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<p>在本章中，我们将考虑对基本孟德尔随机化范式的扩展。
其中包括有多个暴露变量的方法和那些旨在估计标准孟德尔随机化调查中所估计的参数以外的参数。
我们依次考虑了多变量孟德尔随机化、网络孟德尔随机化、非线性孟德尔随机化、因子孟德尔随机化、双向孟德尔随机化和孟德尔随机化的元分析。</p>
<h1 id="multivariable-mendelian-randomization">Multivariable Mendelian
randomization</h1>
<p>多变量孟德尔随机化是对标准孟德尔随机化的扩展，当很难找到与某一特定暴露具体相关的遗传变异，但有可能找到与一组相关暴露具体相关的遗传变异时，就可以使用。
例如，很难找到与高密度脂蛋白胆固醇相关的、与低密度脂蛋白胆固醇和甘油三酯无关的遗传变异。</p>
<p>在多变量孟德尔随机化中，<strong>允许遗传变体与一个以上的暴露相关</strong>，只要它们不与任何暴露-结果关联的混杂物相关，并且它们不直接影响结果--也就是说，任何与结果的遗传关联都是通过一个或多个暴露来中介的。</p>
<p>标准（即单变量）孟德尔随机化评估了暴露的遗传预测值是否与结果相关。
多变量孟德尔随机化评估多个暴露的遗传预测值是否与多变量模型中的结果有关。该技术依赖于某些遗传变异与某些暴露的关联性比其他暴露更强。
例如，尽管身体质量指数的大多数遗传预测因素同时影响脂肪质量和无脂肪质量，但一些变体按比例更强烈地影响脂肪质量，而其他变体更强烈地影响无脂肪质量。
因此，多变量孟德尔随机化可用于分离脂肪质量和无脂肪质量的因果效应。</p>
<p>对于个体水平的数据，多变量孟德尔随机化可以用两阶段最小二乘法（对于连续结果）或相关的两阶段法（对于其他结果）来实现。</p>
<h1 id="network-mendelian-randomization">Network Mendelian
randomization</h1>
<p>在孟德尔随机化环境中评估中介的另一种方法是网络孟德尔随机化</p>
<p>假设效应的线性和同质性，暴露对结果的总效应应该等于暴露对结果的直接效应加上通过中介的间接效应。
这种间接效应可以表示为暴露对媒介物的影响乘以媒介物对结果的影响。</p>
<h1 id="non-linear-mendelian-randomization">Non-linear Mendelian
randomization</h1>
<p>在某些情况下，暴露对结果的因果效应可能是非线性的。暴露和结果之间的因果关系可能有一个阈值形状，这意味着暴露的增加只影响到高于或低于阈值的结果。
或者暴露的增加对暴露值较高或较低的个体的结果有较大的平均影响。</p>
<h1 id="factorial-mendelian-randomization">Factorial Mendelian
randomization</h1>
<p>因子孟德尔随机化是利用遗传变异来回答有关相互作用的问题</p>
<p>主要有以下两个使用场景</p>
<ol type="1">
<li>通过使用遗传变异作为暴露的预测因子来估计暴露之间的因果效应互动；</li>
<li>通过使用遗传变异作为特定治疗的代理来识别干预措施之间的互动。</li>
</ol>
<h1 id="bidirectional-mendelian-randomization">Bidirectional Mendelian
randomization</h1>
<p>在某些情况下，暴露和结果之间的因果效应的方向可能并不明确。双向孟德尔随机化评估了暴露对结果的影响，也评估了结果对暴露的影响</p>
<h1 id="mendelian-randomization-and-meta-analysis">Mendelian
randomization and meta-analysis</h1>
<p>最后一种超出标准孟德尔随机化分析的情况是，当数据来自多个来源的时候。</p>
<p>可以使用元分析文献中的标准方法（如反方差加权）来合并来自多个研究的估计值。
当有多个研究的多个遗传变异的数据时，研究者可以选择将研究内的遗传关联数据合并为研究特定的因果估计，然后通过元分析进行合并，或者将各研究中每个变异的遗传关联数据合并为变异的遗传关联，然后用IVW方法进行合并。</p>
<p>如果研究者对调查特定变体因果估计之间的异质性特别感兴趣，建议将每个变体的跨研究的遗传关联结合起来。</p>
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<div class="post-toc-content"><ol class="nav"><li class="nav-item nav-level-1"><a class="nav-link" href="#multivariable-mendelian-randomization"><span class="nav-number">1.</span> <span class="nav-text">Multivariable Mendelian
randomization</span></a></li><li class="nav-item nav-level-1"><a class="nav-link" href="#network-mendelian-randomization"><span class="nav-number">2.</span> <span class="nav-text">Network Mendelian
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randomization</span></a></li><li class="nav-item nav-level-1"><a class="nav-link" href="#bidirectional-mendelian-randomization"><span class="nav-number">5.</span> <span class="nav-text">Bidirectional Mendelian
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