Rank‐Based Robust Tests for Quantitative‐Trait Genetic Association Studies

Li, Qizhai; Li, Zhengbang; Zheng, Gang; Gao, Guimin; Yu, Kai

doi:10.1002/gepi.21723

Cited by 9 publications

(12 citation statements)

References 31 publications

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“…The minimum value of anti-CCP for these 868 individuals is 20.053. As shown in Li et al 6, neither the original values of anti-CCP (p-value using Shapiro-Wilk test is 10 −16 ) nor their logarithm transformation ones (p-value is 3.9 × 10 −9 ) follow the normal distribution. It is most likely that the observed anti-CCP measurements come from a truncated distribution since the anti-CCP is often measured to confirm RA and so it is not measured in controls.…”

Section: Resultsmentioning

confidence: 89%

“…The Kruskal-Wallis test1, the Jonckheere-Terpstra test23, the U-statistics-based tests45, and the nonparametric trend test6 can be used to evaluate the association between the genetic variants and the quantitative traits for the non-normal trait values. The distributions or the approximate distributions of the test statistics are derived under the null hypothesis that the genotypes are not associated with the traits.…”

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confidence: 99%

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Nonparametric Risk and Nonparametric Odds in Quantitative Genetic Association Studies

Zhang

2015

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The coefficient in a linear regression model is commonly employed to evaluate the genetic effect of a single nucleotide polymorphism associated with a quantitative trait under the assumption that the trait value follows a normal distribution or is appropriately normally distributed after a certain transformation. When this assumption is violated, the distribution-free tests are preferred. In this work, we propose the nonparametric risk (NR) and nonparametric odds (NO), obtain the asymptotic normal distribution of estimated NR and then construct the confidence intervals. We also define the genetic models using NR, construct the test statistic under a given genetic model and a robust test, which are free of the genetic uncertainty. Simulation studies show that the proposed confidence intervals have satisfactory cover probabilities and the proposed test can control the type I error rates and is more powerful than the exiting ones under most of the considered scenarios. Application to gene of PTPN22 and genomic region of 6p21.33 from the Genetic Analysis Workshop 16 for association with the anticyclic citrullinated protein antibody further show their performances.

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Section: Resultsmentioning

confidence: 89%

mentioning

confidence: 99%

Nonparametric Risk and Nonparametric Odds in Quantitative Genetic Association Studies

Zhang

2015

Sci Rep

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“…For

m = 1, 2, ..., M

, we define

\begin{matrix} true \\ right Z_{m} & = & left \frac{1}{\sqrt{n}} ({, \frac{\sqrt{3} η_{1 m}}{{(1 - η_{1 m})}^{1 / 2}}, ({\bar{R}}_{1 m} - \frac{n + 1}{2})) \\ left + (}, \frac{2 \sqrt{3} η_{2 m}}{{(1 - η_{2 m})}^{1 / 2}}, ({\bar{R}}_{2 m} - \frac{n + 1}{2})) . \end{matrix}

Z_{m}

is the numerator of the nonparametric trend test proposed by Li et al. (). The variance of

Z_{m}

is given by Theorem 1 in Li et al.…”

Section: Methodsmentioning

confidence: 99%

“…However, these methods cannot be applied to quantitative traits directly, especially when the trait values do not follow the normal distribution. Although some nonparametric association tests such as the JT test (Jonckheere, ; Terpstra, ), the KW test (Kruskal and Wallis, ), and the nonparametric trend test (Li et al., ) can be applied to non‐normal traits, these tests focus on single SNP analysis.…”

Section: Introductionmentioning

confidence: 99%

“…We first construct the nonparametric trend test statistic (NTT) for each SNP following Li et al. () and then derive the variance‐covariance matrix of the NTTs for a set of markers. Finally, we construct the test statistics for the set of markers using the framework of HT, SSU and the inverse of the square root weighted test statistic (ISRW).…”

Section: Introductionmentioning

confidence: 99%

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Rank-Based Tests for Identifying Multiple Genetic Variants Associated with Quantitative Traits

Yuan

Han

et al. 2014

Annals of Human Genetics

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SummaryWe consider the analysis of multiple genetic variants within a gene or a region that are expected to confer risks to human complex diseases with quantitative traits, where the trait values do not follow the normal distribution even after some transformations. We rank the phenotypic values, calculate a score to measure the trend effect of a particular allele for each marker, and then construct three statistics based on the quadratic frameworks of methods Hotelling T 2 , the summation of squared univariate statistic and the inverse of the square root weighted statistics to combine the scores for different marker loci. Simulation results show that the above three test statistics can control the type I error rate well and are more robust than standard tests constructed based on linear regression. Application to GAW16 data for rheumatoid arthritis successfully detects the association between the HLA-DRB1 gene and anticyclic citrullinated protein measure, while the standard methods based on normal assumption cannot detect this association.

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Subclinical nephrosclerosis is linked to left ventricular hypertrophy independent of classical atherogenic factors

et al. 2013

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Recently, cardio-renal interactions have been considered to be important and it has been demonstrated that mild renal dysfunction is associated with left ventricular hypertrophy (LVH). However, the correlation between LVH and subclinical renal damage is unclear. We investigated this association by assessing pretransplant biopsies from living kidney donors with normal renal function. We retrospectively categorized 238 living kidney donors into tertiles according to the percentage of global glomerulosclerosis (%GGS) observed in pretransplant biopsies (low, 0-3.45% (n=80); moderate, 3.46-11.76% (n=78); high, ⩾11.77% (n=80)) to analyze trends in their left ventricular mass index (LVMI) measured by echocardiography and baseline factors. LVH was defined as LVMI >110 g m(-2) in female and >125 g m(-2) in male subjects. We used a logistic regression model to evaluate any correlations between %GGS and LVH. LVMI increased significantly with increasing tertiles of %GGS, as did the prevalence of left ventricular remodeling and LVH. According to multivariate logistic regression analysis, subjects with high %GGS tertiles had a sevenfold greater risk of LVH than did those with low tertiles, even after adjusting for age, sex, systolic blood pressure, history of diabetes mellitus, total serum cholesterol and glomerular filtration rate (GFR) measured by a radioisotopic technique. There is an association between GGS and LVH in subjects with normal renal function. This association is significant after adjustment for age, sex, blood pressure, GFR and other atherogenic factors.

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Rank‐Based Robust Tests for Quantitative‐Trait Genetic Association Studies

Cited by 9 publications

References 31 publications

Nonparametric Risk and Nonparametric Odds in Quantitative Genetic Association Studies

Nonparametric Risk and Nonparametric Odds in Quantitative Genetic Association Studies

Rank-Based Tests for Identifying Multiple Genetic Variants Associated with Quantitative Traits

Subclinical nephrosclerosis is linked to left ventricular hypertrophy independent of classical atherogenic factors

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