Twin, adoption, and family studies have established the heritability of suicide attempts and suicide. Identifying specific suicide diathesis-related genes has proven more difficult. As with psychiatric disorders in general, methodological difficulties include complexity of the phenotype for suicidal behavior and distinguishing suicide diathesis-related genes from genes associated with mood disorders and other suicide-associated psychiatric illness. Adopting an endophenotype approach involving identification of genes associated with heritable intermediate phenotypes, including biological and/or behavioral markers more proximal to genes, is an approach being used for other psychiatric disorders. Therefore, a workshop convened by the American Foundation for Suicide Prevention, the Department of Psychiatry at Columbia University, and the National Institute of Mental Health sought to identify potential target endophenotypes for genetic studies of suicidal behavior. The most promising endophenotypes were trait aggression/impulsivity, early-onset major depression, neurocognitive function, and cortisol social stress response. Other candidate endophenotypes requiring further investigation include serotonergic neurotransmission, second messenger systems, and borderline personality disorder traits.

Epigenetic effects in mammals depend largely on heritable genomic methylation patterns. We describe a computational pattern recognition method that is used to predict the methylation landscape of human brain DNA. This method can be applied both to CpG islands and to non-CpG island regions. It computes the methylation propensity for an 800-bp region centered on a CpG dinucleotide based on specific sequence features within the region. We tested several classifiers for classification performance, including K means clustering, linear discriminant analysis, logistic regression, and support vector machine. The best performing classifier used the support vector machine approach. Our program (called HDFINDER) presently has a prediction accuracy of 86%, as validated with CpG regions for which methylation status has been experimentally determined. Using HDFINDER, we have depicted the entire genomic methylation patterns for all 22 human autosomes.DNA methylation ͉ epigenomics ͉ methylation prediction ͉ CpG islands A lthough progress recently has been made toward wholegenome DNA methylation profiling by using molecular techniques, computational epigenomics is still in its infancy (1). Global analyses of DNA methylation have been focused mainly on two themes: the discovery of methylated CpG islands (CGI) and allele-specific cytosine methylation. Computational prediction of CGIs was introduced in 1987 by . They defined CGIs as regions of Ͼ200 bp with GϩC content of Ͼ0.5 and the observed͞expected CpG ratio Ͼ0.6. Takai and Jones (3) later proposed a more stringent definition that requires CGIs to be Ͼ500 bp long, CG content Ͼ55%, and the CpG ratio Ͼ0.65. This latter method is successful in excluding Alu repeats, many of which were annotated as CGIs when the former criteria were used. Matsuo et al. (4) have provided statistical evidence for erosion of mouse CGIs as compared with human ones. They suggested that an accumulation of TpGs and CpAs observed in mouse, presumably due to the higher rate of deamination of the methylated CpGs, results in a lower CpG ratio in mouse. Antequerra and Bird (5) performed comparative analysis on human and mouse and came to a similar conclusion. Yang et al.(6) proposed a computational method to identify genes with significant differences in gene expression between two parental alleles by searching the UniGene database for the presence of monoallelically expressed (or imprinted) genes in the human genome. Wang et al. (7) compared human and mouse sequences for all known imprinted genes and found 15 motifs that are significantly enriched in the imprinted genes. However, currently there is no algorithm that can predict DNA methylation patterns based on the genomic sequence alone. Because almost nothing is known of the mechanisms that target specific sequences for de novo methylation, a key question that arises is whether there are DNA sequences that are more prone or resistant to methylation.To answer this question, we use data that was generated by enzymatic fractionation of 30 Mb of human brain DNA...

Impaired brain serotonin neurotransmission is a potential component of the diathesis of major depression. Tryptophan hydroxylase-2 (TPH2), is the rate limiting biosynthetic isoenzyme for serotonin that is preferentially expressed in the brain and a cause of impaired serotonin transmission. Here, we identify a novel TPH2 short isoform with truncated catalytic domain expressed in human brainstem, prefrontal cortex, hippocampus and amygdala. An exploratory study of 166 Caucasian subjects revealed association with major depression or suicide of a novel single nucleotide polymorphism (SNP) g.22879A > G located in exon 6 of this short isoform. This SNP and additional SNPs were discovered through a systematic characterization of the genetic architecture of the TPH2 gene for further genetic and functional investigations of its relationship to major depression and other psychopathology.

Objectives Suicide is partly heritable but the responsible genes have not been identified. We conducted a gene-centric, low coverage single nucleotide polymorphism (SNP) pilot genome-wide association study (GWAS) seeking new candidate regions in suicides with and without depression, combined with gene expression assay of brain tissue. Methods Ninety-nine Caucasian subjects, including 68 who completed suicide and 31 who died suddenly from other causes, were genotyped postmortem using GeneChip® Mapping 50K Xba. Clinical data were obtained from relatives. SNPs with Hardy – Weinberg equilibrium P values below 0.001 were excluded from analysis. Illumina chip expression arrays assayed the transcriptome in prefrontal cortex in a drug-free subgroup. Results GWAS analysis (cutoff P < 0.001) yielded 58 SNPs, 22 of them in or near 19 known genes, with risk allele-associated odds ratios between 2.7 and 6.9. Diagnosis of mood disorder did not explain the associations. Some of the SNPs matched into four functional groups in gene ontology. Gene expression in the prefrontal and the anterior cingulate cortex for these 19 genes was measured on a separate, though overlapping, sample of suicides and seven of 19 genes showed altered expression in suicides as compared with controls, especially in immune system related genes. Conclusions Matching GWAS findings with expression data assesses functional effect of new candidate genes in suicide, and is an alternative form of confirmation or replication study. Results highlight a role for neuroimmunological effects in suicidal behaviour.

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