on MDD). The higher genetic susceptibility to SA can increase the polygenic risk of OUD (OR=1.08, false discovery rate [FDR] =1.71 ×10−3), while the higher susceptibility to OUD can also increase the risk of SA (OR=1.09, FDR =1.73 ×10−6). However, predictive abilities for both were much weakened after controlling for influence of psychiatric diseases. A combination of different MR analyses suggested a possible causal association from SA to OUD (2-sample univariable MR: OR=1.14,P= 0.001; multivariable MR: OR=1.08,P= 0.001).CONCLUSIONS AND RELEVANCEThis study provided new genetic evidence underlying the strong OUD-SA comorbidity. While controlling for the influence of psychiatric diseases, there is still some clue on possible causal association between SA genetic liability and the risk for OUD. Future prevention strategy for each phenotype needs to take into consideration of screening for the other one.Key PointsQuestionDoes opioid use disorder (OUD) have a potentially causal role in the risk for suicide attempts (SA), or vice versa?FindingsIn this observational study with raw data from >150,000 UK Biobank samples and genome-wide association summary statistics from >600,000 individuals, positive phenotypic and genetic associations were observed between OUD and SA, whether or not controlling for major psychiatric disorders. Comprehensive Mendelian randomization analyses (one-sample and two-sample) suggested the genetic liability for SA was associated with increased risk for OUD. However, they were still underpowered to reveal the putative causal association from OUD to SA.MeaningThis genomics-based study supports a strong genetic association underlying the OUD-SA comorbidity. Though both phenotypes are intertwined with other psychiatric disorders, there also exists an independent bidirectional relationship between OUD and SA.

The identification of rare variants that contribute to complex diseases is challenging due to low statistical power. Here we propose a novel and powerful rare variants association test based on the deviation of the observed mutational burden in a genomic region from a baseline mutation burden predicted by weighted recursive truncated negative-binomial regression (RUNNER) on genomic features available from public data. Simulation studies show that RUNNER is substantially more powerful than state-of-the-art rare variant association methods (including SKAT, CMC and KBAC), while maintaining correct type 1 error rates under population stratification and in small samples. Applied to real data, RUNNER “rediscovered” known genes of Hirschsprung disease missed by current methods, and detected promising new candidate genes, including NXPE4 for Hirschsprung disease and CXCL16 for Alzheimer’s disease. The proposed approach provides a powerful and robust method to identify rare risk variants for complex diseases.

The driver tissues or cell-types of many human diseases, in which susceptibility genes cause the diseases, remain elusive. We developed a framework to detect the causal-tissues of complex diseases or traits according to selective expression of disease-associated genes in genome-wide association study (GWAS). The core method of the framework is a new robust z-score to estimate genes’ expression selectivity. Through extensive computing simulations and comparative analyses in a large-scale schizophrenia GWAS, we demonstrate the robust z-score is more sensitive than existing methods to detect multiple selectively expressed tissues, which further lead to the estimation of more biological sensible driver tissues. The effectiveness of this framework is further validated in five representative complex diseases with the usage of GWAS summary statistics and transcript-level expression in GTEx project. Finally, we also demonstrate that the prioritized tissues and the robust selective expression can enhance characterization of directly associated genes of a disease as well. Interesting results include the estimation of lung as a driver tissue of rheumatoid arthritis, consistent with clinical observations of morbidity between rheumatoid arthritis and lung diseases.

Genomic identification of driver mutations and genes in cancer cells are critical for precision medicine. Due to difficulty in modeling distribution of background mutations, existing statistical methods are often underpowered to discriminate driver genes from passenger genes. Here we propose a novel statistical approach, weighted iterative zero-truncated negative-binomial regression (WITER), to detect cancer-driver genes showing an excess of somatic mutations. By solving the problem of inaccurately modeling background mutations, this approach works even in small or moderate samples. Compared to alternative methods, it detected more significant and cancer-consensus genes in all tested cancers. Applying this approach, we estimated 178 driver genes in 26 different cancers types. In silico validation confirmed 90.5% of predicted genes as likely known drivers and 7 genes unique for individual cancers as likely new drivers. The technical advances of WITER enable the detection of driver genes in TCGA datasets as small as 30 subjects, rescuing more genes missed by alternative tools.

Publisher's version (útgefin grein), Previous studies have shown an increased risk for mental health problems in children born to both younger and older parents compared to children of average-aged parents. We previously used a novel design to reveal a latent mechanism of genetic association between schizophrenia and age at first birth in women (AFB). Here, we use independent data from the UK Biobank (N = 38,892) to replicate the finding of an association between predicted genetic risk of schizophrenia and AFB in women, and to estimate the genetic correlation between schizophrenia and AFB in women stratified into younger and older groups. We find evidence for an association between predicted genetic risk of schizophrenia and AFB in women (P-value = 1.12E-05), and we show genetic heterogeneity between younger and older AFB groups (P-value = 3.45E-03). The genetic correlation between schizophrenia and AFB in the younger AFB group is −0.16 (SE = 0.04) while that between schizophrenia and AFB in the older AFB group is 0.14 (SE = 0.08). Our results suggest that early, and perhaps also late, age at first birth in women is associated with increased genetic risk for schizophrenia in the UK Biobank sample. These findings contribute new insights into factors contributing to the complex bio-social risk architecture underpinning the association between parental age and offspring mental health., This research is supported by the Australian National Health and Medical Research Council (1080157, 1087889, 1103418, 1127440), and the Australian Research Council (DP160102126, FT160100229). This research has been conducted using the UK Biobank Resource. UK Biobank (http://www.ukbiobank.ac.uk) Research Ethics Committee (REC) approval number is 11/NW/0382. Our reference number approved by UK Biobank is 14575.