Biology subjects

Ong, J.-S.

Publications and source records attributed to Ong, J.-S..

3 recordsLinked to original sources

bioRxiv2020

Coffee consumption and risk of breast cancer: a Mendelian Randomization study

BackgroundObservational studies have reported either null or weak protective associations for coffee consumption and risk of breast cancer. MethodsWe conducted a two-sample Mendelian randomization randomization (MR) analysis to evaluate the relationship between coffee consumption and breast cancer risk using 33 single-nucleotide polymorphisms (SNPs) associated with coffee consumption from a genome-wide association (GWA) study on 212,119 female UK Biobank participants of White British ancestry. Risk estimates for breast cancer were retrieved from publicly available GWA summary statistics from the Breast Cancer Association Consortium (BCAC) on 122,977 cases (of which 69,501 were estrogen receptor (ER)-positive, 21,468 ER-negative) and 105,974 controls of European ancestry. Random-effects inverse variance weighted (IVW) MR analyses were performed along with several sensitivity analyses to assess the impact of potential MR assumption violations. ResultsOne cup per day increase in genetically predicted coffee consumption in women was not associated with risk of total (IVW random-effects; odds ratio (OR): 0.91, 95% confidence intervals (CI): 0.80-1.02, P: 0.12, P for instrument heterogeneity: 7.17e-13), ER-positive (OR=0.90, 95% CI: 0.79-1.02, P: 0.09) and ER-negative breast cancer (OR: 0.88, 95% CI: 0.75-1.03, P: 0.12). Null associations were also found in the sensitivity analyses using MR-Egger (total breast cancer; OR: 1.00, 95% CI: 0.80-1.25), weighted median (OR: 0.97, 95% CI: 0.89-1.05) and weighted mode (OR: 1.00, CI: 0.93-1.07). ConclusionsThe results of this large MR study do not support an association of genetically predicted coffee consumption on breast cancer risk, but we cannot rule out existence of a weak inverse association.

cancer biology↗

bioRxiv2019

Investigating Genetic Heterogeneity in Major Depression Through Item-level Genetic Analyses of the PHQ-9

BackgroundMajor Depressive Disorder (MDD) is a clinically heterogeneous disorder. Previous large-scale genetic studies of MDD have explored genetic risk factors of MDD case-control status or aggregated sums of depressive symptoms, ignoring possible clinical or genetic heterogeneity. AimIn this study, we present the results of symptom-level genetic analyses and compare SNP-based heritability (h2 O_SCPCAPSNPC_SCPCAP) and genetic correlations across major depression symptoms. We further investigate genetic correlations with a range of psychiatric disorders and other associated traits. Methods: We have analysed data from the UK biobank and included 148,752 subjects of white British ancestry with genotype data who completed nine items of a self-rated measure of depression: the Patient Health Questionnaire (PHQ-9). Genome-Wide Association analyses were conducted for nine symptoms and two composite measures. LD score regression analysis was used to calculate SNP-based heritability (h2 O_SCPCAPSNPC_SCPCAP) and genetic correlations (rg) across symptoms and to investigate genetic correlations with 25 external phenotypes. Confirmatory factor analyses were applied to test whether one, two, or three-factor models best fit the pattern of genetic correlations across the nine symptoms. ResultsWe identified 9 novel genome-wide significant genomic loci, with no overlap in loci across depression symptoms. h2 O_SCPCAPSNPC_SCPCAP ranged from 3% (suicidal ideation) to 11% (fatigue). Genetic correlations range from 0.54 to 0.96 (all p < 1.39x10-3) with 30 of 36 correlations being significantly smaller than 1. A 3-factor model provided the best fit to the genetic correlation matrix, with factors representing "psychological", "neurovegetative", and "psychomotor / concentration" symptoms. The genetic correlations with external phenotypes showed large variation across the nine symptoms. DiscussionPatterns of h2 O_SCPCAPSNPC_SCPCAP and genetic correlations differed across the nine symptoms of depression. Our findings suggest that the large phenotypic heterogeneity observed for MDD is recapitulated at a genetic level. Future studies should investigate how genetic heterogeneity in MDD influences the efficacy of clinical interventions.

genetics↗

bioRxiv2018

Genome-wide association analysis of lifetime cannabis use (N=184,765) identifies new risk loci, genetic overlap with mental health, and a causal influence of schizophrenia on cannabis use

Cannabis use is a heritable trait [1] that has been associated with adverse mental health outcomes. To identify risk variants and improve our knowledge of the genetic etiology of cannabis use, we performed the largest genome-wide association study (GWAS) meta-analysis for lifetime cannabis use (N=184,765) to date. We identified 4 independent loci containing genome-wide significant SNP associations. Gene-based tests revealed 29 genome-wide significant genes located in these 4 loci and 8 additional regions. All SNPs combined explained 10% of the variance in lifetime cannabis use. The most significantly associated gene, CADM2, has previously been associated with substance use and risk-taking phenotypes [2-4]. We used S-PrediXcan to explore gene expression levels and found 11 unique eGenes. LD-score regression uncovered genetic correlations with smoking, alcohol use and mental health outcomes, including schizophrenia and bipolar disorder. Mendelian randomisation analysis provided evidence for a causal positive influence of schizophrenia risk on lifetime cannabis use.

genetics↗