Genetics and Bioinformatics Core Laboratory

到目前为止，我们已经在超过118个基因中测试了大量的单核苷酸多态(SNPs)，包括一些较不成熟但有趣的候选基因，如PROSH、RGS4、CHRNA7、PIP5K2A和PPP3CC。在我们的研究成果中，我们已经对180条先证者染色体的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，并对GAD1上游1.5kb的区域进行了测序。在这些基因中发现了21个新的SNP，其中15个在临床样本中进行了基因分型。我们对180条染色体上GRM3的外显子和剪接点进行了重新测序，从而发现了一些罕见的SNP。我们同样对KCNH2、ErbB4、PIk3d、FGF20、DAARP和COMT的风险区域进行了重新排序，并在这些基因中发现了新的变异体。我们经常将我们的Taqman基因分析通过重新基因分型(Avg.准确率&gt；99%)和双链测序(Avg.对于大多数SNP检测，&gt；99%)。在ABI SDS软件中手动调用并确认基因分型。我们使用程序Merlin执行孟德尔检查和更高阶(例如，多个重组子)错误检查。微卫星基因分型已经与NIMH情绪和焦虑计划合作进行。 我们使用GOLD软件包并行地使用病例和对照的D素数和R2统计量来测量标记之间的连锁不平衡(LD)。所有的SNP都被测试是否偏离了哈代-温伯格平衡。对于大量的基因座，我们使用SNPHAP来重建单倍型并估计它们在无关个体中的频率。对于离散临床表型的家系关联研究，我们使用程序FBAT、TDTPHASE和TRANSPASE进行未知期单倍型估计。在STATA和COCAPHASE中使用Logistic回归分析单个SNPs和SNP单倍型的病例对照分析。所有P值都是根据程序提供的10,000个排列或自举经验计算得出的。对数量性状(如中间表型)的关联测试由FBAT和QTDT执行，这允许对基于家系的样本进行关联和传递不平衡的方差分量测试。所使用的正交化模型对种群分层是稳健的，因为类似于传统的TDT，它只考虑来自杂合子双亲的传播。为了控制由于不同种族的等位基因频率差异而可能产生的伪影，仅限于高加索人的分析是平行进行的。我们还建立了一个非连锁SNPs小组，用作病例对照关联研究的潜在基因组控制小组，包括中间表型分析，以解决潜在的人群混合人工产物。 在我们的基因组学项目中，我们获取了大量的易感基因的遗传变异数据，并在我们的数据集中完成了遗传风险基因的目录。作为GCAP计划的一部分，我们通过外包大大提高了基因分型的吞吐量。我们预计，在接下来的两年里，大约每4个月我们将至少对768个SNP进行基因分型，对已建立的基因进行后续工作，并测试新的基因。此外，我们将SNP检测的大部分重新测序工作外包给DNA测序公司。所有外显子、剪接点和上游区的10kb将在初始过程中被重新测序，然后一些基因的某些区域被进一步测序(例如内含子或阳性单倍型)和/或更多的个体。由于大多数功能性SNPs和突变不在蛋白质编码区，因此充分鉴定死后人脑几个区域的转录物种类至关重要。为了做到这一点，我们常规地执行基本的mRNA转录鉴定技术，如5‘和3’RACE以及从多个大脑区域筛选全长转录本和标准化的cDNA文库。这项工作也用于指导定量RT-PCR和原位杂交表达的研究。 另一个至关重要的项目是基因-基因相互作用的统计分析。很可能某些基因和等位基因组合上位性地相互作用，产生比个体优势比预测的风险更大的风险。即使在每个基因中没有主效应的情况下，一些基因组合也可能会增加风险。我们正在使用范德比尔特开发的数据驱动分析方法，称为多因素降维(MDR)，试图检测预测疾病状态的相互作用的等位基因集。我们还与CART、MARS和Treenet程序的发起人Salford Systems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丰富的数据来模拟和测试复杂的基因-基因和基因-环境相互作用，并建立一些客观标准，用于将统计遗传(疾病和中间表型)数据与收敛的生物学数据相结合，以衡量给定基因/单倍型、表型相关性的总体重要性，并评估归因风险。

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.