Variants (such as rs9939609) in the fat mass- and obesity-associated (FTO) gene have been associated with obesity, type 2 diabetes, some cancers, and alcohol consumption. This study tested the associations of 167 single-nucleotide polymorphisms (SNPs) within FTO gene with alcohol dependence (AD) using two Caucasian samples: the Collaborative Study on the Genetics of Alcoholism (COGA) sample (660 AD cases and 400 controls) and the Study of Addiction: Genetics and Environment (SAGE) sample (623 cases and 1,016 controls). Logistic regression analysis of AD as a binary trait was performed using the PLINK software. For the SAGE sample, the top three SNPs showing associations with AD were rs8062891, rs1108086, and rs1420318 (p = 0.00088, 0.00086 and 0.00086, respectively). Two SNPs (rs12597786 and rs7204609) associated with AD in the SAGE sample (p = 0.017 and 0.034, respectively) were replicated in the COGA sample (p = 0.017 and 0.014, respectively). Through meta-analysis of two samples using PLINK, the top three SNPs associated with AD were rs8062891, rs12597786, and rs7204609 (p = 0.00064, 0.00076 and 0.0011, respectively). Haplotype analysis in the SAGE sample further supported the associations with AD in single-marker analysis. In addition, we found association of rs17817449 (which has a strong linkage disequilibrium with rs9939609) with AD in the SAGE sample (p = 0.00339). The findings provide evidence of joint intervention and prevention of AD and obesity.

HLA-DRA gene polymorphisms might play an important role in alcohol dependence (AD). We examined genetic associations of 29 single-nucleotide polymorphisms (SNPs) within the HLA-DRA gene with AD using two Caucasian samples - the Collaborative Study on the Genetics of Alcoholism (COGA) sample (660 AD cases and 400 controls) and the Study of Addiction: Genetics and Environment (SAGE) sample (623 cases and 1,016 controls). Logistic regression analysis using PLINK showed that 16 SNPs were associated with AD in the COGA sample and 13 SNPs were associated with AD in the SAGE sample (p < 0.05). The best novel signal was SNP rs2239803 associated with AD in both samples (p = 0.000817 for the COGA sample and p = 0.0026 for the SAGE sample, respectively) while one flanking SNP rs4935356 also showed strong association in both samples (p = 0.00219 and 0.0026 for the COGA and SAGE samples, respectively). Furthermore, these two SNPs revealed stronger associations in meta-analysis of these two samples (p = 8.97 × 10-6 and 2.02 × 10-5 for rs2239803 and rs4935356, respectively). In addition, the G-A haplotype from these two SNPs revealed a significant association with AD in both the COGA and SAGE samples (p = 0.0007 and 0.0019, respectively). These findings highlight the novel associations with HLA-DRA that may play an important role in the etiology of AD.

Variants (such as rs9939609) in the fat mass- and obesity-associated (FTO) gene have been associated with obesity, type 2 diabetes, some cancers, and alcohol consumption. This study tested the associations of 167 single-nucleotide polymorphisms (SNPs) within FTO gene with alcohol dependence (AD) using two Caucasian samples: the Collaborative Study on the Genetics of Alcoholism (COGA) sample (660 AD cases and 400 controls) and the Study of Addiction: Genetics and Environment (SAGE) sample (623 cases and 1,016 controls). Logistic regression analysis of AD as a binary trait was performed using the PLINK software. For the SAGE sample, the top three SNPs showing associations with AD were rs8062891, rs1108086, and rs1420318 (p = 0.00088, 0.00086 and 0.00086, respectively). Two SNPs (rs12597786 and rs7204609) associated with AD in the SAGE sample (p = 0.017 and 0.034, respectively) were replicated in the COGA sample (p = 0.017 and 0.014, respectively). Through meta-analysis of two samples using PLINK, the top three SNPs associated with AD were rs8062891, rs12597786, and rs7204609 (p = 0.00064, 0.00076 and 0.0011, respectively). Haplotype analysis in the SAGE sample further supported the associations with AD in single-marker analysis. In addition, we found association of rs17817449 (which has a strong linkage disequilibrium with rs9939609) with AD in the SAGE sample (p = 0.00339). The findings provide evidence of joint intervention and prevention of AD and obesity.

HLA-DRA gene polymorphisms might play an important role in alcohol dependence (AD). We examined genetic associations of 29 single-nucleotide polymorphisms (SNPs) within the HLA-DRA gene with AD using two Caucasian samples - the Collaborative Study on the Genetics of Alcoholism (COGA) sample (660 AD cases and 400 controls) and the Study of Addiction: Genetics and Environment (SAGE) sample (623 cases and 1,016 controls). Logistic regression analysis using PLINK showed that 16 SNPs were associated with AD in the COGA sample and 13 SNPs were associated with AD in the SAGE sample (p < 0.05). The best novel signal was SNP rs2239803 associated with AD in both samples (p = 0.000817 for the COGA sample and p = 0.0026 for the SAGE sample, respectively) while one flanking SNP rs4935356 also showed strong association in both samples (p = 0.00219 and 0.0026 for the COGA and SAGE samples, respectively). Furthermore, these two SNPs revealed stronger associations in meta-analysis of these two samples (p = 8.97 × 10-6 and 2.02 × 10-5 for rs2239803 and rs4935356, respectively). In addition, the G-A haplotype from these two SNPs revealed a significant association with AD in both the COGA and SAGE samples (p = 0.0007 and 0.0019, respectively). These findings highlight the novel associations with HLA-DRA that may play an important role in the etiology of AD.

The PDZ and LIM domain 5 (PDLIM5) gene may play a role in cancer, bipolar disorder, major depression, alcohol dependence and schizophrenia; however, little is known about the interaction effect of steroid and PDLIM5 gene on cancer. This study examined 47 single-nucleotide polymorphisms (SNPs) within the PDLIM5 gene in the Marshfield sample with 716 cancer patients (any diagnosed cancer, excluding minor skin cancer) and 2848 noncancer controls. Multiple logistic regression model in PLINK software was used to examine the association of each SNP with cancer. Bayesian logistic regression in PROC GENMOD in SAS statistical software, ver. 9.4 was used to detect gene–steroid interactions influencing cancer. Single marker analysis using PLINK identified 12 SNPs associated with cancer (P < 0.05); especially, SNP rs6532496 revealed the strongest association with cancer (P = 6.84 × 10−3); while the next best signal was rs951613 (P = 7.46 × 10−3). Classic logistic regression in PROC GENMOD showed that both rs6532496 and rs951613 revealed strong gene–steroid interaction effects (OR = 2.18, 95% CI = 1.31−3.63 with P = 2.9 × 10−3 for rs6532496 and OR = 2.07, 95% CI = 1.24 −3.45 with P = 5.43 × 10−3 for rs951613, respectively). Results from Bayesian logistic regression showed stronger interaction effects (OR = 2.26, 95% CI = 1.2 −3.38 for rs6532496 and OR = 2.14, 95% CI = 1.14 −3.2 for rs951613, respectively). All the 12 SNPs associated with cancer revealed significant gene–steroid interaction effects (P < 0.05); whereas 13 SNPs showed gene–steroid interaction effects without main effect on cancer. SNP rs4634230 revealed the strongest gene–steroid interaction effect (OR = 2.49, 95% CI = 1.5 −4.13 with P = 4.0 × 10−4 based on the classic logistic regression and OR = 2.59, 95% CI = 1.4 −3.97 from Bayesian logistic regression; respectively). This study provides evidence of common genetic variants within the PDLIM5 gene and interactions between PLDIM5 gene polymorphisms and steroid use influencing cancer.

The DPYSL2 gene at 8p22-p21 is expressed widely in neuronal tissues and has been implicated in multiple psychiatric disorders such as Alzheimer's disease and schizophrenia. We therefore hypothesized that DPYSL2 gene polymorphisms may play a role in alcohol dependence (AD). We investigated the genetic associations of 57 single-nucleotide polymorphisms (SNPs) within the DPYSL2 gene with AD using two Caucasian samples - the Collaborative Study on the Genetics of Alcoholism (COGA) sample (660 AD cases and 400 controls), and the Study of Addiction: Genetics and Environment (SAGE) sample (623 cases and 1,016 controls). The SNP rs11995227 was most significantly associated with AD (p = 0.000122) in the COGA sample while one flanking SNP rs7832576 revealed the second most significant association with AD (p = 0.00163) in the COGA sample and association with AD (p = 0.0195) in the SAGE sample. Meta-analysis of two samples showed both rs119952227 and rs7832576 were associated with AD (p = 0.000363 and 0.000184, respectively). Furthermore, the C-A haplotype from rs11995227 and rs7832576 revealed significant association with AD (p = 0.0000899) in the COGA sample while the T-G haplotype revealed association with AD both in the COGA and SAGE samples (p = 0.00098 and 0.021, respectively). These findings suggest that genetic variants in DPYSL2 may play a role in susceptibility to AD.

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.

Indiana University-Purdue University Indianapolis (IUPUI) / Externally visible characteristics (EVCs) predicted from an unknown sample of DNA are particularly useful in forensics as they can provide information beyond that of an STR profile. Current EVCs which are highly studied and well-predicted include iris, hair, and skin color. Notably, models predicting iris color, such as IrisPlex, are the most accurate with up to ~95% accuracy; however, some inaccurate predictions occur, as is evidenced by the ~5%. Often, these are due to green or hazel eyes, which are frequently viewed as intermediate. Though, some of the inaccurate predictions are due to true-blue being predicted as brown and vice versa. Previous research has theorized the possibility of two SNPs, rs12913832 and rs1800407, acting as a functional haplotype affecting iris color. rs12913832 is recognized as the most predictive SNP for iris color and highly significant in other pigmentation phenotypes; presently, rs1800407 is the second-ranked SNP in the IrisPlex 6-SNP system. Both SNPs are highly variable in Europe, where the majority of variation in iris color originates. In the present study, we explore the SNP variation present in the genetic regions of OCA2-HERC2 as well as possible haplotypes. Our research centers around the functional haplotype and the addition of SNPs to the functional haplotype. In addition, three different ways of classifying the phenotype are assessed simultaneously. First, using a 4-point categorical phenotype—blue/blue grey, blue/green yellow, hazel/light brown, and dark brown. Second, calculating a continuous scale from a quantitative phenotype in which the percentage of each categorical color has been measured. Third, using the IrisPlex 6-SNP system to predict eye color and identify individuals which have been inaccurately predicted. Exploration of the SNP and haplotype variation resulted in two SNPs for both the categorical and quantitative phenotypes which were significantly correlated with hazel/light brown—rs1448484 and rs61335644, both as independent SNPs and when assessed in a haplotype with rs1800407-rs12913832. SNP rs1448484 has been associated with skin pigmentation previously and is located in a possible transcription factor binding site. SNP rs61335644 is not presently associated with pigmentation but is in complete LD with two SNPs in and around regulatory regions present in HERC2. Finally, the addition of rs1448484 and rs61335644 into the current IrisPlex 6-SNP system slightly improved each of the tested performance metrics for hazel/light brown and dark brown. Within the inaccurately predicted phenotypes, rs1800407 is confirmed to affect both inaccurately predicted groups and is the most significant SNP. Additionally, rs121918166, a missense variant in OCA2, is the second most significant SNP in true blue predicted as brown. Both SNPs were also the two most significant haplotypes with at least one allele being derived. Therefore, the next steps should include the addition of the functional haplotype and rs121918166 into the current IrisPlex model, and further testing of rs1448484 and rs61335644 on a molecular level. Consequently, the current IrisPlex model should also be reassessed on an independent test set using the 4-point categorical scale rather than the present 3-point scale.

京都大学 / 0048 / 新制・課程博士 / 博士(農学) / 甲第20427号 / 農博第2212号 / 新制||農||1048(附属図書館) / 学位論文||H29||N5048(農学部図書室) / 京都大学大学院農学研究科応用生物科学専攻 / (主査)准教授 谷口 幸雄, 教授 今井 裕, 教授 廣岡 博之 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DFAM

付記する学位プログラム名: 霊長類学・ワイルドライフサイエンス・リーディング大学院 / 京都大学 / 新制・課程博士 / 博士(理学) / 甲第24471号 / 理博第4970号 / 新制||理||1709(附属図書館) / 京都大学大学院理学研究科生物科学専攻 / (主査)教授 村山 美穂, 教授 伊谷 原一, 教授 平田 聡 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DGAM