Gruppo Interdipartimentale di Bioinformatica e Biologia Computazionale, Università di Napoli Federico II-Università di Salerno, Naples, Italy.
PLoS One. 2009 Nov 20;4(11):e7927. doi: 10.1371/journal.pone.0007927.
Genetic differences both between individuals and populations are studied for their evolutionary relevance and for their potential medical applications. Most of the genetic differentiation among populations are caused by random drift that should affect all loci across the genome in a similar manner. When a locus shows extraordinary high or low levels of population differentiation, this may be interpreted as evidence for natural selection. The most used measure of population differentiation was devised by Wright and is known as fixation index, or F(ST). We performed a genome-wide estimation of F(ST) on about 4 millions of SNPs from HapMap project data. We demonstrated a heterogeneous distribution of F(ST) values between autosomes and heterochromosomes. When we compared the F(ST) values obtained in this study with another evolutionary measure obtained by comparative interspecific approach, we found that genes under positive selection appeared to show low levels of population differentiation. We applied a gene set approach, widely used for microarray data analysis, to detect functional pathways under selection. We found that one pathway related to antigen processing and presentation showed low levels of F(ST), while several pathways related to cell signalling, growth and morphogenesis showed high F(ST) values. Finally, we detected a signature of selection within genes associated with human complex diseases. These results can help to identify which process occurred during human evolution and adaptation to different environments. They also support the hypothesis that common diseases could have a genetic background shaped by human evolution.
个体和群体之间的遗传差异因其进化相关性和潜在的医学应用而受到研究。大多数群体间的遗传分化是由随机漂变引起的,这种漂变应该以相似的方式影响基因组中的所有基因座。当一个基因座表现出异常高或低的群体分化水平时,这可能被解释为自然选择的证据。最常用的群体分化衡量标准是由 Wright 设计的,称为固定指数,或 F(ST)。我们在 HapMap 项目数据中约 400 万个 SNPs 上进行了全基因组 F(ST)估计。我们证明了常染色体和异染色质之间 F(ST)值的不均匀分布。当我们将本研究中获得的 F(ST)值与通过比较种间方法获得的另一个进化衡量标准进行比较时,我们发现正选择下的基因似乎显示出较低的群体分化水平。我们应用了一种广泛用于微阵列数据分析的基因集方法,来检测受选择影响的功能途径。我们发现一个与抗原加工和呈递相关的途径显示出低 F(ST)水平,而几个与细胞信号、生长和形态发生相关的途径显示出高 F(ST)值。最后,我们在与人类复杂疾病相关的基因中检测到了选择的特征。这些结果可以帮助确定在人类进化和适应不同环境的过程中发生了哪些过程。它们还支持了这样一种假设,即常见疾病可能有一个由人类进化塑造的遗传背景。