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: 8547060
• 负责人: Steven C. Hunt
• 金额: $ 50.39万
• 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/8547060
• 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; risk variant; 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）：重度肥胖（BMIe35 kg/m2）通常早发，是疾病和早期死亡的强大危险因素，除非进行减肥手术，否则难以治疗，长期维持体重通常是不成功的。重度肥胖有一个重要的遗传因素，连锁、候选基因和使用单核苷酸多态性（snp）的全基因组关联研究（GWAS）已经检测到与肥胖相关的多个遗传区域和特定基因。大多数变异在人群中是常见的（约占5%），每种变异对肥胖的解释都小于2%。这些基因的总和解释了不到10%的BMI变异，尽管40-60%的变异被认为是遗传的。必须有其他重要的遗传变异可以通过其他方法发现。将使用改良的GWAS单倍型方法和利用全外显子组重测序的家系特异性共享基因组序列方法来鉴定含有低频变异的严重肥胖相关基因组区域。该应用程序旨在克服检测与严重肥胖相关的不常见或罕见变异的障碍。第一个障碍是，罕见变异不太可能与GWAS中使用的常见变异处于高度连锁不平衡状态。然而，我们建议推断常见snp的长单倍型，以捕获驻留在这些单倍型上的未测量的罕见变异。其次，一个区域或基因的等位基因异质性（在基因的不同位置发生的因果突变，例如肥胖的MC4R或乳腺癌的BRCA1）阻止了单个snp检测关联。在每个基因或区域中识别的罕见风险单倍型将被分解成单倍型风险集，以适应等位基因的异质性，并增加估计频率和统计能力。第三，因果变异很难与不相关病例和对照中的许多中性变异区分开来。扩展犹他家系将测试在许多肥胖亲属中确定的变体的共分离，以淘汰中性变体。第四，通过确保孟德尔传播，重测序错误更容易通过系谱识别和消除。第五，即使是系谱特异性的罕见变异也可以在8个或更多的远房亲属中至少有15个减数分裂的扩展系谱中被识别出来。外显子组捕获和NextGen测序将识别这些罕见的变异。最后，在我们的谱系中，许多常见的变异与严重的肥胖有关，对这些常见变异的调整将减少遗传变异，从而更容易检测到罕见的变异。来自各种大型研究的10,000多名受试者将用于鉴定，复制和鉴定已确定的变体。高信息性的谱系、病例/对照和基于人群的队列将用于进一步揭示严重肥胖的遗传基础的复杂性，并将导致罕见变异的识别和对严重肥胖发展的生理途径的理解，从而提出预防或减少严重肥胖的方法。