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GWAS如何确定候选区间和causal-gene.html
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GWAS如何确定候选区间和causal gene
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<p>随着高通量测序成本的急剧下降，越来越多的GWAS研究通过全基因组重测序获得标记。全基因组重测序的高密度标记，使得通过GWAS研究快速找到causal gene甚至causal mutation成为可能。但是通过GWAS得到一个信号后，怎么确定与这个信号关联的基因呢？ </p>
<a id="more"></a>
<p>基本的思路就是找与这个信号强连锁的区域，一般来说r<sup>2</sup>大于0.6的区域视为强连锁的区域。简单粗糙的做法：我们可以通过群体的全基因组LD-decay分析，找到LD decay到r<sup>2</sup>等于0.6时所对应的距离，将GWAS超过阈值的信号前后各延伸这个距离作为候选区间。<br>但是基因组不同区域的连锁程度差异很大，上面一刀切的做法可能使我们漏掉一些基因，或者多调查许多关联性并不强的基因。而且很多作物的连锁性很强，导致候选基因很多，这样就大大增加了工作难度。下面介绍一个非常简单高效的方法（<a href="http://www.nature.com/ng/journal/v48/n8/abs/ng.3596.html" target="_blank" rel="external">参考文献</a>）。 </p>
<p align="center"><br> <img src="http://onzjn6hm6.bkt.clouddn.com/20170409_heatmap.png" width="300"><br></p>
<ol>
<li><p>找到信号后，向前后延伸一段距离（可以根据全基因组的LD-decay水平大概估计），计算这段区域内所有标记pairwise r<sup>2</sup>，将r<sup>2</sup>大于0.6的block作为候选区间。pairwise r<sup>2</sup>可以用<a href="http://zzz.bwh.harvard.edu/plink/" target="_blank" rel="external">PLINK</a>计算：</p>
<figure class="highlight bash"><table><tr><td class="gutter"><pre><div class="line">1</div><div class="line">2</div><div class="line">3</div></pre></td><td class="code"><pre><div class="line">plink --noweb --bfile <bfile_prefix> \</div><div class="line"> --chr 5 --from-bp 13641890 --to-bp 17641890 \</div><div class="line"> --matrix --r2 --out <out_prefix></div></pre></td></tr></table></figure>
<p>画图用R,输入文件为plink计算的到的r<sup>2</sup>矩阵，以及标记的位置:</p>
<figure class="highlight r"><table><tr><td class="gutter"><pre><div class="line">1</div><div class="line">2</div><div class="line">3</div><div class="line">4</div><div class="line">5</div><div class="line">6</div><div class="line">7</div><div class="line">8</div><div class="line">9</div></pre></td><td class="code"><pre><div class="line"><span class="comment">#!/usr/bin/env Rscript</span></div><div class="line"><span class="keyword">library</span>(LDheatmap)</div><div class="line">argv <- commandArgs(<span class="literal">TRUE</span>)</div><div class="line">ldmatrix <- as.matrix(read.table(argv[<span class="number">1</span>],sep=<span class="string">' '</span>))</div><div class="line">pos <- as.numeric(unlist(read.table(argv[<span class="number">2</span>], head=<span class="literal">FALSE</span>)))</div><div class="line">pdf(argv[<span class="number">3</span>])</div><div class="line">rgb.palette <- colorRampPalette(rev(c(<span class="string">"yellow"</span>, <span class="string">"orange"</span>, <span class="string">"red"</span>)), space = <span class="string">"rgb"</span>)</div><div class="line">LDheatmap(ldmatrix, genetic.distances=pos, color=rgb.palette(<span class="number">100</span>), flip=<span class="literal">TRUE</span>)</div><div class="line">dev.off()</div></pre></td></tr></table></figure>
</li>
<li><p>将这个区域内的标记按照其对基因功能的影响程度分为5类： </p>
<blockquote>
<p>1） 标记与性状显著关联（-log<sub>10</sub>P大于阈值），且该标记影响氨基酸编码，或者位于剪接位点；<br>2） 标记与性状显著关联，且位于基因起始密码子上游2 kb内；<br>3） 标记与性状显著关联，且位于基因内，除开1）和2）之外的标记<br>4） 标记与性状显著关联，位于基因间区<br>5） 标记与性状不显著关联 </p>
</blockquote>
</li>
</ol>
<p>然后，按照这5类的顺序，依次调查，一般来说，属于1）类的可能性很大，而且基因一般就几个，这样就大大减少了工作难度。</p>
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