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<h4 id="Two-extended-haplotype-blocks-are-associated-with-adaptation-to-high-altitude-habitats-in-East-African-honey-bees-gt-gt-gt"><a href="#Two-extended-haplotype-blocks-are-associated-with-adaptation-to-high-altitude-habitats-in-East-African-honey-bees-gt-gt-gt" class="headerlink" title="Two extended haplotype blocks are associated with adaptation to high altitude habitats in East African honey bees >>>"></a>Two extended haplotype blocks are associated with adaptation to high altitude habitats in East African honey bees <a href="http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1006792">>>></a></h4><p>PLoS Genet 13(5): e1006792.<br>作者通过对东非意蜂高海拔和低海拔群体进行全基因组测序，发现高海拔和低海拔群体基因组之间存在两个遗传差异非常大的区段。其中一个区段存在一系列章鱼胺受体，推测与高海拔和低海拔群体的学习和觅食行为差异有关。这两个区段极大的遗传差异暗示了这些群体古老的起源，祖先群体很可能在传播到当前区域之前就存在不同的环境适应性了。</p>
<h4 id="Pollutants-and-Insecticides-Drive-Local-Adaptation-in-African-Malaria-Mosquitoes-gt-gt-gt"><a href="#Pollutants-and-Insecticides-Drive-Local-Adaptation-in-African-Malaria-Mosquitoes-gt-gt-gt" class="headerlink" title="Pollutants and Insecticides Drive Local Adaptation in African Malaria Mosquitoes >>>"></a>Pollutants and Insecticides Drive Local Adaptation in African Malaria Mosquitoes <a href="https://academic.oup.com/mbe/article/34/5/1261/2996747/Pollutants-and-Insecticides-Drive-Local-Adaptation">>>></a></h4><p>Mol Biol Evol (2017) 34 (5): 1261-1275.<br>对喀麦隆地区941只疟蚊进行简化基因组测序，得到8000个SNP标记。分析发现这些蚊子存在城市和乡村的群体遗传分化，城市污染地区的蚊子存在对杀虫剂的适应性。 </p>
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<p>在处理一些结构化的文件，如fasta文件，我们往往需要读完一条记录再进行处理，如计算每条序列的长度，GC含量等等。这个时候通常的做法是按行读，先把内容存在一个临时变量，直到读到下一个区块分隔符（如fasta文件的‘>‘）的时候再处理上一条记录。如果文件不大，也可以一次读进内存，然后用split分割。前一种方法逻辑有点绕，而且容易忘记处理最后一条记录。而后一种方法对于大文件的处理就不适用了。</p>
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<p><a href="http://home.uchicago.edu/rhudson1/source/mksamples.html">ms</a>是中性进化条件下，群体数据模拟最常用的软件。群体数据模拟在群体遗传学研究中非常重要，可以用来评估各统计量的可靠性，研究某种群体历史下各统计量的估计值，帮助我们理解真实数据所经历的群体历史。</p>
<p>基本用法：</p>
<figure class="highlight bash"><table><tr><td class="gutter"><pre><div class="line">1</div></pre></td><td class="code"><pre><div class="line">ms nsam nreps -t θ</div></pre></td></tr></table></figure>
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主成分分析及其解读
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<p>主成分分析是群体遗传学中常用的分析手段，一般用来 1)分析群体中存在的群体结构(分层)； 2) 推断群体历史； 3) 关联分析中对群体结构进行校正。目前用于主成分分析的数据主要为高密度的SNP标记，其主要思想是将多个线性相关的变量(SNP)，通过一系列矩阵转换，变成少数几个变异解释度大的线性无关变量(特征向量)。 </p>
<p align="center"><br> <img src="http://onzjn6hm6.bkt.clouddn.com/pca.png" width="400"/><br></p>
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分染色体并行计算中科学分配任务
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