Complex Trait Analysis of Erythropoiesis in Normal Populations

描述（由申请人提供）：大量的遗传贡献是稳定状态外周血计数的基础。值得注意的是，基线血液学参数是一般人群早期死亡、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。在小鼠的初步定量性状位点/位点（QTL）分析中，我们已经确定了影响多种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）。基于这些初步研究，我们假设存在显著影响正常造血的新基因，并提出一种无偏倚的方法来开始揭示这一遗传网络，重点关注红细胞生成特征。具体目标是：目标1。分析F2交叉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动物和多态性标记，以缩小现有的RBC计数、Hgb、Hct、MCV和MCH QTL的QTL间隔；(b)建立和分析3个新杂交，以确定额外的QTL，每个杂交至少产生500只F2动物。目标2。使用基因组学、统计学和生物信息学方法缩小QTL间隔。我们将利用基因组和计算机方法来缩小QTL间隔。计算机分析将包括联合交叉分析，一种由Gary Churchill博士开发的统计方法，以及单倍型分析。目标3。鉴定和分析候选基因。我们将对目标1和目标2中确定的最健壮的QTL进行候选基因分析。此外，我们将公开所有确定的QTL区间，允许任何研究者通过关联研究立即在动物模型和人群中进行候选基因分析。

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.