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Modeling Multiple Responses via Bootstrapping Margins with an Application to Genetic Association Testing.

ABSTRACT: The need for analysis of multiple responses arises from many applications. In behavioral science, for example, comorbidity is a common phenomenon where multiple disorders occur in the same person. The advantage of jointly analyzing multiple correlated responses has been examined and documented. Due to the difficulties of modeling multiple responses, nonparametric tests such as generalized Kendall's Tau have been developed to assess the association between multiple responses and risk factors. These procedures have been applied to genomewide association studies of multiple complex traits. Unfortunately, those nonparametric tests only provide the significance of the association but not the magnitude. We propose a Gaussian copula model with discrete margins for modeling multivariate binary responses. This model separates marginal effects from between-trait correlations. We use a bootstrapping margins approach to constructing Wald's statistic for the association test. Although our derivation is based on the fully parametric Gaussian copula framework for simplicity, the underlying assumptions to apply our method can be weakened. The bootstrapping margins approach only requires the correct specification of the model margins. Our simulation and real data analysis demonstrate that our proposed method not only increases power over some existing association tests, but also provides further insight into genetic association studies of multivariate traits.

SUBMITTER: Zhao J

PROVIDER: S-EPMC4629876 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Modeling Multiple Responses via Bootstrapping Margins with an Application to Genetic Association Testing.

Zhao Jiwei J Zhang Heping H

Statistics and its interface 20160101 1

The need for analysis of multiple responses arises from many applications. In behavioral science, for example, comorbidity is a common phenomenon where multiple disorders occur in the same person. The advantage of jointly analyzing multiple correlated responses has been examined and documented. Due to the difficulties of modeling multiple responses, nonparametric tests such as generalized Kendall's Tau have been developed to assess the association between multiple responses and risk factors. The ...[more]

PMID: 26543519

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Project description:BackgroundPre-implantation genetic testing for monogenic disorders (PGT-M) is an effective approach to reducing the incidence of birth defects by preventing the transmission of inherited diseases to offspring. However, there are still controversies regarding the detection methods and transplantation of embryos. This paper aims to evaluate the effectiveness of different detection technologies applied to PGT-M through a retrospective analysis of clinical detection data.MethodsThe carrier status of pathogenic mutations and chromosomal copy number variants (CNVs) in 892 embryos was characterized using next-generation sequencing (NGS), single-nucleotide polymorphism (SNP) array, and PCR-based detection technologies. Clinical data from PGT-M cases were retrospectively analyzed to assess the effectiveness of these detection methods in identifying genetic abnormalities in embryos.ResultsA total of 829 embryos were analyzed, with 63 being unsuccessful. Our study revealed that the success rate of detecting deletional mutations using Gap-PCR 84.9%, which is lower than that of SNP array (98.7%) and NGS (92.5%). However, no significant difference was observed when detecting point mutations using any of the methods. These findings suggest that, when detecting deletional mutations, SNP array and NGS are more suitable choices compared to Gap-PCR. While SNP array may have a lower resolution and success rate (80.5%) in analyzing CNVs compared to NGS (95.5%), it may still be useful for revealing certain abnormal types.ConclusionIn conclusion, this study found that SNP analysis is advantageous for identifying polygenic and deletional mutations, whereas NGS is more cost-efficient for detecting common monogenic diseases. Additionally, SNP-based haplotyping and PCR-based direct detection of mutations can be used together to enhance the accuracy and success rates of PGT-M. Our findings offer valuable insights for PGT technicians in choosing suitable detection methods for patients.

Project description:Multiple sclerosis (MS) is the most common cause of chronic neurologic disability beginning in early to middle adult life. Results from recent genome-wide association studies (GWAS) have substantially lengthened the list of disease loci and provide convincing evidence supporting a multifactorial and polygenic model of inheritance. Nevertheless, the knowledge of MS genetics remains incomplete, with many risk alleles still to be revealed.We used a discovery GWAS dataset (8,844 samples, 2,124 cases and 6,720 controls) and a multi-step logistic regression protocol to identify novel genetic associations. The emerging genetic profile included 350 independent markers and was used to calculate and estimate the cumulative genetic risk in an independent validation dataset (3,606 samples). Analysis of covariance (ANCOVA) was implemented to compare clinical characteristics of individuals with various degrees of genetic risk. Gene ontology and pathway enrichment analysis was done using the DAVID functional annotation tool, the GO Tree Machine, and the Pathway-Express profiling tool.In the discovery dataset, the median cumulative genetic risk (P-Hat) was 0.903 and 0.007 in the case and control groups, respectively, together with 79.9% classification sensitivity and 95.8% specificity. The identified profile shows a significant enrichment of genes involved in the immune response, cell adhesion, cell communication/signaling, nervous system development, and neuronal signaling, including ionotropic glutamate receptors, which have been implicated in the pathological mechanism driving neurodegeneration. In the validation dataset, the median cumulative genetic risk was 0.59 and 0.32 in the case and control groups, respectively, with classification sensitivity 62.3% and specificity 75.9%. No differences in disease progression or T2-lesion volumes were observed among four levels of predicted genetic risk groups (high, medium, low, misclassified). On the other hand, a significant difference (F = 2.75, P = 0.04) was detected for age of disease onset between the affected misclassified as controls (mean = 36 years) and the other three groups (high, 33.5 years; medium, 33.4 years; low, 33.1 years).The results are consistent with the polygenic model of inheritance. The cumulative genetic risk established using currently available genome-wide association data provides important insights into disease heterogeneity and completeness of current knowledge in MS genetics.

Dataset Information

Modeling Multiple Responses via Bootstrapping Margins with an Application to Genetic Association Testing.

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Modeling Multiple Responses via Bootstrapping Margins with an Application to Genetic Association Testing.

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