随机森林方法在致病SNPs检测中的应用

通过全基因组关联研究发现了大量复杂疾病相关变异，近来关注的焦点又集中在了如何利用单核苷酸多态性数据进行深入分析，期待发现更多复杂疾病的易感基因。随机森林是一种新型的集成分类决策器，可以在对样本分类的同时，计算预测变量的重要性值。该文将随机森林方法应用于全基因组数据，实验结果表明该方法可以作为致病SNPs检测的有效参考方法。     

Pathway-based analysis for genome-wide association studies of schizophrenia to provide new insight in schizophrenia study

Schizophrenia (SZ) is an inheritable complex mental disease. There have been several genome-wide association studies (GWASs) of SZ to identify novel genetic susceptibility factors. To further interpret SZ GWASs, pathway-based analysis (PBA), which considers the combined effect of variants and identifies pathways associated with traits, provides a feasible solution to discover the biological function and mechanism of SZ. Furthermore, to investigate the common pathways between SZ and bipolar disorder (BD) wil...     

Problems and promises: How to tell the story of a Genome Wide Association Study?

The promise of treatments for common complex diseases (CCDs) is understood as an important force driving large scale genetics research over the last few decades. This paper considers the phenomenon of the Genome Wide Association Study (GWAS) via one high profile example, the Wellcome Trust Case Control Consortium (WTCCC). The WTCCC despite not fulfilling promises of new health interventions is still understood as an important step towards tackling CCDs clinically. The ‘sociology of expectations’ has considered many examples of failure to fulfil promises and the subsequent negative consequences including disillusionment, disappointment and disinvestment. In order to explore why some domains remain resilient in the face of apparent failure, I employ the concept of the ‘problematic’ found in the work of Giles Deleuze. This alternative theoretical framework challenges the idea that the failure to reach promised goals results in largely negative outcomes for a given field. I will argue that collective scientific action is motivated not only by hopes for the future but also by the drive to create solutions to the actual setbacks and successes which scientists encounter in their day-to-day work. I draw on eighteen interviews.     

全基因组关联分析显示基因ANXA8和C10orf11为影响肌少症的候选基因

为了寻找与瘦体重(lean body mass,LBM)相关的单核苷酸多态性(single nucleotide polymorphism, SNP)位点及易感基因,在1 000个不相关的白人中采用Affymetix 500K芯片扫描了500 000个SNPs,并进行全基因组关联分析(genome-wide association study,GWAS),显著结果在1 625个中国人样本和2 283个欧洲白人样本中进行验证,并将验证结果与研究结果进行荟萃分析。研究发现SNPsrs7905603,rs9416083,rs4409772,rs2894310与LBM关联,其中rs7905603位于基因ANXA8,其他3个SNPs位于基因C10orf11。荟萃分析得到的合并p值分别为2.08×10-5,7.44×10~(-6),6.73×10~(-6),6.76×10~(-6)。ANXA8和C10orf11基因是影响LBM变异的候选基因,这对肌少症的认识提供了新的理论依据。     

一种分析全基因组上位性的新方法

传统基于单位点的全基因组关联研究存在重复性低、难以解释性等缺陷,而采用基于机器学习的上位性分析中面临计算复杂度高、预测准确度不足等问题.本文提出一种分析全基因组上位性的新方法,该方法采用二阶段框架的上位性分析方法,它包含特征过滤阶段以及上位性组合优化阶段,在特征过滤阶段提出了多准则融合策略,从多个不同角度评价遗传变异位点,以保证易感的弱效位点能被保留,然后采用多准测排序融合策略剔除与疾病状态关联程度低的遗传变异,进一步在上位性组合优化阶段采用贪婪算法启发式地搜索组合空间,以降低时间复杂度,最后采用支持向量机作为上位性评价模型.实验中采用不同的连锁不平衡参数与经典算法SNPruler与ACO的性能进行对比,实验结果表明:本文方法能有效保留弱效位点,一定程度上提高了疾病预测的正确度.     

脑影像与基因组数据关联的交互可视化方法研究

可视化是基因组数据分析的一个重要部分,而下一代测序技术和基于阵列的分析方法产生的数据集的数量和多样性对现有的可视化工具提出了重大挑战.针对于这些问题,通过比对已有的可视化工具,总结其优势与不足,设计了一个可视化系统,从全基因组关联研究的数据中挖掘出显著的基因组数据信息,并且将此信息与脑影像结合起来.将研究者关注的信息生成热图（Heatmap Plot）,在Heatmap图中的数据信息可以显示相应表型对应的曼哈顿图,以及相应单核苷酸多态性对应的脑影像中的表型信息（不同区域使用不同p-value进行颜色绘制）,给予研究者更加直接的可视化表现.     

Galton's Quincunx: Probabilistic causation in developmental behavior genetics

In what sense are associations between particular markers and complex behaviors made by genome-wide association studies (GWAS) and related techniques discoveries of, or entries into the study of, the causes of those behaviors? In this paper, we argue that when applied to individuals, the kinds of probabilistic ‘causes’ of complex traits that GWAS-style studies can point towards do not provide the kind of causal information that is useful for generating explanations; they do not, in other words, point towards useful explanations of why particular individuals have the traits that they do. We develop an analogy centered around Galton's “Quincunx” machine; while each pin might be associated with outcomes of a certain sort, in any particular trial, that pin might be entirely bypassed even if the ball eventually comes to rest in the box most strongly associated with that pin. Indeed, in any particular trial, the actual outcome of a ball hitting a pin might be the opposite of what is usually expected. While we might find particular pins associated with outcomes in the aggregate, these associations will not provide causally relevant information for understanding individual outcomes. In a similar way, the complexities of development likely render impossible any moves from population-level statistical associations between genetic markers and complex behaviors to an understanding of the causal processes by which individuals come to have the traits that they in fact have.     

全基因组关联研究中的遗传位点分析方法

提出多种模型方法,对高维位点数据进行分析,为基因定位和复杂疾病性状遗传等方面的研究提供新的技术支持。为了实现关联位点在基因中的定位,首先建立映射模型,对每个位点的碱基对重新编码;然后,提出将质量控制模型与关联分析模型相结合的方法,确定位点的关联程度;随后利用基于随机森林的重要性排序,筛选与该遗传疾病最相关的致病位点;最后,设计出高维RBF神经网络,得到每个位点对性状的相关性系数,探索出与疾病多类性状相关的位点。结合多种检验方式,验证所建模型能够较为准确地定位与疾病相关的位点及基因。各类模型具有极强的推广性,广泛适用于筛选占有各自权值的大样本数据。