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    <meta name="description" content="MR定义 孟德尔随机化是一种基于全基因组测序数据(GWAS数据)，利用单核首酸多态性(SNPs)作为工具变量(IV)，用于揭示因果关系的新型流行病学方法，相较于队列研究等观察性研究，暴露在出生前便已确定，较少受到反向因果及混杂因素的影响，因而能够有效减少偏倚。  MR的核心是运用遗传学数据作为桥梁，来探索某一暴露和某一结局之间的因果关联。与RCT将参与者随机分配到试验组或对照组类似，MR研究基于影">
  
  
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            <span id="subtitle">【学习】孟德尔随机化</span>
          
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            <h1 id="seo-header">【学习】孟德尔随机化</h1>
            
              <p id="updated-time" class="note note-info" style="">
                
                  
                    Last updated on March 19, 2024 pm
                  
                
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              <div class="markdown-body">
                
                <h2 id="MR定义">MR定义</h2>
<p>孟德尔随机化是一种基于全基因组测序数据(GWAS数据)，利用单核首酸多态性(SNPs)作为工具变量(IV)，用于揭示因果关系的新型流行病学方法，相较于队列研究等观察性研究，暴露在出生前便已确定，较少受到反向因果及混杂因素的影响，因而能够有效减少偏倚。<br>
<img src="https://img.limour.top/2023/10/14/652a61ab222a4.webp" srcset="https://jscdn.limour.top/gh/Limour-dev/Sakurairo_Vision/load_svg/inload.svg" lazyload alt="RCT与MR的比较"><br>
MR的核心是运用遗传学数据作为桥梁，来探索某一暴露和某一结局之间的因果关联。与RCT将参与者随机分配到试验组或对照组类似，MR研究基于影响危险因素的一个或多个等位基因，对参与基因进行&quot;随机化&quot;(自然的随机化)，以确定这些遗传变异的携带者与非携带者相比，是否具有不同的疾病发生风险，因此，孟德尔随机化可以被认为类似于<a target="_blank" rel="noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2458144">&quot;自然的随机对照试验&quot;</a>。<a target="_blank" rel="noopener" href="https://mr-dictionary.mrcieu.ac.uk/">MR的相关术语</a></p>
<h2 id="理论假设">理论假设</h2>
<ol>
<li>the variant is associated with the exposure</li>
<li>the variant is not associated with the outcome via a confounding pathway</li>
<li>the variant does not affect the outcome directly, only possibly indirectly via the exposure</li>
</ol>
<p><img src="https://img.limour.top/2023/10/14/652a651b96f98.webp" srcset="https://jscdn.limour.top/gh/Limour-dev/Sakurairo_Vision/load_svg/inload.svg" lazyload alt="孟德尔随机化框架的有向无环图表示"></p>
<ol>
<li>关联性假设：变异与暴露有关</li>
<li>独立性假设：变异与结果之间没有通过混杂途径相关</li>
<li>排他性假设：变异不直接影响结果，只可能通过暴露途径间接影响</li>
</ol>
<ul>
<li>关联性假设：p值，F统计量，R^2</li>
<li>排他性假设：与结局的相关性计算时，p值要大于0.05</li>
<li><a target="_blank" rel="noopener" href="https://doi.org/10.1093/ije/dyv080">MR-Egger</a>回归相比线性回归可以弱化对排他性假设的要求</li>
</ul>
<h2 id="适用范围">适用范围</h2>
<ul>
<li>不确定先有鸡还是先有蛋，比如，到底是抑郁导致肺癌还是肺癌导致了抑郁？</li>
<li>暴露因素难以测量，或者花费昂贵。例如，水溶性维生素等生物标志物的检测金标准可能成本太高，大样本无法承受，或者空腹血糖的测量需要隔夜空腹，可能不现实。</li>
<li>暴露与结局数据来自同一人群，且不存在或存在少量可接受范围内的样本重叠</li>
</ul>
<h2 id="配置环境">配置环境</h2>
<ul>
<li><a href="/-ji-lu--an-zhuang-sheng-xin-de-dai-ma-bian-xie-huan-jing">基础编程环境</a></li>
<li><a href="/-fu-ke-GitHub-wen-jian-jia-su">GitHub 下载加速</a></li>
<li><a href="/-ji-lu-SOCKS5-zhuan-QUIC">可能需要用到的加速服务</a></li>
</ul>
<figure class="highlight bash"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br></pre></td><td class="code"><pre><span class="line">conda create -n MR -c conda-forge r-devtools -y</span><br><span class="line">conda activate MR</span><br><span class="line">conda install -c conda-forge r-irkernel -y</span><br><span class="line">Rscript -e <span class="string">&quot;IRkernel::installspec(name=&#x27;MR&#x27;, displayname=&#x27;MR&#x27;)&quot;</span></span><br><span class="line"><span class="comment"># Rscript -e &quot;usethis::edit_r_environ()&quot; # 设置 GITHUB_PAT</span></span><br><span class="line"><span class="comment"># nano ~/.Renviron # MRCIEU 真是超喜欢GITHUB，要访问一万次 api.github.com</span></span><br><span class="line">conda install -c conda-forge r-rmarkdown -y</span><br><span class="line">conda install -c conda-forge r-meta -y</span><br><span class="line">wget https://github.com/MRCIEU/TwoSampleMR/archive/refs/heads/master.zip -O TwoSampleMR.zip</span><br><span class="line">Rscript -e <span class="string">&quot;devtools::install_local(&#x27;TwoSampleMR.zip&#x27;)&quot;</span></span><br><span class="line">wget https://github.com/MRCIEU/MRInstruments/archive/refs/heads/master.zip -O MRInstruments.zip</span><br><span class="line">Rscript -e <span class="string">&quot;devtools::install_local(&#x27;MRInstruments.zip&#x27;)&quot;</span></span><br><span class="line">conda install -c conda-forge r-susier -y</span><br><span class="line">conda install -c bioconda bioconductor-variantannotation -y</span><br><span class="line">wget https://github.com/MRCIEU/gwasglue/archive/refs/heads/master.zip -O gwasglue.zip</span><br><span class="line">Rscript -e <span class="string">&quot;devtools::install_local(&#x27;gwasglue.zip&#x27;)&quot;</span></span><br><span class="line"><span class="comment"># wget https://github.com/MRCIEU/genetics.binaRies/archive/refs/heads/master.zip -O genetics.binaRies.zip</span></span><br><span class="line"><span class="comment"># Rscript -e &quot;devtools::install_local(&#x27;genetics.binaRies.zip&#x27;)&quot;</span></span><br><span class="line">conda install -c bioconda plink -y</span><br><span class="line"><span class="comment"># whereis plink # /opt/conda/envs/MR/bin/plink</span></span><br><span class="line">Rscript -e <span class="string">&#x27;install.packages(&quot;MendelianRandomization&quot;)&#x27;</span></span><br></pre></td></tr></table></figure>
<h2 id="数据来源">数据来源</h2>
<ul>
<li>精神病学基因组：<a target="_blank" rel="noopener" href="https://pgc.unc.edu/">PGC</a></li>
<li>社会科学遗传学：<a target="_blank" rel="noopener" href="https://www.thessgac.org/">SSGAC</a></li>
<li>大脑健康和疾病：<a target="_blank" rel="noopener" href="https://ctg.cncr.nl/software/summary_statistics">CTG</a></li>
<li>MRCIEU汇总数据库：<a target="_blank" rel="noopener" href="https://gwas.mrcieu.ac.uk/">IEU</a></li>
<li>GWAS研究目录：<a target="_blank" rel="noopener" href="https://www.ebi.ac.uk/gwas/search">NHGRI-EBI</a></li>
<li><a href="/shi-yong-GATK-zhao-SNP">自己分析出数据</a></li>
<li><a target="_blank" rel="noopener" href="https://od.limour.top/archives/GWAS/MR">更多相关网站</a></li>
</ul>
<h3 id="一些参考数据">一些参考数据</h3>
<figure class="highlight bash"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br></pre></td><td class="code"><pre><span class="line">wget http://fileserve.mrcieu.ac.uk/ld/1kg.v3.tgz</span><br><span class="line">tar -zxvf 1kg.v3.tgz</span><br><span class="line"><span class="comment"># mkdir EUR &amp;&amp; mv EUR.* EUR</span></span><br></pre></td></tr></table></figure>
<h3 id="示例结局数据">示例结局数据</h3>
<ul>
<li>浏览器下载 <a target="_blank" rel="noopener" href="https://figshare.com/ndownloader/files/40036684">ADHD2022_iPSYCH_deCODE_PGC.meta.gz</a></li>
<li><a href="/Rclone-bei-fen-VPS-shu-ju-dao-onedrive">上传到服务器</a></li>
</ul>
<figure class="highlight bash"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># zcat ADHD2022_iPSYCH_deCODE_PGC.meta.gz | head</span></span><br><span class="line">CHR SNP BP A1 A2 FRQ_A_38691 FRQ_U_186843 INFO OR SE P Direction Nca Nco</span><br><span class="line">8 rs62513865 101592213 C T 0.925 0.937 0.981 0.99631 0.0175 0.8325 +---+++0-++-+ 38691 186843</span><br><span class="line">8 rs79643588 106973048 G A 0.91 0.917 1 1.00411 0.0159 0.7967 ++--++-+-+-++ 38691 186843</span><br><span class="line">8 rs17396518 108690829 T G 0.561 0.577 0.998 0.99611 0.0096 0.6876 --++-++??-+-- 37367 184388</span><br><span class="line">8 rs983166 108681675 A C 0.57 0.586 0.996 0.99491 0.0096 0.5956 --++-++++-+-- 38691 186843</span><br><span class="line">8 rs28842593 103044620 T C 0.839 0.836 0.982 0.98314 0.0135 0.2081 ----++0+??--+ 37504 184525</span><br><span class="line">8 rs7014597 104152280 G C 0.824 0.824 0.997 0.99950 0.0122 0.9679 +-++-+++++--- 38691 186843</span><br><span class="line">8 rs3134156 100479917 T C 0.841 0.833 0.997 0.98866 0.0128 0.3762 -+----+--++-- 38691 186843</span><br><span class="line">8 rs6980591 103144592 A C 0.783 0.79 1 1.01106 0.0108 0.3075 ++-++---+++++ 38691 186843</span><br><span class="line">8 rs72670434 108166508 A T 0.642 0.623 0.983 1.00672 0.0103 0.5171 +++-+++--+++- 38691 186843</span><br></pre></td></tr></table></figure>
<figure class="highlight txt"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br></pre></td><td class="code"><pre><span class="line">CHR Chromosome (hg19)</span><br><span class="line">SNP Marker name</span><br><span class="line">BP Base pair location (hg19)</span><br><span class="line">A1 Reference allele for OR (may or may not be minor allele)</span><br><span class="line">A2 Alternative allele</span><br><span class="line">FRQ_A_38691 allele frequency of A1 in 38,691 ADHD cases</span><br><span class="line">FRQ_U_186843 allele frequency of A1 in 38,691 controls</span><br><span class="line">INFO Imputation information score (the reported imputation INFO score is a weighted average across the</span><br><span class="line">cohorts contributing to the meta-analysis for that variant)</span><br><span class="line">OR Odds ratio for the effect of the A1 allele</span><br><span class="line">SE Standard error of the log(OR)</span><br><span class="line">P P-value for association test in the meta-analysis</span><br><span class="line">Direction direction of effect in the included cohorts</span><br><span class="line">Nca number of cases with variant information</span><br><span class="line">Nco number of controls with variant information</span><br></pre></td></tr></table></figure>
<p>其中<code>SNP</code>,<code>Effect allele</code>,<code>Beta(OR)</code>,<code>SE</code>,<code>P</code>这五列是必须的。遇到没有提供EAF的数据，可以<a target="_blank" rel="noopener" href="https://github.com/HaobinZhou/Get_MR">匹配千人基因组数据的EAF</a>，<code>get_eaf_from_1000G</code>。</p>
<h3 id="示例暴露数据">示例暴露数据</h3>
<figure class="highlight bash"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">wget -c https://gwas.mrcieu.ac.uk/files/ieu-a-2/ieu-a-2.vcf.gz</span><br></pre></td></tr></table></figure>
<figure class="highlight r"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br></pre></td><td class="code"><pre><span class="line">VCF_dat <span class="operator">=</span> VariantAnnotation<span class="operator">::</span>readVcf<span class="punctuation">(</span><span class="string">&#x27;~/upload/GWAS/IEU/ieu-a-2.vcf.gz&#x27;</span><span class="punctuation">)</span></span><br><span class="line">exp_dat <span class="operator">=</span> gwasglue<span class="operator">::</span>gwasvcf_to_TwoSampleMR<span class="punctuation">(</span>vcf <span class="operator">=</span> VCF_dat<span class="punctuation">)</span></span><br><span class="line">saveRDS<span class="punctuation">(</span>file <span class="operator">=</span> <span class="string">&#x27;ieu-a-2.exp_dat&#x27;</span><span class="punctuation">,</span> exp_dat<span class="punctuation">)</span></span><br><span class="line">exp_dat <span class="operator">=</span> subset<span class="punctuation">(</span>exp_dat<span class="punctuation">,</span> pval.exposure <span class="operator">&lt;</span> <span class="number">5e-08</span><span class="punctuation">)</span> <span class="comment"># 关联性假设</span></span><br><span class="line"><span class="comment"># 去除连锁不平衡</span></span><br><span class="line"><span class="comment"># exp_dat = TwoSampleMR::clump_data(dat = exp_dat, clump_kb = 10000, clump_r2 = 0.001) # MRCIEU太喜欢用cloud api了</span></span><br><span class="line">fix_ld_clump_local <span class="operator">=</span> <span class="keyword">function</span> <span class="punctuation">(</span>dat<span class="punctuation">,</span> tempfile<span class="punctuation">,</span> clump_kb<span class="punctuation">,</span> clump_r2<span class="punctuation">,</span> clump_p<span class="punctuation">,</span> bfile<span class="punctuation">,</span> plink_bin<span class="punctuation">)</span> <span class="punctuation">&#123;</span></span><br><span class="line">    shell <span class="operator">&lt;-</span> ifelse<span class="punctuation">(</span>Sys.info<span class="punctuation">(</span><span class="punctuation">)</span><span class="punctuation">[</span><span class="string">&quot;sysname&quot;</span><span class="punctuation">]</span> <span class="operator">==</span> <span class="string">&quot;Windows&quot;</span><span class="punctuation">,</span> <span class="string">&quot;cmd&quot;</span><span class="punctuation">,</span> </span><br><span class="line">        <span class="string">&quot;sh&quot;</span><span class="punctuation">)</span></span><br><span class="line">    write.table<span class="punctuation">(</span>data.frame<span class="punctuation">(</span>SNP <span class="operator">=</span> dat<span class="punctuation">[[</span><span class="string">&quot;rsid&quot;</span><span class="punctuation">]</span><span class="punctuation">]</span><span class="punctuation">,</span> P <span class="operator">=</span> dat<span class="punctuation">[[</span><span class="string">&quot;pval&quot;</span><span class="punctuation">]</span><span class="punctuation">]</span><span class="punctuation">)</span><span class="punctuation">,</span> </span><br><span class="line">        file <span class="operator">=</span> tempfile<span class="punctuation">,</span> row.names <span class="operator">=</span> <span class="built_in">F</span><span class="punctuation">,</span> col.names <span class="operator">=</span> <span class="built_in">T</span><span class="punctuation">,</span> <span class="built_in">quote</span> <span class="operator">=</span> <span class="built_in">F</span><span class="punctuation">)</span></span><br><span class="line">    fun2 <span class="operator">&lt;-</span> paste0<span class="punctuation">(</span>shQuote<span class="punctuation">(</span>plink_bin<span class="punctuation">,</span> type <span class="operator">=</span> shell<span class="punctuation">)</span><span class="punctuation">,</span> <span class="string">&quot; --bfile &quot;</span><span class="punctuation">,</span> </span><br><span class="line">        shQuote<span class="punctuation">(</span>bfile<span class="punctuation">,</span> type <span class="operator">=</span> shell<span class="punctuation">)</span><span class="punctuation">,</span> <span class="string">&quot; --clump &quot;</span><span class="punctuation">,</span> shQuote<span class="punctuation">(</span>tempfile<span class="punctuation">,</span> </span><br><span class="line">            type <span class="operator">=</span> shell<span class="punctuation">)</span><span class="punctuation">,</span> <span class="string">&quot; --clump-p1 &quot;</span><span class="punctuation">,</span> clump_p<span class="punctuation">,</span> <span class="string">&quot; --clump-r2 &quot;</span><span class="punctuation">,</span> </span><br><span class="line">        clump_r2<span class="punctuation">,</span> <span class="string">&quot; --clump-kb &quot;</span><span class="punctuation">,</span> clump_kb<span class="punctuation">,</span> <span class="string">&quot; --out &quot;</span><span class="punctuation">,</span> shQuote<span class="punctuation">(</span>tempfile<span class="punctuation">,</span> </span><br><span class="line">            type <span class="operator">=</span> shell<span class="punctuation">)</span><span class="punctuation">)</span></span><br><span class="line">    print<span class="punctuation">(</span>fun2<span class="punctuation">)</span></span><br><span class="line">    system<span class="punctuation">(</span>fun2<span class="punctuation">)</span></span><br><span class="line">    res <span class="operator">&lt;-</span> read.table<span class="punctuation">(</span>paste<span class="punctuation">(</span>tempfile<span class="punctuation">,</span> <span class="string">&quot;.clumped&quot;</span><span class="punctuation">,</span> sep <span class="operator">=</span> <span class="string">&quot;&quot;</span><span class="punctuation">)</span><span class="punctuation">,</span> header <span class="operator">=</span> <span class="built_in">T</span><span class="punctuation">)</span></span><br><span class="line">    unlink<span class="punctuation">(</span>paste<span class="punctuation">(</span>tempfile<span class="punctuation">,</span> <span class="string">&quot;*&quot;</span><span class="punctuation">,</span> sep <span class="operator">=</span> <span class="string">&quot;&quot;</span><span class="punctuation">)</span><span class="punctuation">)</span></span><br><span class="line">    y <span class="operator">&lt;-</span> subset<span class="punctuation">(</span>dat<span class="punctuation">,</span> <span class="operator">!</span>dat<span class="punctuation">[[</span><span class="string">&quot;rsid&quot;</span><span class="punctuation">]</span><span class="punctuation">]</span> <span class="operator">%in%</span> res<span class="punctuation">[[</span><span class="string">&quot;SNP&quot;</span><span class="punctuation">]</span><span class="punctuation">]</span><span class="punctuation">)</span></span><br><span class="line">    <span class="keyword">if</span> <span class="punctuation">(</span>nrow<span class="punctuation">(</span>y<span class="punctuation">)</span> <span class="operator">&gt;</span> <span class="number">0</span><span class="punctuation">)</span> <span class="punctuation">&#123;</span></span><br><span class="line">        message<span class="punctuation">(</span><span class="string">&quot;Removing &quot;</span><span class="punctuation">,</span> <span class="built_in">length</span><span class="punctuation">(</span>y<span class="punctuation">[[</span><span class="string">&quot;rsid&quot;</span><span class="punctuation">]</span><span class="punctuation">]</span><span class="punctuation">)</span><span class="punctuation">,</span> <span class="string">&quot; of &quot;</span><span class="punctuation">,</span> nrow<span class="punctuation">(</span>dat<span class="punctuation">)</span><span class="punctuation">,</span> </span><br><span class="line">            <span class="string">&quot; variants due to LD with other variants or absence from LD reference panel&quot;</span><span class="punctuation">)</span></span><br><span class="line">    <span class="punctuation">&#125;</span></span><br><span class="line">    <span class="built_in">return</span><span class="punctuation">(</span>subset<span class="punctuation">(</span>dat<span class="punctuation">,</span> dat<span class="punctuation">[[</span><span class="string">&quot;rsid&quot;</span><span class="punctuation">]</span><span class="punctuation">]</span> <span class="operator">%in%</span> res<span class="punctuation">[[</span><span class="string">&quot;SNP&quot;</span><span class="punctuation">]</span><span class="punctuation">]</span><span class="punctuation">)</span><span class="punctuation">)</span></span><br><span class="line"><span class="punctuation">&#125;</span></span><br><span class="line">fuck <span class="operator">=</span> fix_ld_clump_local<span class="punctuation">(</span></span><br><span class="line">    dat <span class="operator">=</span> dplyr<span class="operator">::</span>tibble<span class="punctuation">(</span>rsid<span class="operator">=</span>exp_dat<span class="operator">$</span>SNP<span class="punctuation">,</span> pval<span class="operator">=</span>exp_dat<span class="operator">$</span>pval.exposure<span class="punctuation">)</span><span class="punctuation">,</span></span><br><span class="line">    tempfile <span class="operator">=</span> file.path<span class="punctuation">(</span>getwd<span class="punctuation">(</span><span class="punctuation">)</span><span class="punctuation">,</span><span class="string">&#x27;tmp.ld_clump.exp_dat&#x27;</span><span class="punctuation">)</span><span class="punctuation">,</span></span><br><span class="line">    clump_kb <span class="operator">=</span> <span class="number">10000</span><span class="punctuation">,</span> clump_r2 <span class="operator">=</span> <span class="number">0.001</span><span class="punctuation">,</span> clump_p <span class="operator">=</span> <span class="number">1</span><span class="punctuation">,</span></span><br><span class="line">    <span class="comment"># pop = &quot;EUR&quot;, # Super-population. Options are &quot;EUR&quot;, &quot;SAS&quot;, &quot;EAS&quot;, &quot;AFR&quot;, &quot;AMR&quot;</span></span><br><span class="line">    plink_bin <span class="operator">=</span> <span class="string">&#x27;/opt/conda/envs/MR/bin/plink&#x27;</span><span class="punctuation">,</span> <span class="comment"># 千万别用什么 genetics.binaRies::get_plink_binary()，他们自己编译的文件有问题</span></span><br><span class="line">    bfile <span class="operator">=</span> <span class="string">&quot;/home/jovyan/upload/GWAS/ld/EUR&quot;</span> <span class="comment"># 前缀，不是文件夹也不是文件</span></span><br><span class="line"><span class="punctuation">)</span></span><br><span class="line">exp_dat_clumped <span class="operator">=</span> exp_dat<span class="punctuation">[</span>exp_dat<span class="operator">$</span>SNP <span class="operator">%in%</span> fuck<span class="operator">$</span>rsid<span class="punctuation">,</span><span class="punctuation">]</span></span><br><span class="line">saveRDS<span class="punctuation">(</span>file <span class="operator">=</span> <span class="string">&#x27;ieu-a-2.exp_gwas&#x27;</span><span class="punctuation">,</span> exp_dat_clumped<span class="punctuation">)</span></span><br></pre></td></tr></table></figure>
<h2 id="获取暴露数据">获取暴露数据</h2>
<h3 id="自己的数据">自己的数据</h3>
<figure class="highlight r"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br></pre></td><td class="code"><pre><span class="line">df_gwas <span class="operator">&lt;-</span> data.frame<span class="punctuation">(</span></span><br><span class="line">  SNP <span class="operator">=</span> <span class="built_in">c</span><span class="punctuation">(</span><span class="string">&quot;rs1&quot;</span><span class="punctuation">,</span> <span class="string">&quot;rs2&quot;</span><span class="punctuation">)</span><span class="punctuation">,</span></span><br><span class="line">  beta <span class="operator">=</span> <span class="built_in">c</span><span class="punctuation">(</span><span class="number">1</span><span class="punctuation">,</span> <span class="number">2</span><span class="punctuation">)</span><span class="punctuation">,</span></span><br><span class="line">  se <span class="operator">=</span> <span class="built_in">c</span><span class="punctuation">(</span><span class="number">1</span><span class="punctuation">,</span> <span class="number">2</span><span class="punctuation">)</span><span class="punctuation">,</span></span><br><span class="line">  effect_allele <span class="operator">=</span> <span class="built_in">c</span><span class="punctuation">(</span><span class="string">&quot;A&quot;</span><span class="punctuation">,</span> <span class="string">&quot;T&quot;</span><span class="punctuation">)</span></span><br><span class="line"><span class="punctuation">)</span></span><br><span class="line">head<span class="punctuation">(</span>df_gwas<span class="punctuation">)</span></span><br><span class="line">exp_dat <span class="operator">&lt;-</span> TwoSampleMR<span class="operator">::</span>format_data<span class="punctuation">(</span>df_gwas<span class="punctuation">,</span> type <span class="operator">=</span> <span class="string">&quot;exposure&quot;</span><span class="punctuation">)</span></span><br></pre></td></tr></table></figure>
<h3 id="gwas-catalog">gwas_catalog</h3>
<figure class="highlight r"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br></pre></td><td class="code"><pre><span class="line">df_gwas <span class="operator">&lt;-</span></span><br><span class="line">  subset<span class="punctuation">(</span>MRInstruments<span class="operator">::</span>gwas_catalog<span class="punctuation">,</span></span><br><span class="line">         grepl<span class="punctuation">(</span><span class="string">&quot;Speliotes&quot;</span><span class="punctuation">,</span> Author<span class="punctuation">)</span> <span class="operator">&amp;</span></span><br><span class="line">           Phenotype <span class="operator">==</span> <span class="string">&quot;Body mass index&quot;</span><span class="punctuation">)</span></span><br><span class="line">head<span class="punctuation">(</span>df_gwas<span class="punctuation">)</span></span><br><span class="line">exp_dat <span class="operator">&lt;-</span> TwoSampleMR<span class="operator">::</span>format_data<span class="punctuation">(</span>df_gwas<span class="punctuation">)</span></span><br></pre></td></tr></table></figure>
<h3 id="metab-qtls">metab_qtls</h3>
<figure class="highlight r"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br></pre></td><td class="code"><pre><span class="line">df_gwas <span class="operator">&lt;-</span></span><br><span class="line">  subset<span class="punctuation">(</span>MRInstruments<span class="operator">::</span>metab_qtls<span class="punctuation">,</span></span><br><span class="line">        phenotype <span class="operator">==</span> <span class="string">&quot;Ala&quot;</span></span><br><span class="line">    <span class="punctuation">)</span></span><br><span class="line">head<span class="punctuation">(</span>df_gwas<span class="punctuation">)</span></span><br><span class="line">exp_dat <span class="operator">&lt;-</span> TwoSampleMR<span class="operator">::</span>format_metab_qtls<span class="punctuation">(</span>df_gwas<span class="punctuation">)</span></span><br></pre></td></tr></table></figure>
<h3 id="proteomic-qtls">proteomic_qtls</h3>
<figure class="highlight r"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br></pre></td><td class="code"><pre><span class="line">df_gwas <span class="operator">&lt;-</span></span><br><span class="line">  subset<span class="punctuation">(</span>MRInstruments<span class="operator">::</span>proteomic_qtls<span class="punctuation">,</span></span><br><span class="line">        analyte <span class="operator">==</span> <span class="string">&quot;ApoH&quot;</span></span><br><span class="line">    <span class="punctuation">)</span></span><br><span class="line">head<span class="punctuation">(</span>df_gwas<span class="punctuation">)</span></span><br><span class="line">exp_dat <span class="operator">&lt;-</span> TwoSampleMR<span class="operator">::</span>format_proteomic_qtls<span class="punctuation">(</span>df_gwas<span class="punctuation">)</span></span><br></pre></td></tr></table></figure>
<h3 id="某个基因">某个基因</h3>
<figure class="highlight r"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br></pre></td><td class="code"><pre><span class="line">df_gwas <span class="operator">&lt;-</span></span><br><span class="line">  subset<span class="punctuation">(</span>MRInstruments<span class="operator">::</span>gtex_eqtl<span class="punctuation">,</span></span><br><span class="line">        gene_name <span class="operator">==</span> <span class="string">&quot;IRAK1BP1&quot;</span> <span class="operator">&amp;</span> tissue <span class="operator">==</span> <span class="string">&quot;Adipose Subcutaneous&quot;</span></span><br><span class="line">    <span class="punctuation">)</span></span><br><span class="line">head<span class="punctuation">(</span>df_gwas<span class="punctuation">)</span></span><br><span class="line">exp_dat <span class="operator">&lt;-</span> TwoSampleMR<span class="operator">::</span>format_gtex_eqtl<span class="punctuation">(</span>df_gwas<span class="punctuation">)</span></span><br></pre></td></tr></table></figure>
<h3 id="某个性状的某个甲基化位点相关QTL">某个性状的某个甲基化位点相关QTL</h3>
<figure class="highlight r"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br></pre></td><td class="code"><pre><span class="line">df_gwas <span class="operator">&lt;-</span></span><br><span class="line">  subset<span class="punctuation">(</span>MRInstruments<span class="operator">::</span>aries_mqtl<span class="punctuation">,</span></span><br><span class="line">        cpg <span class="operator">==</span> <span class="string">&quot;cg25212131&quot;</span> <span class="operator">&amp;</span> age <span class="operator">==</span> <span class="string">&quot;Birth&quot;</span></span><br><span class="line">    <span class="punctuation">)</span></span><br><span class="line">head<span class="punctuation">(</span>df_gwas<span class="punctuation">)</span></span><br><span class="line">exp_dat <span class="operator">&lt;-</span> TwoSampleMR<span class="operator">::</span>format_aries_mqtl<span class="punctuation">(</span>df_gwas<span class="punctuation">)</span></span><br></pre></td></tr></table></figure>
<h3 id="IEU的ID">IEU的ID</h3>
<figure class="highlight r"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br></pre></td><td class="code"><pre><span class="line">exp_gwas <span class="operator">&lt;-</span> TwoSampleMR<span class="operator">::</span>extract_instruments<span class="punctuation">(</span>outcomes <span class="operator">=</span> <span class="string">&#x27;ieu-a-2&#x27;</span><span class="punctuation">)</span></span><br><span class="line">head<span class="punctuation">(</span>exp_gwas<span class="punctuation">)</span></span><br><span class="line">saveRDS<span class="punctuation">(</span>file <span class="operator">=</span> <span class="string">&#x27;ieu-a-2.exp_gwas&#x27;</span><span class="punctuation">,</span> exp_gwas<span class="punctuation">)</span> <span class="comment"># 和自己从VCF开始经过clump得到的差不多</span></span><br></pre></td></tr></table></figure>
<h3 id="UK-Biobank">UK_Biobank</h3>
<figure class="highlight r"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br></pre></td><td class="code"><pre><span class="line">hyperten_tophits <span class="operator">&lt;-</span> ieugwasr<span class="operator">::</span>tophits<span class="punctuation">(</span>id<span class="operator">=</span><span class="string">&quot;ukb-b-12493&quot;</span><span class="punctuation">,</span> clump<span class="operator">=</span><span class="number">0</span><span class="punctuation">)</span></span><br><span class="line">hyperten_gwas <span class="operator">&lt;-</span> dplyr<span class="operator">::</span>rename<span class="punctuation">(</span>hyperten_tophits<span class="punctuation">,</span> <span class="built_in">c</span><span class="punctuation">(</span></span><br><span class="line">  <span class="string">&quot;SNP&quot;</span><span class="operator">=</span><span class="string">&quot;rsid&quot;</span><span class="punctuation">,</span></span><br><span class="line">  <span class="string">&quot;effect_allele.exposure&quot;</span><span class="operator">=</span><span class="string">&quot;ea&quot;</span><span class="punctuation">,</span></span><br><span class="line">  <span class="string">&quot;other_allele.exposure&quot;</span><span class="operator">=</span><span class="string">&quot;nea&quot;</span><span class="punctuation">,</span></span><br><span class="line">  <span class="string">&quot;beta.exposure&quot;</span><span class="operator">=</span><span class="string">&quot;beta&quot;</span><span class="punctuation">,</span></span><br><span class="line">  <span class="string">&quot;se.exposure&quot;</span><span class="operator">=</span><span class="string">&quot;se&quot;</span><span class="punctuation">,</span></span><br><span class="line">  <span class="string">&quot;eaf.exposure&quot;</span><span class="operator">=</span><span class="string">&quot;eaf&quot;</span><span class="punctuation">,</span></span><br><span class="line">  <span class="string">&quot;pval.exposure&quot;</span><span class="operator">=</span><span class="string">&quot;p&quot;</span><span class="punctuation">,</span></span><br><span class="line">  <span class="string">&quot;N&quot;</span><span class="operator">=</span><span class="string">&quot;n&quot;</span><span class="punctuation">)</span><span class="punctuation">)</span></span><br><span class="line">fuck <span class="operator">=</span> fix_ld_clump_local<span class="punctuation">(</span></span><br><span class="line">    dat <span class="operator">=</span> dplyr<span class="operator">::</span>tibble<span class="punctuation">(</span>rsid<span class="operator">=</span>hyperten_gwas<span class="operator">$</span>SNP<span class="punctuation">,</span> pval<span class="operator">=</span>hyperten_gwas<span class="operator">$</span>pval.exposure<span class="punctuation">)</span><span class="punctuation">,</span></span><br><span class="line">    tempfile <span class="operator">=</span> file.path<span class="punctuation">(</span>getwd<span class="punctuation">(</span><span class="punctuation">)</span><span class="punctuation">,</span><span class="string">&#x27;tmp.ld_clump.exp_dat&#x27;</span><span class="punctuation">)</span><span class="punctuation">,</span></span><br><span class="line">    clump_kb <span class="operator">=</span> <span class="number">10000</span><span class="punctuation">,</span> clump_r2 <span class="operator">=</span> <span class="number">0.001</span><span class="punctuation">,</span> clump_p <span class="operator">=</span> <span class="number">1</span><span class="punctuation">,</span></span><br><span class="line">    <span class="comment"># pop = &quot;EUR&quot;, # Super-population. Options are &quot;EUR&quot;, &quot;SAS&quot;, &quot;EAS&quot;, &quot;AFR&quot;, &quot;AMR&quot;</span></span><br><span class="line">    plink_bin <span class="operator">=</span> <span class="string">&#x27;/opt/conda/envs/MR/bin/plink&#x27;</span><span class="punctuation">,</span> <span class="comment"># 千万别用什么 genetics.binaRies::get_plink_binary()，他们自己编译的文件有问题</span></span><br><span class="line">    bfile <span class="operator">=</span> <span class="string">&quot;/home/jovyan/upload/GWAS/ld/EUR&quot;</span> <span class="comment"># 前缀，不是文件夹也不是文件</span></span><br><span class="line"><span class="punctuation">)</span></span><br><span class="line">exp_dat_clumped <span class="operator">=</span> hyperten_gwas<span class="punctuation">[</span>hyperten_gwas<span class="operator">$</span>SNP <span class="operator">%in%</span> fuck<span class="operator">$</span>rsid<span class="punctuation">,</span><span class="punctuation">]</span></span><br><span class="line">MR_calc_r2_F<span class="punctuation">(</span></span><br><span class="line">    beta <span class="operator">=</span> exp_dat_clumped<span class="operator">$</span>beta.exposure<span class="punctuation">,</span> <span class="comment"># Vector of Log odds ratio. beta = log(OR)</span></span><br><span class="line">    eaf <span class="operator">=</span> exp_dat_clumped<span class="operator">$</span>eaf.exposure<span class="punctuation">,</span> <span class="comment"># Vector of allele frequencies.</span></span><br><span class="line">    N <span class="operator">=</span> exp_dat_clumped<span class="operator">$</span>N<span class="punctuation">,</span> <span class="comment"># Array of sample sizes</span></span><br><span class="line">    se <span class="operator">=</span> exp_dat_clumped<span class="operator">$</span>se.exposure <span class="comment"># Vector of SE.</span></span><br><span class="line"><span class="punctuation">)</span> <span class="comment"># 取 F&gt;10 的</span></span><br></pre></td></tr></table></figure>
<h2 id="计算统计效力">计算统计效力</h2>
<figure class="highlight r"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># 分类变量</span></span><br><span class="line">tmp_r2 <span class="operator">=</span>TwoSampleMR<span class="operator">::</span>get_r_from_lor<span class="punctuation">(</span></span><br><span class="line">    lor <span class="operator">=</span> exp_dat_clumped<span class="operator">$</span>beta.exposure<span class="punctuation">,</span> <span class="comment"># Vector of Log odds ratio. beta = log(OR)</span></span><br><span class="line">    af <span class="operator">=</span> exp_dat_clumped<span class="operator">$</span>eaf.exposure<span class="punctuation">,</span> <span class="comment"># Vector of allele frequencies.</span></span><br><span class="line">    ncase <span class="operator">=</span> exp_dat_clumped<span class="operator">$</span>ncase.exposure<span class="punctuation">,</span> <span class="comment"># Vector of Number of cases. </span></span><br><span class="line">    ncontrol <span class="operator">=</span> exp_dat_clumped<span class="operator">$</span>ncontrol.exposure<span class="punctuation">,</span> <span class="comment"># Vector of Number of controls. </span></span><br><span class="line">    prevalence <span class="operator">=</span> <span class="number">1</span><span class="punctuation">,</span> <span class="comment"># Vector of Disease prevalence in the population.</span></span><br><span class="line"><span class="punctuation">)</span></span><br><span class="line"><span class="comment"># 连续变量</span></span><br><span class="line">tmp_r2 <span class="operator">=</span>TwoSampleMR<span class="operator">::</span>get_r_from_pn<span class="punctuation">(</span></span><br><span class="line">    p <span class="operator">=</span> exp_dat_clumped<span class="operator">$</span>pval.exposure<span class="punctuation">,</span> <span class="comment"># Array of pvals</span></span><br><span class="line">    n <span class="operator">=</span> exp_dat_clumped<span class="operator">$</span>samplesize.exposure <span class="comment"># Array of sample sizes</span></span><br><span class="line"><span class="punctuation">)</span></span><br></pre></td></tr></table></figure>
<figure class="highlight r"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br></pre></td><td class="code"><pre><span class="line">MR_calc_r2_F <span class="operator">=</span> <span class="keyword">function</span><span class="punctuation">(</span>beta<span class="punctuation">,</span> eaf<span class="punctuation">,</span> N<span class="punctuation">,</span> se<span class="punctuation">)</span><span class="punctuation">&#123;</span></span><br><span class="line">    <span class="comment"># https://doi.org/10.1038/s41467-020-14389-8</span></span><br><span class="line">    <span class="comment"># https://doi.org/10.1371/journal.pone.0120758</span></span><br><span class="line">    r2 <span class="operator">=</span> <span class="punctuation">(</span><span class="number">2</span> <span class="operator">*</span> <span class="punctuation">(</span>beta<span class="operator">^</span><span class="number">2</span><span class="punctuation">)</span> <span class="operator">*</span> eaf <span class="operator">*</span> <span class="punctuation">(</span><span class="number">1</span> <span class="operator">-</span> eaf<span class="punctuation">)</span><span class="punctuation">)</span> <span class="operator">/</span></span><br><span class="line">            <span class="punctuation">(</span><span class="number">2</span> <span class="operator">*</span> <span class="punctuation">(</span>beta<span class="operator">^</span><span class="number">2</span><span class="punctuation">)</span> <span class="operator">*</span> eaf <span class="operator">*</span> <span class="punctuation">(</span><span class="number">1</span> <span class="operator">-</span> eaf<span class="punctuation">)</span> <span class="operator">+</span></span><br><span class="line">            <span class="number">2</span> <span class="operator">*</span> N <span class="operator">*</span> eaf <span class="operator">*</span> <span class="punctuation">(</span><span class="number">1</span> <span class="operator">-</span> eaf<span class="punctuation">)</span> <span class="operator">*</span> se<span class="operator">^</span><span class="number">2</span><span class="punctuation">)</span></span><br><span class="line">    <span class="built_in">F</span> <span class="operator">=</span> r2 <span class="operator">*</span> <span class="punctuation">(</span>N <span class="operator">-</span> <span class="number">2</span><span class="punctuation">)</span> <span class="operator">/</span> <span class="punctuation">(</span><span class="number">1</span> <span class="operator">-</span> r2<span class="punctuation">)</span></span><br><span class="line">    print<span class="punctuation">(</span>mean<span class="punctuation">(</span><span class="built_in">F</span><span class="punctuation">)</span><span class="punctuation">)</span></span><br><span class="line">    <span class="built_in">return</span><span class="punctuation">(</span>dplyr<span class="operator">::</span>tibble<span class="punctuation">(</span>r2<span class="operator">=</span>r2<span class="punctuation">,</span> <span class="built_in">F</span><span class="operator">=</span><span class="built_in">F</span><span class="punctuation">)</span><span class="punctuation">)</span></span><br><span class="line"><span class="punctuation">&#125;</span></span><br><span class="line">MR_calc_r2_F<span class="punctuation">(</span></span><br><span class="line">    beta <span class="operator">=</span> exp_dat_clumped<span class="operator">$</span>beta.exposure<span class="punctuation">,</span> <span class="comment"># Vector of Log odds ratio. beta = log(OR)</span></span><br><span class="line">    eaf <span class="operator">=</span> exp_dat_clumped<span class="operator">$</span>eaf.exposure<span class="punctuation">,</span> <span class="comment"># Vector of allele frequencies.</span></span><br><span class="line">    N <span class="operator">=</span> exp_dat_clumped<span class="operator">$</span>samplesize.exposure<span class="punctuation">,</span> <span class="comment"># Array of sample sizes</span></span><br><span class="line">    se <span class="operator">=</span> exp_dat_clumped<span class="operator">$</span>se.exposure <span class="comment"># Vector of SE.</span></span><br><span class="line"><span class="punctuation">)</span> <span class="comment"># 取 F&gt;10 的</span></span><br></pre></td></tr></table></figure>
<h2 id="获取结局数据">获取结局数据</h2>
<h3 id="IEU">IEU</h3>
<figure class="highlight r"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">out_gwas <span class="operator">=</span> TwoSampleMR<span class="operator">::</span>extract_outcome_data<span class="punctuation">(</span>snps <span class="operator">=</span> exp_gwas<span class="operator">$</span>SNP<span class="punctuation">,</span> outcomes <span class="operator">=</span> <span class="string">&#x27;ieu-a-7&#x27;</span><span class="punctuation">)</span></span><br></pre></td></tr></table></figure>
<h3 id="UK-Biobank-2">UK_Biobank</h3>
<figure class="highlight r"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">anxiety_hyperten_liberal <span class="operator">&lt;-</span> TwoSampleMR<span class="operator">::</span>extract_outcome_data<span class="punctuation">(</span>snps <span class="operator">=</span> exp_dat_clumped<span class="operator">$</span>SNP<span class="punctuation">,</span> outcomes <span class="operator">=</span> <span class="string">&quot;ukb-b-11311&quot;</span><span class="punctuation">)</span></span><br></pre></td></tr></table></figure>
<h3 id="PGC的示例">PGC的示例</h3>
<figure class="highlight r"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br></pre></td><td class="code"><pre><span class="line">df_gwas <span class="operator">=</span> read.table<span class="punctuation">(</span>gzfile<span class="punctuation">(</span><span class="string">&#x27;~/upload/GWAS/PGC/ADHD2022_iPSYCH_deCODE_PGC.meta.gz&#x27;</span><span class="punctuation">)</span><span class="punctuation">,</span> header <span class="operator">=</span> <span class="built_in">T</span><span class="punctuation">)</span></span><br><span class="line">head<span class="punctuation">(</span>df_gwas<span class="punctuation">)</span></span><br><span class="line">df_gwas <span class="operator">=</span> df_gwas<span class="punctuation">[</span>df_gwas<span class="operator">$</span>SNP <span class="operator">%in%</span> exp_gwas<span class="operator">$</span>SNP<span class="punctuation">,</span><span class="punctuation">]</span></span><br><span class="line">out_gwas <span class="operator">=</span> data.frame<span class="punctuation">(</span></span><br><span class="line">    SNP <span class="operator">=</span> df_gwas<span class="operator">$</span>SNP<span class="punctuation">,</span></span><br><span class="line">    chr <span class="operator">=</span> <span class="built_in">as.character</span><span class="punctuation">(</span>df_gwas<span class="operator">$</span>CHR<span class="punctuation">)</span><span class="punctuation">,</span></span><br><span class="line">    pos <span class="operator">=</span> df_gwas<span class="operator">$</span>BP<span class="punctuation">,</span></span><br><span class="line">    beta.outcome <span class="operator">=</span> <span class="built_in">log</span><span class="punctuation">(</span>df_gwas<span class="operator">$</span>OR<span class="punctuation">)</span><span class="punctuation">,</span></span><br><span class="line">    se.outcome <span class="operator">=</span> df_gwas<span class="operator">$</span>SE<span class="punctuation">,</span></span><br><span class="line">    samplesize.outcome <span class="operator">=</span> df_gwas<span class="operator">$</span>Nca <span class="operator">+</span> df_gwas<span class="operator">$</span>Nco<span class="punctuation">,</span></span><br><span class="line">    pval.outcome <span class="operator">=</span> df_gwas<span class="operator">$</span>P<span class="punctuation">,</span></span><br><span class="line">    eaf.outcome <span class="operator">=</span> with<span class="punctuation">(</span>df_gwas<span class="punctuation">,</span> <span class="punctuation">(</span>FRQ_A_38691<span class="operator">*</span>Nca<span class="operator">+</span>FRQ_U_186843<span class="operator">*</span>Nco<span class="punctuation">)</span><span class="operator">/</span><span class="punctuation">(</span>Nca<span class="operator">+</span>Nco<span class="punctuation">)</span><span class="punctuation">)</span><span class="punctuation">,</span></span><br><span class="line">    effect_allele.outcome <span class="operator">=</span> df_gwas<span class="operator">$</span>A1<span class="punctuation">,</span></span><br><span class="line">    other_allele.outcome <span class="operator">=</span> df_gwas<span class="operator">$</span>A2<span class="punctuation">,</span></span><br><span class="line">    outcome <span class="operator">=</span> <span class="string">&#x27;ADHD&#x27;</span><span class="punctuation">,</span></span><br><span class="line">    id.outcome <span class="operator">=</span> <span class="string">&#x27;ADHD2022_iPSYCH_deCODE_PGC&#x27;</span>    </span><br><span class="line"><span class="punctuation">)</span></span><br><span class="line">out_gwas <span class="operator">=</span> subset<span class="punctuation">(</span>out_gwas<span class="punctuation">,</span> pval.outcome <span class="operator">&gt;</span> <span class="number">5e-08</span><span class="punctuation">)</span> <span class="comment"># 排他性假设</span></span><br></pre></td></tr></table></figure>
<h2 id="附加-代理SNP">附加 代理SNP</h2>
<p>一部分暴露的SNPs在结局中找不到，可以找和这部分SNPs连锁不平衡的SNPs来代替。相关网站：<a target="_blank" rel="noopener" href="https://snipa.org/snipa3/">snipa</a></p>
<h2 id="Harmonization">Harmonization</h2>
<ul>
<li>将Exposure-SNP及Outcome-SNP等位基因方向协同</li>
<li>根据EAF大小，剔除不能判断方向的回文SNP</li>
<li>剔除incompatible SNP</li>
</ul>
<figure class="highlight r"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br></pre></td><td class="code"><pre><span class="line">dat <span class="operator">&lt;-</span> TwoSampleMR<span class="operator">::</span>harmonise_data<span class="punctuation">(</span></span><br><span class="line"> exposure_dat <span class="operator">=</span> exp_gwas<span class="punctuation">,</span> </span><br><span class="line"> outcome_dat <span class="operator">=</span> out_gwas</span><br><span class="line"><span class="punctuation">)</span></span><br></pre></td></tr></table></figure>
<h2 id="附加-一键报告">附加 一键报告</h2>
<figure class="highlight r"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">TwoSampleMR<span class="operator">::</span>mr_report<span class="punctuation">(</span>dat<span class="punctuation">,</span> output_type <span class="operator">=</span> <span class="string">&quot;md&quot;</span><span class="punctuation">)</span></span><br></pre></td></tr></table></figure>
<h2 id="MR分析">MR分析</h2>
<h3 id="回归分析">回归分析</h3>
<figure class="highlight r"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br></pre></td><td class="code"><pre><span class="line">TwoSampleMR<span class="operator">::</span>mr_method_list<span class="punctuation">(</span><span class="punctuation">)</span> <span class="comment"># 查看mr支持的MR分析方法</span></span><br><span class="line">mr_regression <span class="operator">=</span> TwoSampleMR<span class="operator">::</span>mr<span class="punctuation">(</span>dat<span class="punctuation">,</span> method_list <span class="operator">=</span> <span class="built_in">c</span><span class="punctuation">(</span><span class="string">&#x27;mr_ivw&#x27;</span><span class="punctuation">,</span> <span class="string">&#x27;mr_egger_regression&#x27;</span><span class="punctuation">,</span> <span class="string">&#x27;mr_weighted_median&#x27;</span><span class="punctuation">)</span><span class="punctuation">)</span></span><br><span class="line">mr_regression_or <span class="operator">=</span> TwoSampleMR<span class="operator">::</span>generate_odds_ratios<span class="punctuation">(</span>mr_res <span class="operator">=</span> mr_regression<span class="punctuation">)</span> <span class="comment"># 分类变量</span></span><br><span class="line"><span class="punctuation">&#123;</span>pdf<span class="punctuation">(</span>file <span class="operator">=</span> <span class="string">&#x27;MR.BMIvsADHD.plot.pdf&#x27;</span><span class="punctuation">,</span> width <span class="operator">=</span> <span class="number">6</span><span class="punctuation">,</span> height <span class="operator">=</span> <span class="number">6</span><span class="punctuation">)</span>; <span class="comment"># 导出 PDF 开始</span></span><br><span class="line">print<span class="punctuation">(</span>TwoSampleMR<span class="operator">::</span>mr_scatter_plot<span class="punctuation">(</span>mr_results <span class="operator">=</span> mr_regression<span class="punctuation">,</span> dat <span class="operator">=</span> dat<span class="punctuation">)</span><span class="punctuation">)</span>; <span class="comment"># 返回的是一个ggplot2对象</span></span><br><span class="line">dev.off<span class="punctuation">(</span><span class="punctuation">)</span><span class="punctuation">&#125;</span> <span class="comment"># 导出 PDF 结束</span></span><br></pre></td></tr></table></figure>
<p><img src="https://img.limour.top/2023/10/15/652bd26b9010d.webp" srcset="https://jscdn.limour.top/gh/Limour-dev/Sakurairo_Vision/load_svg/inload.svg" lazyload alt="mr_scatter_plot"></p>
<h3 id="异质性检测">异质性检测</h3>
<ul>
<li>有异质性用随机效应模型<code>ivw</code>，无异质性用固定效应模型（也可以用随机效应模型，两者结果一致）</li>
<li>异质性可能带来多效性，如果没有多效性，则可以说异质性没有带来多效性</li>
</ul>
<figure class="highlight r"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br></pre></td><td class="code"><pre><span class="line">TwoSampleMR<span class="operator">::</span>mr_heterogeneity<span class="punctuation">(</span>dat<span class="punctuation">)</span> <span class="comment"># ivw的 Q_pval &lt; 0.05 则说明有异质性</span></span><br><span class="line">heterogeneity_presso <span class="operator">=</span> TwoSampleMR<span class="operator">::</span>run_mr_presso<span class="punctuation">(</span>dat<span class="punctuation">,</span> NbDistribution <span class="operator">=</span> <span class="number">3000</span><span class="punctuation">)</span> <span class="comment"># NbDistribution越高分辨率越高，找不到离群的SNP时需要提高</span></span><br><span class="line">heterogeneity_presso<span class="punctuation">[[</span><span class="number">1</span><span class="punctuation">]</span><span class="punctuation">]</span><span class="operator">$</span>`MR-PRESSO results`<span class="operator">$</span>`Global Test`<span class="operator">$</span>Pvalue <span class="comment"># &lt; 0.05 说明有异质性</span></span><br><span class="line">heterogeneity_presso<span class="punctuation">[[</span><span class="number">1</span><span class="punctuation">]</span><span class="punctuation">]</span><span class="operator">$</span>`MR-PRESSO results`<span class="operator">$</span>`Distortion Test`<span class="operator">$</span>`Outliers Indices` <span class="comment"># 显示离群的SNP，将其剔除后重新分析</span></span><br></pre></td></tr></table></figure>
<h3 id="水平多效性">水平多效性</h3>
<ul>
<li>P &lt; 0.05 说明不满足独立性假设，建议放弃继续做这个课题</li>
<li>P &lt; 0.05 拒绝了截距为0的假设，说明SNP效应为0时依然有影响(截距存在)，有其他因素在起作用</li>
</ul>
<figure class="highlight r"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">TwoSampleMR<span class="operator">::</span>mr_pleiotropy_test<span class="punctuation">(</span>dat<span class="punctuation">)</span></span><br></pre></td></tr></table></figure>
<h3 id="敏感性分析">敏感性分析</h3>
<ul>
<li>Leave-one-out analysis</li>
<li>所有结果都不应该存在跨过0的情况，否则说明结果不稳定，不再能说明因果关系</li>
</ul>
<figure class="highlight r"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br></pre></td><td class="code"><pre><span class="line">mr_loo <span class="operator">&lt;-</span> TwoSampleMR<span class="operator">::</span>mr_leaveoneout<span class="punctuation">(</span>dat<span class="punctuation">)</span></span><br><span class="line"><span class="punctuation">&#123;</span>pdf<span class="punctuation">(</span>file <span class="operator">=</span> <span class="string">&#x27;MR.BMIvsADHD.leaveoneout.plot.pdf&#x27;</span><span class="punctuation">,</span> width <span class="operator">=</span> <span class="number">6</span><span class="punctuation">,</span> height <span class="operator">=</span> <span class="number">6</span><span class="punctuation">)</span>; <span class="comment"># 导出 PDF 开始</span></span><br><span class="line">print<span class="punctuation">(</span>TwoSampleMR<span class="operator">::</span>mr_leaveoneout_plot<span class="punctuation">(</span>leaveoneout_results <span class="operator">=</span> mr_loo<span class="punctuation">)</span><span class="punctuation">)</span>; <span class="comment"># 返回的是一个ggplot2对象</span></span><br><span class="line">dev.off<span class="punctuation">(</span><span class="punctuation">)</span><span class="punctuation">&#125;</span> <span class="comment"># 导出 PDF 结束</span></span><br></pre></td></tr></table></figure>
<h3 id="单SNP分析">单SNP分析</h3>
<ul>
<li>对每个暴露-结果组合进行多次分析，每次使用不同的单 SNP 进行分析</li>
</ul>
<figure class="highlight r"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br></pre></td><td class="code"><pre><span class="line">mr_res_single <span class="operator">&lt;-</span> TwoSampleMR<span class="operator">::</span>mr_singlesnp<span class="punctuation">(</span>dat<span class="punctuation">)</span></span><br><span class="line">TwoSampleMR<span class="operator">::</span>mr_funnel_plot<span class="punctuation">(</span>mr_res_single<span class="punctuation">)</span></span><br><span class="line">TwoSampleMR<span class="operator">::</span>mr_forest_plot<span class="punctuation">(</span>mr_res_single<span class="punctuation">)</span></span><br></pre></td></tr></table></figure>
<h3 id="方向性检测">方向性检测</h3>
<figure class="highlight r"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">TwoSampleMR<span class="operator">::</span>directionality_test<span class="punctuation">(</span>dat<span class="punctuation">)</span> <span class="comment"># TRUE表示确实是暴露导致了结果</span></span><br></pre></td></tr></table></figure>
<h2 id="附加-稳健回归">附加 稳健回归</h2>
<figure class="highlight r"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br></pre></td><td class="code"><pre><span class="line">dat2 <span class="operator">&lt;-</span> TwoSampleMR<span class="operator">::</span>dat_to_MRInput<span class="punctuation">(</span>dat<span class="punctuation">)</span></span><br><span class="line">mr_ivw_robust <span class="operator">&lt;-</span> MendelianRandomization<span class="operator">::</span>mr_ivw<span class="punctuation">(</span>dat2<span class="punctuation">[[</span><span class="number">1</span><span class="punctuation">]</span><span class="punctuation">]</span><span class="punctuation">,</span> model<span class="operator">=</span> <span class="string">&quot;default&quot;</span><span class="punctuation">,</span> <span class="comment"># “random”指的就是随机效应模型，“fixed”指的是固定效应模型</span></span><br><span class="line">                                             robust <span class="operator">=</span> <span class="literal">TRUE</span><span class="punctuation">,</span> penalized <span class="operator">=</span> <span class="literal">TRUE</span><span class="punctuation">,</span>correl <span class="operator">=</span> <span class="literal">FALSE</span><span class="punctuation">,</span> <span class="comment"># 参数penalized代表下调异常值的权重</span></span><br><span class="line">                                             weights <span class="operator">=</span><span class="string">&quot;simple&quot;</span><span class="punctuation">,</span> psi <span class="operator">=</span> <span class="number">0</span><span class="punctuation">,</span>distribution <span class="operator">=</span> <span class="string">&quot;normal&quot;</span><span class="punctuation">,</span>alpha <span class="operator">=</span> <span class="number">0.05</span><span class="punctuation">)</span></span><br></pre></td></tr></table></figure>
<h2 id="附加-绘制森林图">附加 绘制森林图</h2>
<ul>
<li><a href="/Forest-plot-displays-the-results-of-regression-analysis">美化森林图</a></li>
</ul>
<h2 id="附加-计算Power">附加 计算Power</h2>
<ul>
<li><a target="_blank" rel="noopener" href="https://doi.org/10.1093/ije/dyt179">Calculating statistical power in Mendelian randomization studies</a></li>
<li><a target="_blank" rel="noopener" href="https://shiny.cnsgenomics.com/mRnd/">Power calculations for Mendelian Randomization</a></li>
<li>Sample size: 结局总的样本量，不是暴露的样本量</li>
<li>K: 结局中病例的比例，case/(case+control)</li>
<li>OR: IVW的OR值，exp(beta)</li>
<li>R2: MR_calc_r2_F 计算得到的所有R2的sum</li>
</ul>

                
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          res.push('.markdown-body > ' + item.trim());
        }
        if (CONFIG.anchorjs.placement === 'left') {
          anchors.options.class = 'anchorjs-link-left';
        }
        anchors.add(res.join(', '));
      }
    });
  });
</script>


  <script>Fluid.plugins.imageCaption();</script>

  <script  src="/js/local-search.js" ></script>


<!-- 主题的启动项，将它保持在最底部 -->
<!-- the boot of the theme, keep it at the bottom -->
<script  src="/js/boot.js" ></script>


  <noscript>
    <div class="noscript-warning">Blog works best with JavaScript enabled</div>
  </noscript>
<!-- hexo injector body_end start -->
  <script defer src="/theme-inject/timeliness.js"></script>
<!-- hexo injector body_end end --></body>
</html>