Biological Insights from Genetic Investigation of ANthropometric Traits (GIANT) Across the Allelic Spectrum

跨等位基因谱的人体测量特征 (GIANT) 遗传研究的生物学见解

• 批准号: 10226942
• 负责人: JOEL N HIRSCHHORN
• 金额: $ 71.51万
• 依托单位: BOSTON CHILDREN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-06-08 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10226942
• 关键词: Adult; Alleles; Benchmarking; Biological; Biological Factors; Biological Process; Biology; Body mass index; Cardiovascular Diseases; Cessation of life; Childhood; Code; Collection; Computer software; Computing Methodologies; Data; Data Set; Developmental Process; Diabetes Mellitus; Disease; Environment; Epidemic; Epigenetic Process; Frequencies; Funding; Future; Generations; Genes; Genetic; Genetic Variation; Genetic study; Genotype; Goals; Growth; Haplotypes; Height; Heritability; Human Genetics; Individual; Infrastructure; Investigation; Knowledge; Light; Malignant Neoplasms; Measures; Medical; Mendelian randomization; Meta-Analysis; Metabolic; Methods; Modeling; Obesity; Pathway interactions; Plant Roots; Polygenic Traits; Predisposition; Public Health; Regulatory Element; Resistance; Resources; Route; Sample Size; Sampling; Signal Transduction; Site; Testing; Therapeutic Intervention; Untranslated RNA; Variant; Waist-Hip Ratio; Work; base; causal variant; design; effective therapy; exome; genetic approach; genetic testing; genome wide association study; genome-wide; genomic data; human disease; improved; innovation; insight; large datasets; novel; novel therapeutics; obesity genetics; obesity risk; rare variant; success; trait; whole genome

For diseases without safe and long-term effective therapies, such as obesity, human genetics offers an unbiased route to biological insights that may provide valuable new therapeutic hypotheses. Genome-wide association studies (GWAS) have implicated both known and novel genes for many polygenic traits, including obesity. However, moving from genetic discovery to biological insight requires overcoming some key hurdles. Because associations from GWAS typically identify clusters of correlated noncoding variants, associated loci typically do not pinpoint either specific regulatory elements or causal genes. In addition, little is known about the function of most genes, so it is often difficult to recognize the biological implications of new discoveries. Fortunately, there is a path forward – considering associated loci in combination can reveal shared biology and causal genes not apparent from any individual association – but powerful computational methods and large numbers of associated loci are needed for this approach to work. For height, a model polygenic trait with many known loci, this approach highlights many relevant pathways and genes, both known and novel. Similar insights have only just begun to emerge when applied to measures of obesity, where there are fewer known loci and likely less well-annotated causal biology. The main goal of these genetic studies is to achieve a clearer view of underlying biology, and progress has been more dramatic for height than for obesity. As such, the current success with height shows the promise for a greatly expanded genetic discovery effort for obesity. This proposal aims to fulfill the promise of human genetics to provide critical insights into the root biological causes of obesity. It builds on the collaborative infrastructure we successfully created within the GIANT consortium and have used to discover most of the common variants known to be associated with anthropometric traits. The work will leverage newly feasible genetic approaches and unprecedented sample sizes to study anthropometric measures of obesity (a major public health problem and unmet medical need) and height (the classical model polygenic trait). To increase the number of genetic discoveries, which is vital to recognizing underlying biology, the proposal encompasses the largest collection of genotyped samples yet assembled (up to 2 million individuals from multiple ancestries), imputed to state-of-the-art reference panels. Association analysis for anthropometric traits will also be performed in large whole genome and whole exome sequence data sets (N>100,000), to discover rare variants that may have larger effects and more precisely pinpoint causal genes/regulatory elements. Computational methods that integrate genetic, expression and epigenetic data will be benchmarked on results from height, and then applied to recognize shared biology across obesity-associated loci and across the allelic spectrum, providing insights into likely causal genes and mechanisms. Finally, Mendelian randomization will be used to infer causal relationships between obesity and circulating metabolites, to define metabolic consequences of obesity as well as new therapeutic opportunities.

对于没有安全和长期有效疗法的疾病，例如肥胖症，人类遗传学提供了一种方法 获得生物学见解的公正途径可能会提供有价值的新治疗假设。全基因组 关联研究（GWAS）表明许多多基因性状涉及已知基因和新基因，包括 肥胖。然而，从基因发现转向生物学洞察需要克服一些关键障碍。 由于 GWAS 的关联通常会识别相关非编码变异的簇，因此相关位点 通常不会查明特定的调控元件或因果基因。此外，人们知之甚少 大多数基因的功能，因此通常很难认识到新发现的生物学意义。 幸运的是，有一条前进的道路——组合考虑相关基因座可以揭示共同的生物学和 因果基因在任何个体关联中并不明显——但强大的计算方法和大型 这种方法的发挥作用需要大量相关基因座。对于身高来说，模型多基因性状具有多种特征 已知基因座，这种方法突出了许多已知和新颖的相关途径和基因。相似的 当应用于肥胖测量时，见解才刚刚开始出现，而对肥胖的了解还较少。 位点和可能注释较少的因果生物学。这些遗传学研究的主要目标是获得更清晰的结果 从基础生物学的角度来看，身高方面的进展比肥胖方面的进展更为显着。因此， 目前在身高方面取得的成功表明，肥胖症基因发现工作有望得到极大扩展。 该提案旨在履行人类遗传学的承诺，为根生物体提供重要的见解 肥胖的原因。它建立在我们在 GIANT 内成功创建的协作基础设施的基础上 联盟并已用于发现已知与相关的大多数常见变体 人体测量特征。这项工作将利用新的可行的遗传方法和前所未有的样本 研究肥胖的人体测量指标（一个重大的公共卫生问题和未满足的医疗需求） 和身高（经典模型多基因性状）。增加基因发现的数量，这对于 该提案认识到基础生物学，涵盖了迄今为止最大的基因分型样本集合 集合（来自多个血统的多达 200 万人），归因于最先进的参考小组。 人体测量特征的关联分析也将在大型全基因组和全外显子组中进行 序列数据集（N>100,000），以发现可能具有更大影响和更精确的罕见变异 查明因果基因/调控元件。整合遗传、表达和 表观遗传数据将以高度结果为基准，然后应用于识别共享生物学 跨越与肥胖相关的基因座和整个等位基因谱，提供对可能的因果基因和 机制。最后，孟德尔随机化将用于推断肥胖与肥胖之间的因果关系。 循环代谢物，以确定肥胖的代谢后果以及新的治疗机会。

项目成果

The efficacy of detecting variants with small effects on the Affymetrix 6.0 platform using pooled DNA.

• DOI: 10.1007/s00439-011-0974-0
• 发表时间: 2011-11
• 期刊: Human genetics
• 影响因子: 5.3
• 作者: Chiang CW;Gajdos ZK;Korn JM;Butler JL;Hackett R;Guiducci C;Nguyen TT;Wilks R;Forrester T;Henderson KD;Le Marchand L;Henderson BE;Haiman CA;Cooper RS;Lyon HN;Zhu X;McKenzie CA;Palmert MR;Hirschhorn JN
• 通讯作者: Hirschhorn JN

Genetic Evidence for a Causal Role of Obesity in Diabetic Kidney Disease.

• DOI: 10.2337/db15-0254
• 发表时间: 2015-12
• 期刊: Diabetes
• 影响因子: 7.7
• 作者: Todd JN;Dahlström EH;Salem RM;Sandholm N;Forsblom C;FinnDiane Study Group;McKnight AJ;Maxwell AP;Brennan E;Sadlier D;Godson C;Groop PH;Hirschhorn JN;Florez JC
• 通讯作者: Florez JC

Using metabolite profiling to construct and validate a metabolite risk score for predicting future weight gain.

使用代谢物分析构建和验证代谢物风险评分，以预测未来的体重增加。

• DOI: 10.1371/journal.pone.0222445
• 发表时间: 2019
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Geidenstam,Nina;Hsu,Yu-HanH;Astley,ChristinaM;Mercader,JosepM;Ridderstråle,Martin;Gonzalez,MariaE;Gonzalez,Clicerio;Hirschhorn,JoelN;Salem,RanyM
• 通讯作者: Salem,RanyM

MixFit: Methodology for Computing Ancestry-Related Genetic Scores at the Individual Level and Its Application to the Estonian and Finnish Population Studies.

MixFit：计算个人水平上与祖先相关的遗传评分的方法及其在爱沙尼亚和芬兰人群研究中的应用。

• DOI: 10.1371/journal.pone.0170325
• 发表时间: 2017
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Haller T;Leitsalu L;Fischer K;Nuotio ML;Esko T;Boomsma DI;Kyvik KO;Spector TD;Perola M;Metspalu A
• 通讯作者: Metspalu A

Clear detection of ADIPOQ locus as the major gene for plasma adiponectin: results of genome-wide association analyses including 4659 European individuals.

• DOI: 10.1016/j.atherosclerosis.2009.11.035
• 发表时间: 2010-02
• 期刊: ATHEROSCLEROSIS
• 影响因子: 5.3
• 作者: Heid, Iris M.;Henneman, Peter;Hicks, Andrew;Coassin, Stefan;Winkler, Thomas;Aulchenko, Yurii S.;Fuchsberger, Christian;Song, Kijoung;Hivert, Marie-France;Waterworth, Dawn M.;Timpson, Nicholas J.;Richards, J. Brent;Perry, John R. B.;Tanaka, Toshiko;Amin, Najaf;Kollerits, Barbara;Pichler, Irene;Oostra, Ben A.;Thorand, Barbara;Frants, Rune R.;Illig, Thomas;Dupuis, Josee;Glaser, Beate;Spector, Tim;Guralnik, Jack;Egan, Josephine M.;Florez, Jose C.;Evans, David M.;Soranzo, Nicole;Bandinelli, Stefania;Carlson, Olga D.;Frayling, Timothy M.;Burling, Keith;Smith, George Davey;Mooser, Vincent;Ferrucci, Luigi;Meigs, James B.;Vollenweider, Peter;van Dijk, Ko Willems;Pramstaller, Peter;Kronenberg, Florian;van Duijn, Cornelia M.
• 通讯作者: van Duijn, Cornelia M.