Hsin-Chou Yang

A Genome-Wide Association Study Reveals a Quantitative Trait Locus of Adiponectin on CDH13 That Predicts Cardiometabolic Outcomes

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.

Breast cancer risk associated with genotypic polymorphism of the mitosis-regulating gene Aurora-A/STK15/BTAK

Aneuploidy, an abnormal number of chromosomes, is relatively common and occurs early in breast cancer development. This observation supports a breast tumorigenic contribution of mechanisms responsible for maintaining chromosome number stability in which centrosomes play an essential role. We therefore speculated that the Aurora‐A/STK15/BTAK gene, implicated in the regulation of centrosome duplication, may be associated with breast tumorigenesis. To test this hypothesis, we conducted a case‐control study of 709 primary breast cancer patients and 1,972 healthy controls, examining single‐nucleotide polymorphisms (SNPs), including a suggested functional Phe31Ile SNP, in Aurora‐A. We were also interested in knowing whether any association between Aurora‐A and breast cancer was modified by reproductive risk factors reflecting susceptibility to estrogen exposure. Our hypothesis is that, since estrogen is known to promote breast cancer development via its mitogenic effect leading to malignant proliferation on breast epithelium and since Aurora‐A is involved in regulating mitosis, the discovery of a joint effect between the Aurora‐A genotype and reproductive risk factors on cancer risk might yield valuable clues to the association of breast tumorigenesis with estrogen. Support for this hypothesis came from the following observations. (i) Two SNPs in Aurora‐A were significantly associated with breast cancer risk (p < 0.05). (ii) Haplotype analyses, based on different combinations of multiple SNPs in Aurora‐A, revealed a strong association with breast cancer risk; interestingly, the genotypic distribution of the suggested functional Phe31Ile SNP was not significantly different between breast cancer patients and controls, but the specific haplotype containing the putative at‐risk Ile allele was more common in patients. (iii) This association between risk and putative high‐risk genotypes was stronger and more significant in women thought to be more susceptible to estrogen, i.e., those with a longer interval between menarche and first full‐term pregnancy. (iv) The protective effect conferred by a history of full‐term pregnancy was significant only in women with a putative low‐risk genotype of Aurora‐A. Our study provides new findings supporting the mutator role of Aurora‐A in breast cancer development, suggesting that breast cancer could be driven by genomic instability associated with variant Aurora‐A, the tumorigenic contribution of which could be enhanced as a result of increased mitosis due to estrogen exposure.

Genome-Wide Association Study of Young-Onset Hypertension in the Han Chinese Population of Taiwan

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.

Machine learning and data mining in complex genomic data—a review on the lessons learned in Genetic Analysis Workshop 19

In the analysis of current genomic data, application of machine learning and data mining techniques has become more attractive given the rising complexity of the projects. As part of the Genetic Analysis Workshop 19, approaches from this domain were explored, mostly motivated from two starting points. First, assuming an underlying structure in the genomic data, data mining might identify this and thus improve downstream association analyses. Second, computational methods for machine learning need to be developed further to efficiently deal with the current wealth of data.In the course of discussing results and experiences from the machine learning and data mining approaches, six common messages were extracted. These depict the current state of these approaches in the application to complex genomic data. Although some challenges remain for future studies, important forward steps were taken in the integration of different data types and the evaluation of the evidence. Mining the data for underlying genetic or phenotypic structure and using this information in subsequent analyses proved to be extremely helpful and is likely to become of even greater use with more complex data sets.

Identification of IGF1, SLC4A4, WWOX, and SFMBT1 as Hypertension Susceptibility Genes in Han Chinese with a Genome-Wide Gene-Based Association Study

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.

A genome-wide study of preferential amplification/hybridization in microarray-based pooled DNA experiments

Microarray-based pooled DNA methods overcome the cost bottleneck of simultaneously genotyping more than 100 000 markers for numerous study individuals. The success of such methods relies on the proper adjustment of preferential amplification/hybridization to ensure accurate and reliable allele frequency estimation. We performed a hybridization-based genome-wide single nucleotide polymorphisms (SNPs) genotyping analysis to dissect preferential amplification/hybridization. The majority of SNPs had less than 2-fold signal amplification or suppression, and the lognormal distributions adequately modeled preferential amplification/hybridization across the human genome. Comparative analyses suggested that the distributions of preferential amplification/hybridization differed among genotypes and the GC content. Patterns among different ethnic populations were similar; nevertheless, there were striking differences for a small proportion of SNPs, and a slight ethnic heterogeneity was observed. To fulfill appropriate and gratuitous adjustments, databases of preferential amplification/hybridization for African Americans, Caucasians and Asians were constructed based on the Affymetrix GeneChip Human Mapping 100 K Set. The robustness of allele frequency estimation using this database was validated by a pooled DNA experiment. This study provides a genome-wide investigation of preferential amplification/hybridization and suggests guidance for the reliable use of the database. Our results constitute an objective foundation for theoretical development of preferential amplification/hybridization and provide important information for future pooled DNA analyses.

Kernel-Based Association Test

Association mapping (i.e., linkage disequilibrium mapping) is a powerful tool for positional cloning of disease genes. We propose a kernel-based association test (KBAT), which is a composite function of “P-values of single-locus association tests” and “kernel weights related to intermarker distances and/or linkage disequilibria.” The KBAT is a general form of some current test statistics. This method can be applied to the study of candidate genes and can scan each chromosome using a moving average procedure. We evaluated the performance of the KBAT through simulation studies that considered evolutionary parameters, disease models, sample sizes, kernel functions, test statistics, window attributes, empirical P-value estimations, and genetic/physical maps. The results showed that the KBAT had a well-controlled false positive rate and high power compared to existing methods. In addition, the KBAT was also applied to analyze a genomewide data set from the Collaborative Study on the Genetics of Alcoholism. Important genes associated with alcoholism dependence were identified. In summary, the merits of the KBAT are multifold: the KBAT is robust against the inclusion of nuisance markers, is invariant to the map scale, and accommodates different types of genomic data, study designs, and study purposes. The proposed methods are packaged in the user-friendly software, KBAT, available at http://www.stat.sinica.edu.tw/hsinchou/genetics/association/KBAT.htm.

Genome-wide gene-based association study

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.

A genome-wide scanning and fine mapping study of COGA data

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.

Population size estimation using local sample coverage for open populations

Abstract An unsolved problem in the analysis of capture–recapture experiments is the estimation of the size of an open population when the capture probabilities are heterogeneous across the population. Here, we extend a kernel smoothing approach of Huggins and Yip (Biometrics 55 (1999) 387) to the martingale estimating functions based on sample coverage of Chao et al. (J. Statist. Plann. Inference 92 (2001) 213) and solve this problem when there are frequent capture occasions. Simulation results are shown to examine the performance of the proposed estimation procedure. A real data set is used for illustration.