ABSTRACT Although recent studies have revealed the association between the gut microbiota and obesity, the causality remains elusive. We performed a Mendelian Randomization (MR) analysis to determine whether there is a causal relationship between gut microbiota and abdominal obesity. We used a two-sample MR approach to assess the causal effect from gut microbiota to obesity based on genome-wide association studies (GWAS) summary statistics. The GWAS summary statistics of gut microbiota obtained from UK-twins cohort (N=1,126) were used as discovery sample exposure, and the GWAS summary statistics from the Genetic Environmental Microbial (GEM) project (N=1,098) were used as replication sample exposure. Trunk fat mass (TFM) summary statistics from the UK Biobank (UKB) cohort (N=330,762) were used as outcome. Bacteria were grouped into taxa features at family level. A total of 16 families were analyzed in the discovery sample. Family Barnesiellaceae was associated with TFM at the nominal significance level ( b =-3.81×10 −4 , P =1.96×10 −3 ). The causal association was successfully replicated in the replication sample ( b =-7.34×10 −3 , P =2.77×10 −2 ). Our findings provided evidence of causal relationship from microbiota to fat development, and may be helpful in selecting potential causal bacteria for manipulating candidate gut microbiota to therapy obesity. IMPORTANCE Obesity, as a global public health problem, is one of the most important risk factors contributing to the overall global burden of disease, and is associated with an increased risk of cardiovascular disease, type 2 diabetes, and certain cancers. Recent studies have shown that gut microbiota is closely related to the development of obesity, but the causal relationship is unclear. Therefore, it is necessary to identify the causality between gut microbiota and obesity. The significance of our research is in identifying the causal relationship from specific bacteria to fat development, which will provide the new insights into the microbiota mediated the fat development mechanism.
Literature data on the effects of CYP17 MspA1 polymorphism on age at menarche (AAM) are inconsistent. To reexamine this controversy, we performed a meta-analysis.In total 16 studies containing more than 11000 individuals of various ethnicities were selected for the analyses. For 11 case-control studies, odds ratio (OR) was employed to evaluate the risk of late AAM for each study, using homozygote at the wild-type allele as a control group. For the 5 studies with continuous outcomes, the effect size was estimated using the Hedges' adjusted g, which is calculated based on the standardized mean difference between groups of subjects with early and late AAM.We did not find evidence for association of the MspA1 polymorphism with AAM in the combined case-control sample with mixed ethnic background (OR=1.03, 95% CI: 0.90-1.18, P=0.66), in the monoethnic case-control sample of Caucasian females (OR=1.09, 95% CI: 0.99-1.20, P=0.08) and in the combined sample with continuous traits (Hedges' g=0.33 and -0.041, 95% CI: -0.14-0.80 and -0.18-0.10, P values 0.17 and 0.56 for the pooled population sample and monoethnic sample of Caucasian females, respectively).Our study showed that CYP17 MspA1 polymorphism was not a significant independent risk factor of AAM. Further studies are needed to clarify the effects of the interaction between this gene and other genetic and/or environment factors on AAM.
Abstract The large‐scale open access whole‐exome sequencing (WES) data of the UK Biobank ~200,000 participants is accelerating a new wave of genetic association studies aiming to identify rare and functional loss‐of‐function (LoF) variants associated with complex traits and diseases. We proposed to merge the WES genotypes and the genome‐wide genotyping (GWAS) genotypes of 167,000 UKB homogeneous European participants into a combined reference panel, and then to impute 241,911 UKB homogeneous European participants who had the GWAS genotypes only. We then used the imputed data to replicate association identified in the discovery WES sample. The average imputation accuracy measure r 2 is modest to high for LoF variants at all minor allele frequency intervals: 0.942 at MAF interval (0.01, 0.5), 0.807 at (1.0 × 10 −3 , 0.01), 0.805 at (1.0 × 10 −4 , 1.0 × 10 −3 ), 0.664 at (1.0 × 10 −5 , 1.0 × 10 −4 ) and 0.410 at (0, 1.0 × 10 −5 ). As applications, we studied associations of LoF variants with estimated heel BMD and four lipid traits. In addition to replicating dozens of previously reported genes, we also identified three novel associations, two genes PLIN1 and ANGPTL3 for high‐density‐lipoprotein cholesterol and one gene PDE3B for triglycerides. Our results highlighted the strength of WES based genotype imputation as well as provided useful imputed data within the UKB cohort.
MicroRNAs (miRNAs) are critical post-transcriptional regulators. Based on a previous genome-wide association (GWA) scan, we conducted a polymorphism in microRNA target sites (poly-miRTS)-centric multistage meta-analysis for lumbar spine (LS)-, total hip (HIP)- and femoral neck (FN)-bone mineral density (BMD). In stage I, 41 102 poly-miRTSs were meta-analyzed in seven cohorts with a genome-wide significance (GWS) α = 0.05/41 102 = 1.22 × 10−6. By applying α = 5 × 10−5 (suggestive significance), 11 poly-miRTSs were selected, with FGFRL1 rs4647940 and PRR5 rs3213550 as top signals for FN-BMD (P = 7.67 × 10−6 and 1.58 × 10−5) in gender-combined sample. In stage II in silico replication (two cohorts), FGFRL1 rs4647940 was the only signal marginally replicated for FN-BMD (P = 5.08 × 10−3) at α = 0.10/11 = 9.09 × 10-3. PRR5 rs3213550 was also selected based on biological significance. In stage III de novo genotyping replication (two cohorts), FGFRL1 rs4647940 was the only signal significantly replicated for FN-BMD (P = 7.55 × 10−6) at α = 0.05/2 = 0.025 in gender-combined sample. Aggregating three stages, FGFRL1 rs4647940 was the single stage I-discovered and stages II- and III-replicated signal attaining GWS for FN-BMD (P = 8.87 × 10−12). Dual-luciferase reporter assays demonstrated that FGFRL1 3′ untranslated region harboring rs4647940 appears to be hsa-miR-140-5p's target site. In a zebrafish microinjection experiment, dre-miR-140-5p is shown to exert a dramatic impact on craniofacial skeleton formation. Taken together, we provided functional evidence for a novel FGFRL1 poly-miRTS rs4647940 in a previously known 4p16.3 locus, and experimental and clinical genetics studies have shown both FGFRL1 and hsa-miR-140-5p are important for bone formation.
The power of genetic association analyses is often compromised by missing genotypic data which contributes to lack of significant findings, e.g., in in silico replication studies. One solution is to impute untyped SNPs from typed flanking markers, based on known linkage disequilibrium (LD) relationships. Several imputation methods are available and their usefulness in association studies has been demonstrated, but factors affecting their relative performance in accuracy have not been systematically investigated. Therefore, we investigated and compared the performance of five popular genotype imputation methods, MACH, IMPUTE, fastPHASE, PLINK and Beagle, to assess and compare the effects of factors that affect imputation accuracy rates (ARs). Our results showed that a stronger LD and a lower MAF for an untyped marker produced better ARs for all the five methods. We also observed that a greater number of haplotypes in the reference sample resulted in higher ARs for MACH, IMPUTE, PLINK and Beagle, but had little influence on the ARs for fastPHASE. In general, MACH and IMPUTE produced similar results and these two methods consistently outperformed fastPHASE, PLINK and Beagle. Our study is helpful in guiding application of imputation methods in association analyses when genotype data are missing.
Abstract The large-scale open access whole-exome sequencing (WES) data of the UK Biobank ~200,000 participants is accelerating a new wave of genetic association studies aiming to identify rare and functional loss-of-function (LoF) variants associated with a broad range of complex traits and diseases, however the community is in short of stringent replication of new associations. In this study, we proposed to merge the WES genotypes and the genome-wide genotyping (GWAS) genotypes of 167,000 UKB Caucasian participants into a combined reference panel, and then to impute 241,911 UKB Caucasian participants who had the GWAS genotypes only. We then proposed to use the imputed data to replicate association identified in the discovery WES sample. Using a leave-100-out imputation strategy in the reference panel, we showed that average imputation accuracy measure r 2 is modest to high at LoF variants of all minor allele frequency (MAF) intervals including ultra-rare ones: 0.942 at MAF interval [1%, 50%], 0.807 at [0.1%, 1.0%), 0.805 at [0.01%, 0.1%), 0.664 at [0.001%, 0.01%) and 0.410 at (0, 0.001%). As applications, we studied single variant level and gene level associations of LoF variants with estimated heel BMD (eBMD) and 4 lipid traits: high-density-lipoprotein cholesterol (HDL-C), low-density-lipoprotein cholesterol (LDL-C), triglycerides (TG) and total cholesterol (TC). In addition to replicating dozens of previously reported genes such as MEPE for eBMD and PCSK9 for more than one lipid trait, the results also identified 2 novel gene-level associations: PLIN1 (cumulative MAF=0.10%, discovery BETA=0.38, P=1.20×10 −13 ; replication BETA=0.25, P=1.03×10 −6 ) and ANGPTL3 (cumulative MAF=0.10%, discovery BETA=−0.36, P=4.70×10 −11 ; replication BETA=−0.30, P=6.60×10 −11 ) for HDL-C, as well as one novel single variant level association (11:14843853:C:T, MAF=0.11%, discovery BETA=−0.31, P=2.70×10 −9 ; replication BETA=−0.31, P=8.80×10 −14 , PDE3B ) for TG. Our results highlighted the strength of WES based genotype imputation as well as provided useful imputed data within the UKB cohort.
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