Extension of the Haseman-Elston Regression Model to Longitudinal Data

Won, Sungho,Elston, Robert C.,Park, Taesung S. Karger AG 2006 Human heredity Vol.61 No.2

<P>We propose an extension to longitudinal data of the Haseman and Elston regression method for linkage analysis. The proposed model is a mixed model having several random effects. As response variable, we investigate the sibship sample mean corrected cross-product (smHE) and the BLUP-mean corrected cross product (pmHE), comparing them with the original squared difference (oHE), the overall mean corrected cross-product (rHE), and the weighted average of the squared difference and the squared mean-corrected sum (wHE). The proposed model allows for the correlation structure of longitudinal data. Also, the model can test for gene × time interaction to discover genetic variation over time. The model was applied in an analysis of the Genetic Analysis Workshop 13 (GAW13) simulated dataset for a quantitative trait simulating systolic blood pressure. Independence models did not preserve the test sizes, while the mixed models with both family and sibpair random effects tended to preserve size well.</P><P>Copyright © 2006 S. Karger AG, Basel</P>

What Is the Significance of Difference in Phenotypic Variability across SNP Genotypes?

Sun, X.,Elston, R.,Morris, N.,Zhu, X. University of Chicago Press [etc.] 2013 American journal of human genetics Vol.93 No.2

We studied the general problem of interpreting and detecting differences in phenotypic variability among the genotypes at a locus, from both a biological and a statistical point of view. The scales on which we measure interval-scale quantitative traits are man-made and have little intrinsic biological relevance. Before claiming a biological interpretation for genotype differences in variance, we should be sure that no monotonic transformation of the data can reduce or eliminate these differences. We show theoretically that for an autosomal diallelic SNP, when the three corresponding means are distinct so that the variance can be expressed as a quadratic function of the mean, there implicitly exists a transformation that will tend to equalize the three variances; we also demonstrate how to find a transformation that will do this. We investigate the validity of Bartlett's test, Box's modification of it, and a modified Levene's test to test for differences in variances when normality does not hold. We find that, although they may detect differences in variability, these tests do not necessarily detect differences in variance. The same is true for permutation tests that use these three statistics.

Predicting Barrett's Esophagus in Families: An Esophagus Translational Research Network (BETRNet) Model Fitting Clinical Data to a Familial Paradigm

Sun, Xiangqing,Elston, Robert C.,Barnholtz-Sloan, Jill S.,Falk, Gary W.,Grady, William M.,Faulx, Ashley,Mittal, Sumeet K.,Canto, Marcia,Shaheen, Nicholas J.,Wang, Jean S.,Iyer, Prasad G.,Abrams, Julia American Association for Cancer Research 2016 Cancer Epidemiology, Biomarkers & Prevention Vol.25 No.5

<P><B>Background:</B> Barrett's esophagus is often asymptomatic and only a small portion of Barrett's esophagus patients are currently diagnosed and under surveillance. Therefore, it is important to develop risk prediction models to identify high-risk individuals with Barrett's esophagus. Familial aggregation of Barrett's esophagus and esophageal adenocarcinoma, and the increased risk of esophageal adenocarcinoma for individuals with a family history, raise the necessity of including genetic factors in the prediction model. Methods to determine risk prediction models using both risk covariates and ascertained family data are not well developed.</P><P><B>Methods:</B> We developed a Barrett's Esophagus Translational Research Network (BETRNet) risk prediction model from 787 singly ascertained Barrett's esophagus pedigrees and 92 multiplex Barrett's esophagus pedigrees, fitting a multivariate logistic model that incorporates family history and clinical risk factors. The eight risk factors, age, sex, education level, parental status, smoking, heartburn frequency, regurgitation frequency, and use of acid suppressant, were included in the model. The prediction accuracy was evaluated on the training dataset and an independent validation dataset of 643 multiplex Barrett's esophagus pedigrees.</P><P><B>Results:</B> Our results indicate family information helps to predict Barrett's esophagus risk, and predicting in families improves both prediction calibration and discrimination accuracy.</P><P><B>Conclusions:</B> Our model can predict Barrett's esophagus risk for anyone with family members known to have, or not have, had Barrett's esophagus. It can predict risk for unrelated individuals without knowing any relatives' information.</P><P><B>Impact:</B> Our prediction model will shed light on effectively identifying high-risk individuals for Barrett's esophagus screening and surveillance, consequently allowing intervention at an early stage, and reducing mortality from esophageal adenocarcinoma. <I>Cancer Epidemiol Biomarkers Prev; 25(5); 727–35. ©2016 AACR</I>.</P>

Identification of gene-gene interactions in the presence of missing data using the multifactor dimensionality reduction method

Namkung, Junghyun,Elston, Robert C.,Yang, Jun-Mo,Park, Taesung Wiley Subscription Services, Inc., A Wiley Company 2009 Genetic epidemiology Vol.33 No.7

<P>Gene-gene interaction is believed to play an important role in understanding complex traits. Multifactor dimensionality reduction (MDR) was proposed by Ritchie et al. [2001. Am J Hum Genet 69:138–147] to identify multiple loci that simultaneously affect disease susceptibility. Although the MDR method has been widely used to detect gene-gene interactions, few studies have been reported on MDR analysis when there are missing data. Currently, there are four approaches available in MDR analysis to handle missing data. The first approach uses only complete observations that have no missing data, which can cause a severe loss of data. The second approach is to treat missing values as an additional genotype category, but interpretation of the results may then be not clear and the conclusions may be misleading. Furthermore, it performs poorly when the missing rates are unbalanced between the case and control groups. The third approach is a simple imputation method that imputes missing genotypes as the most frequent genotype, which may also produce biased results. The fourth approach, Available, uses all data available for the given loci to increase power. In any real data analysis, it is not clear which MDR approach one should use when there are missing data. In this article, we consider a new EM Impute approach to handle missing data more appropriately. Through simulation studies, we compared the performance of the proposed EM Impute approach with the current approaches. Our results showed that Available and EM Impute approaches perform better than the three other current approaches in terms of power and precision. Genet. Epidemiol. 33:646–656, 2009. © 2009 Wiley-Liss, Inc.</P>

Log-linear model-based multifactor dimensionality reduction method to detect gene–gene interactions

Yeoun Lee, S.,Chung, Y.,Elston, R. C.,Kim, Y.,Park, T. Oxford University Press 2007 Bioinformatics Vol.23 No.19

Odds ratio based multifactor-dimensionality reduction method for detecting gene–gene interactions

Chung, Yujin,Lee, Seung Yeoun,Elston, Robert C.,Park, Taesung Oxford University Press 2007 Bioinformatics Vol.23 No.1

<P><B>Motivation:</B> The identification and characterization of genes that increase the susceptibility to common complex multifactorial diseases is a challenging task in genetic association studies. The multifactor dimensionality reduction (MDR) method has been proposed and implemented by Ritchie <I>et al</I>. (2001) to identify the combinations of multilocus genotypes and discrete environmental factors that are associated with a particular disease. However, the original MDR method classifies the combination of multilocus genotypes into high-risk and low-risk groups in an <I>ad hoc</I> manner based on a simple comparison of the ratios of the number of cases and controls. Hence, the MDR approach is prone to false positive and negative errors when the ratio of the number of cases and controls in a combination of genotypes is similar to that in the entire data, or when both the number of cases and controls is small. Hence, we propose the odds ratio based multifactor dimensionality reduction (OR MDR) method that uses the odds ratio as a new quantitative measure of disease risk.</P><P><B>Results:</B> While the original MDR method provides a simple binary measure of risk, the OR MDR method provides not only the odds ratio as a quantitative measure of risk but also the ordering of the multilocus combinations from the highest risk to lowest risk groups. Furthermore, the OR MDR method provides a confidence interval for the odds ratio for each multilocus combination, which is extremely informative in judging its importance as a risk factor. The proposed OR MDR method is illustrated using the dataset obtained from the CDC Chronic Fatigue Syndrome Research Group.</P><P><B>Availability:</B> The program written in R is available.</P><P><B>Contact:</B> tspark@snu.ac.kr</P>

ONETOOL for the analysis of family-based big data

Song, Yeunjoo E,Lee, Sungyoung,Park, Kyungtaek,Elston, Robert C,Yang, Hyeon-Jong,Won, Sungho Oxford University Press 2018 Bioinformatics Vol.34 No.16

<P><B>Abstract</B></P><P><B>Motivation</B></P><P>Despite the need for separate tools to analyze family-based data, there are only a handful of tools optimized for family-based big data compared to the number of tools available for analyzing population-based data.</P><P><B>Results</B></P><P>ONETOOL implements the properties of well-known existing family data analysis tools and recently developed methods in a computationally efficient manner, and so is suitable for analyzing the vast amount of variant data available from sequencing family members, providing a rich choice of analysis methods for big data on families.</P><P><B>Availability and implementation</B></P><P>ONETOOL is freely available from http://healthstat.snu.ac.kr/software/onetool/.</P><P><B>Supplementary information</B></P><P>Supplementary data are available at <I>Bioinformatics</I> online.</P>

On the association analysis of CNV data: a fast and robust family-based association method

Liu, Meiling,Moon, Sanghoon,Wang, Longfei,Kim, Sulgi,Kim, Yeon-Jung,Hwang, Mi Yeong,Kim, Young Jin,Elston, Robert C.,Kim, Bong-Jo,Won, Sungho BioMed Central 2017 BMC bioinformatics Vol.18 No.1

<P><B>Background</B></P><P>Copy number variation (CNV) is known to play an important role in the genetics of complex diseases and several methods have been proposed to detect association of CNV with phenotypes of interest. Statistical methods for CNV association analysis can be categorized into two different strategies. First, the copy number is estimated by maximum likelihood and association of the expected copy number with the phenotype is tested. Second, the observed probe intensity measurements can be directly used to detect association of CNV with the phenotypes of interest.</P><P><B>Results</B></P><P>For each strategy we provide a statistic that can be applied to extended families. The computational efficiency of the proposed methods enables genome-wide association analysis and we show with simulation studies that the proposed methods outperform other existing approaches. In particular, we found that the first strategy is always more efficient than the second strategy no matter whether copy numbers for each individual are well identified or not. With the proposed methods, we performed genome-wide CNV association analyses of hematological trait, hematocrit, on 521 Korean family samples.</P><P><B>Conclusions</B></P><P>We found that statistical analysis with the expected copy number is more powerful than the statistic with the probe intensity measurements regardless of the accuracy of the estimation of copy numbers.</P><P><B>Electronic supplementary material</B></P><P>The online version of this article (doi:10.1186/s12859-017-1622-z) contains supplementary material, which is available to authorized users.</P>