Rare Variant Associations With Severe Obesity in Utah Pedigrees

犹他州谱系中与严重肥胖相关的罕见变异

• 批准号: 8334704
• 负责人: Steven C. Hunt
• 金额: $ 49.28万
• 依托单位: UNIVERSITY OF UTAH
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-26 至 2015-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8334704
• 关键词: Affect; BRCA1 gene; Birth; Body Weight decreased; British; Candidate Disease Gene; Custom; DNA; DNA Resequencing; Data; Detection; Disease; Distant; Ensure; Exons; Frequencies; Genes; Genetic; Genetic Variation; Genome; Genomic Segment; Genomics; Genotype; Grant; Haplotypes; Heterogeneity; Human; Individual; Intervention; Lead; Linkage Disequilibrium; Location; Low Density Lipoprotein Receptor; Maintenance; Meiosis; Methodology; Methods; Morbid Obesity; Mutation; Non obese; Obesity; Pathway interactions; Physiological; Population; Relative (related person); Risk; Risk Factors; Sampling; Series; Single Nucleotide Polymorphism; Staging; Sum; Test Result; Testing; Utah; Variant; bariatric surgery; base; case control; cohort; early onset; exome; genetic pedigree; genome wide association study; high risk; improved; malignant breast neoplasm; member; mortality; obesity prevention; population based; prevent; segregation; transmission process

DESCRIPTION (provided by applicant): Severe obesity (BMIe35 kg/m2) often has early onset, is a strong risk factor for disease and early mortality, is difficult to treat short of bariatric surgery, and long-term maintenance of weight loss is usually unsuccessful. There is a significant genetic component to severe obesity, as linkage, candidate gene, and genome-wide association studies (GWAS) using single nucleotide polymorphisms (SNPs) have detected multiple genetic regions and specific genes associated with obesity. Most of the variants are common (>5%) in the population, and each explains <2% of the variance in obesity. The sum of these genes explain <10% of BMI variation, even though 40-60% of the variation is thought to be genetic. There must be significant additional genetic variation that can be uncovered by other methodologies. Severe obesity-associated regions of the genome harboring low frequency variants will be identified using both a modified GWAS haplotype approach and a pedigree-specific shared genomic sequence approach utilizing whole exome resequencing. This application proposes to overcome barriers to detecting less common or rare variants associated with severe obesity. The first barrier is that rare variants are not likely to be in high linkage disequilibrium with the common variants used in GWAS. However, we propose to infer long haplotypes of common SNPs to capture unmeasured rare variants residing on those haplotypes. Second, allelic heterogeneity in a region or gene (causal mutations occurring in different locations across the gene, e.g. MC4R for obesity or BRCA1 for breast cancer) prevents single SNPs from detecting association. The rare risk haplotypes identified across each gene or region will be collapsed into haplotype risk sets to accommodate allelic heterogeneity and increase the estimated frequency and statistical power. Third, causal variants are difficult to distinguish from the many neutral variants in unrelated cases and controls. Extended Utah pedigrees will be tested for co segregation of the identified variants in many obese relatives to weed out the neutral variants. Fourth, resequencing errors are more easily identified and removed using pedigrees by ensuring Mendelian transmission. Fifth, even pedigree-specific rare variants can be identified in extended pedigrees in which there are at least 15 meioses among 8 or more distant affected relatives. Exome capture and NextGen sequencing will identify such rare variants. Finally, a number of common variants are associated with severe obesity in our pedigrees and adjustment for these common variants will reduce the genetic variation so that the rarer variants are more easily detectable. Over 10,000 subjects, combined from various large studies, will be used for identification, replication, and characterization of identified variants. The highly informative set of pedigrees, cases/controls, and population-based cohorts will be used to further unravel the complexity of the genetic underpinnings of severe obesity and should lead to the identification of rare variants and an understanding of the physiological pathways through which severe obesity develops, suggesting ways to prevent or reduce severe obesity.

描述（由申请人提供）：严重的肥胖症（BMIE35 kg/m2）通常早发，是疾病和早期死亡的强大危险因素，难以治疗减肥手术，长期维持体重减轻通常不成功。严重的肥胖症具有重要的遗传成分，因为使用单核苷酸多态性（SNP）的联系，候选基因和全基因组关联研究（GWAS）已经检测到了多个遗传区域和与肥胖相关的特定基因。大多数变体在人群中是常见的（> 5％），每个变体都示意肥胖方差的2％。这些基因的总和解释了BMI变异的<10％，即使40-60％的变异被认为是遗传的。其他方法论必须有明显的遗传变异。使用整个外显子组重新配置的整个外显子组，将使用改良的GWAS单倍型方法和谱系特异性的共享基因组序列方法来鉴定具有低频变体基因组的严重肥胖相关区域。 该应用建议克服障碍，以检测与严重肥胖相关的较少常见或稀有变体。第一个障碍是，罕见的变体不太可能与GWAS中使用的常见变体处于高连接状态。但是，我们建议推断出长的常见SNP单倍型，以捕获存在于这些单倍型上的未衡量的稀有变体。其次，一个区域或基因的等位基因异质性（因果突变发生在整个基因的不同位置，例如肥胖症的MC4R或用于乳腺癌的BRCA1）阻止单个SNP检测关联。在每个基因或区域中鉴定出的罕见风险单倍型将折叠成单倍型风险集，以适应等位基因异质性并增加估计的频率和统计能力。第三，在无关的情况和对照中，因果变异很难与许多中性变体区分开。犹他州的延长血统会进行测试，以隔离许多肥胖的亲属中所鉴定的变体，以清除中性变体。第四，通过确保Mendelian传播，更容易地更容易地识别和删除重新定制错误。第五，即使是谱系特异性的稀有变体也可以在延长的谱系中鉴定出，其中8个或更多远处受影响的亲属中至少有15倍的变体。外显子捕获和NextGen测序将确定这种罕见的变体。最后，许多常见变体与我们的血统中的严重肥胖有关，对这些常见变体的调整将减少遗传变异，从而更容易检测到稀有变体。超过10,000名来自各种大型研究的受试者将用于识别，复制和表征已识别的变体。高度信息丰富的血统，病例/对照组和基于人群的同类将用于进一步揭示严重肥胖症的遗传基础的复杂性，并应导致对稀有变体的鉴定以及对严重肥胖的生理途径的理解，并提出严重肥胖的发展，并提出预防或减少严重肥胖的方法。