Linkage disequilibrium analysis in the LOC93081-KDELC1-BIVM region on 13q in bipolar disorder

Genetic variants of genes in the NER pathway associated with risk of breast cancer: A large-scale analysis of 14 published GWAS datasets in the DRIVE study

A recent hypothesis-free pathway-level analysis of genome-wide association study (GWAS) datasets suggested that the overall genetic variation measured by single nucleotide polymorphisms (SNPs) in the nucleotide excision repair (NER) pathway genes was associated with breast cancer (BC) risk, but no detailed SNP information was provided. To substantiate this finding, we performed a larger meta-analysis of 14 previously published GWAS datasets in the Discovery, Biology and Risk of Inherited Variants in Breast Cancer (DRIVE) study with 53,107 subjects of European descent. Using a hypothesis-driven approach, we selected 138 candidate genes from the NER pathway using the "Molecular Signatures Database (MsigDB)" and "PathCards". All SNPs were imputed using IMPUTE2 with the 1000 Genomes Project Phase 3. Logistic regression was used to estimate BC risk, and pooled ORs for each SNP were obtained from the meta-analysis using the false discovery rate for multiple test correction. RegulomeDB, HaploReg, SNPinfo and expression quantitative trait loci (eQTL) analysis were used to assess the SNP functionality. We identified four independent SNPs associated with BC risk, BIVM-ERCC5 rs1323697_C (OR = 1.06, 95% CI = 1.03-1.10), GTF2H4 rs1264308_T (OR = 0.93, 95% CI = 0.89-0.97), COPS2 rs141308737_C deletion (OR = 1.06, 95% CI = 1.03-1.09) and ELL rs1469412_C (OR = 0.93, 95% CI = 0.90-0.96). Their combined genetic score was also associated with BC risk (OR = 1.12, 95% CI = 1.08-1.16, p_trend < 0.0001). The eQTL analysis revealed that BIVM-ERCC5 rs1323697 C and ELL rs1469412 C alleles were correlated with increased mRNA expression levels of their genes in 373 lymphoblastoid cell lines (p = 0.022 and 2.67 × 10^-22 , respectively). These SNPs might have roles in the BC etiology, likely through modulating their corresponding gene expression.

Molecular genetics of bipolar disorder and depression

In this review, all papers relevant to the molecular genetics of bipolar disorder published from 2004 to the present (mid 2006) are reviewed, and major results on depression are summarized. Several candidate genes for schizophrenia may also be associated with bipolar disorder: G72, DISC1, NRG1, RGS4, NCAM1, DAO, GRM3, GRM4, GRIN2B, MLC1, SYNGR1, and SLC12A6. Of these, association with G72 may be most robust. However, G72 haplotypes and polymorphisms associated with bipolar disorder are not consistent with each other. The positional candidate approach showed an association between bipolar disorder and TRPM2 (21q22.3), GPR50 (Xq28), Citron (12q24), CHMP1.5 (18p11.2), GCHI (14q22-24), MLC1 (22q13), GABRA5 (15q11-q13), BCR (22q11), CUX2, FLJ32356 (12q23-q24), and NAPG (18p11). Studies that focused on mood disorder comorbid with somatic symptoms, suggested roles for the mitochondrial DNA (mtDNA) 3644 mutation and the POLG mutation. From gene expression analysis, PDLIM5, somatostatin, and the mtDNA 3243 mutation were found to be related to bipolar disorder. Whereas most previous positive findings were not supported by subsequent studies, DRD1 and IMPA2 have been implicated in follow-up studies. Several candidate genes in the circadian rhythm pathway, BmaL1, TIMELESS, and PERIOD3, are reported to be associated with bipolar disorder. Linkage studies show many new linkage loci. In depression, the previously reported positive finding of a gene-environmental interaction between HTTLPR (insertion/deletion polymorphism in the promoter of a serotonin transporter) and stress was not replicated. Although the role of the TPH2 mutation in depression had drawn attention previously, this has not been replicated either. Pharmacogenetic studies show a relationship between antidepressant response and HTR2A or FKBP5. New technologies for comprehensive genomic analysis have already been applied. HTTLPR and BDNF promoter polymorphisms are now found to be more complex than previously thought, and previous papers on these polymorphisms should be treated with caution. Finally, this report addresses some possible causes for the lack of replication in this field.