Genetics and Bioinformatics Core Laboratory

遗传学与生物信息学核心实验室

• 批准号: 7735226
• 负责人: Daniel Weinberger
• 金额: $ 217.31万
• 依托单位: NATIONAL INSTITUTE OF MENTAL HEALTH
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/7735226
• 关键词: Address; Admixture; Alleles; Animal Model; Anxiety; Autopsy; Bioinformatics; Biological; Biological Assay; Biological Models; Brain; Brain Diseases; Brain region; COMT gene; Candidate Disease Gene; Cataloging; Catalogs; Catechol O-Methyltransferase; Caucasians; Caucasoid Race; Chromosomes; Clinical; Clinical Trials; Cognition; Collaborations; Complex; Computer Simulation; Computer software; DNA; DNA Sequence; Data; Data Set; Disease; Equilibrium; ErbB4 gene; Ethnic group; Exons; FGF20 gene; Family; Frequencies; Gene Combinations; Gene Frequency; Genes; Genetic; Genetic Risk; Genetic Variation; Genomics; Genotype; Gold; Haplotypes; Hereditary Disease; Human; Image; In Situ Hybridization; Individual; Introns; Laboratories; Laboratory Research; Length; Linkage Disequilibrium; Literature; Logistic Regressions; Measures; Messenger RNA; Methods; Microsatellite Repeats; Modeling; Moods; Morphologic artifacts; Mutation; National Institute of Mental Health; Neurofibromin 2; Neuropsychology; Numbers; Odds Ratio; Online Systems; Open Reading Frames; Outsourcing; PIP5K2A gene; PPP3CC gene; Parents; Phase; Phenotype; Phosphatidylinositol-4-Phosphate 5-Kinase Type II Alpha; Polymorphism Analysis; Population; Proteins; Psychotic Disorders; RNA Splicing; Race; Recombinants; Reproducibility; Research Personnel; Reverse Transcriptase Polymerase Chain Reaction; Risk; Sampling; Schizophrenia; Screening procedure; Single Nucleotide Polymorphism; Site; Spottings; Stratification; Susceptibility Gene; System; Technology; Testing; Transcript; Variant; Work; base; cDNA Library; case control; clinical phenotype; data mining; follow-up; gene interaction; genetic variant; human FGF20 protein; human PPP3CC protein; novel; proband; programs; statistics; trait; transmission process

To date, we have tested numerous single nucleotide polymorphisms (SNPs) in well over 118 genes, including some of the less established but intriguing candidates such as PRODH, RGS4, CHRNA7, PIP5K2A, and PPP3CC. Among our accomplishments, we have fully sequenced the 10 exons and flanking sequences of 180 proband chromosomes for dysbindin, sequenced two exons of MRDS1, and sequenced 1.5 kb of the GAD1 upstream region. A total of 21 new SNPs were discovered in these genes, 15 of which were genotyped in the clinical samples. We have re-sequenced the exons and splice sites of GRM3 in 180 chromosomes, which led to the discovery of a few rare SNPs. We have likewise re-sequenced risk regions of KCNH2, ErbB4, PIk3d, FGF20, DAARP, and COMT and identified novel variants in these genes as well. We routinely submit our Taqman genotype assay to reproducibility checks by re-genotyping (avg. accuracy >99%) and spot accuracy checks done by double stranded sequencing (avg. >99% for most SNP assays). Genotypes are called manually within the ABI SDS software and confirmed. We perform Mendelian checks and higher order (e.g. multiple recombinants) error checking with the program MERLIN. Microsatellite genotyping has been performed in collaboration with the NIMH Mood and Anxiety Program. We measure linkage disequilibrium (LD) between markers with the D prime and r2 statistics from cases and controls in parallel using the GOLD software package. All SNPs are tested for departures from Hardy-Weinberg equilibrium. For large numbers of loci, we use SNPHAP to reconstruct haplotypes and estimate their frequencies in unrelated individuals. For family based association studies of the discrete clinical phenotype, we use the programs FBAT, TDTPHASE and TRANSMIT for unknown phase haplotype estimation. Case-control analysis of individual SNPs and SNP haplotypes is done using logistic regression in STATA and COCAPHASE. All P values are computed empirically with 10,000 permutations or bootstraps as the programs provide. Tests of association to quantitative traits such as the intermediate phenotypes are performed by the FBAT and QTDT, which allows variance-components testing of family-based samples for association and transmission disequilibrium. The orthogonal model used is robust to population stratification because, analogous to the conventional TDT, it only considers transmissions from heterozygous parents. To control for possible artifacts due to allele frequency differences across ethnic groups, analysis limited to Caucasians is performed in parallel. We have also established a panel unlinked SNPs to use as a potential genomic control panel for case control association studies, including intermediate phenotype analyses, to address potential population admixture artifacts. In our genomics project we acquire extensive genetic variation data in our susceptibility genes and complete the catalog of genetic risk genes in our datasets. As part of the GCAP program, we have greatly increased the genotyping throughput by outsourcing. We project that about every 4 months for the next 2 years we will genotype a minimum of 768 SNPs, perform follow-up work on established genes and test novel genes. In addition, we outsource the majority of re-sequencing for SNP detection to DNA sequencing companies. All exons, splice sites, and 10 kb of the upstream region will be re-sequenced in an initial pass, then some regions of some genes are sequenced further (e.g. the introns or positive haplotypes) and/or more individuals. Because most functional SNPs and mutations are not in protein coding regions, it is critical to fully characterize transcripts species in several regions of post mortem human brain. To accomplish this, we routinely execute basic mRNA transcript characterization technologies such as 5' and 3' RACE and screening of full-length transcripts, normalized cDNA libraries from multiple brain regions. This work also serves to guide quantitative RT-PCR and in situ hybridization expression studies. Another project of central importance is the statistical analyses of gene-gene interactions. It is likely that certain gene and allele combinations interact epistatically to produce risk greater than that predicted by the individual odds ratios. It is also likely that some gene combinations will increase risk even in the absence of main effects in each gene. We are using the data driven analytic approach developed at Vanderbilt called multifactor dimensionality reduction (MDR) in an attempt to detect sets of interacting alleles that predict disease status. We also engage in collaborative discussions with Salford Systems, originator of the programs CART, MARS, and TREENET, to explore and execute other data mining strategies. Our statistical geneticist uses the wealth of data to model and test complex gene-gene and gene-environmental interactions, and establish some objective criteria for integrating statistical genetic (disease and intermediate phenotype) data with convergent biological data both to gauge overall significance of given genotype/haplotype, phenotype correlations and to evaluate attributable risk.

迄今为止，我们已经测试了超过118个基因的许多单核苷酸多态性（SNP），其中包括一些较少但有趣但有趣的候选者，例如PODH，RGS4，CHRNA7，CHRNA7，PIP5K2A和PPP3CC。在我们的成就中，我们对dysbindin的180个概率和染色体的10个外显子和侧翼序列进行了全面测序，对MRDS1的两个外显子进行了测序，并测序了1.5 kb的GAD1上游区域。在这些基因中，总共发现了21个新的SNP，其中15个是临床样品中的基因分型。我们已经在180种染色体中重新确定了GRM3的外显子和剪接位点，从而发现了一些罕见的SNP。同样，我们也重新确定了KCNH2，ERBB4，PIK3D，FGF20，DAARP和COMT的风险区域，并确定了这些基因中的新型变体。我们通常通过重新生成（AVG。精度> 99％）和通过双链测序进行（大多数SNP分析的AVG。> 99％）进行的taqman基因型测定法进行可重复性检查。基因型在ABI SDS软件中手动称为并确认。我们执行Mendelian检查和高阶（例如多个重组）的高级检查，并使用程序Merlin进行错误检查。微卫星基因分型已与NIMH MOOD和焦虑计划合作进行。 我们通过使用Gold软件包并行对具有D Prime和R2统计数据的标记和R2统计的标记之间的连锁不平衡（LD）测量。所有SNP都经过测试，以脱离Hardy-Weinberg平衡。对于大量基因座，我们使用SNPHAP重建单倍型并估计其在无关的个体中的频率。对于基于家庭的临床表型的基于家庭的关联研究，我们使用FBAT，TDTPHase和Transmit程序进行未知相单倍型估计。单个SNP和SNP单倍型的病例对照分析是使用Stata和Cocaphase中的逻辑回归进行的。所有p值均以10,000个置换或引导程序的经验计算。 FBAT和QTDT对定量性状（例如中间表型）进行的关联测试，该测试允许对基于家庭的样本进行方差 - 组件测试，以实现关联和传播不平衡。所使用的正交模型对人口分层是可靠的，因为类似于常规的TDT，它仅考虑杂合父母的传播。为了控制由于种族之间的等位基因频率差异而导致的可能的伪影，并行进行了限于高加索人的分析。我们还建立了一个未链接的SNP，用作病例控制关联研究的潜在基因组控制面板，包括中间表型分析，以解决潜在的人群混合伪像。 在我们的基因组学项目中，我们在易感基因中获取广泛的遗传变异数据，并完成数据集中遗传风险基因的目录。作为GCAP计划的一部分，我们通过外包大大增加了基因分型吞吐量。我们预测，在接下来的两年中，大约每4个月每4个月都将至少基因型768个SNP，对既定基因进行后续工作并测试新基因。此外，我们将大多数重新测量SNP检测的重新测序外包外包给DNA测序公司。上游区域的所有外显子，剪接位点和10 kb都将在初次通过中重新测试，然后对某些基因的某些区域进行进一步测序（例如内含子或阳性单倍型）和/或更多个体。由于大多数功能性SNP和突变都不在蛋白质编码区域中，因此在验尸后的几个区域充分表征转录本物种至关重要。为了实现这一目标，我们通常执行基本的mRNA转录物特征技术，例如5'和3'种族以及筛选全长转录本，从多个大脑区域进行标准化的cDNA文库。这项工作还用于指导定量RT-PCR和原位杂交表达研究。 另一个核心重要性的项目是基因 - 基因相互作用的统计分析。某些基因和等位基因组合在上词相互作用可能会产生大于单个赔率比的风险。即使在每个基因中没有主要影响的情况下，某些基因组合也可能会增加风险。我们正在使用在范德比尔特开发的数据驱动的分析方法，称为多因素维度降低（MDR），以检测预测疾病状况的相互作用等位基因集。我们还与Salford Systems，计划推车，火星和Treenet的发起人进行合作讨论，以探索和执行其他数据挖掘策略。我们的统计遗传学家使用大量数据来模拟和测试复杂的基因基因和基因 - 环境相互作用，并建立了与统计遗传（疾病和中间表型）数据相结合的一些客观标准，并与收敛的生物学数据相结合，都可以评估给定基因型/单倍型，表型纠纷和评估属性的整体重要性。