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1.  A Genome-Wide Association Study Reveals a Quantitative Trait Locus of Adiponectin on CDH13 That Predicts Cardiometabolic Outcomes 
Diabetes  2011;60(9):2417-2423.
OBJECTIVE
The plasma adiponectin level, a potential upstream and internal facet of metabolic and cardiovascular diseases, has a reasonably high heritability. Whether other novel genes influence the variation in adiponectin level and the roles of these genetic variants on subsequent clinical outcomes has not been thoroughly investigated. Therefore, we aimed not only to identify genetic variants modulating plasma adiponectin levels but also to investigate whether these variants are associated with adiponectin-related metabolic traits and cardiovascular diseases.
RESEARCH DESIGN AND METHODS
We conducted a genome-wide association study (GWAS) to identify quantitative trait loci (QTL) associated with high molecular weight forms of adiponectin levels by genotyping 382 young-onset hypertensive (YOH) subjects with Illumina HumanHap550 SNP chips. The culpable single nucleotide polymorphism (SNP) variants responsible for lowered adiponectin were then confirmed in another 559 YOH subjects, and the association of these SNP variants with the risk of metabolic syndrome (MS), type 2 diabetes mellitus (T2DM), and ischemic stroke was examined in an independent community–based prospective cohort, the CardioVascular Disease risk FACtors Two-township Study (CVDFACTS, n = 3,350).
RESULTS
The SNP (rs4783244) most significantly associated with adiponectin levels was located in intron 1 of the T-cadherin (CDH13) gene in the first stage (P = 7.57 × 10−9). We replicated and confirmed the association between rs4783244 and plasma adiponectin levels in an additional 559 YOH subjects (P = 5.70 × 10−17). This SNP was further associated with the risk of MS (odds ratio [OR] = 1.42, P = 0.027), T2DM in men (OR = 3.25, P = 0.026), and ischemic stroke (OR = 2.13, P = 0.002) in the CVDFACTS.
CONCLUSIONS
These findings indicated the role of T-cadherin in modulating adiponectin levels and the involvement of CDH13 or adiponectin in the development of cardiometabolic diseases.
doi:10.2337/db10-1321
PMCID: PMC3161336  PMID: 21771975
2.  Integrative analysis of single nucleotide polymorphisms and gene expression efficiently distinguishes samples from closely related ethnic populations 
BMC Genomics  2012;13:346.
Background
Ancestry informative markers (AIMs) are a type of genetic marker that is informative for tracing the ancestral ethnicity of individuals. Application of AIMs has gained substantial attention in population genetics, forensic sciences, and medical genetics. Single nucleotide polymorphisms (SNPs), the materials of AIMs, are useful for classifying individuals from distinct continental origins but cannot discriminate individuals with subtle genetic differences from closely related ancestral lineages. Proof-of-principle studies have shown that gene expression (GE) also is a heritable human variation that exhibits differential intensity distributions among ethnic groups. GE supplies ethnic information supplemental to SNPs; this motivated us to integrate SNP and GE markers to construct AIM panels with a reduced number of required markers and provide high accuracy in ancestry inference. Few studies in the literature have considered GE in this aspect, and none have integrated SNP and GE markers to aid classification of samples from closely related ethnic populations.
Results
We integrated a forward variable selection procedure into flexible discriminant analysis to identify key SNP and/or GE markers with the highest cross-validation prediction accuracy. By analyzing genome-wide SNP and/or GE markers in 210 independent samples from four ethnic groups in the HapMap II Project, we found that average testing accuracies for a majority of classification analyses were quite high, except for SNP-only analyses that were performed to discern study samples containing individuals from two close Asian populations. The average testing accuracies ranged from 0.53 to 0.79 for SNP-only analyses and increased to around 0.90 when GE markers were integrated together with SNP markers for the classification of samples from closely related Asian populations. Compared to GE-only analyses, integrative analyses of SNP and GE markers showed comparable testing accuracies and a reduced number of selected markers in AIM panels.
Conclusions
Integrative analysis of SNP and GE markers provides high-accuracy and/or cost-effective classification results for assigning samples from closely related or distantly related ancestral lineages to their original ancestral populations. User-friendly BIASLESS (Biomarkers Identification and Samples Subdivision) software was developed as an efficient tool for selecting key SNP and/or GE markers and then building models for sample subdivision. BIASLESS was programmed in R and R-GUI and is available online at http://www.stat.sinica.edu.tw/hsinchou/genetics/prediction/BIASLESS.htm.
doi:10.1186/1471-2164-13-346
PMCID: PMC3453505  PMID: 22839760
Single nucleotide polymorphism (SNP); Allele frequency; Gene expression; HapMap; Classification analysis; Ancestry informative marker (AIM)
3.  Identification of IGF1, SLC4A4, WWOX, and SFMBT1 as Hypertension Susceptibility Genes in Han Chinese with a Genome-Wide Gene-Based Association Study 
PLoS ONE  2012;7(3):e32907.
Hypertension is a complex disorder with high prevalence rates all over the world. We conducted the first genome-wide gene-based association scan for hypertension in a Han Chinese population. By analyzing genome-wide single-nucleotide-polymorphism data of 400 matched pairs of young-onset hypertensive patients and normotensive controls genotyped with the Illumina HumanHap550-Duo BeadChip, 100 susceptibility genes for hypertension were identified and also validated with permutation tests. Seventeen of the 100 genes exhibited differential allelic and expression distributions between patient and control groups. These genes provided a good molecular signature for classifying hypertensive patients and normotensive controls. Among the 17 genes, IGF1, SLC4A4, WWOX, and SFMBT1 were not only identified by our gene-based association scan and gene expression analysis but were also replicated by a gene-based association analysis of the Hong Kong Hypertension Study. Moreover, cis-acting expression quantitative trait loci associated with the differentially expressed genes were found and linked to hypertension. IGF1, which encodes insulin-like growth factor 1, is associated with cardiovascular disorders, metabolic syndrome, decreased body weight/size, and changes of insulin levels in mice. SLC4A4, which encodes the electrogenic sodium bicarbonate cotransporter 1, is associated with decreased body weight/size and abnormal ion homeostasis in mice. WWOX, which encodes the WW domain-containing protein, is related to hypoglycemia and hyperphosphatemia. SFMBT1, which encodes the scm-like with four MBT domains protein 1, is a novel hypertension gene. GRB14, TMEM56 and KIAA1797 exhibited highly significant differential allelic and expressed distributions between hypertensive patients and normotensive controls. GRB14 was also found relevant to blood pressure in a previous genetic association study in East Asian populations. TMEM56 and KIAA1797 may be specific to Taiwanese populations, because they were not validated by the two replication studies. Identification of these genes enriches the collection of hypertension susceptibility genes, thereby shedding light on the etiology of hypertension in Han Chinese populations.
doi:10.1371/journal.pone.0032907
PMCID: PMC3315540  PMID: 22479346
4.  Region-based and pathway-based QTL mapping using a p-value combination method 
BMC Proceedings  2011;5(Suppl 9):S43.
Quantitative trait locus (QTL) mapping using deep DNA sequencing data is a challenging task. In this study we performed region-based and pathway-based QTL mappings using a p-value combination method to analyze the simulated quantitative traits Q1 and Q4 and the exome sequencing data. The aims were to evaluate the performance of the QTL mapping approaches that were used and to suggest plausible strategies for QTL mapping of DNA sequencing data. We conducted single-locus QTL mappings using a linear regression model with adjustments for age and smoking status, and we also conducted region-based and pathway-based QTL mappings using a truncated product method for combining p-values from the single-locus QTL mapping. To account for the features of rare variants and common single-nucleotide polymorphisms (SNPs), we considered independently rare-variant-only, common-SNP-only, and combined analyses. An analysis of 200 simulated replications showed that the three region-based methods reasonably controlled type I error, whereas the combined analysis yielded the greatest statistical power. Rare-variant-only, common-SNP-only, and combined analyses were also applied to pathway-based QTL mappings. We found that pathway-based QTL mappings had a power of approximately 100% when the significance of the vascular endothelial growth factor pathway was evaluated, but type I errors were slightly inflated. Our approach complements single-locus QTL mapping. An integrated approach using single-locus, combined region-based, and combined pathway-based analyses should yield promising results for QTL mapping of DNA sequencing data.
doi:10.1186/1753-6561-5-S9-S43
PMCID: PMC3287880  PMID: 22373302
5.  SAQC: SNP Array Quality Control 
BMC Bioinformatics  2011;12:100.
Background
Genome-wide single-nucleotide polymorphism (SNP) arrays containing hundreds of thousands of SNPs from the human genome have proven useful for studying important human genome questions. Data quality of SNP arrays plays a key role in the accuracy and precision of downstream data analyses. However, good indices for assessing data quality of SNP arrays have not yet been developed.
Results
We developed new quality indices to measure the quality of SNP arrays and/or DNA samples and investigated their statistical properties. The indices quantify a departure of estimated individual-level allele frequencies (AFs) from expected frequencies via standardized distances. The proposed quality indices followed lognormal distributions in several large genomic studies that we empirically evaluated. AF reference data and quality index reference data for different SNP array platforms were established based on samples from various reference populations. Furthermore, a confidence interval method based on the underlying empirical distributions of quality indices was developed to identify poor-quality SNP arrays and/or DNA samples. Analyses of authentic biological data and simulated data show that this new method is sensitive and specific for the detection of poor-quality SNP arrays and/or DNA samples.
Conclusions
This study introduces new quality indices, establishes references for AFs and quality indices, and develops a detection method for poor-quality SNP arrays and/or DNA samples. We have developed a new computer program that utilizes these methods called SNP Array Quality Control (SAQC). SAQC software is written in R and R-GUI and was developed as a user-friendly tool for the visualization and evaluation of data quality of genome-wide SNP arrays. The program is available online (http://www.stat.sinica.edu.tw/hsinchou/genetics/quality/SAQC.htm).
doi:10.1186/1471-2105-12-100
PMCID: PMC3101186  PMID: 21501472
6.  A new analysis tool for individual-level allele frequency for genomic studies 
BMC Genomics  2010;11:415.
Background
Allele frequency is one of the most important population indices and has been broadly applied to genetic/genomic studies. Estimation of allele frequency using genotypes is convenient but may lose data information and be sensitive to genotyping errors.
Results
This study utilizes a unified intensity-measuring approach to estimating individual-level allele frequencies for 1,104 and 1,270 samples genotyped with the single-nucleotide-polymorphism arrays of the Affymetrix Human Mapping 100K and 500K Sets, respectively. Allele frequencies of all samples are estimated and adjusted by coefficients of preferential amplification/hybridization (CPA), and large ethnicity-specific and cross-ethnicity databases of CPA and allele frequency are established. The results show that using the CPA significantly improves the accuracy of allele frequency estimates; moreover, this paramount factor is insensitive to the time of data acquisition, effect of laboratory site, type of gene chip, and phenotypic status. Based on accurate allele frequency estimates, analytic methods based on individual-level allele frequencies are developed and successfully applied to discover genomic patterns of allele frequencies, detect chromosomal abnormalities, classify sample groups, identify outlier samples, and estimate the purity of tumor samples. The methods are packaged into a new analysis tool, ALOHA (Allele-frequency/Loss-of-heterozygosity/Allele-imbalance).
Conclusions
This is the first time that these important genetic/genomic applications have been simultaneously conducted by the analyses of individual-level allele frequencies estimated by a unified intensity-measuring approach. We expect that additional practical applications for allele frequency analysis will be found. The developed databases and tools provide useful resources for human genome analysis via high-throughput single-nucleotide-polymorphism arrays. The ALOHA software was written in R and R GUI and can be downloaded at http://www.stat.sinica.edu.tw/hsinchou/genetics/aloha/ALOHA.htm.
doi:10.1186/1471-2164-11-415
PMCID: PMC2996943  PMID: 20602748
7.  Assessment of gene-covariate interactions by incorporating covariates into association mapping 
BMC Proceedings  2009;3(Suppl 7):S85.
The HLA region is considered to be the main genetic risk factor for rheumatoid arthritis. Previous research demonstrated that HLA-DRB1 alleles encoding the shared epitope are specific for disease that is characterized by antibodies to cyclic citrullinated peptides (anti-CCP). In the present study, we incorporated the shared epitope and either anti-CCP antibodies or rheumatoid factor into linkage disequilibrium mapping, to assess the association between the shared epitope or antibodies with the disease gene identified. Incorporating the covariates into the association mapping provides a mechanism 1) to evaluate gene-gene and gene-environment interactions and 2) to dissect the pathways underlying disease induction/progress in quantitative antibodies.
PMCID: PMC2795988  PMID: 20018081
8.  Genome-wide gene-based association study 
BMC Proceedings  2009;3(Suppl 7):S135.
Genome-wide association studies, which analyzes hundreds of thousands of single-nucleotide polymorphisms to identify disease susceptibility genes, are challenging because the work involves intensive computation and complex modeling. We propose a two-stage genome-wide association scanning procedure, consisting of a single-locus association scan for the first stage and a gene-based association scan for the second stage. Marginal effects of single-nucleotide polymorphisms are examined by using the exact Armitage trend test or logistic regression, and gene effects are examined by using a p-value combination method. Compared with some existing single-locus and multilocus methods, the proposed method has the following merits: 1) convenient for definition of biologically meaningful regions, 2) powerful for detection of minor-effect genes, 3) helpful for alleviation of a multiple-testing problem, and 4) convenient for result interpretation. The method was applied to study Genetic Analysis Workshop 16 Problem 1 rheumatoid arthritis data, and strong association signals were found. The results show that the human major histocompatibility complex region is the most important genomic region associated with rheumatoid arthritis. Moreover, previously reported genes including PTPN22, C5, and IL2RB were confirmed; novel genes including HLA-DRA, BTNL2, C6orf10, NOTCH4, TAP2, and TNXB were identified by our analysis.
PMCID: PMC2795909  PMID: 20018002
9.  Genome-Wide Association Study of Young-Onset Hypertension in the Han Chinese Population of Taiwan 
PLoS ONE  2009;4(5):e5459.
Young-onset hypertension has a stronger genetic component than late-onset counterpart; thus, the identification of genes related to its susceptibility is a critical issue for the prevention and management of this disease. We carried out a two-stage association scan to map young-onset hypertension susceptibility genes. The first-stage analysis, a genome-wide association study, analyzed 175 matched case-control pairs; the second-stage analysis, a confirmatory association study, verified the results at the first stage based on a total of 1,008 patients and 1,008 controls. Single-locus association tests, multilocus association tests and pair-wise gene-gene interaction tests were performed to identify young-onset hypertension susceptibility genes. After considering stringent adjustments of multiple testing, gene annotation and single-nucleotide polymorphism (SNP) quality, four SNPs from two SNP triplets with strong association signals (−log10(p)>7) and 13 SNPs from 8 interactive SNP pairs with strong interactive signals (−log10(p)>8) were carefully re-examined. The confirmatory study verified the association for a SNP quartet 219 kb and 495 kb downstream of LOC344371 (a hypothetical gene) and RASGRP3 on chromosome 2p22.3, respectively. The latter has been implicated in the abnormal vascular responsiveness to endothelin-1 and angiotensin II in diabetic-hypertensive rats. Intrinsic synergy involving IMPG1 on chromosome 6q14.2-q15 was also verified. IMPG1 encodes interphotoreceptor matrix proteoglycan 1 which has cation binding capacity. The genes are novel hypertension targets identified in this first genome-wide hypertension association study of the Han Chinese population.
doi:10.1371/journal.pone.0005459
PMCID: PMC2674219  PMID: 19421330
10.  Using the longest significance run to estimate region-specific p-values in genetic association mapping studies 
BMC Bioinformatics  2008;9:246.
Background
Association testing is a powerful tool for identifying disease susceptibility genes underlying complex diseases. Technological advances have yielded a dramatic increase in the density of available genetic markers, necessitating an increase in the number of association tests required for the analysis of disease susceptibility genes. As such, multiple-tests corrections have become a critical issue. However the conventional statistical corrections on locus-specific multiple tests usually result in lower power as the number of markers increases. Alternatively, we propose here the application of the longest significant run (LSR) method to estimate a region-specific p-value to provide an index for the most likely candidate region.
Results
An advantage of the LSR method relative to procedures based on genotypic data is that only p-value data are needed and hence can be applied extensively to different study designs. In this study the proposed LSR method was compared with commonly used methods such as Bonferroni's method and FDR controlling method. We found that while all methods provide good control over false positive rate, LSR has much better power and false discovery rate. In the authentic analysis on psoriasis and asthma disease data, the LSR method successfully identified important candidate regions and replicated the results of previous association studies.
Conclusion
The proposed LSR method provides an efficient exploratory tool for the analysis of sequences of dense genetic markers. Our results show that the LSR method has better power and lower false discovery rate comparing with the locus-specific multiple tests.
doi:10.1186/1471-2105-9-246
PMCID: PMC2430975  PMID: 18503718
11.  MPDA: Microarray pooled DNA analyzer 
BMC Bioinformatics  2008;9:196.
Background
Microarray-based pooled DNA experiments that combine the merits of DNA pooling and gene chip technology constitute a pivotal advance in biotechnology. This new technique uses pooled DNA, thereby reducing costs associated with the typing of DNA from numerous individuals. Moreover, use of an oligonucleotide gene chip reduces costs related to processing various DNA segments (e.g., primers, reagents). Thus, the technique provides an overall cost-effective solution for large-scale genomic/genetic research. However, few publicly shared tools are available to systematically analyze the rapidly accumulating volume of whole-genome pooled DNA data.
Results
We propose a generalized concept of pooled DNA and present a user-friendly tool named Microarray Pooled DNA Analyzer (MPDA) that we developed to analyze hybridization intensity data from microarray-based pooled DNA experiments. MPDA enables whole-genome DNA preferential amplification/hybridization analysis, allele frequency estimation, association mapping, allelic imbalance detection, and permits integration with shared data resources online. Graphic and numerical outputs from MPDA support global and detailed inspection of large amounts of genomic data. Four whole-genome data analyses are used to illustrate the major functionalities of MPDA. The first analysis shows that MPDA can characterize genomic patterns of preferential amplification/hybridization and provide calibration information for pooled DNA data analysis. The second analysis demonstrates that MPDA can accurately estimate allele frequencies. The third analysis indicates that MPDA is cost-effective and reliable for association mapping. The final analysis shows that MPDA can identify regions of chromosomal aberration in cancer without paired-normal tissue.
Conclusion
MPDA, the software that integrates pooled DNA association analysis and allelic imbalance analysis, provides a convenient analysis system for extensive whole-genome pooled DNA data analysis. The software, user manual and illustrated examples are freely available online at the MPDA website listed in the Availability and requirements section.
doi:10.1186/1471-2105-9-196
PMCID: PMC2387178  PMID: 18412951
12.  PDA: Pooled DNA analyzer 
BMC Bioinformatics  2006;7:233.
Background
Association mapping using abundant single nucleotide polymorphisms is a powerful tool for identifying disease susceptibility genes for complex traits and exploring possible genetic diversity. Genotyping large numbers of SNPs individually is performed routinely but is cost prohibitive for large-scale genetic studies. DNA pooling is a reliable and cost-saving alternative genotyping method. However, no software has been developed for complete pooled-DNA analyses, including data standardization, allele frequency estimation, and single/multipoint DNA pooling association tests. This motivated the development of the software, 'PDA' (Pooled DNA Analyzer), to analyze pooled DNA data.
Results
We develop the software, PDA, for the analysis of pooled-DNA data. PDA is originally implemented with the MATLAB® language, but it can also be executed on a Windows system without installing the MATLAB®. PDA provides estimates of the coefficient of preferential amplification and allele frequency. PDA considers an extended single-point association test, which can compare allele frequencies between two DNA pools constructed under different experimental conditions. Moreover, PDA also provides novel chromosome-wide multipoint association tests based on p-value combinations and a sliding-window concept. This new multipoint testing procedure overcomes a computational bottleneck of conventional haplotype-oriented multipoint methods in DNA pooling analyses and can handle data sets having a large pool size and/or large numbers of polymorphic markers. All of the PDA functions are illustrated in the four bona fide examples.
Conclusion
PDA is simple to operate and does not require that users have a strong statistical background. The software is available at .
doi:10.1186/1471-2105-7-233
PMCID: PMC1539032  PMID: 16643673
13.  A genome-wide scanning and fine mapping study of COGA data 
BMC Genetics  2005;6(Suppl 1):S30.
A thorough genetic mapping study was performed to identify predisposing genes for alcoholism dependence using the Collaborative Study on the Genetics of Alcoholism (COGA) data. The procedure comprised whole-genome linkage and confirmation analyses, single locus and haplotype fine mapping analyses, and gene × environment haplotype regression. Stratified analysis was considered to reduce the ethnic heterogeneity and simultaneously family-based and case-control study designs were applied to detect potential genetic signals. By using different methods and markers, we found high linkage signals at D1S225 (253.7 cM), D1S547 (279.2 cM), D2S1356 (64.6 cM), and D7S2846 (56.8 cM) with nonparametric linkage scores of 3.92, 4.10, 4.44, and 3.55, respectively. We also conducted haplotype and odds ratio analyses, where the response was the dichotomous status of alcohol dependence, explanatory variables were the inferred individual haplotypes and the three statistically significant covariates were age, gender, and max drink (the maximum number of drinks consumed in a 24-hr period). The final model identified important AD-related haplotypes within a candidate region of NRXN1 at 2p21 and a few others in the inter-gene regions. The relative magnitude of risks to the identified risky/protective haplotypes was elucidated.
doi:10.1186/1471-2156-6-S1-S30
PMCID: PMC1866755  PMID: 16451640
14.  Androgenic Alopecia Is Associated with Less Dietary Soy, Higher Blood Vanadium and rs1160312 1 Polymorphism in Taiwanese Communities 
PLoS ONE  2013;8(12):e79789.
Background
Although the genetic basis of androgenic alopecia has been clearly established, little is known about its non-genetic causes, such as environmental and lifestyle factors.
Objective
This study investigated blood and urine heavy metals concentrations, environmental exposure factors, personal behaviors, dietary intakes and the genotypes of related susceptibility genes in patients with androgenic alopecia (AGA).
Design
Age, AGA level, residence area, work hours, sleep patterns, cigarette usage, alcohol consumption, betel nut usage, hair treatments, eating habits, body heavy metals concentrations and rs1998076, rs913063, rs1160312 and rs201571 SNP genotype data were collected from 354 men. Logistic regression analysis was performed to examine whether any of the factors displayed odds ratios (ORs) indicating association with moderate to severe AGA (≧IV). Subsequently, Hosmer-Lemeshow, Nagelkerke R2 and accuracy tests were conducted to help establish an optimal model.
Results
Moderate to severe AGA was associated with the AA genotype of rs1160312 (22.50, 95% CI 3.99–126.83), blood vanadium concentration (0.02, 95% CI 0.01–0.04), and regular consumption of soy bean drinks (0.23, 95% CI 0.06–0.85), after adjustment for age. The results were corroborated by the Hosmer-Lemeshow test (P = 0.73), Nagelkerke R2 (0.59), accuracy test (0.816) and area under the curve (AUC; 0.90, 0.847–0.951) analysis.
Conclusions
Blood vanadium and frequent soy bean drink consumption may provide protect effects against AGA. Accordingly, blood vanadium concentrations, the AA genotype of rs1160312 and frequent consumption of soy bean drinks are associated with AGA.
doi:10.1371/journal.pone.0079789
PMCID: PMC3875420  PMID: 24386074
15.  The DAO Gene Is Associated with Schizophrenia and Interacts with Other Genes in the Taiwan Han Chinese Population 
PLoS ONE  2013;8(3):e60099.
Background
Schizophrenia is a highly heritable disease with a polygenic mode of inheritance. Many studies have contributed to our understanding of the genetic underpinnings of schizophrenia, but little is known about how interactions among genes affect the risk of schizophrenia. This study aimed to assess the associations and interactions among genes that confer vulnerability to schizophrenia and to examine the moderating effect of neuropsychological impairment.
Methods
We analyzed 99 SNPs from 10 candidate genes in 1,512 subject samples. The permutation-based single-locus, multi-locus association tests, and a gene-based multifactorial dimension reduction procedure were used to examine genetic associations and interactions to schizophrenia.
Results
We found that no single SNP was significantly associated with schizophrenia. However, a risk haplotype, namely A-T-C of the SNP triplet rsDAO7-rsDAO8-rsDAO13 of the DAO gene, was strongly associated with schizophrenia. Interaction analyses identified multiple between-gene and within-gene interactions. Between-gene interactions including DAO*DISC1, DAO*NRG1 and DAO*RASD2 and a within-gene interaction for CACNG2 were found among schizophrenia subjects with severe sustained attention deficits, suggesting a modifying effect of impaired neuropsychological functioning. Other interactions such as the within-gene interaction of DAO and the between-gene interaction of DAO and PTK2B were consistently identified regardless of stratification by neuropsychological dysfunction. Importantly, except for the within-gene interaction of CACNG2, all of the identified risk haplotypes and interactions involved SNPs from DAO.
Conclusions
These results suggest that DAO, which is involved in the N-methyl-d-aspartate receptor regulation, signaling and glutamate metabolism, is the master gene of the genetic associations and interactions underlying schizophrenia. Besides, the interaction between DAO and RASD2 has provided an insight in integrating the glutamate and dopamine hypotheses of schizophrenia.
doi:10.1371/journal.pone.0060099
PMCID: PMC3610748  PMID: 23555897
16.  Fine-Mapping Angiotensin-Converting Enzyme Gene: Separate QTLs Identified for Hypertension and for ACE Activity 
PLoS ONE  2013;8(3):e56119.
Angiotensin-converting enzyme (ACE) has been implicated in multiple biological system, particularly cardiovascular diseases. However, findings associating ACE insertion/deletion polymorphism with hypertension or other related traits are inconsistent. Therefore, in a two-stage approach, we aimed to fine-map ACE in order to narrow-down the function-specific locations. We genotyped 31 single nucleotide polymorphisms (SNPs) of ACE from 1168 individuals from 305 young-onset (age ≤40) hypertension pedigrees, and found four linkage disequilibrium (LD) blocks. A tag-SNP, rs1800764 on LD block 2, upstream of and near the ACE promoter, was significantly associated with young-onset hypertension (p = 0.04). Tag-SNPs on all LD blocks were significantly associated with ACE activity (p-value: 10–16 to <10–33). The two regions most associated with ACE activity were found between exon13 and intron18 and between intron 20 and 3′UTR, as revealed by measured haplotype analysis. These two major QTLs of ACE activity and the moderate effect variant upstream of ACE promoter for young-onset hypertension were replicated by another independent association study with 842 subjects.
doi:10.1371/journal.pone.0056119
PMCID: PMC3587614  PMID: 23469169
17.  A Genome-Wide Homozygosity Association Study Identifies Runs of Homozygosity Associated with Rheumatoid Arthritis in the Human Major Histocompatibility Complex 
PLoS ONE  2012;7(4):e34840.
Rheumatoid arthritis (RA) is a chronic inflammatory disorder with a polygenic mode of inheritance. This study examined the hypothesis that runs of homozygosity (ROHs) play a recessive-acting role in the underlying RA genetic mechanism and identified RA-associated ROHs. Ours is the first genome-wide homozygosity association study for RA and characterized the ROH patterns associated with RA in the genomes of 2,000 RA patients and 3,000 normal controls of the Wellcome Trust Case Control Consortium. Genome scans consistently pinpointed two regions within the human major histocompatibility complex region containing RA-associated ROHs. The first region is from 32,451,664 bp to 32,846,093 bp (−log10(p)>22.6591). RA-susceptibility genes, such as HLA-DRB1, are contained in this region. The second region ranges from 32,933,485 bp to 33,585,118 bp (−log10(p)>8.3644) and contains other HLA-DPA1 and HLA-DPB1 genes. These two regions are physically close but are located in different blocks of linkage disequilibrium, and ∼40% of the RA patients' genomes carry these ROHs in the two regions. By analyzing homozygote intensities, an ROH that is anchored by the single nucleotide polymorphism rs2027852 and flanked by HLA-DRB6 and HLA-DRB1 was found associated with increased risk for RA. The presence of this risky ROH provides a 62% accuracy to predict RA disease status. An independent genomic dataset from 868 RA patients and 1,194 control subjects of the North American Rheumatoid Arthritis Consortium successfully validated the results obtained using the Wellcome Trust Case Control Consortium data. In conclusion, this genome-wide homozygosity association study provides an alternative to allelic association mapping for the identification of recessive variants responsible for RA. The identified RA-associated ROHs uncover recessive components and missing heritability associated with RA and other autoimmune diseases.
doi:10.1371/journal.pone.0034840
PMCID: PMC3335047  PMID: 22536334

Results 1-17 (17)