METHODS: To clarify the shared molecular genetic basis of major depressive disorder and bipolar disorder and to highlight disorder-specific associations, we meta-analyzed data from the latest Psychiatric Genomics Consortium genome-wide association studies of major depression (including data from 23andMe) and bipolar disorder, and an additional major depressive disorder cohort from UK Biobank (total: 185,285 cases, 439,741 controls; nonoverlapping N = 609,424).
RESULTS: Seventy-three loci reached genome-wide significance in the meta-analysis, including 15 that are novel for mood disorders. More loci from the Psychiatric Genomics Consortium analysis of major depression than from that for bipolar disorder reached genome-wide significance. Genetic correlations revealed that type 2 bipolar disorder correlates strongly with recurrent and single-episode major depressive disorder. Systems biology analyses highlight both similarities and differences between the mood disorders, particularly in the mouse brain cell types implicated by the expression patterns of associated genes. The mood disorders also differ in their genetic correlation with educational attainment-the relationship is positive in bipolar disorder but negative in major depressive disorder.
CONCLUSIONS: The mood disorders share several genetic associations, and genetic studies of major depressive disorder and bipolar disorder can be combined effectively to enable the discovery of variants not identified by studying either disorder alone. However, we demonstrate several differences between these disorders. Analyzing subtypes of major depressive disorder and bipolar disorder provides evidence for a genetic mood disorders spectrum.
METHODS: We analyzed by digital droplet PCR (ddPCR) to determine presence of the MYD88 L265P and CD79B Y196 hotspot mutations in cfDNA isolated from plasma of 24 PCNSL patients with active disease. Corresponding tumor samples were available for 14 cases. Based on the false positive rate observed in 8 healthy control samples, a stringent cut-off for the MYD88 L265P and CD79B Y196 mutation were set at 0.3% and 0.5%, respectively.
RESULTS: MYD88 L265P and CD79B Y196 mutations were detected in 9/14 (64%) and 2/13 (15%) tumor biopsies, respectively. In cfDNA samples, the MYD88 L265P mutation was detected in 3/24 (12.5%), while the CD79B Y196 mutation was not detected in any of the 23 tested cfDNA samples. Overall, MYD88 L265P and/or CD79B Y196 were detected in cfDNA in 3/24 cases (12.5%). The detection rate of the combined analysis did not improve the single detection rate for either MYD88 L265P or CD79B Y196.
CONCLUSION: The low detection rate of MYD88 L265P and CD79B Y196 mutations in cfDNA in the plasma of PCNSL patients argues against its use in routine diagnostics. However, detection of MYD88 L265P by ddPCR in cfDNA in the plasma could be considered in challenging cases.
METHODS: Two-hundred unrelated Emirati parents of patients selected for bone marrow transplantation were genotyped for HLA class I (A, B, C) and class II (DRB1, DQB1) genes using reverse sequence specific oligonucleotide bead-based multiplexing. HLA haplotypes were assigned with certainty by segregation (pedigree) analysis, and haplotype frequencies were obtained by direct counting. HLA class I and class II frequencies in Emiratis were compared to data from other populations using standard genetic distances (SGD), Neighbor-Joining (NJ) phylogenetic dendrograms, and correspondence analysis.
RESULTS: The studied HLA loci were in Hardy-Weinberg Equilibrium. We identified 17 HLA-A, 28 HLA-B, 14 HLA-C, 13 HLA-DRB1, and 5 HLA-DQB1 alleles, of which HLA-A*02 (22.2%), -B*51 (19.5%), -C*07 (20.0%), -DRB1*03 (22.2%), and -DQB1*02 (32.8%) were the most frequent allele lineages. DRB1*03~DQB1*02 (21.2%), DRB1*16~DQB1*05 (17.3%), B*35~C*04 (11.7%), B*08~DRB1*03 (9.7%), A*02~B*51 (7.5%), and A*26~C*07~B*08~DRB1*03~DQB1*02 (4.2%) were the most frequent two- and five-locus HLA haplotypes. Correspondence analysis and dendrograms showed that Emiratis were clustered with the Arabian Peninsula populations (Saudis, Omanis and Kuwaitis), West Mediterranean populations (North Africans, Iberians) and Pakistanis, but were distant from East Mediterranean (Turks, Albanians, Greek), Levantine (Syrians, Palestinians, Lebanese), Iranian, Iraqi Kurdish, and Sub-Saharan populations.
CONCLUSIONS: Emiratis were closely related to Arabian Peninsula populations, West Mediterranean populations and Pakistanis. However, the contribution of East Mediterranean, Levantine Arab, Iranian, and Sub-Saharan populations to the Emiratis' gene pool appears to be minor.