Efficient identification of trait-associated loss-of-function variants in the UK Biobank cohort by exome-sequencing based genotype imputation.

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 r2 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.

Human brain proteome-wide association study provides insights into the genetic components of protein abundance in obesity.

BACKGROUNDS: Genome-wide association studies have identified multiple genetic variants associated with obesity. However, most obesity-associated loci were waiting to be translated into new biological insights. Given the critical role of brain in obesity development, we sought to explore whether obesity-associated genetic variants could be mapped to brain protein abundances. METHODS: We performed proteome-wide association studies (PWAS) and colocalization analyses to identify genes whose cis-regulated brain protein abundances were associated with obesity-related traits, including body fat percentage, trunk fat percentage, body mass index, visceral adipose tissue, waist circumference, and waist-to-hip ratio. We then assessed the druggability of the identified genes and conducted pathway enrichment analysis to explore their functional relevance. Finally, we evaluated the effects of the significant PWAS genes at the brain transcriptional level. RESULTS: By integrating human brain proteomes from discovery (ROSMAP, N = 376) and validation datasets (BANNER, N = 198) with genome-wide summary statistics of obesity-related phenotypes (N ranged from 325,153 to 806,834), we identified 51 genes whose cis-regulated brain protein abundance was associated with obesity. These 51 genes were enriched in 11 metabolic processes, e.g., small molecule metabolic process and metabolic pathways. Fourteen of the 51 genes had high drug repurposing value. Ten of the 51 genes were also associated with obesity at the transcriptome level, suggesting that genetic variants likely confer risk of obesity by regulating mRNA expression and protein abundance of these genes. CONCLUSIONS: Our study provides new insights into the genetic component of human brain protein abundance in obesity. The identified proteins represent promising therapeutic targets for future drug development.

Causal Effects of Plasma Proteome on Osteoporosis and Osteoarthritis.

The two-sample Mendelian randomization (MR) study revealed a causal association of plasma proteins with osteoporosis (OP) and osteoarthritis (OA). Bone mineral density (BMD) is the gold standard for the clinical assessment of OP. Recent studies have shown that plasma proteins play an essential role in the regulation of bone development. However, the causal association of plasma proteins with BMD and OA remains unclear. We estimated the effects of 2889 plasma proteins on 2 BMD phenotypes and 6 OA phenotypes using two-sample MR analysis based on the genome-wide association study summary statistics. Then, we performed sensitivity analysis and reverse-direction MR analysis to evaluate the robustness of the MR analysis results, followed by gene ontology (GO) enrichment analysis and KEGG pathway analysis to explore the functional relevance of the identified plasma proteins. Overall, we observed a total of 257 protein-estimated heel BMD associations, 17 protein-total-body BMD associations, 2 protein-all-OA associations, and 2 protein-knee-OA associations at PFDR < 0.05. Reverse-direction MR analysis demonstrated that there was little evidence of the causal association of BMD and OA with plasma proteins. GO enrichment analysis and KEGG pathway analysis identified multiple pathways, which may be involved in the development of OP and OA. Our findings recognized plasma proteins that could be used to regulate changes in OP and OA, thus, providing new insights into protein-mediated mechanisms of bone development.

Screening Plasma Proteins for the Putative Drug Targets for Carpal Tunnel Syndrome.

Carpal tunnel syndrome (CTS) is one of the most common work-related musculoskeletal disorders. The present study sought to identify putative causal proteins for CTS. We conducted a two-sample Mendelian randomization (MR) analysis to evaluate the causal association between 2859 plasma proteins (N = 35,559) and CTS (N = 1,239,680) based on the published GWAS summary statistics. Then we replicated the significant associations using an independent plasma proteome GWAS (N = 10,708). Sensitivity analyses were conducted to validate the robustness of MR results. Multivariate MR and mediation analyses were conducted to evaluate the mediation effects of body mass index (BMI), type 2 diabetes (T2D), and arm tissue composition on the association between putative causal proteins and CTS. Colocalization analysis was used to examine whether the identified proteins and CTS shared causal variant(s). Finally, we evaluated druggability of the identified proteins. Ten plasma proteins were identified as putative causal markers for CTS, including sCD14, PVR, LTOR3, CTSS, SIGIRR, IFNL3, ASPN, TM11D, ASIP, and ITIH1. Sensitivity analyses and reverse MR analysis validated the robustness of their causal effects. Arm tissue composition, BMI, and T2D may play a fully/partial mediating role in the causal relationships of ASIP, TM11D, IFNL3, PVR, and LTOR3 with CTS. The association of ASPN and sCD14 with CTS were supported by colocalization analysis. Druggability assessment demonstrated that sCD14, CTSS, TM11D, and IFNL3 were potential drug therapeutic targets. The present study identified several potential plasma proteins that were causally associated with CTS risk, providing new insights into the pathogenesis of protein-mediated CTS and offering potential targets for new therapies.

Three pleiotropic loci associated with bone mineral density and lean body mass.

Both bone mineral density (BMD) and lean body mass (LBM) are important physiological measures with strong genetic determination. Besides, BMD and LBM might have common genetic factors. Aiming to identify pleiotropic genomic loci underlying BMD and LBM, we performed bivariate genome-wide association study meta-analyses of femoral neck bone mineral density and LBM at arms and legs, and replicated in the large-scale UK Biobank cohort sample. Combining the results from discovery meta-analysis and replication sample, we identified three genomic loci at the genome-wide significance level (p < 5.0 × 10-8): 2p23.2 (lead SNP rs4477866, discovery p = 3.47 × 10-8, replication p = 1.03 × 10-4), 16q12.2 (rs1421085, discovery p = 2.04 × 10-9, replication p = 6.47 × 10-14) and 18q21.32 (rs11152213, discovery p = 3.47 × 10-8, replication p = 6.69 × 10-6). Our findings not only provide useful insights into lean mass and bone mass development, but also enhance our understanding of the potential genetic correlation between BMD and LBM.

Identification of pleiotropic loci underlying hip bone mineral density and trunk lean mass.

Bone mineral density (BMD) and lean body mass (LBM) not only have a considerable heritability each, but also are genetically correlated. However, common genetic determinants shared by both traits are largely unknown. In the present study, we performed a bivariate genome-wide association study (GWAS) meta-analysis of hip BMD and trunk lean mass (TLM) in 11,335 subjects from 6 samples, and performed replication in estimated heel BMD and TLM in 215,234 UK Biobank (UKB) participants. We identified 2 loci that nearly attained the genome-wide significance (GWS, p < 5.0 × 10-8) level in the discovery GWAS meta-analysis and that were successfully replicated in the UKB sample: 11p15.2 (lead SNP rs12800228, discovery p = 2.88 × 10-7, replication p = 1.95 × 10-4) and 18q21.32 (rs489693, discovery p = 1.67 × 10-7, replication p = 1.17 × 10-3). The above 2 pleiotropic loci may play a pleiotropic role for hip BMD and TLM development. So our findings provide useful insights that further enhance our understanding of genetic interplay between BMD and LBM.

Mendelian Randomization Analysis Reveals Causal Effects of the Human Gut Microbiota on Abdominal Obesity.

BACKGROUND: Although recent studies have revealed an association between the composition of the gut microbiota and obesity, whether specific gut microbiota cause obesity has not been determined. OBJECTIVES: The aim of this study is to determine the causal relationship between specific gut microbiota and abdominal obesity. Based on genome-wide association study (GWAS) summary statistics, we performed a 2-sample Mendelian randomization (MR) analysis to evaluate whether the gut microbiota affects abdominal obesity. METHODS: Gut microbiota GWAS in 1126 twin pairs (age range, 18-89 years; 89% were females) from the TwinsUK study were used as exposure data. The primary outcome tested was trunk fat mass (TFM) GWAS in 492,805 participants (age range, 40-69 years; 54% were females) from the UK Biobank. The gut microbiota were classified at family, genus, and species levels. A feature was defined as a distinct family, genus, or species. MR analysis was mainly performed by an inverse variance-weighted test or Wald ratio test, depending on the number of instrumental variables (IVs) involved. A sensitivity analysis was performed on significant results by a weighted median test and a weighted genetic risk score (GRS) analysis. RESULTS: Results of MR analyses provided evidence of a causal association between 3 microbiota features and TFM, including 1 family [Lachnosiraceae; P = 0.02; ß = 0.001 (SEE, 4.28 × 10-4)], 1 genus [Bifidobacterium; P = 5.0 × 10-9; ß = -0.08 (SEE, 0.14)], and 1 species [Prausnitzii; P = 0.03; ß = -0.007 (SEE, 0.003)]. Both the weighted median test and GRS analysis successfully validated the association of the genetically predicted family, Lachnosiraceae (Pweighted median = 0.03; PGRS = 0.004). CONCLUSIONS: Our findings provided evidence of a causal association between gut microbiota and TFM in UK adults and identified specific bacteria taxa that may regulate the fat metabolism, thus offering new direction for the treatment of obesity.

Two novel pleiotropic loci associated with osteoporosis and abdominal obesity.

Aiming to uncover a shared genetic basis of abdominal obesity and osteoporosis, we performed a bivariate GWAS meta-analysis of femoral neck BMD (FNK-BMD) and trunk fat mass adjusted by trunk lean mass (TFMadj) in 11,496 subjects from 6 samples, followed by in silico replication in the large-scale UK Biobank (UKB) cohort. A series of functional investigations were conducted on the identified variants. Bivariate GWAS meta-analysis identified two novel pleiotropic loci 12q15 (lead SNP rs73134637, p = 3.45 × 10-7) and 10p14 (lead SNP rs2892347, p = 2.63 × 10-7) that were suggestively associated and that were replicated in the analyses of related traits in the UKB sample (osteoporosis p = 0.06 and 0.02, BMI p = 0.03 and 4.61 × 10-3, N up to 499,520). Cis-eQTL analysis demonstrated that allele C at rs73134637 was positively associated with IFNG expression in whole blood (N = 369, p = 0.04), and allele A at rs11254759 (10p14, p = 9.49 × 10-7) was negatively associated with PRKCQ expression in visceral adipose tissue (N = 313, p = 0.04) and in lymphocytes (N = 117, p = 0.03). As a proof-of-principle experiment, the function of rs11254759, which is 235 kb 5'-upstream from PRKCQ gene, was investigated by the dual-luciferase reporter assay, which clearly showed that the haplotype carrying rs11254759 regulated PRKCQ expression by upregulating PRKCQ promoter activity (p = 4.60 × 10-7) in an allelic specific manner. Mouse model analysis showed that heterozygous PRKCQ deficient mice presented decreased fat mass compared to wild-type control mice (p = 3.30 × 10-3). Mendelian randomization analysis demonstrated that both FNK-BMD and TFMadj were causally associated with fracture risk (p = 1.26 × 10-23 and 1.18 × 10-11). Our findings may provide useful insights into the genetic association between osteoporosis and abdominal obesity.

Four pleiotropic loci associated with fat mass and lean mass.

BACKGROUND: Fat mass and lean mass are two biggest components of body mass. Both fat mass and lean mass are under strong genetic determinants and are correlated. METHODS: We performed a bivariate genome-wide association meta-analysis of (lean adjusted) leg fat mass and (fat adjusted) leg lean mass in 12,517 subjects from 6 samples, and followed by in silico replication in large-scale UK biobank cohort sample (N = 370 097). RESULTS: We identified four loci that were significant at the genome-wide significance (GWS, α = 5.0 × 10-8) level at the discovery meta-analysis, and successfully replicated in the replication sample: 2q36.3 (rs1024137, pdiscovery = 3.32 × 10-8, preplication = 4.07 × 10-13), 5q13.1 (rs4976033, pdiscovery = 1.93 × 10-9, preplication = 6.35 × 10-7), 12q24.31 (rs4765528, pdiscovery = 7.19 × 10-12, preplication = 1.88 × 10-11) and 18q21.32 (rs371326986, pdiscovery = 9.04 × 10-9, preplication = 2.35 × 10-95). The above four pleiotropic loci may play a pleiotropic role for fat mass and lean mass development. CONCLUSIONS: Our findings further enhance the understanding of the genetic association between fat mass and lean mass and provide a new theoretical basis for their understanding.

Correction to: Effects of probiotics on type II diabetes mellitus: a meta-analysis.

Following publication of the original article [1], the authors reported an error in one of the author names. In this Correction the incorrect and correct author names are listed.

Effects of probiotics on type II diabetes mellitus: a meta-analysis.

OBJECTIVE: The purpose of the present study was to evaluate the effectiveness of probiotics on type II diabetes mellitus (T2DM). METHODS: We performed a comprehensive search on PubMed, Web of Science, China National Knowledge Infrastructure, Chinese Scientific Journal Databases, Wan Fang database and China biology medicine disc for relevant studies published before June 2019. Glycated hemoglobin A1c (HbA1c), homeostasis model assessment of insulin resistance (HOMA-IR) and fasting blood glucose (FBG) were used as indicators for T2DM. Inverse-variance weighted mean difference (WMD) with 95% confidence interval (CI) was calculated for the mean HbA1c, FBG and HOMA-IR changes from baseline. RESULTS: 15 randomized controlled trials (RCT) with a total of 902 participants were included into the meta-analysis. Considering the clinical heterogeneity caused by variation of dosage and duration of probiotic treatment, random-effects model was used to estimate the pooled WMD. Significantly greater reduction in HbA1c% (WMD = - 0.24, 95% CI [- 0.44, - 0.04], p = 0.02), FBG (WMD = - 0.44 mmol/L, 95% CI [- 0.74, - 0.15], p = 0.003) and HOMA-IR (WMD = - 1.07, 95% CI [- 1.58, - 0.56], p < 0.00001) were observed in probiotics treated group. Further sensitivity analysis verified the reliability and stability of our results. CONCLUSION: The results of our meta-analysis indicated that probiotics treatment may reduce HbA1c, FBG and insulin resistance level in T2DM patients. More clinical data and research into the mechanism of probiotics are needed to clarify the role of probiotics in T2DM.

Identification of a 1p21 independent functional variant for abdominal obesity.

OBJECTIVES: Aiming to uncover the genetic basis of abdominal obesity, we performed a genome-wide association study (GWAS) meta-analysis of trunk fat mass adjusted by trunk lean mass (TFMadj) and followed by a series of functional investigations. SUBJECTS: A total of 11,569 subjects from six samples were included into the GWAS meta-analysis. METHODS: Meta-analysis was performed by a weighted fixed-effects model. In silico replication analysis was performed in the UK-Biobank (UKB) sample (N = 331,093) and in the GIANT study (N up to 110,204). Cis-expression QTL (cis-eQTL) analysis, dual-luciferase reporter assay and electrophoresis mobility shift assay (EMSA) were conducted to examine the functional relevance of the identified SNPs. At last, differential gene expression analysis (DGEA) was performed. RESULTS: We identified an independent SNP rs12409479 at 1p21 (MAF = 0.07, p = 7.26 × 10-10), whose association was replicated by the analysis of TFM in the UKB sample (one-sided p = 3.39 × 10-3), and was cross-validated by the analyses of BMI (one-sided p = 0.03) and WHRadj (one-sided p = 0.04) in the GIANT study. Cis-eQTL analysis demonstrated that allele A at rs12409479 was positively associated with PTBP2 expression level in subcutaneous adipose tissue (N = 385, p = 4.15 × 10-3). Dual-luciferase reporter assay showed that the region repressed PTBP2 gene expression by downregulating PTBP2 promoter activity (p < 0.001), and allele A at rs12409479 induced higher luciferase activity than allele G did (p = 4.15 × 10-3). EMSA experiment implied that allele A was more capable of binding to unknown transcription factors than allele G. Lastly, DGEA showed that the level of PTBP2 expression was higher in individuals with obesity than in individuals without obesity (N = 20 and 11, p = 0.04 and 9.22 × 10-3), suggesting a regulatory role in obesity development. CONCLUSIONS: Taken together, we hypothesize a regulating path from rs12409479 to trunk fat mass development through its allelic specific regulation of PTBP2 gene expression, thus providing some novel insight into the genetic basis of abdominal obesity.

Identification of a novel FGFRL1 MicroRNA target site polymorphism for bone mineral density in meta-analyses of genome-wide association studies.

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.

Bivariate genome-wide association analyses identified genetic pleiotropic effects for bone mineral density and alcohol drinking in Caucasians.

Several studies indicated bone mineral density (BMD) and alcohol intake might share common genetic factors. The study aimed to explore potential SNPs/genes related to both phenotypes in US Caucasians at the genome-wide level. A bivariate genome-wide association study (GWAS) was performed in 2069 unrelated participants. Regular drinking was graded as 1, 2, 3, 4, 5, or 6, representing drinking alcohol never, less than once, once or twice, three to six times, seven to ten times, or more than ten times per week respectively. Hip, spine, and whole body BMDs were measured. The bivariate GWAS was conducted on the basis of a bivariate linear regression model. Sex-stratified association analyses were performed in the male and female subgroups. In males, the most significant association signal was detected in SNP rs685395 in DYNC2H1 with bivariate spine BMD and alcohol drinking (P = 1.94 × 10-8). SNP rs685395 and five other SNPs, rs657752, rs614902, rs682851, rs626330, and rs689295, located in the same haplotype block in DYNC2H1 were the top ten most significant SNPs in the bivariate GWAS in males. Additionally, two SNPs in GRIK4 in males and three SNPs in OPRM1 in females were suggestively associated with BMDs (of the hip, spine, and whole body) and alcohol drinking. Nine SNPs in IL1RN were only suggestively associated with female whole body BMD and alcohol drinking. Our study indicated that DYNC2H1 may contribute to the genetic mechanisms of both spine BMD and alcohol drinking in male Caucasians. Moreover, our study suggested potential pleiotropic roles of OPRM1 and IL1RN in females and GRIK4 in males underlying variation of both BMD and alcohol drinking.

Meta-analysis of genome-wide association data identifies novel susceptibility loci for obesity.

Obesity is a major public health problem with strong genetic determination. Multiple genetic variants have been implicated for obesity by conducting genome-wide association (GWA) studies, primarily focused on body mass index (BMI). Fat body mass (FBM) is phenotypically more homogeneous than BMI and is more appropriate for obesity research; however, relatively few studies have been conducted on FBM. Aiming to identify variants associated with obesity, we carried out meta-analyses of seven GWA studies for BMI-related traits including FBM, and followed these analyses by de novo replication. The discovery cohorts consisted of 21 969 individuals from diverse ethnic populations and a total of over 4 million genotyped or imputed SNPs. The de novo replication cohorts consisted of 6663 subjects from two independent samples. To complement individual SNP-based association analyses, we also carried out gene-based GWA analyses in which all variations within a gene were considered jointly. Individual SNP-based association analyses identified a novel locus 1q21 [rs2230061, CTSS (Cathepsin S)] that was associated with FBM after the adjustment of lean body mass (LBM) (P = 3.57 × 10(-8)) at the genome-wide significance level. Gene-based association analyses identified a novel gene NLK (nemo-like kinase) in 17q11 that was significantly associated with FBM adjusted by LBM. In addition, we confirmed three previously reported obesity susceptibility loci: 16q12 [rs62033400, P = 1.97 × 10(-14), FTO (fat mass and obesity associated)], 18q22 [rs6567160, P = 8.09 × 10(-19), MC4R (melanocortin 4 receptor)] and 2p25 [rs939583, P = 1.07 × 10(-7), TMEM18 (transmembrane protein 18)]. We also found that rs6567160 may exert pleiotropic effects to both FBM and LBM. Our results provide additional insights into the molecular genetic basis of obesity and may provide future targets for effective prevention and therapeutic intervention.

Multistage genome-wide association meta-analyses identified two new loci for bone mineral density.

Aiming to identify novel genetic variants and to confirm previously identified genetic variants associated with bone mineral density (BMD), we conducted a three-stage genome-wide association (GWA) meta-analysis in 27 061 study subjects. Stage 1 meta-analyzed seven GWA samples and 11 140 subjects for BMDs at the lumbar spine, hip and femoral neck, followed by a Stage 2 in silico replication of 33 SNPs in 9258 subjects, and by a Stage 3 de novo validation of three SNPs in 6663 subjects. Combining evidence from all the stages, we have identified two novel loci that have not been reported previously at the genome-wide significance (GWS; 5.0 × 10(-8)) level: 14q24.2 (rs227425, P-value 3.98 × 10(-13), SMOC1) in the combined sample of males and females and 21q22.13 (rs170183, P-value 4.15 × 10(-9), CLDN14) in the female-specific sample. The two newly identified SNPs were also significant in the GEnetic Factors for OSteoporosis consortium (GEFOS, n = 32 960) summary results. We have also independently confirmed 13 previously reported loci at the GWS level: 1p36.12 (ZBTB40), 1p31.3 (GPR177), 4p16.3 (FGFRL1), 4q22.1 (MEPE), 5q14.3 (MEF2C), 6q25.1 (C6orf97, ESR1), 7q21.3 (FLJ42280, SHFM1), 7q31.31 (FAM3C, WNT16), 8q24.12 (TNFRSF11B), 11p15.3 (SOX6), 11q13.4 (LRP5), 13q14.11 (AKAP11) and 16q24 (FOXL1). Gene expression analysis in osteogenic cells implied potential functional association of the two candidate genes (SMOC1 and CLDN14) in bone metabolism. Our findings independently confirm previously identified biological pathways underlying bone metabolism and contribute to the discovery of novel pathways, thus providing valuable insights into the intervention and treatment of osteoporosis.

Exploring the Major Sources and Extent of Heterogeneity in a Genome-Wide Association Meta-Analysis.

Genome-wide association (GWA) meta-analysis has become a popular approach for discovering genetic variants responsible for complex diseases. The between-study heterogeneity effect is a severe issue that may complicate the interpretation of results. Aiming to improve the interpretation of meta-analysis results, we empirically explored the extent and source of heterogeneity effect. We analyzed a previously reported GWA meta-analysis of obesity, in which over 21,000 subjects from seven individual samples were meta-analyzed. We first evaluated the extent and distribution of heterogeneity across the entire genome. We then studied the effects of several potentially confounding factors, including age, ethnicity, gender composition, study type, and genotype imputation on heterogeneity with a random-effects meta-regression model. Of the total 4,325,550 SNPs being tested, heterogeneity was moderate to very large for 25.4% of the total SNPs. Heterogeneity was more severe in SNPs with stronger association signals. Ethnicity, average age, and genotype imputation accuracy had significant effects on the heterogeneity. Exploring the effects of ethnicity can provide clues to the potential ethnic-specific effects for two loci known to affect obesity, MC4R, and MTCH2. Our analysis can help to clarify understanding of the obesity mechanism and may provide guidance for an effective design of future GWA meta-analysis.

A new method for estimating effect size distribution and heritability from genome-wide association summary results.

Accurately estimating the distribution and heritability of SNP effects across the genome could help explain the mystery of missing heritability. In this study, we propose a novel statistical method for estimating the distribution and heritability of SNP effects from genome-wide association studies (GWASs), and compare its performance to several existing methods using both simulations and real data. Specifically, we study the full range of GWAS summary results and link observed p values and unobserved effect sizes by (non-central) Chi-square distribution. By modeling the observed full set of association signals using a multinomial distribution, we build a likelihood function of SNP effect sizes using parametric and non-parametric maximum likelihood frameworks. Simulation studies show that the proposed method can accurately estimate effect sizes and the number of associated SNPs. As real applications, we analyze publicly available GWAS summary results for height, body mass index (BMI), and bone mineral density (BMD). Our analyses show that there are over 10,000 SNPs that might be associated with height, and the total heritability attributable to these SNPs exceeds 70 %. The heritabilities for BMI and BMD are ~10 and ~15 %, respectively. The results indicate that the proposed method has the potential to improve the accuracy of estimates of heritability and effect size for common SNPs in large-scale GWAS meta-analyses. These improved estimates may contribute to an enhanced understanding of the genetic basis of complex traits.

FISH: fast and accurate diploid genotype imputation via segmental hidden Markov model.

MOTIVATION: Fast and accurate genotype imputation is necessary for facilitating gene-mapping studies, especially with the ever increasing numbers of both common and rare variants generated by high-throughput-sequencing experiments. However, most of the existing imputation approaches suffer from either inaccurate results or heavy computational demand. RESULTS: In this article, aiming to perform fast and accurate genotype-imputation analysis, we propose a novel, fast and yet accurate method to impute diploid genotypes. Specifically, we extend a hidden Markov model that is widely used to describe haplotype structures. But we model hidden states onto single reference haplotypes rather than onto pairs of haplotypes. Consequently the computational complexity is linear to size of reference haplotypes. We further develop an algorithm 'merge-and-recover (MAR)' to speed up the calculation. Working on compact representation of segmental reference haplotypes, the MAR algorithm always calculates an exact form of transition probabilities regardless of partition of segments. Both simulation studies and real-data analyses demonstrated that our proposed method was comparable to most of the existing popular methods in terms of imputation accuracy, but was much more efficient in terms of computation. The MAR algorithm can further speed up the calculation by several folds without loss of accuracy. The proposed method will be useful in large-scale imputation studies with a large number of reference subjects. AVAILABILITY: The implemented multi-threading software FISH is freely available for academic use at https://sites.google.com/site/lzhanghomepage/FISH.