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）：重度肥胖（BMI e 35 kg/m2）通常起病较早，是疾病和早期死亡的一个强风险因素，除了减肥手术外难以治疗，长期维持体重减轻通常不成功。严重肥胖有重要的遗传成分，因为使用单核苷酸多态性（SNP）的连锁、候选基因和全基因组关联研究（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（>5%），每种变异解释肥胖的变异<2%。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。必须有显著的额外的遗传变异，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谱系特异性共享基因组序列方法来鉴定携带低频变体的基因组的重度肥胖相关区域。 本申请提出克服检测与严重肥胖症相关的不太常见或罕见变体的障碍。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。然而，我们建议推断常见SNP的长单倍型，以捕获那些单倍型上存在的不可测量的罕见变异。第二，区域或基因中的等位基因异质性（在基因的不同位置发生的因果突变，例如肥胖症的MC 4 R或乳腺癌的BRCA 1）阻止单个SNP检测关联。在每个基因或区域中鉴定的罕见风险单倍型将被分解成单倍型风险集，以适应等位基因异质性并增加估计频率和统计功效。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。将对扩展的犹他州家系进行测试，以确定在许多肥胖亲属中鉴定的变异的共分离，以剔除中性变异。第四，通过确保孟德尔传播，使用谱系更容易识别和去除重测序错误。第五，即使是家系特异性罕见变异也可以在8个或更多的远亲中至少有15个减数分裂的扩展家系中鉴定出来。外显子组捕获和NextGen测序将识别这种罕见的变异。最后，在我们的谱系中，许多常见变异与严重肥胖有关，对这些常见变异的调整将减少遗传变异，以便更容易检测到更罕见的变异。来自各种大型研究的超过10，000名受试者将用于识别，复制和表征已识别的变体。这组信息丰富的家系、病例/对照和基于人群的队列将用于进一步揭示严重肥胖症遗传基础的复杂性，并将导致识别罕见变异和了解严重肥胖症发展的生理途径，从而提出预防或减少严重肥胖症的方法。