Computational protocol: A GWAS follow‐up of obesity‐related SNPs in SYPL2 reveals sex‐specific association with hip circumference

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Protocol publication

[…] The first SNP selected for genotyping was the rare variant previously associated with BMI and located at SYPL2 exon 4 (rs62623713 A>G [chr1:109476817/hg19]) . Because exome sequencing is not able to identify common SNPs located within untranscribed regions, additional tagging SNPs were added to the association study in order to cover most of the genetic variability within the SYPL2 locus. Selection of additional tagging SNPs within the SYPL2 locus and surrounding regions (2.5 kb upstream and downstream) was carried using the tagger selection algorithm of the Haploview software (Massachusetts Institute of Technology, Cambridge, MA, USA) and considering the CEU panel (Utah residents with Northern and Western European ancestry) of the latest release of HapMap (release 28, Phase II + III data). Using this tagging SNP selection, we identified rs9661614 T>C (chr1:109479215; intron variant) and rs485660 G>A (chr1:109480810; 3ʹ‐UTR variant) located in the vicinity of rs62623713 (2.4 and 4.0 kb, respectively). These two additional tagging SNPs covered 100% of SYPL2 genetic variability considering common genetic variants with minor allele frequencies higher than 5% and high linkage disequilibrium (LD; r 2 > 0.8). Selected SNPs were genotyped in both the obesity and the infogene cohorts using TaqMan probes (Applied Biosystems, Foster, CA, USA). Genomic DNA was extracted from the blood buffy coat using the GenElute Blood Genomic DNA kit (Sigma, St. Louis, MO, USA). Genotypes were determined using the 7500 Fast Real‐Time PCR System (Applied Biosystems), and they were analysed using the high‐throughput array technology QuantStudio 12 K Flex System, coupled with Taqman OpenArray Technology (Life Technologies, Carlsbad, CA, USA). Haplotype reconstruction and individual diplotype assignments were inferred from genotype data using plink v1.07 (PLINK, Boston, MA, USA) and phase v2.1.1 software (University of Washington, Seattle, WA, USA), with default parameters and using the CEU panel of the 1000 Genomes Project (Phase 3) as the reference population. [...] A two‐stage association study was carried out. Statistical analyses were first performed in the obesity cohort, which was randomly subdivided into two smaller sub‐cohorts (discovery and replication) to test for associations separately, and formed by 1,513 (472 men and 1,041 women) and 1,511 (471 men and 1,040 women) subjects, respectively. In order not to be too restrictive at this exploratory stage, a nominal P ≤ 0.05 found in both discovery and replication sub‐cohorts was used to determine an association to be further tested in the entire obesity cohort. Significant associations were further validated by means of a joint analysis in the combined cohort composed of the obesity and the infogene cohorts. False discovery rate‐corrected P (FDR‐corrected P ≤ 0.05) was applied for multiple‐testing correction when testing for associations in the obesity and the combined cohorts.Association tests were performed using the analysis of variance (general linear models, type III sum of squares) under an additive model of inheritance, and adjusted for the effects of age, sex and BMI. Genotype by sex (G × S) and genotype by BMI (G × B) interaction terms were added into separate models one at a time. Pairwise comparisons among genotype groups were performed using least square means, and statistically significant differences were determined with Bonferroni adjusted P‐values (P bon ≤ 0.01). Quantitative anthropometric and metabolic traits tested for associations were waist (WC) and hip circumference (HC), WHR, triglycerides (TG), HDL‐cholesterol (HDL‐C), LDL‐cholesterol (LDL‐C) and total cholesterol, total cholesterol to HDL‐C ratio, fasting glucose and blood pressure (systolic and diastolic). Variables that were non‐normally distributed were transformed to approximate a normal distribution (inverse transformed: TG and HDL‐C; log10 transformed: fasting glucose and total cholesterol to HDL‐C ratio). Diplotype‐based association tests were performed using diplotypes composed of SNPs showing statistically significant associations independently. The analysis of variance adjusted by the same variables was also applied for diplotype‐based tests.The proportion of phenotypic variance explained by the genotype was calculated as the ratio of the type III sum of squares because of the SNP effect to the sum of squares of the model. With the statistical significance set to α = 0.05 and β = 0.10, the statistical power to detect significant associations was higher than 99% in both the obesity and the combined cohorts. Statistical analyses were performed using sas software version 9.3 (SAS Institute, Cary, NC, USA). Statistical power analyses were performed using G*Power (version (Heinrich Heine‐Universität, Düsseldorf, Germany) . […]

Pipeline specifications

Software tools Haploview, PLINK, G*Power
Applications Miscellaneous, WES analysis, GWAS
Organisms Homo sapiens
Diseases Hip Dislocation