METHODS: Data were combined from 3024 MDD cases and 2741 control subjects from nine cohorts contributing to the MDD Working Group of the Psychiatric Genomics Consortium. MDD-PRS were based on a discovery sample of ∼110,000 independent individuals. CT was assessed as exposure to sexual or physical abuse during childhood. In a subset of 1957 cases and 2002 control subjects, a more detailed five-domain measure additionally included emotional abuse, physical neglect, and emotional neglect.
RESULTS: MDD was associated with the MDD-PRS (odds ratio [OR] = 1.24, p = 3.6 × 10-5, R2 = 1.18%) and with CT (OR = 2.63, p = 3.5 × 10-18 and OR = 2.62, p = 1.4 ×10-5 for the two- and five-domain measures, respectively). No interaction was found between MDD-PRS and the two-domain and five-domain CT measure (OR = 1.00, p = .89 and OR = 1.05, p = .66).
CONCLUSIONS: No meta-analytic evidence for interaction between MDD-PRS and CT was found. This suggests that the previously reported interaction effects, although both statistically significant, can best be interpreted as chance findings. Further research is required, but this study suggests that the genetic heterogeneity of MDD is not attributable to genome-wide moderation of genetic effects by CT.
METHODS: A case-control study was conducted involving 600 people with type 2 diabetes (300 chronic kidney disease cases, 300 controls) who participated in The Malaysian Cohort project. Retrospective subanalysis was performed on the chronic kidney disease cases to assess chronic kidney disease progression from the recruitment phase. We genotyped 32 single nucleotide polymorphisms using mass spectrometry. The probability of chronic kidney disease and predicted rate of newly detected chronic kidney disease progression were estimated from the significant gene-environment interaction analyses.
RESULTS: Four single nucleotide polymorphisms (eNOS rs2070744, PPARGC1A rs8192678, KCNQ1 rs2237895 and KCNQ1 rs2283228) and five environmental factors (age, sex, smoking, waist circumference and HDL) were significantly associated with chronic kidney disease. Gene-environment interaction analyses revealed significant probabilities of chronic kidney disease for sex (PPARGC1A rs8192678), smoking (eNOS rs2070744, PPARGC1A rs8192678 and KCNQ1 rs2237895), waist circumference (eNOS rs2070744, PPARGC1A rs8192678, KCNQ1 rs2237895 and KCNQ1 rs2283228) and HDL (eNOS rs2070744 and PPARGC1A rs8192678). Subanalysis indicated that the rate of newly detected chronic kidney disease progression was 133 cases per 1000 person-years (95% CI: 115, 153), with a mean follow-up period of 4.78 (SD 0.73) years. There was a significant predicted rate of newly detected chronic kidney disease progression in gene-environment interactions between KCNQ1 rs2283228 and two environmental factors (sex and BMI).
CONCLUSIONS: Our findings suggest that the gene-environment interactions of eNOS rs2070744, PPARGC1A rs8192678, KCNQ1 rs2237895 and KCNQ1 rs2283228 with specific environmental factors could modify the probability for chronic kidney disease.