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D Y Lin

Publications and source records attributed to D Y Lin.

At least 19 recordsLinked to original sources
PubMed2026

Genomic basis of developmental defects of enamel and sex-specific effects.

We conducted a multi-ancestry genome-wide association study (GWAS) of developmental defects of enamel (DDE) in the primary dentition among 6,061 U.S. preschool-aged children (3-5 years). We investigated four DDE phenotypes (demarcated opacities, diffuse opacities, hypoplastic defects, and a combined DDE trait) leveraging main-effect models, joint gene-sex interaction testing (2df), and sex-stratified analyses. SNP-based heritability for the combined DDE trait was estimated at 20%, with concordance analyses robustly supporting a genetic etiology. We identified 39 unique genome-wide significant loci (P<5&#xd7;10 ), with five surpassing a study-wide Bonferroni-corrected statistical significance criterion (P<1.25&#xd7;10 9), including Y RNA and ALDH1A1. The main-effect GWAS identified 20 loci, including HBS1L and MYB, genes regulating hematopoiesis with plausible roles in amelogenesis. Joint test and sex-stratified analyses revealed 19 additional loci, including ALDH1A1, TENM2, and DLGAP2, demonstrating sex-specific heterogeneity. Nineteen loci exhibited sex-specific differences after Bonferroni correction (P<2x10-3), including genes involved in retinoic acid signaling (ALDH1A1), odontogenesis (TENM2), and neurodevelopment (DLGAP2, CDH10). Pathway enrichment highlighted ectodermal and synapse organization networks, suggesting shared etiological mechanisms between DDE and systemic conditions like neurofibromatosis and autism spectrum disorder. Notably, no locus generalized in an external GWAS of permanent dentition DDE, underscoring fundamental biological differences in the genetic architectures governing primary versus permanent enamel formation. Crucially, a comprehensive cross-trait pleiotropy lookup against early childhood caries (ECC) revealed no shared genetic architecture, supporting the notion that the established clinical and epidemiological association between DDE and ECC is likely driven by structural defects increasing caries lesion susceptibility rather than genetic pleiotropy. By integrating gene-sex interaction testing, this study offers novel insights into the complex, sexually dimorphic genetic etiology of DDE and augments the biological evidence base that can support the development of precision pediatric dentistry.

developmental defects of enamel↗
PubMed2006

Efficient semiparametric estimation of haplotype-disease associations in case-cohort and nested case-control studies.

Estimating the effects of haplotypes on the age of onset of a disease is an important step toward the discovery of genes that influence complex human diseases. A haplotype is a specific sequence of nucleotides on the same chromosome of an individual and can only be measured indirectly through the genotype. We consider cohort studies which collect genotype data on a subset of cohort members through case-cohort or nested case-control sampling. We formulate the effects of haplotypes and possibly time-varying environmental variables on the age of onset through a broad class of semiparametric regression models. We construct appropriate nonparametric likelihoods, which involve both finite- and infinite-dimensional parameters. The corresponding nonparametric maximum likelihood estimators are shown to be consistent, asymptotically normal, and asymptotically efficient. Consistent variance-covariance estimators are provided, and efficient and reliable numerical algorithms are developed. Simulation studies demonstrate that the asymptotic approximations are accurate in practical settings and that case-cohort and nested case-control designs are highly cost-effective. An application to a major cardiovascular study is provided.

Age of Onset↗
PubMed2006

Evaluating statistical significance in two-stage genomewide association studies.

Genomewide association studies are being conducted to unravel the genetic etiology of complex human diseases. Because of cost constraints, these studies typically employ a two-stage design, under which a large panel of markers is examined in a subsample of subjects, and the most-promising markers are then examined in all subjects. This report describes a simple and efficient method to evaluate statistical significance for such genome studies. The proposed method, which properly accounts for the correlated nature of polymorphism data, provides accurate control of the overall false-positive rate and is substantially more powerful than the standard Bonferroni correction, especially when the markers are in strong linkage disequilibrium.

Data Interpretation, Statistical↗
PubMed2006

Improving the power of association tests for quantitative traits in family studies.

Association mapping based on family studies can identify genes that influence complex human traits while providing protection against population stratification. Because no gene is likely to have a very large effect on a complex trait, most family studies have limited power. Among the commonly used family-based tests of association for quantitative traits, the quantitative transmission-disequilibrium tests (QTDT) based on the variance-components model is the most flexible and most powerful. This method assumes that the trait values are normally distributed. Departures from normality can inflate the type I error and reduce the power. Although the family-based association tests (FBAT) and pedigree disequilibrium tests (PDT) do not require normal traits, nonnormality can also result in loss of power. In many cases, approximate normality can be achieved by transforming the trait values. However, the true transformation is unknown, and incorrect transformations may compromise the type I error and power. We propose a novel class of association tests for arbitrarily distributed quantitative traits by allowing the true transformation function to be completely unspecified and empirically estimated from the data. Extensive simulation studies showed that the new methods provide accurate control of the type I error and can be substantially more powerful than the existing methods. We applied the new methods to the Collaborative Study on the Genetics of Alcoholism and discovered significant association of single nucleotide polymorphisms (SNP) tsc0022400 on chromosome 7 with the quantitative electrophysiological phenotype TTTH1, which was not detected by any existing methods. We have implemented the new methods in a freely available computer program.

Alcoholism↗
PubMed2006

Semiparametric variance-component models for linkage and association analyses of censored trait data.

Variance-component (VC) models are widely used for linkage and association mapping of quantitative trait loci in general human pedigrees. Traditional VC methods assume that the trait values within a family follow a multivariate normal distribution and are fully observed. These assumptions are violated if the trait data contain censored observations. When the trait pertains to age at onset of disease, censoring is inevitable because of loss to follow-up and limited study duration. Censoring also arises when the trait assay cannot detect values below (or above) certain thresholds. The latent trait values tend to have a complex distribution. Applying traditional VC methods to censored trait data would inflate type I error and reduce power. We present valid and powerful methods for the linkage and association analyses of censored trait data. Our methods are based on a novel class of semiparametric VC models, which allows an arbitrary distribution for the latent trait values. We construct appropriate likelihood for the observed data, which may contain left or right censored observations. The maximum likelihood estimators are approximately unbiased, normally distributed, and statistically efficient. We develop stable and efficient numerical algorithms to implement the corresponding inference procedures. Extensive simulation studies demonstrate that the proposed methods outperform the existing ones in practical situations. We provide an application to the age at onset of alcohol dependence data from the Collaborative Study on the Genetics of Alcoholism. A computer program is freely available.

Age of Onset↗
PubMed2005

A powerful and robust method for mapping quantitative trait loci in general pedigrees.

The variance-components model is the method of choice for mapping quantitative trait loci in general human pedigrees. This model assumes normally distributed trait values and includes a major gene effect, random polygenic and environmental effects, and covariate effects. Violation of the normality assumption has detrimental effects on the type I error and power. One possible way of achieving normality is to transform trait values. The true transformation is unknown in practice, and different transformations may yield conflicting results. In addition, the commonly used transformations are ineffective in dealing with outlying trait values. We propose a novel extension of the variance-components model that allows the true transformation function to be completely unspecified. We present efficient likelihood-based procedures to estimate variance components and to test for genetic linkage. Simulation studies demonstrated that the new method is as powerful as the existing variance-components methods when the normality assumption holds; when the normality assumption fails, the new method still provides accurate control of type I error and is substantially more powerful than the existing methods. We performed a genomewide scan of monoamine oxidase B for the Collaborative Study on the Genetics of Alcoholism. In that study, the results that are based on the existing variance-components method changed dramatically when three outlying trait values were excluded from the analysis, whereas our method yielded essentially the same answers with or without those three outliers. The computer program that implements the new method is freely available.

Chromosome Mapping↗
PubMed2005

Model-checking techniques for stratified case-control studies.

We present graphical and numerical methods for assessing the adequacy of the logistic regression model for stratified case-control data. The proposed methods are derived from the cumulative sum of residuals over the covariate or linear predictor. Under the assumed model, the cumulative residual process converges weakly to a zero-mean Gaussian process whose distribution can be approximated via Monte Carlo simulation. The observed cumulative residual pattern can then be compared both visually and analytically to a number of simulated realizations from the approximate null distribution. These comparisons enable one to examine the functional form of each covariate, the logistic link function as well as the overall model adequacy. Simulation studies demonstrate that the proposed methods perform well in practical settings. Illustration with an oesophageal cancer study is provided.

Adult↗
PubMed2005

Regression methods for cost-effectiveness analysis with censored data.

A system of seemingly unrelated regression equations is proposed for prognostic factor adjustment and subgroup analysis when comparing two groups in a cost-effectiveness analysis with censored data. Because of the induced dependent censoring on costs and quality-adjusted survival, inverse probability weighting is employed for parameter estimation. The method is illustrated with data from two recent examples using both survival time and quality-adjusted survival time as the measures of effectiveness.

Amiodarone↗
PubMed2005

Estimating haplotype-disease associations with pooled genotype data.

The genetic dissection of complex human diseases requires large-scale association studies which explore the population associations between genetic variants and disease phenotypes. DNA pooling can substantially reduce the cost of genotyping assays in these studies, and thus enables one to examine a large number of genetic variants on a large number of subjects. The availability of pooled genotype data instead of individual data poses considerable challenges in the statistical inference, especially in the haplotype-based analysis because of increased phase uncertainty. Here we present a general likelihood-based approach to making inferences about haplotype-disease associations based on possibly pooled DNA data. We consider cohort and case-control studies of unrelated subjects, and allow arbitrary and unequal pool sizes. The phenotype can be discrete or continuous, univariate or multivariate. The effects of haplotypes on disease phenotypes are formulated through flexible regression models, which allow a variety of genetic hypotheses and gene-environment interactions. We construct appropriate likelihood functions for various designs and phenotypes, accommodating Hardy-Weinberg disequilibrium. The corresponding maximum likelihood estimators are approximately unbiased, normally distributed, and statistically efficient. We develop simple and efficient numerical algorithms for calculating the maximum likelihood estimators and their variances, and implement these algorithms in a freely available computer program. We assess the performance of the proposed methods through simulation studies, and provide an application to the Finland-United States Investigation of NIDDM Genetics Study. The results show that DNA pooling is highly efficient in studying haplotype-disease associations. As a by-product, this work provides valid and efficient methods for estimating haplotype-disease associations with unpooled DNA samples.

Algorithms↗
PubMed2005

Maximum likelihood estimation of haplotype effects and haplotype-environment interactions in association studies.

The associations between haplotypes and disease phenotypes offer valuable clues about the genetic determinants of complex diseases. It is highly challenging to make statistical inferences about these associations because of the unknown gametic phase in genotype data. We describe a general likelihood-based approach to inferring haplotype-disease associations in studies of unrelated individuals. We consider all possible phenotypes (including disease indicator, quantitative trait, and potentially censored age at onset of disease) and all commonly used study designs (including cross-sectional, case-control, cohort, nested case-control, and case-cohort). The effects of haplotypes on phenotype are characterized by appropriate regression models, which allow various genetic mechanisms and gene-environment interactions. We present the likelihood functions for all study designs and disease phenotypes under Hardy-Weinberg disequilibrium. The corresponding maximum likelihood estimators are approximately unbiased, normally distributed, and statistically efficient. We provide simple and efficient numerical algorithms to calculate the maximum likelihood estimators and their variances, and implement these algorithms in a freely available computer program. Extensive simulation studies demonstrate that the proposed methods perform well in realistic situations. An application to the Carolina Breast Cancer Study reveals significant haplotype effects and haplotype-smoking interactions in the development of breast cancer.

Algorithms↗
PubMed2005

Semiparametric methods for mapping quantitative trait loci with censored data.

Statistical methods for the detection of genes influencing quantitative traits with the aid of genetic markers are well developed for normally distributed, fully observed phenotypes. Many experiments are concerned with failure-time phenotypes, which have skewed distributions and which are usually subject to censoring because of random loss to follow-up, failures from competing causes, or limited duration of the experiment. In this article, we develop semiparametric statistical methods for mapping quantitative trait loci (QTLs) based on censored failure-time phenotypes. We formulate the effects of the QTL genotype on the failure time through the Cox (1972, Journal of the Royal Statistical Society, Series B 34, 187-220) proportional hazards model and derive efficient likelihood-based inference procedures. In addition, we show how to assess statistical significance when searching several regions or the entire genome for QTLs. Extensive simulation studies demonstrate that the proposed methods perform well in practical situations. Applications to two animal studies are provided.

Animals↗
PubMed2005

Goodness-of-fit methods for generalized linear mixed models.

We develop graphical and numerical methods for checking the adequacy of generalized linear mixed models (GLMMs). These methods are based on the cumulative sums of residuals over covariates or predicted values of the response variable. Under the assumed model, the asymptotic distributions of these stochastic processes can be approximated by certain zero-mean Gaussian processes, whose realizations can be generated through Monte Carlo simulation. Each observed process can then be compared, both visually and analytically, to a number of realizations simulated from the null distribution. These comparisons enable one to assess objectively whether the observed residual patterns reflect model misspecification or random variation. The proposed methods are particularly useful for checking the functional form of a covariate or the link function. Extensive simulation studies show that the proposed goodness-of-fit tests have proper sizes and are sensitive to model misspecification. Applications to two medical studies lead to improved models.

Anti-HIV Agents↗
PubMed2004

An efficient Monte Carlo approach to assessing statistical significance in genomic studies.

MOTIVATION: Multiple hypothesis testing is a common problem in genome research, particularly in microarray experiments and genomewide association studies. Failure to account for the effects of multiple comparisons would result in an abundance of false positive results. The Bonferroni correction and Holm's step-down procedure are overly conservative, whereas the permutation test is time-consuming and is restricted to simple problems. RESULTS: We developed an efficient Monte Carlo approach to approximating the joint distribution of the test statistics along the genome. We then used the Monte Carlo distribution to evaluate the commonly used criteria for error control, such as familywise error rates and positive false discovery rates. This approach is applicable to any data structures and test statistics. Applications to simulated and real data demonstrate that the proposed approach provides accurate error control, and can be substantially more powerful than the Bonferroni and Holm methods, especially when the test statistics are highly correlated.

Algorithms↗
PubMed2004

Haplotype-based association analysis in cohort studies of unrelated individuals.

Exploring the associations between haplotypes and disease phenotypes is an important step toward the discovery of genes that influence complex human diseases. When unrelated subjects are sampled, haplotypes are often ambiguous because of the unknown gametic phase of the measured sites along a chromosome. We consider cohort studies of unrelated subjects which collect data on potentially censored ages of onset of disease along with unphased genotypes and possibly time-varying environmental factors. We formulate the effects of haplotypes and environmental variables on the time to disease occurrence through a semiparametric Cox proportional hazards model, which can accommodate a variety of genetic mechanisms as well as gene-environment interactions. We develop a simple and fast expectation-maximization algorithm to maximize the likelihood for the relative risks and other parameters based on the observable data of unphased genotypes and potentially censored ages of onset. The resultant estimators are consistent, efficient, and asymptotically normal. Simulation studies show that, for practical situations, the parameter estimators are virtually unbiased, the association tests maintain type I errors near nominal levels, the confidence intervals have proper coverage probabilities, and the efficiency loss due to unknown gametic phase is small.

Algorithms↗
PubMed2004

Assessing genomewide statistical significance in linkage studies.

Assessment of genomewide statistical significance in multipoint linkage analysis is a thorny problem. The existing analytical solutions rely on strong assumptions (i.e., infinitely dense or equally spaced genetic markers that are fully informative and completely observed, and a single type of relative pair) which are rarely satisfied in real human studies, while simulation-based methods are computationally intensive and may not be applicable to complex data structures and sophisticated genetic models. Here, we propose a conceptually simple and numerically efficient Monte Carlo procedure for determining genomewide significance levels that is applicable to all linkage studies. The pedigree structure is completely general; the marker data are totally arbitrary in respect to number, spacing, informativeness, and missingness; the trait can be qualitative, quantitative, or multivariate; the alternative hypothesis can be two-sided or one-sided; and the statistic can be parametric or nonparametric. The usefulness of the proposed approach is demonstrated through extensive simulation studies and an application to the nuclear family data from the Tenth Genetic Analysis Workshop.

Computer Simulation↗
PubMed2004

Mapping quantitative trait loci with censored observations.

The existing statistical methods for mapping quantitative trait loci (QTL) assume that the phenotype follows a normal distribution and is fully observed. These assumptions may not be satisfied when the phenotype pertains to the survival time or failure time, which has a skewed distribution and is usually subject to censoring due to random loss of follow-up or limited duration of the experiment. In this article, we propose an interval-mapping approach for censored failure time phenotypes. We formulate the effects of QTL on the failure time through parametric proportional hazards models and develop efficient likelihood-based inference procedures. In addition, we show how to assess genome-wide statistical significance. The performance of the proposed methods is evaluated through extensive simulation studies. An application to a mouse cross is provided.

Chromosome Mapping↗
PubMed2004

An efficient resampling method for assessing genome-wide statistical significance in mapping quantitative trait Loci.

Assessing genome-wide statistical significance is an important and difficult problem in multipoint linkage analysis. Due to multiple tests on the same genome, the usual pointwise significance level based on the chi-square approximation is inappropriate. Permutation is widely used to determine genome-wide significance. Theoretical approximations are available for simple experimental crosses. In this article, we propose a resampling procedure to assess the significance of genome-wide QTL mapping for experimental crosses. The proposed method is computationally much less intensive than the permutation procedure (in the order of 10(2) or higher) and is applicable to complex breeding designs and sophisticated genetic models that cannot be handled by the permutation and theoretical methods. The usefulness of the proposed method is demonstrated through simulation studies and an application to a Drosophila backcross.

Animals↗