Computational protocol: Phenotype and Tissue Expression as a Function of Genetic Risk in Polycystic Ovary Syndrome

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Protocol publication

[…] Patient DNA was isolated from whole blood according to manufacturer’s specifications (Qiagen, USA) and genotyped using the HumanOmniExpress BeadChip (Illumina, San Diego). Associations between five SNPs not previously examined in our data ( and ) and PCOS phenotypes were evaluated [–].Linear regression using an additive genetic model was used to test for association of PCOS risk variants with 30 log-transformed quantitative traits in the combined sample of PCOS cases and controls in the discovery Boston cohort. A p value < 0.0003 was considered significant after Bonferroni correction for 12 evaluable gene variants () and 15 independent traits (FSH, LH, prolactin, 17-OH progesterone, testosterone, cholesterol, sex hormone binding globulin (SHBG), estradiol, blood pressure, pulse, thyroid stimulating hormone (TSH), body mass index (BMI), fasting glucose, fasting insulin and ovarian volume), with other variables highly correlated.Associations between genetic variants and response to metformin were analyzed using Chi square [] and one way ANOVA. To correct for multiple testing, a p value of 0.0021 was considered significant to correct for comparisons of 2 independent outcomes (change in baseline glucose and glucose mediated glucose disposal, and testosterone and ovulation) and 12 evaluable variants.SNPs from all loci associated with PCOS in European and Chinese GWAS and available on the HumanOmniExpress BeadChip were examined in relation to adipose and skin expression [–].Skin (n = 10) and adipose tissue (n = 33) were isolated, tissue homogenized and RNA extracted (RNeasy or RNeasy lipid kits, Qiagen, USA). RNA libraries were prepared using Illumina TruSeq strand RNA sample prep with RiboZero treatment and sequencing was performed using the Illumina HiSeq 50 cycle single-read sequencing.Transcript annotations for hg19 (build 74) were downloaded from Ensembl. Splice junction sequences were generated using the USeq (v8.8.2) MakeTranscriptome application using a radius of 46. Novoindex (2.8) was used to create a transcriptome index using the combined splice junction and hg19 chromosome sequences.Reads were aligned to the transcriptome index described above using Novoalign (v2.08.01), allowing up to 50 alignments for each read. USeq (v8.8.2) SamTranscriptomeParser was used to convert the coordinates of reads aligning to splice junctions to genomic space. Reads aligning to multiple locations in the genome with equal confidence were discarded.Ensembl Build 74 transcript annotations were collapsed into gene annotations using USeq (v8.8.2) MergeUCSCGeneTable. USeq (v8.8.8) DefinedRegionDifferentialSeq was used to generate read counts for each gene. DESeq2 was used to test for differential expression in any of the genotypes using the likelihood ratio test (LRT) option []. Genes with significant adjusted p-values were used in Ingenuity Pathway Analysis (IPA®, Ingenuity Systems, QIAGEN) and for eQTL expression analysis. For eQTL expression analysis, all genes within an LD block defined by an r2 = 0.1 were considered. In addition, expression in fat and skin for all genes within 1 MB of the variant of interest were examined, which encompassed the LD block defined above. Pathway analysis was performed using IPA®, which creates algorithmically generated pathways from expression data and compares the ratio of overlap to known pathways. The upstream regulator data is generated by determining differentially expressed genes between genotypes and comparing them to genes regulated by the upstream molecules to determine the significance of enrichment of the gene expression data for the genes downstream of an upstream regulator. For both pathway and upstream regulator analysis, significance is calculated using the Fisher exact test. […]

Pipeline specifications

Software tools NovoAlign, DESeq2, IPA
Application RNA-seq analysis
Organisms Homo sapiens
Diseases Polycystic Ovary Syndrome
Chemicals Metformin