Genetics and Bioinformatics Core Laboratory

我们小组的目标是利用与大脑发育和功能测量的遗传关联的证据作为进一步表征分子途径的基础。一项研究证明了这一点，该研究着眼于精神分裂症患者的健康兄弟姐妹的基因保护。通过CBDB，我们可以获得大量健康兄弟姐妹的样本，这不仅使我们能够区分状态和潜在的特质临床现象；它还允许对一个家庭内部保护的遗传学进行调查。这是我们家庭样本的独特潜力的另一个例子。由于一个家庭中的兄弟姐妹从父母那里继承风险相关等位基因和风险保护性等位基因的可能性相同，因此可以合理地预测，健康的兄弟姐妹将表现出与患病后代相似的保护性等位基因和风险等位基因的扭曲传播。我们在PRODH（脯氨酸脱氢酶）基因的研究中对此进行了测试，其中三个影响脯氨酸氧化酶活性的功能性等位基因，因此具有明确的生物学效应，在我们的家庭中表现出扭曲的遗传给受影响的后代。我们重点研究了包含三个已证实影响酶活性的等位基因的单倍型，并使用FBAT（基于家庭的关联测试）发现包含与痘活性增加相关的等位基因的单倍型对受影响后代的传播显著。与此形成鲜明对比的是，在未受影响的兄弟姐妹中发现了与痘活性降低相关的单倍型的显著过传。在未来的研究中，包括GWAS（全基因组关联研究）分析，我们将采用这种新策略来识别与我们家庭中未受影响状态相关的保护相关等位基因和基因（可能与风险基因不同）。这种方法有可能确定新的治疗靶点。

Our group aims to use evidence of genetic association with measures of brain development and function as a basis for further characterization of molecular pathways. One study which demonstrates this is looking at the genetic protection in healthy siblings of subjects with schizophrenia. Through the CBDB, we have access to a large sample of healthy siblings, which allows us not only to differentiate state from potential trait clinical phenomena; it also permits an investigation of the genetics of protection within a family. This is another example of the unique potential of our family samples. Because siblings within a family each have the same likelihood of inheriting risk associated and risk protective alleles from their parents, it is reasonable to predict that healthy siblings will show distorted transmission of protective alleles similarly to ill offspring and distorted transmission of risk alleles. We have tested this in a study of the gene PRODH (proline dehydrogenase), in which three functional alleles impacting on POX (proline oxidase) enzyme activity, therefore having a clear biological effect, showed distorted transmission to affected offspring in our families. We focused on the haplotype containing the three alleles that have been confirmed to affect enzyme activity, and using FBAT (family-based association test) found significant over transmission of the haplotype containing alleles related to increased POX activity to affected offspring. In dramatic contrast, significant over transmission of the haplotype related to decreased POX activity was found in the unaffected siblings. We will be adapting this novel strategy to identifying protection associated alleles and genes (which may not be the same as risk genes) related to unaffected status in our families in future studies including our GWAS (genome-wide association studies) analyses. This approach has the potential to define new targets for treatment. To date, we have tested numerous single nucleotide polymorphisms (SNPs) in well over 120 genes, including some of the less established but intriguing candidates such as PRODH, RGS4 (regulator of G-protein signaling 4), CHRNA7 (nicotinic, alpha polypeptide 7), PIP5K2A (phosphatidylinositol-5-phosphate 4-kinase, type II, alpha), and PPP3CC (protein phosphatase 3, catalytic subunit, gamma). Among our many accomplishments, we have fully sequenced the 10 exons and flanking sequences of 180 proband chromosomes for dysbindin, sequenced two exons of MRDS1 (orofacial clefting chromosomal breakpoint region 1), and sequenced 1.5 kb of the GAD1 (Glutamic acid decarboxylase 1) upstream region. A total of 21 new SNPs (single nucleotide polymorphisms) 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 (glutamate receptor, metabotropic 3) in 180 chromosomes, which led to the discovery of a few rare SNPs. We have likewise re-sequenced risk regions of KCNH2 (potassium voltage-gated channel), ErbB4 (v-erb-a erythroblastic leukemia viral oncogene homolog 4), PI3K (phosphotidylinositol 3 kinase), FGF20 (fibroblast growth factor 20), DARPP (dopamine- and cAMP-regulated phosphoprotein), and COMT (catechol-o-methyltransferase) 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 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 the program 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 programs. 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 (quantitative transmission disequilibrium test), 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 of 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 and reduced costs by purchasing high throughput equipment for in-house testing. 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 (real time-polymerase chain reactions) 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.