Supplementary Material for: Identification of Rare Variants from Exome Sequence in a Large Pedigree with Autism

We carried out analyses with the goal of identifying rare variants in exome sequence data that contribute to disease risk for a complex trait. We analyzed a large, 47-member, multigenerational pedigree with 11 cases of autism spectrum disorder, using genotypes from 3 technologies representing increasing resolution: a multiallelic linkage marker panel, a dense diallelic marker panel, and variants from exome sequencing. Genome-scan marker genotypes were available on most subjects, and exome sequence data was available on 5 subjects. We used genome-scan linkage analysis to identify and prioritize the chromosome 22 region of interest, and to select subjects for exome sequencing. Inheritance vectors (IVs) generated by Markov chain Monte Carlo analysis of multilocus marker data were the foundation of most analyses. Genotype imputation used IVs to determine which sequence variants reside on the haplotype that co-segregates with the autism diagnosis. Together with a rare-allele frequency filter, we identified only one rare variant on the risk haplotype, illustrating the potential of this approach to prioritize variants. The associated gene, MYH9, is biologically unlikely, and we speculate that for this complex trait, the key variants may lie outside the exome.本研究以鉴定与复杂性状疾病风险相关的外显子组（exome）测序数据中的罕见变异为目标开展分析。我们对一个包含47名成员、存在11例自闭症谱系障碍（Autism Spectrum Disorder, ASD）病例的大型多代家系进行分析，采用三种分辨率依次提升的技术获取基因型数据：多等位基因连锁标记面板、高密度双等位基因标记面板，以及外显子组测序得到的变异位点。多数受试者拥有全基因组扫描标记的基因型数据，其中5名受试者具备外显子组测序数据。本研究通过全基因组扫描连锁分析，定位并优先筛选出22号染色体上的候选区域，同时筛选出用于外显子组测序的受试者。基于多位点标记数据的马尔可夫链蒙特卡洛（Markov Chain Monte Carlo, MCMC）分析生成的遗传向量（Inheritance Vectors, IVs），为绝大多数分析提供了核心基础。基因型填充借助IVs确定哪些序列变异位于与自闭症诊断共分离的单体型之上。结合罕见等位基因频率过滤策略，我们仅在该风险单体型上鉴定出1个罕见变异，印证了该方法在变异优先筛选方面的应用潜力。该关联基因MYH9的生物学功能与该疾病的关联性看似缺乏合理性，据此我们推测，对于该复杂性状而言，关键变异可能存在于外显子组之外。