A resource for the genetic analysis of complex traits

复杂性状遗传分析的资源

• 批准号: 8255484
• 负责人: Stuart John Macdonald
• 金额: $ 42.09万
• 依托单位: UNIVERSITY OF KANSAS LAWRENCE
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-07-15 至 2015-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8255484
• 关键词: Affect; Alleles; Alzheimer' s Disease; American; Animal Model; Biological Assay; Caffeine; Cataloging; Catalogs; Chromosomes; Communities; Complex; DNA; DNA Resequencing; DNA Sequence; Data; Detection; Diabetes Mellitus; Diagnosis; Disease; Disease Progression; Dissection; Dopamine; Drosophila genome; Drosophila genus; Drosophila melanogaster; Drug Tolerance; Elements; Environmental Risk Factor; Frequencies; Future; Generations; Genetic; Genetic Polymorphism; Genetic Recombination; Genetic Risk; Genetic Variation; Genome; Genotype; Heart Diseases; Heritability; Inbreeding; Incidence; Individual; Inherited; Investigation; Joints; Laboratories; Link; Linkage Disequilibrium; Maintenance; Malignant Neoplasms; Maps; Measurement; Measures; Medicine; Meiosis; Methodology; Methods; Mus; Nicotine; Nucleotides; Online Systems; Output; Parents; Pattern; Pharmaceutical Preparations; Phase; Phenotype; Population; Positioning Attribute; Probability; Quantitative Trait Loci; Reading; Recombinants; Relative (related person); Resistance; Resolution; Resources; Risk; Robin bird; Sequence Alignment; Site; Starvation; Stress; Structure; System; Technology; Validation; Variant; Work; case-by-case basis; design; disorder control; disorder risk; fly; genetic analysis; genome sequencing; genome wide association study; genome-wide; novel strategies; population based; prevent; research study; segregation; trait

DESCRIPTION (provided by applicant): Complex diseases are controlled by a large number of potentially interacting genetic and environmental factors. Each year millions of Americans are diagnosed with complex diseases such as diabetes, heart disease, Alzheimer's, and various forms of cancer. If the precise genetic loci contributing to variation in disease risk could be characterized, disease incidence might be predicted, and treatments could be modified on a case-by-case basis to halt disease progression and ameliorate suffering (so-called personalized medicine). Unfortunately, progress towards identifying the catalog of genetic risk alleles has been slow, in part because it is unclear what we are searching for: Risk alleles may be common in the population, individually conferring only slight disease risk, or may be rare, but with much larger effects. Our proposal explores a novel approach to characterize genetic loci that combines the strengths of typical QTL (Quantitative Trait Locus) and association mapping, while minimizing some of their weaknesses. A feature of the approach is that it provides a small set of candidate causative sites for each QTL mapped: Previous methodologies have had great difficulty moving from QTL to the precise nucleotides involved. The main aim of the proposal is to develop an integrated framework for the genetic dissection of complex traits in the model organism Drosophila melanogaster. A pair of synthetic laboratory populations will each be initiated with eight inbred founder lines, and following many generations of maintenance a large panel of RILs (Recombinant Inbred Lines) will be derived. The genome of each RIL will be a fine-scale mosaic of segments inherited from the eight founder strains. Following genotyping the panel will serve as a valuable community resource in which to map QTL with high resolution, and we will facilitate these user-directed experiments by creating a web-based analysis portal. The approach yields considerable power to map QTL relative to commonly applied association study methods, and offers the extremely important ability to estimate the frequency of mapped QTL, and in turn the contribution of common alleles to complex trait variation. By resequencing the genomes of the founder strains, for each QTL one can additionally identify the small handful of putatively causative DNA variants to target for future work. We will map QTL for four stress- and drug-tolerance demonstration traits, chosen for their ease of measurement and modest heritability. Each trait will be scored in three mapping designs: directly in homozygous RILs, in the heterozygous progeny of round-robin RIL-RIL crosses, and after crossing RILs to a common inbred reference strain. The two heterozygous testcrosses will examine the effect of inbreeding on QTL mapping directly in RILs. These testcrosses are relatively simple to carry out in flies, and the results may have important implications for the design of experiments using the mouse "Collaborative Cross" eight-way RILs, a system in which the testcrosses would be considerably more cumbersome and expensive to perform.

描述（由申请人提供）：复杂疾病由大量潜在相互作用的遗传和环境因素控制。每年有数百万美国人被诊断患有复杂的疾病，如糖尿病，心脏病，阿尔茨海默氏症和各种形式的癌症。如果能够确定导致疾病风险变化的精确遗传位点，就可以预测疾病的发生率，并根据具体情况修改治疗方法，以阻止疾病进展并减轻痛苦（所谓的个性化医疗）。不幸的是，识别遗传风险等位基因目录的进展一直很缓慢，部分原因是我们还不清楚我们在寻找什么：风险等位基因可能在人群中很常见，单独只赋予轻微的疾病风险，或者可能很罕见，但影响要大得多。我们的建议探索了一种新的方法来表征遗传位点，结合了典型的QTL（数量性状基因座）和关联作图的优势，同时最大限度地减少了他们的一些弱点。该方法的一个特点是，它为每个QTL定位提供了一小组候选致病位点：以前的方法很难从QTL移动到所涉及的精确核苷酸。 该提案的主要目的是为模式生物黑腹果蝇复杂性状的遗传解剖建立一个综合框架。一对合成实验室群体将各自用8个近交建立系开始，并且在许多代的维持之后，将衍生出一大组RILs（重组近交系）。每个RIL的基因组将是从八个创始菌株遗传的片段的精细尺度镶嵌。在基因分型之后，该小组将作为一个宝贵的社区资源，在其中以高分辨率绘制QTL，我们将通过创建基于网络的分析门户网站来促进这些用户指导的实验。相对于常用的关联研究方法，该方法产生了相当大的能力来定位QTL，并提供了极其重要的能力来估计定位QTL的频率，进而估计常见等位基因对复杂性状变异的贡献。通过对创始菌株的基因组进行重测序，对于每个QTL，人们可以另外鉴定少数致病DNA变体以供将来工作靶向。 我们将绘制四个压力和耐药性示范性状的QTL，选择他们的易于测量和适度的遗传力。每种性状将在三种作图设计中评分：直接在纯合RIL中，在RIL-RIL杂交的杂合后代中，以及将RIL与常见的近交参考品系杂交后。两个杂合测交将直接在RIL中检查近交对QTL作图的影响。这些testcrosses是相对简单的苍蝇进行，结果可能有重要的影响，实验设计使用小鼠“协作交叉”八路RILs，系统中的testcrosses将是相当麻烦和昂贵的执行。