Computational protocol: Common Variation in the Fat Mass and Obesity-Associated (FTO) Gene Confers Risk of Obesity and Modulates BMI in the Chinese Population

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Protocol publication

[…] We selected potentially functional and common SNPs from the 3′ end of the neighboring RPGRIP1L gene to the 5′ flanking region of the FTO gene by using FASTSNP (Functional Analysis and Selection Tool for Single Nucleotide Polymorphism) software (). The FASTSNP software is a web server that allows users to efficiently identify and prioritize SNPs according to their putative functional effects. The prioritization strategy is based on the priority proposed by Tabor et al. (). All SNPs in the FTO gene with possible function effects are predicted as “intronic enhancers,” which are intronic SNPs with a potential transcriptional factor binding site. We selected only SNPs with known minor allele frequencies >15% in the Chinese population. Twenty SNPs were selected. Among them, SNP rs1421092 is located in intron 1 of the RPGRIP1L gene. Other SNPs are located in intron 1 (rs11861870), intron 4 (rs2388405), intron 7 (rs2287142), intron 8 (rs16952730, rs16952777, rs4784338, rs1107355, rs12443572, rs13331869, rs12600060, rs16952987, rs918031, and rs1008400), and the 5′ flanking region (rs1588413, rs11076022, rs11076023, rs12597712, rs2072518, and rs2075205) of the FTO gene. Because all FTO variants (rs7193144, rs8050136, rs9939609, rs9930506, rs1121980, and rs17817449) with known associations with obesity and type 2 diabetes in European populations (–) are <15% in the Chinese population and are in strong LD (mean r2 = 0.87 and mean D′ = 1) according to the HapMap Chinese in Beijing (CHB) databank (), we additionally selected rs9939609 as a tag SNP capturing these SNPs. Genotyping was performed in a total of 21 SNPs using the GenomeLab SNPstream genotyping platform (Beckman Coulter, Fullerton, CA) and its accompanying SNPstream software suite. On average, 97.96% of attempted genotypes were successful, except for rs2388405 and rs2287142, in which genotyping failed in all samples. The concordance rate of genotyping duplication was 99.62%, based on 40 duplicate samples for each SNP. These 19 SNPs captured 96 of 407 alleles (23%), with frequencies >5% at an r2 of 0.8 across a region of 419 kb containing the FTO gene from the HapMap CHB database (build 35 release) (). [...] Genotype data of both case and control groups were used to estimate intermarker LD by pairwise D′ and r2. We used the solid spine of the LD method implemented in the Haploview software (available at http://www.broad.mit.edu/mpg/haploview/) to define an LD block with an extended spine of D′ > 0.8 (). To compare the LD structures of a 419-kb region containing the FTO gene between Chinese and European populations, we used the genotype data from the CHB (Han Chinese) and CEU (Centre d'Etude du Polymorphisme Humain, Utah residents of northern and western European ancestry) databases in the HapMap (build 35). The LD structures were visualized using the Haploview software.A Hardy-Weinberg equilibrium test was performed for each SNP for the control group before marker-trait association analysis. The associations of each SNP with obesity and type 2 diabetes were estimated using logistic regression under a log-additive model implemented in the PLINK software (available at http://pngu.mgh.harvard.edu/∼purcell/plink/) (). We tested the model fit for disease association by comparing additive, dominant, and recessive models using logistic regression. Nominal two-sided P values were reported and were corrected for multiple testing by permutation for 10,000 times.For quantitative trait analyses, all metabolic traits including BMI were logarithmically transformed and standardized to the Z score units. The associations of each SNP with metabolic traits and the per-allele effect size on metabolic traits were estimated using linear regression in an additive genetic model in PLINK. We tested the model fit for metabolic traits association by comparing additive, dominant, and recessive models using linear regression. Nominal two-sided P values were reported and were corrected for multiple testing by permutation for 10,000 times.In the case-control association study for obesity and association analysis for quantitative metabolic traits, we combined samples from different study populations. We used the Cochran's Q test for heterogeneity and the I2 statistics to estimate heterogeneity between study populations. Meta-analysis of obesity association for the combined samples was performed using the fixed-effects Cochran-Mantel-Haenszel method implemented in PLINK. The combined odds ratio and significance level was estimated using the study population as strata. For quantitative metabolic trait association analyses in the combined samples, we used inverse variance methods implemented in the Comprehensive Meta-Analysis Software version 2 (Biostat, Englewood, NJ) to estimate the combined effect size on BMI and significance level.To provide an approximate estimate of the per-allele effect size in BMI units (kg/m2), we used the methods adopted by Frayling et al. (). The Z score unit differences were translated into BMI units using the SD of BMI in the general Chinese population (3.01 kg/m2 in healthy control subjects of this study).The population-attributable risk fraction was estimated with data from the control group, calculated as follows: 1 − (1/[p2 ORhomo + 2p {1 − p} ORhetero+ {1 − p}2]), where p is the risk-allele frequency, ORhomo is the odds ratio for homozygotes, and ORhetero is the odds ratio for heterozygotes. Power calculations were performed using a Genetic Power Calculator (available at http://pngu.mgh.harvard.edu/∼purcell/gpc/) (). […]

Pipeline specifications

Software tools FastSNP, Haploview, PLINK
Application GWAS
Diseases Ataxia Telangiectasia, Diabetes Mellitus, Machado-Joseph Disease