Complex Trait Analysis of Erythropoiesis in Normal Populations

描述(申请人提供)：稳定状态下的外周血细胞计数是一个重要的遗传因素。值得注意的是，基线血液学参数是普通人群早期死亡率、心脏病和中风以及镰状细胞病和地中海贫血疾病严重程度的重要独立危险因素。在对小鼠的初步数量性状基因座(QTL)分析中，我们已经确定了影响多个基线血液学参数的特定染色体区域，包括红细胞(RBC)计数、血红蛋白(HGB)和红细胞压积(Hct)水平以及平均红细胞体积(MCV)。在这些初步研究的基础上，我们假设存在显著影响正常造血的新基因，并提出了一种公正的方法来开始解开这个遗传网络，重点放在红细胞生成特征上。其具体目的是：目的1.分析F2杂交组合以确定影响基线血液学性状的染色体区域。我们将(A)增加额外的F2动物和多态标记，以缩小现有的RBC计数、HGB、Hct、MCV和MCH QTL的QTL区间；以及(B)建立和分析三个新的杂交组合，以识别额外的QTL，每个杂交组合至少产生500个F2动物。目的2.利用基因组学、统计学和生物信息学方法缩小QTL区间。我们将利用基因组和电子计算方法来缩小QTL区间。在计算机分析中，将包括联合交叉分析，一种由Co-PI博士Gary Churchill开发的统计方法，以及单倍型分析。目的3.确定和分析候选基因。我们将对AIMS 1和AIMS 2中确定的最可靠的QTL进行候选基因分析。此外，我们将公开所有已确定的QTL区间，允许任何研究人员立即通过关联研究在动物模型和人类种群中进行候选基因分析。 与公共卫生的相关性：调节外周血液特征的基因深刻影响着人类的疾病结局。识别外周血基线性状的主要遗传决定因素将增强我们对血液形成的理解，并为血液学和心血管病理学提供新的诊断和治疗靶点。

DESCRIPTION (provided by applicant): A substantial genetic contribution underlies steady state peripheral blood counts. Notably, baseline hematological parameters are significant, independent risk factors for early mortality, heart disease and stroke in the general population, and for disease severity in sickle cell disease and thalassemia. In preliminary quantitative trait locus/loci (QTL) analyses in mice, we have identified specific chromosomal regions influencing multiple baseline hematological parameters, including red blood cell (RBC) count, hemoglobin (Hgb) and hematocrit (Hct) levels, and mean corpuscular volume (MCV). Based on these preliminary studies, we hypothesize that novel genes which significantly impact normal hematopoiesis exist and propose an unbiased approach to begin unraveling this genetic network, focusing on erythropoietic traits. The specific aims are to: Aim 1. Analyze F2 intercrosses to identify chromosome regions influencing baseline hematological traits. We will (a) add additional F2 animals and polymorphic markers to narrow existing QTL intervals identified for RBC count, Hgb, Hct, MCV, and MCH QTL; and (b) establish and analyze three new crosses to identify additional QTL, generating at least 500 F2 animals in each. Aim 2. Narrow QTL intervals using genomic, statistical, and bioinformatic approaches. We will utilize genomic and in silico approaches to narrow QTL intervals. In silico analyses will include combined cross analysis, a statistical method developed by Co-Pi Dr. Gary Churchill, and haplotype analysis. Aim 3. Identify and analyze candidate genes. We will pursue candidate gene analysis for the most robust QTL identified in Aims 1 and 2. Furthermore, we will make available publicly all QTL intervals identified, allowing any investigator to immediately pursue candidate gene analysis in animal models and in human populations by association studies. Relevance to Public Health: Genes regulating peripheral blood traits profoundly influence disease outcome in man. Identifying the primary genetic determinants of baseline peripheral blood traits will enhance our understanding of blood formation and provide novel diagnostic and therapeutic targets for hematological and cardiovascular pathologies.