METHODS: A total of 1114 subjects comprising of 536 PD patients and 578 healthy controls of Malay ancestry were recruited and genotyped using Taqman® allelic discrimination assays.

RESULTS: The G allele of rs10513789 (OR = 0.83, p = 0.001) and A allele of rs12637471 (OR = 0.79, p = 0.007) in the MCCC1/LAMP3 locus were associated with a protective effect against developing PD in the Malay population. A recessive model of penetrance showed a protective effect of the GG genotype for rs10513789 and the AA genotype for rs12637471. No association with PD was found with the other MCCC1/LAMP3 rs12493050 variant or with the DGKQ (rs11248060) variant. No significant associations were found between the four variants with the age at PD diagnosis.

Objective: To determine whether subgroups of patients with MDD stratified according to the A/W criterion had a different degree of genetic overlap with obesity-related traits (body mass index [BMI] and levels of C-reactive protein [CRP] and leptin).

Design, Setting, and Patients: This multicenter study assembled genome-wide genotypic and phenotypic measures from 14 data sets of the Psychiatric Genomics Consortium. Data sets were drawn from case-control, cohort, and population-based studies, including 26 628 participants with established psychiatric diagnoses and genome-wide genotype data. Data on BMI were available for 15 237 participants. Data were retrieved and analyzed from September 28, 2015, through May 20, 2017.

Main Outcomes and Measures: Lifetime DSM-IV MDD was diagnosed using structured diagnostic instruments. Patients with MDD were stratified into subgroups according to change in the DSM-IV A/W symptoms as decreased or increased.

Results: Data included 11 837 participants with MDD and 14 791 control individuals, for a total of 26 628 participants (59.1% female and 40.9% male). Among participants with MDD, 5347 (45.2%) were classified in the decreased A/W and 1871 (15.8%) in the increased A/W subgroups. Common genetic variants explained approximately 10% of the heritability in the 2 subgroups. The increased A/W subgroup showed a strong and positive genetic correlation (SE) with BMI (0.53 [0.15]; P = 6.3 × 10-4), whereas the decreased A/W subgroup showed an inverse correlation (-0.28 [0.14]; P = .06). Furthermore, the decreased A/W subgroup had a higher polygenic risk for increased BMI (odds ratio [OR], 1.18; 95% CI, 1.12-1.25; P = 1.6 × 10-10) and levels of CRP (OR, 1.08; 95% CI, 1.02-1.13; P = 7.3 × 10-3) and leptin (OR, 1.09; 95% CI, 1.06-1.12; P = 1.7 × 10-3).

METHODS: Consecutive biopsy-proven NAFLD patients (n = 191) and healthy controls (n = 188) were enrolled and genotyped for HLA-DQA1 and HLA-DQB1 alleles using the sequence-specific oligonucleotide-polymerase chain reaction method.

RESULTS: No association was found between the HLA alleles and NAFLD or NASH in a case-control setting. Nevertheless, among NAFLD patients, the frequency of HLA-DQB1*06 allele was significantly the lowest in NASH with significant fibrosis (10.4%) and approximately similar for NASH without significant fibrosis (22.9%) and NAFL (22.5%) (P = 0.004). It is noteworthy that the association remains significant after correction for multiple comparisons (Pc  = 0.04). Multivariate analysis revealed that HLA-DQB1*06 allele is also associated with fibrosis score (P = 0.001); the result remains significant after correction for multiple comparisons.

METHODS: The case-control portion of the study was conducted in nine UK centers with men ages 50-69 years who underwent prostate-specific antigen screening for prostate cancer within the Prostate Testing for Cancer and Treatment (ProtecT) trial. Two data sources were used to appraise causality: a genome-wide association study (GWAS) of metabolites in 24,925 participants and a GWAS of prostate cancer in 44,825 cases and 27,904 controls within the Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL) consortium.

RESULTS: Thirty-five metabolites were strongly associated with prostate cancer (P < 0.0014, multiple-testing threshold). These fell into four classes: (i) lipids and lipoprotein subclass characteristics (total cholesterol and ratios, cholesterol esters and ratios, free cholesterol and ratios, phospholipids and ratios, and triglyceride ratios); (ii) fatty acids and ratios; (iii) amino acids; (iv) and fluid balance. Fourteen top metabolites were proxied by genetic variables, but MR indicated these were not causal.

CONCLUSIONS: We identified 35 circulating metabolites associated with prostate cancer presence, but found no evidence of causality for those 14 testable with MR. Thus, the 14 MR-tested metabolites are unlikely to be mechanistically important in prostate cancer risk.

METHODS: This is a cross-sectional study that conducted genome-wide copy number analysis using CytoScan 750K array on salivary samples from Malay subjects with NSCL/P with or without hypodontia aged 7-13 years. To confirm the significant results, simple logistic regression was employed to conduct statistical data analysis using SPSS software.

RESULTS: The results indicated the most common recurrent copy neutral LOH (cnLOH) observed at 1p33-1p32.3, 1q32.2-1q42.13 and 6p12.1-6p11.1 loci in 8 (13%), 4 (7%), and 3 (5%) of the NSCL/P subjects, respectively. The cnLOHs at 1p33-1p32.3 (D1S197), 1q32.2-1q42.13 (D1S160), and 6p12.1-6p11.1 (D1S1661) were identified observed in NSCL/P and noncleft children using microsatellite analysis markers as a validation analysis. The regions affected by the cnLOHs at 1p33-1p32.3, 1q32.2-1q42.13, and 6p12.1-6p11.1 loci contained selected genes, namely FAF1, WNT3A and BMP5, respectively. There was a significant association between the D1S197 (1p33-32.3) markers containing the FAF1 gene among NSCL/P subjects with or without hypodontia compared with the noncleft subjects (p-value = 0.023).

METHODS: We performed an allelic association analysis in patients with SLE, followed by a meta-analysis assessing genome-wide association data across 11 independent cohorts (n = 28,872). In silico bioinformatics analysis and experimental validation in SLE-relevant cell lines were applied to determine the functional consequences of rs34330.

RESULTS: We replicated a genetic association between SLE and rs34330 (meta-analysis P = 5.29 × 10-22 , odds ratio 0.84 [95% confidence interval 0.81-0.87]). Follow-up bioinformatics and expression quantitative trait locus analysis suggested that rs34330 is located in active chromatin and potentially regulates several target genes. Using luciferase and chromatin immunoprecipitation-real-time quantitative polymerase chain reaction, we demonstrated substantial allele-specific promoter and enhancer activity, and allele-specific binding of 3 histone marks (H3K27ac, H3K4me3, and H3K4me1), RNA polymerase II (Pol II), CCCTC-binding factor, and a critical immune transcription factor (interferon regulatory factor 1 [IRF-1]). Chromosome conformation capture revealed long-range chromatin interactions between rs34330 and the promoters of neighboring genes APOLD1 and DDX47, and effects on CDKN1B and the other target genes were directly validated by clustered regularly interspaced short palindromic repeat (CRISPR)-based genome editing. Finally, CRISPR/dead CRISPR-associated protein 9-based epigenetic activation/silencing confirmed these results. Gene-edited cell lines also showed higher levels of proliferation and apoptosis.

METHODS: We did a genome-wide association study of NKTCL in multiple populations from east Asia. We recruited a discovery cohort of 700 cases with NKTCL and 7752 controls without NKTCL of Han Chinese ancestry from 19 centres in southern, central, and northern regions of China, and four independent replication samples including 717 cases and 12 650 controls. Three of these independent samples (451 cases and 5301 controls) were from eight centres in the same regions of southern, central, and northern China, and the fourth (266 cases and 7349 controls) was from 11 centres in Hong Kong, Taiwan, Singapore, and South Korea. All cases had primary NKTCL that was confirmed histopathologically, and matching with controls was based on geographical region and self-reported ancestry. Logistic regression analysis was done independently by geographical regions, followed by fixed-effect meta-analyses, to identify susceptibility loci. Bioinformatic approaches, including expression quantitative trait loci, binding motif and transcriptome analyses, and biological experiments were done to fine-map and explore the functional relevance of genome-wide association loci to the development of NKTCL.

FINDINGS: Genetic data were gathered between Jan 1, 2008, and Jan 23, 2019. Meta-analysis of all samples (a total of 1417 cases and 20 402 controls) identified two novel loci significantly associated with NKTCL: IL18RAP on 2q12.1 (rs13015714; p=2·83 × 10-16; odds ratio 1·39 [95% CI 1·28-1·50]) and HLA-DRB1 on 6p21.3 (rs9271588; 9·35 × 10-26 1·53 [1·41-1·65]). Fine-mapping and experimental analyses showed that rs1420106 at the promoter of IL18RAP was highly correlated with rs13015714, and the rs1420106-A risk variant had an upregulatory effect on IL18RAP expression. Cell growth assays in two NKTCL cell lines (YT and SNK-6 cells) showed that knockdown of IL18RAP inhibited cell proliferation by cell cycle arrest in NKTCL cells. Haplotype association analysis showed that haplotype 47F-67I was associated with reduced risk of NKTCL, whereas 47Y-67L was associated with increased risk of NKTCL. These two positions are component parts of the peptide-binding pocket 7 (P7) of the HLA-DR heterodimer, suggesting that these alterations might account for the association at HLA-DRB1, independent of the previously reported HLA-DPB1 variants.

INTERPRETATION: Our findings provide new insights into the development of NKTCL by showing the importance of inflammation and immune regulation through the IL18-IL18RAP axis and antigen presentation involving HLA-DRB1, which might help to identify potential therapeutic targets. Taken in combination with additional genetic and other risk factors, our results could potentially be used to stratify people at high risk of NKTCL for targeted prevention.