CANDIDATE GENE STUDIES OF OBESITY GUIDED BY WHOLE GENOME ASSOCIATION DATA

描述（由申请人提供）：肥胖症在美国的流行持续不减，并伴有大量并发症，包括糖尿病、癌症、心血管疾病和死亡。虽然最近肥胖症的增加是由于生活方式的改变， 或抵抗肥胖的能力受到遗传因素的强烈影响。由研究人员领导的全基因组关联（GWA）研究已经确定了人类基因组中的数十个位点（位点），其中常见的DNA序列变异与体重指数（BMI）和其他肥胖指标相关。虽然成功，这些GWA研究只能评估常见的变异，而且大多数变异都是非编码的，因此很难确定每个相关基因座中的多个基因中哪些是相关的。虽然从GWA研究中确定的基因座仍然可以突出相关的生物学途径，但相关基因的不确定性是完成功能和计算后续研究的关键障碍，这些研究将详细揭示GWA研究所暗示的生物学。 因此，我们将集中精力研究罕见的变异，特别是罕见的编码变异，这可以更精确地确定影响肥胖的基因。我们还将重新分析现有的大型（N~ 200，000）GWA数据集，使用包含更罕见遗传变异的新“参考面板”，以便能够测试更罕见的遗传变异， 与BMI和其他肥胖指标的关系。我们将在复制样本中测试和跟踪编码和非编码变异;我们将非编码变异与基因组注释相关联，从而进一步了解机制（目标1）。此外，我们将从GWA研究确定的基因座中选择600个基因，并对表型良好的样本和从BMI分布尾部抽取的样本中的这些基因的编码区进行集中的靶向测序（目标2）。为了检测频率更低的编码变异，因此超出了GWA研究的范围，我们将使用“外显子组芯片”对从BMI分布尾部抽取的样本进行更全面的基因分型研究，该芯片全面调查频率> 0.5%的非同义变异。我们将联合收割机将这些数据与其他可用的全外显子组基因型和序列数据相结合，以鉴定显示与BMI和其他肥胖指标相关的个体变体（以及因此的基因）（目标3）。最后，我们将整合基因分型和测序数据与其他数据集（基因表达，蛋白质-蛋白质相互作用），以确定额外的600个基因进行进一步的靶向测序，以确定影响BMI和其他肥胖指标的罕见变异的基因，并将这些研究扩展到非欧洲血统的样本（目标4）。成功地确定与肥胖相关的基因将是揭示影响人类肥胖的关键途径的关键下一步，这反过来又可以指导治疗和预防工作。 公共卫生相关性：肥胖是一个紧迫的公共卫生问题，长期有效和安全的治疗方法很少，肥胖的遗传易感性在人群中差异很大。了解变异增加或减少肥胖风险的潜在基因将揭示新的生物学根源，从而指导新的或改进的疗法和干预措施的开发。我们计划将重点放在迄今为止尚未得到充分研究的低频遗传变异上，目的是确定影响人类肥胖的特定基因。

DESCRIPTION (provided by applicant): The obesity epidemic in the U.S. continues unabated, and is accompanied by substantial complications, including diabetes, cancer, cardiovascular disease, and death. Although the recent rise in obesity is due to lifestyle changes, susceptibility or resistance to obesity is strongly influenced by genetic factors. Genome-wide association (GWA) studies, led by the investigators, have identified dozens of places (loci) in the human genome where common DNA sequence variation is associated with body mass index (BMI), and other measures of obesity. Although successful, these GWA studies have only been able to assess common variation, and most of these variants are noncoding, making it difficult to be confident about which of the multiple genes in each of the associated loci are relevant. Although loci identified from GWA studies can still highlight relevant biological pathways, the uncertainty about the relevant genes represents a key obstacle to completing the functional and computational follow-up studies that would reveal in detail the biology that has been hinted at by the GWA studies. Accordingly, we will focus our efforts on studying rare variation, and particularly rare coding variation, which can more precisely pinpoint genes that influence obesity. We will also reanalyze existing large (N~200,000) GWA data sets, using new "reference panels" containing rarer genetic variants to be able to test rarer genetic variation for association with BMI and other measures of obesity. We will test and follow-up in replication samples both coding and noncoding variation; we will correlate noncoding variation with genomic annotations that could provide further insight into mechanism (Aim 1). In addition, we will select 600 genes from within the loci identified by GWA studies and perform focused, targeted sequencing of the coding regions of these genes in well-phenotyped samples and samples drawn from the tails of the BMI distribution (Aim 2). To test coding variants that are of even lower frequency and therefore beyond the reach of GWA studies, we will perform more comprehensive genotyping studies in samples drawn from the tails of the BMI distribution, using an "exome chip" that comprehensively surveys nonsynonymous variation with frequency >0.5%. We will combine these data with additional available whole exome genotype and sequence data to identify individual variants (and hence genes) that show association with BMI and other measures of obesity (Aim 3). Finally, we will integrate the genotyping and sequencing data with other data sets (gene expression, protein-protein interaction) to identify an additional 600 genes for further targeted sequencing to identify genes with collections of rare variants that influence BMI and other measures of obesity, and extend these studies into samples of non-European ancestries (Aim 4). Successfully pinpointing genes that are associated with obesity would be a critical next step in uncovering key pathways that influence obesity in humans, which in turn could guide efforts at therapy and prevention. PUBLIC HEALTH RELEVANCE: Obesity is a pressing public health problem for which there are few long-term effective and safe treatments, and genetic susceptibility to obesity varies widely across the population. Knowledge of the underlying genes where variation increases or decreases risk of obesity would shed light on new biological root causes that in turn could guide the development of new or improved therapies and intervention. We plan to focus on lower frequency genetic variation that has not been well-studied until now, with the goal of identifying specific genes that influence human obesity.