We performed a genome-wide association analysis to identify genetic variants influencing age at onset (AAO) and examine gene×gender interactions for AAO in schizophrenia (SCZ) using a European-American sample (1,162 cases). Linear regression model in PLINK was used to test for associations with AAO while the GxE option was chosen to test for the influence of gene×gender interactions. The most significant association with AAO was observed with SNP rs7819815 (P=3.10×10-7) at 8q24.22. The next best signal was at 4q25 in COL25A1 gene (rs17039583, P=4.30×10-6) and the third region was at 4p16.1 (rs17407555, P=4.56×10-6, near RAF1P1, and rs4697924, P=1.23×10-5 within WDR1 gene). Conditional analysis on chromosome 4 indicated that 4p16.1 and 4q25 loci were independent. Furthermore, 2 SNPs (rs16834822 and rs16834824) at 1q43 in RYR2 showed strong associations in the female sample (P=2.10×10-6 and 2.33×10-6, respectively) and strong gene×gender interactions in influencing AAO (P=9.23×10-7 and 1.15×10-6, respectively) while the second best region showing gene×gender interaction was at 7q22.3 (rs179863, P=2.33×10-6). Using an independent sample of 1,068 cases, we could not replicate the associations for above top SNPs; however, we found nominal significance associations for their flanking SNPs (P<0.05). These findings provide evidence of several genetic variants influencing AAO of SCZ.

Schizophrenia and bipolar disorder both have strong inherited components. Recent studies have indicated that schizophrenia and bipolar disorder may share more than half of their genetic determinants. In this study, we performed a meta-analysis (combined analysis) for genome-wide association data of the Affymetrix Genome-Wide Human SNP array 6.0 to detect genetic variants influencing both schizophrenia and bipolar disorder using European-American samples (653 bipolar cases and 1034 controls, 1172 schizophrenia cases and 1379 controls). The best associated SNP rs11789399 was located at 9q33.1 (p=2.38×10-6, 5.74×10-4, and 5.56×10-9, for schizophrenia, bipolar disorder and meta-analysis of schizophrenia and bipolar disorder, respectively), where one flanking gene, ASTN2 (220kb away) has been associated with attention deficit/hyperactivity disorder and schizophrenia. The next best SNP was rs12201676 located at 6q15 (p=2.67×10-4, 2.12×10-5, 3.88×10-8 for schizophrenia, bipolar disorder and meta-analysis, respectively), near two flanking genes, GABRR1 and GABRR2 (15 and 17kb away, respectively). The third interesting SNP rs802568 was at 7q35 within CNTNAP2 (p=8.92×10-4, 1.38×10-5, and 1.62×10-7 for schizophrenia, bipolar disorder and meta-analysis, respectively). Through meta-analysis, we found two additional associated genes NALCN (the top SNP is rs2044117, p=4.57×10-7) and NAP5 (the top SNP is rs10496702, p=7.15×10-7). Haplotype analyses of above five loci further supported the associations with schizophrenia and bipolar disorder. These results provide evidence of common genetic variants influencing schizophrenia and bipolar disorder. These findings will serve as a resource for replication in other populations to elucidate the potential role of these genetic variants in schizophrenia and bipolar disorder.

A comprehensive understanding about how genetic variants and mutations contribute to phenotypic variations and alterations entails experimental technologies and analytical methodologies that are able to detect genetic variants/mutations from various biological samples in a timely and accurate manner. High-throughput sequencing technology represents the latest achievement in a series of efforts to facilitate genetic variants discovery and genotyping and promises to transform the way we tackle healthcare and biomedical problems. The tremendous amount of data generated by this new technology, however, needs to be processed and analyzed in an accurate and efficient way in order to fully harness its potential. Structural variation (SV) encompasses a wide range of genetic variations with different sizes and generated by diverse mechanisms. Due to the technical difficulties of reliably detecting SVs, their characterization lags behind that of SNPs and indels. In this dissertation I presented two novel computational methods: one for detecting transposable element (TE) transpositions and the other for detecting SVs in general using a local assembly approach. Both methods are able to pinpoint breakpoint junctions at single-nucleotide resolution and estimate variant allele frequencies in the sample. I also applied those methods to study the impact of TE transpositions on the genomic stability, the inheritance patterns of TE insertions in the population and the molecular mechanisms and potential functional consequences of somatic SVs in cancer genomes.

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第19402号 / 医博第4053号 / 新制||医||1012(附属図書館) / 32427 / 京都大学大学院医学研究科医学専攻 / (主査)教授 佐藤 俊哉, 教授 小川 誠司, 教授 横出 正之 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第20278号 / 医博第4237号 / 新制||医||1021(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 森田 智視, 教授 佐藤 俊哉, 教授 中山 健夫 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

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Here we present the genome-wide association study of body weight at 8 weeks of age based onthe advanced intercross pedigree of two chicken lines gone through bi-directional selection.With improved marker density (~3M SNPs) and larger sample size (2667 individuals from F2-F15), 34 loci with suggestive significance are detected, of which 18 loci are novel, and the rest17 loci are consistent with the results of previous quantitative trait locus mapping studies onthis trait with smaller number of genetic markers and fewer individuals. The component traits,referring to traits related to body weight and possibly contributing to the body weight as well,are also measured and analysed. The combined result showed that one locus with significantmarginal effect on BW8 is associated with early growth, breast muscle development and shankdevelopment, while another locus with late development and bursa development.

The Norway spruce is of great importance from both an ecological- and economic standpoint. Information about which genes that causes certain phenotypic traits in the species is therefore highly valuable. The purpose of this project was to apply machine learning to find such genotype-phenotype correlations. The purpose was also to compare the results from different machine learning algorithms to a more traditional linear mixed model GWAS (where correlation to the phenotype is estimated for each SNP one by one) to find which is the better method for GWAS. The machine learning algorithms tested were decision tree, support vector machine and support vector regression. The phenotypes analyzed were wood density and initiation frequency of zygotic embryogenesis (ZE). The latter is related to a new method for cloning. The genetic data consisted of single-nucleotide polymorphisms (SNPs). Due to the large genome size of Norway spruce and due to limitations in the packages used in R two different approaches were taken to reduce the sample size. The first approach used Kendall’s rank correlation coefficient to remove redundant SNPs and the second used an iterative approach to the machine learning model. The iterative approach was proven to be the best and support vector machine/regression was found to be better than decision tree for both phenotypes. Support vector regression from the iterative approach resulted in a squared correlation coefficient of 0.83 for density and 0.94 for ZE initiation frequency. Note that these very high values should be interpreted with caution, as it is possible that some of the significant correlations are only due to random chance. Even a small chance for random correlations will result in findings when the number of SNPs are this large (1908552 SNPs). The significant SNPs identified by the machine learning models were compared to SNPs identified by the linear mixed model GWAS. This indeed showed some overlaps of significant SNPs, which increases the credibility of my results. However, further investigation of the identified significant SNPs is needed to determine their functional mode of action. My conclusion is that using machine learning to predict phenotypic traits from SNP data can be a good choice. However, the model might not use all correlated SNPs, just enough to get a good prediction. Therefore, for the purpose of finding significant SNPs, the linear mixed model approach might be better. In other words, the method used should be determined by the purpose of the study.

京都大学 / 新制・課程博士 / 博士(医学) / 甲第24494号 / 医博第4936号 / 新制||医||1063(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 村川 泰裕, 教授 小杉 眞司, 教授 松田 文彦 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

Bipolar disorder is a relatively common form of mental illness that depends strongly on genetic inheritance for expression. The author of this study has sought to evaluate whether the gender of subjects influences which genetic variants are associated with the disease. A portion of the cases from a previously published study were analyzed using PLINK software and the association of single-nucleotide polymorphisms was evaluated separately for all cases, for female subjects alone, and for male subjects alone. The results obtained for male subjects alone reached higher levels of statistical significance than when both genders were evaluated together or when female subjects were evaluated alone. The most significantly scoring polymorphisms were distinctly different for the 2 genders. In particular, a site downstream of the ion exchanger SLC24A3 and upstream of the Rab5-interacting protein RIN2 gene on chromosome 20 (rs6046396) yielded very high significance in men (p=3.91 X 10-9).