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: 8194511
• 负责人: Steven C. Hunt
• 金额: $ 34.55万
• 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/8194511
• 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. PUBLIC HEALTH RELEVANCE: This grant seeks to identify genes containing rare DNA changes that lead to severe obesity. Identifying these genes and understanding what they do may suggest physiological pathways that might be used for intervention or prevention of obesity. Using pedigrees to identify these genes greatly improves the likelihood of identifying the rare DNA changes.

描述(申请人提供)：严重肥胖(BMIe 35 kg/m2)通常起病早，是疾病和早期死亡的强烈危险因素，除非进行减肥手术，否则很难治疗，而且长期维持减肥通常不成功。重度肥胖与遗传因素密切相关，单核苷酸多态(SNPs)的连锁、候选基因和全基因组关联研究发现了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(5%)，每个变异都解释了肥胖2%的变异。这些基因的总和解释了10%的BMI变异，尽管40%-60%的变异被认为是遗传的。必须有其他方法可以发现的显著的额外遗传变异。含有低频率变异的基因组中与严重肥胖相关的区域将使用改进的GWAS单倍型方法和利用整个外显子组重测序的家系特异性共享基因组序列方法来识别。这项应用建议克服检测与严重肥胖症相关的不常见或罕见变异的障碍。第一个障碍是，罕见的变异不太可能与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SNPs的长单倍型，以捕获驻留在这些单倍型上的未测量的稀有变异。其次，一个区域或基因的等位基因异质性(发生在整个基因不同位置的因果突变，例如肥胖症的MC4R或乳腺癌的BRCA1)阻止了单个SNPs检测关联。每个基因或区域识别的罕见风险单倍型将被折叠成单倍型风险集，以适应等位基因异质性，并增加估计频率和统计能力。第三，在不相关的病例和对照中，因果变异很难与许多中性变异区分开来。犹他州的扩展家系将在许多肥胖亲属中测试已识别的变异的共同分离，以剔除中性变异。第四，通过确保孟德尔遗传，利用谱系可以更容易地识别和消除重测序错误。第五，在8个或更远的远亲中至少有15个减数分裂的扩展家系中，甚至可以识别出特定于家系的稀有变异。外显子组捕获和NextGen测序将识别这些罕见的变体。最后，在我们的家系中，许多常见的变异与严重的肥胖症有关，对这些常见的变异进行调整将减少遗传变异，从而使较罕见的变异更容易被检测到。来自各种大型研究的10,000多名受试者将被用于识别、复制和表征已识别的变异。这组信息丰富的家系、病例/对照和基于人群的队列将被用来进一步揭开严重肥胖症遗传基础的复杂性，并将导致识别罕见的变异和对严重肥胖症发生的生理途径的理解，提出预防或减少严重肥胖症的方法。 与公共健康相关：这项拨款旨在识别包含导致严重肥胖的罕见DNA变化的基因。识别这些基因并了解它们的作用可能会提示可能用于干预或预防肥胖的生理途径。使用系谱来识别这些基因极大地提高了识别罕见DNA变化的可能性。