Associations between genetic loci, environment factors and mental disorders: a genome-wide survival analysis using the UK Biobank data

It is well-accepted that both environment and genetic factors contribute to the development of mental disorders (MD). However, few genetic studies used time-to-event data analysis to identify the susceptibility genetic variants associated with MD and explore the role of environment factors in these associations. In order to detect novel genetic loci associated with MD based on the time-to-event data and identify the role of environmental factors in them, this study recruited 376,806 participants from the UK Biobank cohort. The MD outcomes (including overall MD status, anxiety, depression and substance use disorders (SUD)) were defined based on in-patient hospital, self-reported and death registry data collected in the UK Biobank. SPACOX approach was used to identify the susceptibility loci for MD using the time-to-event data of the UK Biobank cohort. And then we estimated the associations between identified candidate loci, fourteen environment factors and MD through a phenome-wide association study and mediation analysis. SPACOX identified multiple candidate loci for overall MD status, depression and SUD, such as rs139813674 (P value = 8.39 × 10–9, ZNF684) for overall MD status, rs7231178 (DCC, P value = 2.11 × 10–9) for depression, and rs10228494 (FOXP2, P value = 6.58 × 10–10) for SUD. Multiple environment factors could influence the associations between identified loci and MD, such as confide in others and felt hated. Our study identified novel candidate loci for MD, highlighting the strength of time-to-event data based genetic association studies. We also observed that multiple environment factors could influence the association between susceptibility loci and MD.

Risk Alleles for Multiple Myeloma Susceptibility in ADME Genes

The cause of multiple myeloma (MM) remains largely unknown. Several pieces of evidence support the involvement of genetic and multiple environmental factors (i.e., chemical agents) in MM onset. The inter-individual variability in the bioactivation, detoxification, and clearance of chemical carcinogens such as asbestos, benzene, and pesticides might increase the MM risk. This inter-individual variability can be explained by the presence of polymorphic variants in absorption, distribution, metabolism, and excretion (ADME) genes. Despite the high relevance of this issue, few studies have focused on the inter-individual variability in ADME genes in MM risk. To identify new MM susceptibility loci, we performed an extended candidate gene approach by comparing high-throughput genotyping data of 1936 markers in 231 ADME genes on 64 MM patients and 59 controls from the CEU population. Differences in genotype and allele frequencies were validated using an internal control group of 35 non-cancer samples from the same geographic area as the patient group. We detected an association between MM risk and ADH1B rs1229984 (OR = 3.78; 95% CI, 1.18–12.13; p = 0.0282), PPARD rs6937483 (OR = 3.27; 95% CI, 1.01–10.56; p = 0.0479), SLC28A1 rs8187737 (OR = 11.33; 95% CI, 1.43–89.59; p = 0.005), SLC28A2 rs1060896 (OR = 6.58; 95% CI, 1.42–30.43; p = 0.0072), SLC29A1 rs8187630 (OR = 3.27; 95% CI, 1.01–10.56; p = 0.0479), and ALDH3A2 rs72547554 (OR = 2.46; 95% CI, 0.64–9.40; p = 0.0293). The prognostic value of these genes in MM was investigated in two public datasets showing that shorter overall survival was associated with low expression of ADH1B and SLC28A1. In conclusion, our proof-of-concept findings provide novel insights into the genetic bases of MM susceptibility.

Splicing QTL analysis focusing on coding sequences reveals pathogenicity of disease susceptibility loci.

Splicing QTL (sQTL) are one of the major causal mechanisms in GWAS loci, but their role in disease pathogenesis is poorly understood. One reason is the huge complexity of alternative splicing events producing many unknown isoforms. Here, we proposed two novel approaches, namely integration and selection, for this complexity by focusing on protein-structure of isoforms. First, we integrated isoforms with the same coding sequence (CDS) and identified 369-601 integrated-isoform ratio QTLs (i2-rQTLs), which altered protein-structure, in six immune subsets. Second, we selected CDS incomplete isoforms annotated in GENCODE and identified 175-337 isoform-ratio QTL (i-rQTL). By comprehensive long-read capture RNA-seq among these incomplete isoforms, we revealed 29 full-length isoforms with novel CDSs associated with GWAS traits. Furthermore, we have shown that disease-causal sQTL genes can be identified by evaluating their trans-eQTL effects. Our approaches highlight the understudied role of protein-altering sQTLs and are broadly applicable to other tissues and diseases.

Twenty-Five Novel Loci for Carotid Intima-Media Thickness: A Genome-Wide Association Study in >45 000 Individuals and Meta-Analysis of >100 000 Individuals

Objective: Carotid artery intima-media thickness (cIMT) is a widely accepted marker of subclinical atherosclerosis. Twenty susceptibility loci for cIMT were previously identified and the identification of additional susceptibility loci furthers our knowledge on the genetic architecture underlying atherosclerosis. Approach and Results: We performed 3 genome-wide association studies in 45 185 participants from the UK Biobank study who underwent cIMT measurements and had data on minimum, mean, and maximum thickness. We replicated 15 known loci and identified 20 novel loci associated with cIMT at P <5×10 −8 . Seven novel loci ( ZNF385D , AD AMTS9 , EDNRA , HAND2 , MYOCD , ITCH/EDEM2/ matrix metalloproteinase [ MMP ] 24 , and MRTFA ) were identified in all 3 phenotypes. An additional new locus ( LOXL1 ) was identified in the meta-analysis of the 3 phenotypes. Sex interaction analysis revealed sex differences in 7 loci including a novel locus ( SYNE3 ) in males. Meta-analysis of UK Biobank data with a previous meta-analysis led to identification of three novel loci ( APOB, FIP1L1, and LOXL4 ). Transcriptome-wide association analyses implicated additional genes ARHGAP42 , NDRG4 , and KANK2 . Gene set analysis showed an enrichment in extracellular organization and the PDGF (platelet-derived growth factor) signaling pathway. We found positive genetic correlations of cIMT with coronary artery disease r g =0.21 ( P =1.4×10 -7 ), peripheral artery disease r g =0.45 ( P =5.3×10 -5 ), and systolic blood pressure r g =0.30 ( P =4.0×10 -18 ). A negative genetic correlation between average of maximum cIMT and high-density lipoprotein was found r g =−0.12 ( P =7.0×10 -4 ). Conclusions: Genome-wide association meta-analyses in >100 000 individuals identified 25 novel loci associated with cIMT providing insights into genes and tissue-specific regulatory mechanisms of proatherosclerotic processes. We found evidence for shared biological mechanisms with cardiovascular diseases.

Genetic Analysis for Coronary Artery Disease Toward Diverse Populations

Coronary artery disease is one of the leading causes of death in the world, and as such, it is one of the diseases for which genetic analyses have been actively conducted. In the early days, analyses of families with the aggregation of early-onset myocardial infarction, such as those with familial hypercholesterolemia, was the main focus, but since the practical application of genome-wide association study, the analysis of coronary artery disease as a common disease has progressed, and many disease-susceptibility loci have been identified. In addition, with the advancement of technologies, it has become possible to identify relatively rare genetic variants in a population-based analysis. These advances have not only revealed the detailed disease mechanisms but have also enabled the quantification of individual genetic risk and the development of new therapeutic agents. In this paper, some of those items, which are important to know in the current genetic analyses for coronary artery disease, are discussed.

Genetic regulation of RNA splicing in human pancreatic islets

Genetic variants that influence transcriptional regulation in pancreatic islets play a major role in the susceptibility to type 2 diabetes (T2D). For many susceptibility loci, however, the mechanisms are unknown. We examined splicing QTLs (sQTLs) in islets from 399 donors and observed that genetic variation has a widespread influence on splicing of genes with important functions in islet biology. In parallel, we profiled expression QTLs, and used transcriptome-wide association and co-localization studies to assign islet sQTLs or eQTLs to T2D susceptibility signals that lacked candidate effector genes. We found novel T2D associations, including an sQTL that creates a nonsense isoform in ERO1B, a regulator of ER-stress and proinsulin biosynthesis. The expanded list of T2D risk effectors revealed overrepresented pathways, including regulators of G-protein-mediated cAMP production. This data exposes an underappreciated layer of genetic regulation in pancreatic islets, and nominates molecular mediators of T2D susceptibility.