Human population based genetic studies to elucidate the biology of NAFLD

 描述(申请人提供)：非酒精性脂肪性肝病(NAFLD)是由肝脏脂肪变性(脂肪堆积)引起的，在美国影响多达2900万成年人，并将成为 到2020年成为全球肝病的主要原因。预防或治疗非酒精性脂肪性肝病的有效方法很少。需要更好地了解NAFLD的病因，以提高其诊断和治疗水平。我们先前确定NAFLD是可遗传的(受遗传影响)，并在5个与肝脏脂肪变性相关的基因座上发现了常见的单核苷酸多态(SNPs)(次要等位基因频率(MAF)和GT；5%)，首先在约7000名欧洲血统的个体中，然后在不同的祖先中。5个座位上的变异解释了约20%的遗传力，这表明存在更有影响力的因果变异。我们现在的目标是确定1.因果遗传变异2.它们发挥作用的基因(S)，以及3.它们导致疾病的机制(S)。我们将我们的发现样本的样本大小和祖先多样性增加了两倍，从而提高了我们识别和精细绘制因果变异的能力。我们将把全基因组的基因类型归因于新的更密集的和祖先多样化的归属小组，提高我们识别跨祖先的原因常见的和现在的低频率(MAF 1-5%)变异的能力。我们将使用新的分析方法，不仅进行单一变异，而且还进行跨祖先的条件性和基于基因的分析，从而使我们能够有效地识别因果单一变异和基因。我们将使用新的变体和基因注释算法来确定变体和基因的优先顺序，以便进行功能跟踪。使用这种方法，我们确定了新的假定原因NAFLD变种和先前分析遗漏的基因。最后，我们开发了一个NAFLD的人类细胞系模型，我们使用该模型来表明，按照上述方法优先改变三个基因的功能会导致肝脏甘油三酯积累增加，这表明这是这些基因导致NAFLD的共同机制。我们假设，促进NAFLD的人类基因变异具有跨祖先的影响，并以细胞自主的方式在肝细胞中发挥作用，以增加甘油三酯的积累。为了验证这一假设，我们将协调来自10个发现队列(n&gt；21K)的肝脏脂肪变性的计算机断层扫描表型和基因类型，将基因类型归因于1000个基因组全球参考组，并跨组执行单一变异、基于基因和条件荟萃分析，以识别、精细绘制和注释假定的原因变异和基因。我们将在4,000例组织学证实的NAFLD病例和约3,000名对照中对顶级相关变异进行追踪，以重复我们的发现。我们将在人类肝细胞系中敲除和过度表达可能的致病基因(野生型和变异型)，并确定它们对甘油三酯积累的影响和遗传作用机制。我们将从我们之前的NAFLD荟萃分析中优先考虑的三个基因开始，然后继续从1000个基因组荟萃分析中确定的新基因。这项工作将有助于确定导致NAFLD的遗传和代谢机制，并为开发新的生物标记物以及治疗这种疾病的潜在疗法提供信息。

 DESCRIPTION (provided by applicant): Nonalcoholic fatty liver disease (NAFLD) is caused by hepatic steatosis (lipid accumulation), affects up to 29 million adults in the U.S. and will become the leading cause of liver disease worldwide by 2020. There are few effective ways to prevent or treat NAFLD. A better understanding of NAFLD etiology is needed to improve its diagnosis and treatment. We previously determined that NAFLD is heritable (genetically influenced) and identified common (minor allele frequency (MAF) >5%) single nucleotide polymorphisms (SNPs) at 5 loci that associate with hepatic steatosis, first in ~7,000 individuals of European ancestry and then across ancestries. Variants at the 5 loci explain ~20% of heritability, suggesting the existence of more influential causal variants. We now aim to identify 1. causal genetic variants 2. gene(s) they work through to exert their effects, and 3. mechanism(s) by which they lead to disease. We tripled the sample size and ancestral diversity of our discovery sample, thus improving our ability to identify and fine-map causal variants. We will impute genome- wide genotypes to new denser and ancestrally diverse imputation panels, increasing our ability to identify causal common and now low-frequency (MAF 1-5%) variants across ancestries. We will use new analytical methods to perform not only single variant but also conditional and gene-based analyses across ancestries allowing us to efficiently identify causal single variants and genes. We will use new variant and gene annotations algorithms to prioritize variants and genes for functional follow up. Using this approach we identify new putative causal NAFLD variants and genes missed by previous analyses. Finally, we developed a human cell line model of NAFLD that we use to show that altering the function of three genes prioritized by the above approaches results in increased liver triglyceride accumulation, suggesting this as a common mechanism by which these genes cause NAFLD. We hypothesize that causal NAFLD-promoting human genetic variants have effects across ancestries and exert their effects in a cell-autonomous manner in hepatocytes to increase triglyceride accumulation. To test this hypothesis, we will harmonize the computed tomography hepatic steatosis phenotype and genotypes from 10 discovery cohorts (n>21K), impute genotypes to the 1000 Genomes cosmopolitan reference panel, and perform single variant, gene-based, and conditional meta- analyses across groups to identify, fine-map, and annotate putative causal variants and genes. We will follow- up top associating variants in >4,000 histologically confirmed NAFLD cases and ~3,000 controls to replicate our findings. We will knockdown and overexpress putative causal genes (wild-type and variant) in human liver cell lines and determine their effect on triglyceride accumulation and genetic mechanism of action. We will begin with three genes prioritized from our previous NAFLD meta-analysis and proceed to new genes identified from the 1000 Genomes meta-analysis. This work will help define the genetic and metabolic mechanisms that cause NAFLD and inform development of new biomarkers as well as potential therapeutics for this condition.