Sharing of Genes and Pathways Across Complex Phenotypes: A Multilevel Genome-Wide Analysis

Childhood household dysfunction and psychiatric, criminal, and social outcomes in emerging adulthood. A cousin comparison study

BACKGROUND

Childhood household dysfunction is a well-known risk factor for adverse medical and social outcomes. However, less is known about the extent to which such associations are affected by unmeasured familial confounding.

METHODS

This cohort study is based on Finnish register data on birth cohorts 1987-2000 (n = 835 987). We considered parental hospital-presenting substance use and psychiatric disorders, prison sentences, death, means-tested social assistance, and union dissolution at ages 0-14 as indicators of childhood household dysfunction. The study participants were followed from age 15 until the end of 2020 for hospital-presenting psychiatric disorders and substance use, psychotropic medication purchases, violent and property crime arrests, and not being in education, employment, or training. The associations were estimated using Cox regression, and cousin comparisons were used to account for unmeasured confounders shared within extended families (n = 87 500).

RESULTS

All the exposures were associated with the outcomes in the population-level models, with hazard ratios ranging from 1.3 (95% confidence interval 1.3-1.4) to 2.5 (2.4-2.6). The associations attenuated in the cousin comparisons, on average 12% but with a wide range from -2% to 39% [hazard ratios ranging from 1.2 (1.1-1.4) to 1.9 (1.6-2.3)]. A dose-response relationship between the exposures and the outcomes was observed in the population-level models and the cousin comparisons, with attenuated associations in the latter.

CONCLUSION

Our findings show systematic associations between childhood household dysfunction and subsequent outcomes. Unobserved confounding likely creates upward bias in these associations, but the extent of this confounding depends on the specific exposure-outcome pairs.

MR-Corr2: a two-sample Mendelian randomization method that accounts for correlated horizontal pleiotropy using correlated instrumental variants

MOTIVATION

Mendelian randomization (MR) is a valuable tool to examine the causal relationships between health risk factors and outcomes from observational studies. Along with the proliferation of genome-wide association studies, a variety of two-sample MR methods for summary data have been developed to account for horizontal pleiotropy (HP), primarily based on the assumption that the effects of variants on exposure (γ) and HP (α) are independent. In practice, this assumption is too strict and can be easily violated because of the correlated HP.

RESULTS

To account for this correlated HP, we propose a Bayesian approach, MR-Corr2, that uses the orthogonal projection to reparameterize the bivariate normal distribution for γ and α, and a spike-slab prior to mitigate the impact of correlated HP. We have also developed an efficient algorithm with paralleled Gibbs sampling. To demonstrate the advantages of MR-Corr2 over existing methods, we conducted comprehensive simulation studies to compare for both type-I error control and point estimates in various scenarios. By applying MR-Corr2 to study the relationships between exposure-outcome pairs in complex traits, we did not identify the contradictory causal relationship between HDL-c and CAD. Moreover, the results provide a new perspective of the causal network among complex traits.

AVAILABILITY AND IMPLEMENTATION

The developed R package and code to reproduce all the results are available at https://github.com/QingCheng0218/MR.Corr2.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

MicroRNA sequences modulating inflammation and lipid accumulation in macrophage “foam” cells: Implications for atherosclerosis

Accumulation of macrophage “foam” cells, laden with cholesterol and cholesteryl ester, within the intima of large arteries, is a hallmark of early “fatty streak” lesions which can progress to complex, multicellular atheromatous plaques, involving lipoproteins from the bloodstream and cells of the innate and adaptive immune response. Sterol accumulation triggers induction of genes encoding proteins mediating the atheroprotective cholesterol efflux pathway. Within the arterial intima, however, this mechanism is overwhelmed, leading to distinct changes in macrophage phenotype and inflammatory status. Over the last decade marked gains have been made in understanding of the epigenetic landscape which influence macrophage function, and in particular the importance of small non-coding micro-RNA (miRNA) sequences in this context. This review identifies some of the miRNA sequences which play a key role in regulating “foam” cell formation and atherogenesis, highlighting sequences involved in cholesterol accumulation, those influencing inflammation in sterol-loaded cells, and novel sequences and pathways which may offer new strategies to influence macrophage function within atherosclerotic lesions.

Mutations in sphingolipid metabolism genes are associated with ADHD

Attention deficit hyperactivity disorder (ADHD) is the most prevalent neurodevelopmental disorder in children, with genetic factors accounting for 75-80% of the phenotypic variance. Recent studies have suggested that ADHD patients might present with atypical central myelination that can persist into adulthood. Given the essential role of sphingolipids in myelin formation and maintenance, we explored genetic variation in sphingolipid metabolism genes for association with ADHD risk. Whole-exome genotyping was performed in three independent cohorts from disparate regions of the world, for a total of 1520 genotyped subjects. Cohort 1 (MTA (Multimodal Treatment study of children with ADHD) sample, 371 subjects) was analyzed as the discovery cohort, while cohorts 2 (Paisa sample, 298 subjects) and 3 (US sample, 851 subjects) were used for replication. A set of 58 genes was manually curated based on their roles in sphingolipid metabolism. A targeted exploration for association between ADHD and 137 markers encoding for common and rare potentially functional allelic variants in this set of genes was performed in the screening cohort. Single- and multi-locus additive, dominant and recessive linear mixed-effect models were used. During discovery, we found statistically significant associations between ADHD and variants in eight genes (GALC, CERS6, SMPD1, SMPDL3B, CERS2, FADS3, ELOVL5, and CERK). Successful local replication for associations with variants in GALC, SMPD1, and CERS6 was demonstrated in both replication cohorts. Variants rs35785620, rs143078230, rs398607, and rs1805078, associated with ADHD in the discovery or replication cohorts, correspond to missense mutations with predicted deleterious effects. Expression quantitative trait loci analysis revealed an association between rs398607 and increased GALC expression in the cerebellum.

Knowledge-based analyses reveal new candidate genes associated with risk of hepatitis B virus related hepatocellular carcinoma

BACKGROUND

Recent genome-wide association studies (GWASs) have suggested several susceptibility loci of hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) by statistical analysis at individual single-nucleotide polymorphisms (SNPs). However, these loci only explain a small fraction of HBV-related HCC heritability. In the present study, we aimed to identify additional susceptibility loci of HBV-related HCC using advanced knowledge-based analysis.

METHODS

We performed knowledge-based analysis (including gene- and gene-set-based association tests) on variant-level association p-values from two existing GWASs of HBV-related HCC. Five different types of gene-sets were collected for the association analysis. A number of SNPs within the gene prioritized by the knowledge-based association tests were selected to replicate genetic associations in an independent sample of 965 cases and 923 controls.

RESULTS

The gene-based association analysis detected four genes significantly or suggestively associated with HBV-related HCC risk: SLC39A8, GOLGA8M, SMIM31, and WHAMMP2. The gene-set-based association analysis prioritized two promising gene sets for HCC, cell cycle G1/S transition and NOTCH1 intracellular domain regulates transcription. Within the gene sets, three promising candidate genes (CDC45, NCOR1 and KAT2A) were further prioritized for HCC. Among genes of liver-specific expression, multiple genes previously implicated in HCC were also highlighted. However, probably due to small sample size, none of the genes prioritized by the knowledge-based association analyses were successfully replicated by variant-level association test in the independent sample.

CONCLUSIONS

This comprehensive knowledge-based association mining study suggested several promising genes and gene-sets associated with HBV-related HCC risks, which would facilitate follow-up functional studies on the pathogenic mechanism of HCC.

Genetic overlap between epilepsy and schizophrenia: Evidence from cross phenotype analysis in Hong Kong Chinese population

Epilepsy and schizophrenia are common and typical neurological or mental illness respectively, and sometimes they comorbid in the same patients, however the underlying genetic relationship between the two brain diseases is still not fully understood. To investigate the possible genetic contribution to their comorbidity, we performed polygenic risk score (PRS) analyses and genetic correlation estimation so as to identify the overall genetic overlap between the two diseases. The global schizophrenia PRS is strongly associated with schizophrenia phenotype in Hong Kong population (odds ratio = 1.7, p = 2.26E-16), and focal epilepsy PRS is moderately associated with epilepsy phenotype in Hong Kong population (odds ratio = 1.14, p = 0.013). However the disease-specific PRS can only predict its own well-matched phenotype but not the other ones (p > 0.05). This pattern is further supported by non-significant pairwise genetic correlation and insufficient statistical power for PRS association from the cross-phenotype analyses. Our study reveals there's limited shared genetic aetiology between schizophrenia and epilepsy, and thus supports a model of shared environmental factors to explain the comorbidity between the two phenotypes.