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Functional informed genome‐wide interaction analysis of body mass index, diabetes and colorectal cancer risk

机译:体重指数,糖尿病和结直肠癌风险的功能性通知基因组宽相互作用分析

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Background Body mass index (BMI) and diabetes are established risk factors for colorectal cancer (CRC), likely through perturbations in metabolic traits (e.g. insulin resistance and glucose homeostasis). Identification of interactions between variation in genes and these metabolic risk factors may identify novel biologic insights into CRC etiology. Methods To improve statistical power and interpretation for gene‐environment interaction (G?×?E) testing, we tested genetic variants that regulate expression of a gene together for interaction with BMI (kg/m2) and diabetes on CRC risk among 26?017 cases and 20?692 controls. Each variant was weighted based on PrediXcan analysis of gene expression data from colon tissue generated in the Genotype‐Tissue Expression Project for all genes with heritability ≥1%. We used a mixed‐effects model to jointly measure the G?×?E interaction in a gene by partitioning the interactions into the predicted gene expression levels (fixed effects), and residual G?×?E effects (random effects). G?×?BMI analyses were stratified by sex as BMI‐CRC associations differ by sex. We used false discovery rates to account for multiple comparisons and reported all results with FDR 0.2. Results Among 4839 genes tested, genetically predicted expressions of FOXA1 (P?=?3.15?×?10?5), PSMC5 (P?=?4.51?×?10?4) and CD33 (P?=?2.71?×?10?4) modified the association of BMI on CRC risk for men; KIAA0753 (P?=?2.29?×?10?5) and SCN1B (P?=?2.76?×?10?4) modified the association of BMI on CRC risk for women; and PTPN2 modified the association between diabetes and CRC risk in both sexes (P?=?2.31?×?10?5). Conclusions Aggregating G?×?E interactions and incorporating functional information, we discovered novel genes that may interact with BMI and diabetes on CRC risk.
机译:背景技术体重指数(BMI)和糖尿病是对结肠直肠癌(CRC)的危险因素,可能通过代谢性状的扰动(例如胰岛素抵抗和葡萄糖稳态)。鉴定基因变异与这些代谢风险因素之间的相互作用可能会识别CRC病因的新型生物学洞察。改善基因环境相互作用统计功率和解释的方法(G?×ΔE)测试,我们测试了调节基因表达,以便与BMI(kg / m2)和糖尿病在26?017之间对BMI(kg / m2)和糖尿病进行相互作用的遗传变体。案例和20?692控制。基于来自基因型组织表达项目中的基因表达数据的预Xcan分析,对所有具有遗传性≥1%的基因型组织表达项目产生的基因表达数据进行加权。我们使用了混合效应模型来共同测量基因中的G?×Δe相互作用,通过将相互作用分配到预测的基因表达水平(固定效应)和残留的G?×e效应(随机效应)中。 g?××β?BMI分析是以性别的分层分层,因为BMI-CRC联想因性别而异。我们使用虚假发现速率来解释多个比较,并报告了FDR <0.2的所有结果。结果在4839个基因中测试,遗传预测的FoxA1(p?= 3.15?×10?5),PSMC5(P?= 4.51?×10?4)和CD33(P?= 2.71?×? 10?4)修改了BMI对男性的CRC风险; Kiaa0753(p?=?2.29?×10?5)和scn1b(p?=?2.76?×10?4)修改了BMI对女性的CRC风险的关联;并且PTPN2修改了两性(P?= 2.31?×10?5)中的糖尿病和CRC风险之间的关联。结论聚集G?×ΔE相互作用和掺入功能信息,我们发现了可能与BMI和糖尿病相互作用的新基因对CRC风险。
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