Biomedical subjects

Yuanqing Ye

Publications and source records attributed to Yuanqing Ye.

6 recordsLinked to original sources

PubMed2006

Family-based association tests for ordinal traits adjusting for covariates.

We present a class of family-based association tests (FBATs) for ordinal traits that adjust for the effects of covariates. For complex diseases, especially mental health conditions including nicotine dependence and substance use, the outcome variables are often recorded in an ordinal rather than quantitative scale. The naturally recorded ordinal traits are commonly analyzed either as quantitative traits or are dichotomized. It has been demonstrated repeatedly in recent studies that these commonly used approaches to dealing with ordinal traits are inadequate and result in loss of power. In this report, we make use of conditional likelihood to derive score test statistics that belong to a general class of FBATs. We conducted simulation studies to compare the type I error and power of our proposed test with existing tests. The empirical result suggests that our test produces reasonable type I errors and has power far exceeding (often doubling) those of existing tests. We applied our proposed test to a data set on alcohol dependence and found that six single nucleotide polymorphisms (SNPs) are significantly associated (P-values < or =0.001) with alcohol dependence after adjusting for gender and age. Three of the SNPs (rs619, rs1972373, and rs1571423) or their tightly linked regions have been suggested in the literature from the analysis of the same data, demonstrating the consistent findings between various methods. The other three SNPs (rs485874, rs718251, and rs1869907) are identified for the first time using this data set, underscoring the potential power of our proposed test.

Alcoholism↗

PubMed2006

DOPA decarboxylase gene is associated with nicotine dependence.

INTRODUCTION: Cigarette smoking is a prevalent and harmful behavior. Although the heritability of nicotine dependence (ND) is well documented and many candidate genetic regions have been identified, few of them were confirmed. This may be, in part, due to analytic methods that sacrifice power. METHODS: Using a recently developed, more powerful method for testing association between a genetic marker and an ordinal trait, we analyzed data from 1879 smokers and nonsmokers from 600 nuclear families of African- or European-American (AA or EA) ancestry. This method increases power principally by accounting for differences in severity between affected subjects. RESULTS: To demonstrate the more powerful method, we re-analyzed an existing dataset, which confirmed the association of the DOPA decarboxylase (DDC) gene on chromosome 7p11 with measures of nicotine dependence. Although none of the eight single nucleotide polymorphisms (SNPs) studied were found to be significantly associated with nicotine dependence (unadjusted p-value > 0.01), we identified haplotypes from those SNPs that were significantly associated with nicotine dependence in both AA and EA samples. CONCLUSION: The associated haplotypes differed in the AA and EA samples. The strongest association (p-value = 0.003) was identified between the 'heaviness of smoking index' and haplotype C-A-T-G in SNPs rs921451-rs3735273-rs1451371-rs2060762. However, this association was not found significant in a previous report (p-value = 0.19) that used the same sample, underscoring the importance of using the statistical methods that use more of the available phenotypic information, and thereby better reflect the distribution of the phenotypes.

Black or African American↗

PubMed2005

Multivariate linkage analysis using the electrophysiological phenotypes in the COGA alcoholism data.

Multivariate linkage analysis using several correlated traits may provide greater statistical power to detect susceptibility genes in loci whose effects are too small to be detected in univariate analysis. In this analysis, we apply a new approach and perform a linkage analysis of several electrophysiological phenotypes of the Collaborative Study on the Genetics of Alcoholism data of the Genetic Analysis Workshop 14. Our approach is based on a variance-component model to map candidate genes using repeated or longitudinal measurements. It can take into account covariate effects and time-dependent genetic effects in general pedigree data. We compare our results with the ones obtained by SOLAR using single measurement data. Our multivariate linkage analysis found linkage evidence on two regions on chromosome 4: around marker GABRB1 at 51.4 cM and marker FABP2 at 116.8 cM (unadjusted p-value = 0.00006).

Alcoholism↗

PubMed2005

A genome-wide tree- and forest-based association analysis of comorbidity of alcoholism and smoking.

Genetic mechanisms underlying alcoholism are complex. Understanding the etiology of alcohol dependence and its comorbid conditions such as smoking is important because of the significant health concerns. In this report, we describe a method based on classification trees and deterministic forests for association studies to perform a genome-wide joint association analysis of alcoholism and smoking. This approach is used to analyze the single-nucleotide polymorphism data from the Collaborative Study on the Genetics of Alcoholism in the Genetic Analysis Workshop 14. Our analysis reaffirmed the importance of sex difference in alcoholism. Our analysis also identified genes that were reported in other studies of alcoholism and identified new genes or single-nucleotide polymorphisms that can be useful candidates for future studies.

Alcoholism↗

PubMed2005

Detection of genes for ordinal traits in nuclear families and a unified approach for association studies.

There is growing interest in genomewide association analysis using single-nucleotide polymorphisms (SNPs), because traditional linkage studies are not as powerful in identifying genes for common, complex diseases. Tests for linkage disequilibrium have been developed for binary and quantitative traits. However, since many human conditions and diseases are measured in an ordinal scale, methods need to be developed to investigate the association of genes and ordinal traits. Thus, in the current report we propose and derive a score test statistic that identifies genes that are associated with ordinal traits when gametic disequilibrium between a marker and trait loci exists. Through simulation, the performance of this new test is examined for both ordinal traits and quantitative traits. The proposed statistic not only accommodates and is more powerful for ordinal traits, but also has similar power to that of existing tests when the trait is quantitative. Therefore, our proposed statistic has the potential to serve as a unified approach to identifying genes that are associated with any trait, regardless of how the trait is measured. We further demonstrated the advantage of our test by revealing a significant association (P = 0.00067) between alcohol dependence and a SNP in the growth-associated protein 43.

Alcoholism↗

PubMed2005

Data mining.

Group 14 used data-mining strategies to evaluate a number of issues, including appropriate diagnosis, haplotype estimation, genetic linkage and association studies, and type I error. Methods ranged from exploratory analyses, to machine learning strategies (neural networks, supervised learning, and tree-based methods), to false discovery rate control of type I errors. The general motivations were to find the "story" in the data and to summarize information from a multitude of measures. Several methods illustrated strategies for better trait definition, using summarization of related traits. In the few studies that sought to identify genes for alcoholism, there was little agreement among the different strategies, likely reflecting the complexities of the disease. Nevertheless, Group 14 found that these methods offered strategies to gain a better understanding of the complex pathways by which disease develops.

Alcoholism↗