Using whole genome sequencing to identify risk alleles for susceptibility to schizophrenia

Schizophrenia is a complex idiopathic neuropsychiatric illness that affects approximately 1% of the general population. Family, twin, and adoption studies indicate a high heritability and strong genetic element to the disease with first degree relatives demonstrating an increased risk of about 10% and monozygotic concordance rates as high as 50%. These values represent the probability of developing schizophrenia based on the presence of genetic components. The high heritability has led to individual studies and meta-analyses being able to produce significant evidence of linkage to specific locations, but studies that used large number of pedigrees have failed to produce statistically significant linkage results. Genome Wide Association Studies of schizophrenia have also produced similarly mixed results. One interpretation of these mixed linkage and association results is that factors such as small effect size and uncontrolled phenotypic variation require very large samples to overcome. This thesis focuses on a different interpretation: genuine genetic differences between definable subsets can mask both linkage and association, and that this problem is worsened in studies that use large samples where the entire sample is analyzed as if it were a genetically homogenous group. The work presented herein begins with linkage studies performed on 22 medium- sized Canadian pedigrees (n=304 individuals) of German or Celtic descent initially recruited if at least three subjects with schizophrenia were available for study. Association studies were conducted on an expanded sample of 30 pedigrees (n=573). Subjects in this sample have been followed for up to 20 years allowing for continued observation of diagnostic stability. We have identified linkage disequilibrium between schizophrenia and single nucleotide polymorphisms (SNPs) from six discrete genomic regions located under linkage peaks within this sample. We hypothesize that SNPs that generated compelling evidence of association (PPLD|L >= 0.2) produce these scores because they either are, or are in, high LD (r 2 >= 0.8) with functional variants that increase susceptibility to schizophrenia. To that end, whole genome sequencing data from ten individuals within this study (n=10) was analyzed to generate a list of variants within 500 kb upstream and downstream of each risk SNP. A pipeline was created to determine whether or not each SNP in this list was a candidate for further analysis by assessing its LD to the risk SNPs identified by the association studies described above. SNPs determined to be candidates were then genotyped in the entire sample (n=378) so that association could be accurately assessed. Finally, association scores were compared between risk SNPs and candidate SNPs, with variants having higher PPLD|L scores than the referring SNP identified as potential functional candidates. Six SNPs from one genomic region produced higher PPLD|L scores than the referring SNP and so will replace the referring SNP as candidates for further functional analysis. These six SNPs first will be evaluated for additional candidate SNPs 500 kb up- and down-stream in order to determine the best SNP in the region according to the PPLD|L. Additional SNPs have also been identified in some of the other genomic regions that need to be assessed for LD in the full sample. The SNP or SNPs producing the strongest LD signal in each region will need to be further assessed by functional assays to determine their potential role in schizophrenia susceptibility.

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