Robust Quantitative Trait Association Tests in the Parent‐Offspring Triad Design: Conditional Likelihood‐Based Approaches

Wang, J.-Y.; Tai, John Jen

doi:10.1111/j.1469-1809.2008.00502.x

Cited by 24 publications

(28 citation statements)

References 54 publications

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“…For a sample of n triad families, 10 categories can be classified based on the parental mating type and the offspring genotype (Table ). Following Wang & Tai (), denote the i th parental mating type by

g_{p} = i

i = 1, ..., 6

, the j th offspring genotype by

g_{c} = j

j = 0, 1, 2

, and the m quantitative traits of the k th offspring member of the category (

g_{p} = i

g_{c} = j

) by

{boldy}_{i j k} = {(y_{i j k}^{(1)}, ..., y_{i j k}^{(r)}, ..., y_{i j k}^{(m)})}^{T}

, where

y_{i j k}^{(r)}

is the r th quantitative trait of the offspring in the k th triad family of the category (

g_{p} = i

g_{c} = j

r = 1, ..., m

k = 1, ..., n_{i j}

, and

n_{i j}

is the number of families in the category (

g_{p} = i

g_{c} = j

).…”

Section: Methodsmentioning

confidence: 99%

“…However, since these methods often assume that the true underlying genetic model (mode of inheritance) of a putative locus is additive, they may incur a substantial loss of power when the true underlying genetic model is misspecified in practical studies. To deal with this problem, over the past decade, many population‐ and family‐based robust association analysis methods have been widely proposed in the literature for investigating binary disease traits (Gastwirth & Freidlin, ; Zheng et al., ; Zheng & Tian, ; Yuan et al., ; Joo et al., ), though relatively fewer research studies have been carried out in this regard for quantitative traits (Wang & Tai, ; So & Sham, ).…”

Section: Introductionmentioning

confidence: 99%

“…In the Materials and Methods section, we will first demonstrate how to extract the association information between a marker and multiple quantitative traits through the conditional likelihood (Schaid & Sommer, ; Kistner & Weinberg, ; Wheeler & Cordell, ; Wang & Tai, ). We will establish three optimal multivariate score tests under the recessive, additive, and dominant models, respectively.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Family‐Based Robust Multivariate Association Test Using Maximum Statistic

Hsieh

Chang

Tai

2014

Annals of Human Genetics

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SummaryFor characterizing the genetic mechanisms of complex diseases familial data with multiple correlated quantitative traits are usually collected in genetic studies. To analyze such data, various multivariate tests have been proposed to investigate the association between the underlying disease genes and the multiple traits. Although these multivariate association tests may have better power performance than the univariate association tests, they suffer from loss of testing power when the genetic models of the putative genes are misspecified. To address the problem, in this paper we aim to develop a family-based robust multivariate association test. We will first establish the optimal multivariate score tests for the recessive, additive, and dominant genetic models. Based on these optimal tests, a maximum-type robust multivariate association test is then obtained. Simulations are conducted to compare the power of our method with that of other existing multivariate methods. The results show that the robust multivariate test does manifest the robustness in power over all plausible genetic models. A practical data set is applied to demonstrate the applicability of our approach. The results suggest that the robust multivariate test is more powerful than the robust univariate test when dealing with multiple quantitative traits.

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g_{p} = i

i = 1, ..., 6

, the j th offspring genotype by

g_{c} = j

j = 0, 1, 2

, and the m quantitative traits of the k th offspring member of the category (

g_{p} = i

g_{c} = j

) by

{boldy}_{i j k} = {(y_{i j k}^{(1)}, ..., y_{i j k}^{(r)}, ..., y_{i j k}^{(m)})}^{T}

, where

y_{i j k}^{(r)}

is the r th quantitative trait of the offspring in the k th triad family of the category (

g_{p} = i

g_{c} = j

r = 1, ..., m

k = 1, ..., n_{i j}

, and

n_{i j}

is the number of families in the category (

g_{p} = i

g_{c} = j

).…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Family‐Based Robust Multivariate Association Test Using Maximum Statistic

Hsieh

Chang

Tai

2014

Annals of Human Genetics

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show abstract

“…To increase the feasibility and reliability of clinical prognosis based on gene, the forward selection model was used to select the prognosis-related key genes from seed genes. We implemented prognosis-related key genes selection by the "rbsurv" R package [30][31][32]. The procedure was as followings:…”

Section: Prognosis-related Key Genes Selectionmentioning

confidence: 99%

Identification of a Sixteen-gene Prognostic Biomarker for Lung Adenocarcinoma Using a Machine Learning Method

Geng

Meng

et al. 2020

J. Cancer

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Objectives: Lung adenocarcinoma (LUAD) accounts for a majority of cancer-related deaths worldwide annually. The identification of prognostic biomarkers and prediction of prognosis for LUAD patients is necessary. Materials and Methods: In this study, LUAD RNA-Seq data and clinical data from the Cancer Genome Atlas (TCGA) were divided into TCGA cohort I (n = 338) and II (n = 168). The cohort I was used for model construction, and the cohort II and data from Gene Expression Omnibus (GSE72094 cohort, n = 393; GSE11969 cohort, n = 149) were utilized for validation. First, the survival-related seed genes were selected from the cohort I using the machine learning model (random survival forest, RSF), and then in order to improve prediction accuracy, the forward selection model was utilized to identify the prognosis-related key genes among the seed genes using the clinically-integrated RNA-Seq data. Second, the survival risk score system was constructed by using these key genes in the cohort II, the GSE72094 cohort and the GSE11969 cohort, and the evaluation metrics such as HR, p value and C-index were calculated to validate the proposed method. Third, the developed approach was compared with the previous five prediction models. Finally, bioinformatics analyses (pathway, heatmap, protein-gene interaction network) have been applied to the identified seed genes and key genes. Results and Conclusion:Based on the RSF model and clinically-integrated RNA-Seq data, we identified sixteen key genes that formed the prognostic gene expression signature. These sixteen key genes could achieve a strong power for prognostic prediction of LUAD patients in cohort II (HR = 3.80, p = 1.63e-06, C-index = 0.656), and were further validated in the GSE72094 cohort (HR = 4.12, p = 1.34e-10, C-index = 0.672) and GSE11969 cohort (HR = 3.87, p = 6.81e-07, C-index = 0.670). The experimental results of three independent validation cohorts showed that compared with the traditional Cox model and the use of standalone RNA-Seq data, the machine-learning-based method effectively improved the prediction accuracy of LUAD prognosis, and the derived model was also superior to the other five existing prediction models. KEGG pathway analysis found eleven of the sixteen genes were associated with Nicotine addiction. Thirteen of the sixteen genes were reported for the first time as the LUAD prognosis-related key genes. In conclusion, we developed a sixteen-gene prognostic marker for LUAD, which may provide a powerful prognostic tool for precision oncology.

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“…Therefore, using the rbsurv package of R software, we applied the survival model based on Robust likelihoodbased survival method to screen out key lncRNAs influencing BRCA. [22][23][24] The procedures were as follows:…”

Section: Identification Of Prognosis-related Key Lncrnasmentioning

confidence: 99%

A prognostic 11 long noncoding RNA expression signature for breast invasive carcinoma

Meng

et al. 2019

J of Cellular Biochemistry

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Breast cancer, the most common cancer in women worldwide, is associated with high mortality. The long non‐coding RNAs (lncRNAs) with a little capacity of coding proteins is playing an increasingly important role in the cancer paradigm. Accumulating evidences demonstrate that lncRNAs have crucial connections with breast cancer prognosis while the studies of lncRNAs in breast cancer are still in its primary stage. In this study, we collected 1052 clinical patient samples, a comparatively large sample size, including 13 159 lncRNA expression profiles of breast invasive carcinoma (BRCA) from The Cancer Genome Atlas database to identify prognosis‐related lncRNAs. We randomly separated all of these clinical patient samples into training and testing sets. In the training set, we performed univariable Cox regression analysis for primary screening and played the model for Robust likelihood‐based survival for 1000 times. Then 11 lncRNAs with a frequency more than 600 were selected for prediction of the prognosis of BRCA. Using the analysis of multivariate Cox regression, we established a signature risk‐score formula for 11 lncRNA to identify the relationship between lncRNA signatures and overall survival. The 11 lncRNA signature was validated both in the testing and the complete set and could effectively classify the high‐/low‐risk group with different OS. We also verified our results in different stages. Moreover, we analyzed the connection between the 11 lncRNAs and the genes of ESR1, PGR, and Her2, of which protein products (ESR, PGR, and HER2) were used to classify the breast cancer subtypes widely. The results indicated correlations between 11 lncRNAs and the gene of PGR and ESR1. Thus, a prognostic model for 11 lncRNA expression was developed to classify the BRAC clinical patient samples, providing new avenues in understanding the potential therapeutic methods of breast cancer.

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Robust Quantitative Trait Association Tests in the Parent‐Offspring Triad Design: Conditional Likelihood‐Based Approaches

Cited by 24 publications

References 54 publications

A Family‐Based Robust Multivariate Association Test Using Maximum Statistic

A Family‐Based Robust Multivariate Association Test Using Maximum Statistic

Identification of a Sixteen-gene Prognostic Biomarker for Lung Adenocarcinoma Using a Machine Learning Method

A prognostic 11 long noncoding RNA expression signature for breast invasive carcinoma

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