Identifying Molecular Subtypes of Schizophrenia: A Novel Genomic Approach

描述（由申请人提供）：最近发展的微阵列平台，能够对数十万个单核苷酸多态性（SNP）进行基因分型，已经导致快速鉴定许多复杂疾病的新易感基因。然而，最近的数据从几个大规模的全基因组关联研究（GWAS）的精神分裂症（SCZ）一直令人失望，很明显，即使是几个常见的变异非常温和的影响，将留下相当大的遗传变异SCZ无法解释。在尤里卡的申请中，我们挑战了GWAS的隐含假设：多个共同等位基因的加性效应联合收割机产生表型，并提出了一个互补的假设：一部分精神分裂症病例的特征可能是一个简单的遗传结构，一个或多个同质的分子亚型可能与多基因库的情况下共存。支持这一模型来自一种新的统计方法来分析GWAS数据，我们称之为“全基因组纯合性分析”（WGHA）。应用于我们自己的SCZ数据集（PI：Anil Malhotra），WGHA已经确定了几个罕见的隐性基因座的高多态性，包括几个染色体区域（约200 kb-2 MB），已确定在以前的SCZ连锁和关联研究。尤里卡的这一建议旨在分几个阶段扩大这一初步调查结果。首先，我们将WGHA分析应用于来自遗传分析信息网络（GAIN）倡议的更大的SCZ数据集。接下来，我们将重新分析来自全基因组连锁数据集（PI：Hugh Gurling）的数据，其结果将指导来自NIMH库的SCZ谱系的新的基于SNP的连锁研究。连锁数据的分析将集中在确定具有相似基因座的强隐性遗传的小簇家系。然后，将与冷泉港实验室（PI：W. Richard McCombie）。到拟议的3年资助期的上半年结束时，我们的目标是确定一种或多种隐性精神分裂症亚型的特征，占所有病例的10%。在第3年完成项目时，我们的目标是通过深度重测序确定这些病例背后的一个或多个高度渗透性突变。精神分裂症（SCZ）是美国第五大残疾原因。我们的目标是使用新的统计方法和最先进的基因组测序技术来表征精神分裂症的一个或多个同质遗传亚型，并确定这些亚型的致病突变。这些发现将为精神分裂症的诊断和预测以及了解其生物学创造新的机会。

DESCRIPTION (provided by applicant): The recent development of microarray platforms, capable of genotyping hundreds of thousands of single nucleotide polymorphisms (SNPs), has resulted in the rapid identification of novel susceptibility genes for a number of complex diseases. However, recent data from several large-scale genome-wide association studies (GWAS) of schizophrenia (SCZ) have been disappointing; it is clear that identification of even several common variants of very modest effect will leave considerable genetic variance in SCZ unexplained. In this EUREKA application, we challenge the implicit assumptions underlying GWAS: that multiple common alleles of additive effect combine to produce the phenotype, and suggest a complementary hypothesis: that a proportion of cases of schizophrenia may be characterized by a simpler genetic architecture, and that one or more homogeneous molecular subtypes may co-exist alongside the polygenic pool of cases. Support for this model comes from a novel statistical approach to analyzing GWAS data, which we have termed "whole genome homozygosity analysis" (WGHA). Applied to our own SCZ dataset (PI: Anil Malhotra), WGHA has identified several rare recessive loci of high penetrance, encompassing several chromosomal regions (of approximately 200kb-2MB) that have been identified in prior SCZ linkage and association studies. This EUREKA proposal aims to extend this initial finding in several stages. First, we will apply WGHA analysis to a much larger SCZ dataset derived from the Genetic Analysis Information Network (GAIN) initiative. Next, we will re-analyze data from a whole genome linkage dataset (PI: Hugh Gurling), results of which will guide a novel SNP-based linkage study of SCZ pedigrees from the NIMH repository. Analysis of linkage data will be focused on identifying small clusters of pedigrees with strongly recessive transmission of similar loci. Highly penetrant recessive loci identified and replicated across these analyses will then be interrogated in both case and control samples using next-generation high-throughput deep resequencing technology (Illumina 1G platform), in collaboration with Cold Spring Harbor Laboratory (PI: W. Richard McCombie). By the end of the first half of the proposed 3-year funding period, we aim to have characterized one or more recessive subtypes of schizophrenia, accounting for up to 10% of all cases. At the completion of the project in Year 3, we aim to have identified, through deep resequencing, one or more highly penetrant mutations underlying these cases. Schizophrenia (SCZ) constitutes the fifth leading cause of disability in the US. We aim to use novel statistical approaches and state-of-the-art genomic sequencing technology to characterize one or more homogeneous genetic subtypes of schizophrenia, and to identify the causal mutation(s) underlying such subtype(s). Findings will create new opportunities for diagnosis and prediction of schizophrenia, and for understanding its biology.