Vitt Ursula, Gietzen Darryl, Stevens Kristian, Wingrove Jim, Becha Shanya, Bulloch Sean, Burrill John, Chawla Narinder, Chien Jennifer, Crawford Matthew, Ison Craig, Kearney Liam, Kwong Mary, Park Joe, Policky Jennifer, Weiler Mark, White Renee, Xu Yuming, Daniels Sue, Jacob Howard, Jensen-Seaman Michael I, Lazar Jozef, Stuve Laura, Schmidt Jeanette
Incyte Corporation, Palo Alto, California 94304, USA.
Genome Res. 2004 Apr;14(4):640-50. doi: 10.1101/gr.1932304.
We aligned Incyte ESTs and publicly available sequences to the rat genome and analyzed rat chromosome 1q43-54, a region in which several quantitative trait loci (QTLs) have been identified, including renal disease, diabetes, hypertension, body weight, and encephalomyelitis. Within this region, which contains 255 Ensembl gene predictions, the aligned sequences clustered into 568 Incyte genes and gene fragments. Of the Incyte genes, 261 (46%) overlapped 184 (72%) of the Ensembl gene predictions, whereas 307 were unique to Incyte. The rat-to-human syntenic map displays rearrangement of this region on rat chr. 1 onto human chromosomes 9 and 10. The mapping of corresponding human disease phenotypes to either one of these chromosomes has allowed us to focus in on genes associated with disease phenotypes. As an example, we have used the syntenic information for the rat Rf-1 disease region and the orthologous human ESRD disease region to reduce the size of the original rat QTL to only 11.5 Mb. Using the syntenic information in combination with expression data from ESTs and microarrays, we have selected a set of 66 candidate disease genes for Rf-1. The combination of the results from these different analyses represents a powerful approach for narrowing the number of genes that could play a role in the development of complex diseases.
我们将英赛特公司的EST序列和公开可用序列与大鼠基因组进行比对,并分析了大鼠1号染色体1q43 - 54区域,该区域已鉴定出多个数量性状基因座(QTL),包括肾病、糖尿病、高血压、体重和脑脊髓炎。在这个包含255个Ensembl基因预测的区域内,比对后的序列聚集成568个英赛特基因和基因片段。在英赛特基因中,261个(46%)与184个(72%)的Ensembl基因预测重叠,而307个是英赛特特有的。大鼠与人的同线基因图谱显示该区域在大鼠1号染色体上的排列重排到了人类9号和10号染色体上。将相应的人类疾病表型定位到这些染色体中的任何一条上,使我们能够聚焦于与疾病表型相关的基因。例如,我们利用大鼠Rf - 1疾病区域和直系同源人类终末期肾病疾病区域的同线信息,将原始大鼠QTL的大小缩小到仅11.5 Mb。利用同线信息结合EST和微阵列的表达数据,我们为Rf - 1选择了一组66个候选疾病基因。这些不同分析结果的结合代表了一种强大的方法,可减少可能在复杂疾病发展中起作用的基因数量。