Joint identification of genetic variants for physical activity in Korean population.
ABSTRACT: There has been limited research on genome-wide association with physical activity (PA). This study ascertained genetic associations between PA and 344,893 single nucleotide polymorphism (SNP) markers in 8842 Korean samples. PA data were obtained from a validated questionnaire that included information on PA intensity and duration. Metabolic equivalent of tasks were calculated to estimate the total daily PA level for each individual. In addition to single- and multiple-SNP association tests, a pathway enrichment analysis was performed to identify the biological significance of SNP markers. Although no significant SNP was found at genome-wide significance level via single-SNP association tests, 59 genetic variants mapped to 76 genes were identified via a multiple SNP approach using a bootstrap selection stability measure. Pathway analysis for these 59 variants showed that maturity onset diabetes of the young (MODY) was enriched. Joint identification of SNPs could enable the identification of multiple SNPs with good predictive power for PA and a pathway enriched for PA.
Project description:BACKGROUND:The molecular basis of the Turkish population with suspected maturity-onset diabetes of the young (MODY) has not been identified. This is the first study to investigate the association between HNF1A-gene single-nucleotide polymorphisms (SNPs) and having early-onset, MODY-like diabetes mellitus in the Turkish population. METHODS:All diabetic patients (N?=?565) who presented to our clinic between 2012 and 2015 with a clinical suspicion of MODY were included in the study. Analysis of HNF1A, HNFB, HNF4A, GCK gene mutations was performed using real-time polymerase chain reaction sequencing. After genetic analysis, diabetics (n?=?46) with HNF1A, HNF1B, HNF4A, GCK gene mutations (diagnosed as MODY) and diabetics (n?=?30) with HNF1B, HNF4A, GCK gene SNPs were excluded. Patients with early-onset, MODY-like diabetes (n?=?486) and non-diabetic controls (n?=?263) were included. Genetic analyses for the HNF1A gene p.S487?N (rs2464196), p.A98V (rs1800574) and p.I27L (rs1169288) SNPs were performed using Sanger-based DNA sequencing among the control group. RESULTS:p.S487?N and p.A98V was similar between the diabetics and controls in dominant and recessive models with no association (each, p?>?0.05). p.I27L GT/TT carriers (GT/TT vs. GG, OR?=?1.68, 95% CI: [1. 21-2.13]; p?=?0.035) and p.I27L TT carriers had increased risk of having MODY-like diabetes (GT/GG vs. TT, OR?=?1.56, 95% CI: [1. 14-2.57]; p?=?0.048). Family inheritance of diabetes was significantly more common in patients with the p.I27L TT genotype. The p.I27L SNP was modestly associated with having diabetes after adjusting for body mass index and age (??=?1.45, 95% CI: [1. 2-4.2]; p?=?0.036). CONCLUSIONS:The HNF1A gene p.I27L SNP was modestly associated with having early-onset, MODY-like diabetes in the Turkish population. HNF1A gene p.I27L SNP might contribute to age at diabetes diagnosis and family inheritance.
Project description:Previous studies have shown association of single nucleotide polymorphisms (SNPs) in 3 contiguous genes (PON1, PON2, and PON3) encoding paraoxonase with risk of Alzheimer disease (AD). We evaluated the association of serum paraoxonase activity measured by phenyl acetate (PA) and thiobutyl butyrolactone (TBBL) with risk of AD and with 26 SNPs spanning the PON gene cluster in 266 AD cases and 306 sibling controls from the MIRAGE study. The odds of AD (adjusted for age, gender, and ethnicity) increased 20% for each standard deviation decrease in PA or TBBL activity. There were association signals with activity in all 3 genes. Haplotypes including SNPs spanning the PON genes were generally more significant than haplotypes comprising SNPs from 1 gene. Significant interactions were observed between SNP pairs located across the PON cluster with either serum activity measure as the outcome, and between several PON SNPs and PA activity with AD status as the outcome. Our results suggest that low serum paraoxonase activity is a risk factor for AD. Furthermore, multiple variants in PON influence serum paraoxonase activity and their effects may be synergistic.
Project description:Placental abruption (PA), a pregnancy-related vascular disorder, is a leading cause of maternal and perinatal morbidity and mortality. The success of identifying genetic susceptibility loci for PA, a multi-factorial heritable disorder, has been limited. We conducted a genome-wide association study (GWAS) and candidate gene association study using 470 PA cases and 473 controls from Lima, Peru. Genotyping for common genetic variations (single nucleotide polymorphisms, SNPs) was conducted using the Illumina Cardio-Metabo Chip platform. Common variations in 35 genes that participate in mitochondrial biogenesis (MB) and oxidative phosphorylation (OS) were selected for the candidate gene study. Regression models were fit to examine associations of each SNP with risk of PA. In pathway analyses, we examined functions and functional relationships of genes represented by the top GWAS hits. Genetic risk scores (GRS), based on top hits of the GWAS and candidate gene analyses, respectively, were computed using the risk allele counting method. The top hit in the GWAS analyses was rs1238566 (empirical P-value=1.04e-4 and FDR-adjusted P-value=5.65E-04) in FLI-1 gene, a megakaryocyte-specific transcription factor. Networks of genes involved in lipid metabolism and cell signaling were significantly enriched by the 51 genes whose SNPs were among the top 200 GWAS hits (P-value <2.1e-3). SNPs known to regulate MB (e.g. CAMK2B, NR1H3, PPARG, PRKCA, and THRB) and OP (e.g., COX5A, and NDUF family of genes) were associated with PA risk (P-value <0.05). GRS was significantly associated with PA risk (trend P-value <0.001 and 0.01 for GWAS and candidate gene based GRS, respectively). Our study suggests that integrating multiple analytical strategies in genetic association studies can provide opportunities for identifying genetic risk factors and novel molecular mechanisms that underlie PA.
Project description:Genetic effects contribute to individual differences in smoking behavior. Persistence to smoke despite known harmful health effects is mostly driven by nicotine addiction. As the physiological effects of nicotine are mediated by nicotinic acetylcholine receptors (nAChRs), we aimed at examining whether single nucleotide polymorphisms (SNPs) residing in nAChR subunit (CHRN) genes, other than CHRNA3/CHRNA5/CHRNB4 gene cluster previously showing association in our sample, are associated with smoking quantity or serum cotinine levels.The study sample consisted of 485 Finnish adult daily smokers (age 30-75 years, 59% men) assessed for the number of cigarettes smoked per day (CPD) and serum cotinine level. We first studied SNPs residing on selected nAChR subunit genes (CHRNA2, CHRNA4, CHRNA6/CHRNB3, CHRNA7, CHRNA9, CHRNA10, CHRNB2, CHRNG/CHRND) genotyped within a genome-wide association study for single SNP and multiple SNP associations by ordinal regression. Next, we explored individual haplotype associations using sliding window technique.At one of the 8 loci studied, CHRNG/CHRND (chr2), single SNP (rs1190452), multiple SNP, and 2-SNP haplotype analyses (SNPs rs4973539-rs1190452) all showed statistically significant association with cotinine level. The median cotinine levels varied between the 2-SNP haplotypes from 220 ng/ml (AA haplotype) to 249 ng/ml (AG haplotype). We did not observe significant associations with CPD.These results provide further evidence that the ?-? nAChR subunit gene region is associated with cotinine levels but not with the number of CPD, illustrating the usefulness of biomarkers in genetic analyses.
Project description:Inherited risk of pancreatic cancer has been associated with mutations in several genes, including BRCA2, CDKN2A (p16), PRSS1, and PALB2. We hypothesized that common variants in these genes, single nucleotide polymorphisms (SNP), may also influence risk for pancreatic cancer development.A clinic-based case-control study in non-Hispanic white persons compared 1,143 patients with pancreatic adenocarcinoma with 1,097 healthy controls. Twenty-eight genes directly and indirectly involved in the Fanconi/BRCA pathway (includes BRCA1, BRCA2, and PALB2) were identified and 248 tag SNPs were selected. In addition, 11 SNPs in CDKN2A, PRSS1, and PRSS2 were selected. Association studies were done at the gene level by principal components analysis, whereas recursive partitioning analysis was used to investigate pathway effects. At the individual SNP level, adjusted additive, dominant, and recessive models were investigated, and gene-environment interactions were also assessed.Gene level analyses showed no significant association of any genes with altered pancreatic cancer risk. Multiple single SNP analyses showed associations, which will require replication. Exploratory pathway analyses by recursive partitioning showed no association between SNPs and risk for pancreatic cancer.In a candidate gene and pathway SNP association study analysis, common variations in the Fanconi/BRCA pathway and other candidate familial pancreatic cancer genes are not associated with risk for pancreatic cancer.
Project description:Genome-wide association studies (GWAS) have become a common approach to identifying single nucleotide polymorphisms (SNPs) associated with complex diseases. As complex diseases are caused by the joint effects of multiple genes, while the effect of individual gene or SNP is modest, a method considering the joint effects of multiple SNPs can be more powerful than testing individual SNPs. The multi-SNP analysis aims to test association based on a SNP set, usually defined based on biological knowledge such as gene or pathway, which may contain only a portion of SNPs with effects on the disease. Therefore, a challenge for the multi-SNP analysis is how to effectively select a subset of SNPs with promising association signals from the SNP set.We developed the Optimal P-value Threshold Pedigree Disequilibrium Test (OPTPDT). The OPTPDT uses general nuclear families. A variable p-value threshold algorithm is used to determine an optimal p-value threshold for selecting a subset of SNPs. A permutation procedure is used to assess the significance of the test. We used simulations to verify that the OPTPDT has correct type I error rates. Our power studies showed that the OPTPDT can be more powerful than the set-based test in PLINK, the multi-SNP FBAT test, and the p-value based test GATES. We applied the OPTPDT to a family-based autism GWAS dataset for gene-based association analysis and identified MACROD2-AS1 with genome-wide significance (p-value=2.5×10(-6)).Our simulation results suggested that the OPTPDT is a valid and powerful test. The OPTPDT will be helpful for gene-based or pathway association analysis. The method is ideal for the secondary analysis of existing GWAS datasets, which may identify a set of SNPs with joint effects on the disease.
Project description:In spite of the success of genome-wide association studies (GWASs), only a small proportion of heritability for each complex trait has been explained by identified genetic variants, mainly SNPs. Likely reasons include genetic heterogeneity (i.e., multiple causal genetic variants) and small effect sizes of causal variants, for which pathway analysis has been proposed as a promising alternative to the standard single-SNP-based analysis. A pathway contains a set of functionally related genes, each of which includes multiple SNPs. Here we propose a pathway-based test that is adaptive at both the gene and SNP levels, thus maintaining high power across a wide range of situations with varying numbers of the genes and SNPs associated with a trait. The proposed method is applicable to both common variants and rare variants and can incorporate biological knowledge on SNPs and genes to boost statistical power. We use extensively simulated data and a WTCCC GWAS dataset to compare our proposal with several existing pathway-based and SNP-set-based tests, demonstrating its promising performance and its potential use in practice.
Project description:Complex diseases, such as cancer, arise from complex etiologies consisting of multiple single-nucleotide polymorphisms (SNPs), each contributing a small amount to the overall risk of disease. Thus, many researchers have gone beyond single-SNPs analysis methods, focusing instead on groups of SNPs, for example by analysing haplotypes. More recently, pathway-based methods have been proposed that use prior biological knowledge on gene function to achieve a more powerful analysis of genome-wide association studies (GWAS) data. In this paper we propose a novel Bayesian modeling framework to identify molecular biomarkers for disease prediction. Our method combines pathway-based approaches with multiple SNP analyses of a specified region of interest. The model's development is motivated by SNP data from a lung cancer study. In our approach we define gene-level scores based on SNP allele frequencies and use a linear modeling setting to study the scores association to the observed phenotype. The basic idea behind the definition of gene-level scores is to weigh the SNPs within the gene according to their rarity, based on genotype frequencies expected under the Hardy-Weinberg equilibrium law. This results in scores giving more importance to the unusually low frequencies, i.e. to SNPs that might indicate peculiar genetic differences between subjects belonging to different groups. An additional feature of our approach is that we incorporate information on SNP-to-SNP associations into the model. In particular, we use network priors that model the linkage disequilibrium between SNPs. For posterior inference, we design a stochastic search method that identifies significant biomarkers (genes and SNPs) for disease prediction. We assess performances on simulated data and compare results to existing approaches. We then show the ability of the proposed methodology to detect relevant genes and associated SNPs in a lung cancer dataset.
Project description:Abstract Primary aldosteronism (PA), also known as Conn’s syndrome, is a common curable cause of hypertension. Family studies of essential hypertensive patients suggest that heritable genetic factors play a role in blood pressure regulation1. Interestingly, single nucleotide polymorphisms (SNP) in genes encoding enzymes involved with adrenal steroidogenesis, CYP11B2, CYP11B1 and CYP17A1, associate with increased risk of hypertension2. Therefore, we analysed whether selected SNPs in these genes are associated with PA. We performed an association study using genotype imputation for selected SNPs of the steroidogenic enzyme genes CYP11B2 (rs4546, rs1799998, rs13268025), CYP11B1 (rs6410, rs149845727), and CYP17A1 (rs1004467, rs138009835, rs2150927) from a pilot genome wide association study of Malaysian PA patients and healthy controls which was merged with the Singapore Genome Variation Project (SGVP) population dataset3. Genotype imputation for minor and major alleles was validated using PCR sequencing (n>10 for each genotype). Further, one SNP from each steroidogenic enzyme (CYP11B2:rs1799998, CYP11B1:rs6410 and CYP17A1:rs1004467) was validated using commercial TaqMan genotyping assays on the ABI 7000 Sequence Detection System which was performed on 149 PA patients and 78 non-hypertensive healthy individuals. Case-control genetic association analysis was performed at http://www.oege.org/software/orcalc.html and the association between genotypes and phenotypes was done using the independent-samples Kruskal-Wallis test on SPSS (version 25). The Minor Allele Frequencies (MAFs) for rs1004467, rs6410 and rs1799998 were similar to East Asian populations but differed significantly different from European, African, American and South Asian populations (rs1004467 MAF: C=0.258/298, rs6410 MAF: A=0.265/298, rs1799998 MAF: C=0.225/298). In Chinese patients matched by gender, heterozygotes for rs6410 had significantly increased risk of PA compared to common homozygotes (OR: 3.15, 95% CI: 1.01–9.8, p=0.04). Across patients of different ethnicity, the distribution of aldosterone levels was significantly different (p=0.039). In summary, only SNP rs6410 in Chinese patients matched by gender showed association with PA in our South East Asian cohort. More functional experiments need to be done to find out whether this is causal for PA or whether the SNP is in linkage disequilibrium with the actual functional causative SNPs. Once the functional SNP is known, identification of these germline variants in asymptomatic family members would allow early screening of PA to be offered and potentially provide novel drug targets to treat the disease. References: 1Timberlake et al., Curr Opin Nephrol Hypertens. 2001 Jan;10(1):71-9. 2MacKenzie et al., Int J Mol Sci. 2017 Mar 7;18(3). pii: E579. 3Teo et al., Genome Res. 2009 Nov;19(11):2154-62.
Project description:Individual genetic variation that results in differences in systemic response to xenobiotic exposure is not accounted for as a predictor of outcome in current exposure assessment models.We developed a strategy to investigate individual differences in single-nucleotide polymorphisms (SNPs) as genetic markers associated with naphthyl-keratin adduct (NKA) levels measured in the skin of workers exposed to naphthalene.The SNP-association analysis was conducted in PLINK using candidate-gene analysis and genome-wide analysis. We identified significant SNP-NKA associations and investigated the potential impact of these SNPs along with personal and workplace factors on NKA levels using a multiple linear regression model and the Pratt index.In candidate-gene analysis, a SNP (rs4852279) located near the CYP26B1 gene contributed to the 2-naphthyl-keratin adduct (2NKA) level. In the multiple linear regression model, the SNP rs4852279, dermal exposure, exposure time, task replacing foam, age, and ethnicity all were significant predictors of 2NKA level. In genome-wide analysis, no single SNP reached genome-wide significance for NKA levels (all p ? 1.05 × 10(-5)). Pathway and network analyses of SNPs associated with NKA levels were predicted to be involved in the regulation of cellular processes and homeostasis.These results provide evidence that a quantitative biomarker can be used as an intermediate phenotype when investigating the association between genetic markers and exposure-dose relationship in a small, well-characterized exposed worker population.