Ruzong Fan

Family-based transmission disequilibrium test (TDT) and case-control association studies reveal surfactant protein A (SP-A) susceptibility alleles for respiratory distress syndrome (RDS) and possible race differences

A key cause of respiratory distress syndrome (RDS) in the prematurely born infant is deficiency of pulmonary surfactant, a lipoprotein complex. Both low levels of surfactant protein A (SP‐A) and SP‐A alleles have been associated with RDS. Using the candidate gene approach, we performed family‐based linkage studies to discern linkage of SP‐A to RDS and identify SP‐A susceptibility or protective alleles. Moreover, we performed case–control studies of whites and blacks to detect association between RDS and SP‐A alleles. Transmission disequilibrium test (TDT) analysis revealed that the frequency of transmission (from parent to the offspring with RDS) of alleles 6A2 and 1A0 and of 1A0/6A2 haplotype in RDS was increased, whereas transmission of alleles 1A5 and 6A4 and of haplotype 1A5/6A4 was decreased. Extended TDT analysis further strengthened the observations made. The case–control studies showed that in whites or blacks with RDS the frequencies of specific genotypes, 1A0 and 6A2 or 1A0, were increased, respectively, but the frequency of specific 6A3 genotypes was increased in certain white subgroups and decreased in blacks. Regression analysis revealed gestational age (GA) and 6A3 genotypes are significant factors in blacks with RDS. In whites with RDS, GA and antenatal steroids are important factors. The data together indicate linkage between SP‐A and RDS; certain SP‐A alleles/haplotypes are susceptibility (1A0, 6A2, 1A0/6A2) or protective (1A5, 6A4, 1A5/6A4) factors for RDS. Some differences between blacks and whites with regard to SP‐A alleles may exist.

Genetic variants of surfactant proteins A, B, C, and D in bronchopulmonary dysplasia

BPD_28D (O2 dependency at 28 days of life) and BPD_36W (O2 dependency at 36 wks post-menstrual age) are diseases of prematurely born infants exposed to mechanical ventilation and/or oxygen supplementation. In order to determine whether genetic variants of surfactant proteins (SPs-A, B, C, and D) and SP-B-linked microsatellite markers are risk factors in BPD, we performed a family based association study using a Greek study group of 71 neonates (<30 wks gestational age) from 60 families with, 52 BPD_28D and 19 BPD_36W, affected infants. Genotyping was performed using newly designed pyrosequencing assays and previously published methods. Associations between genetic variants of SPs and BPD subgroups were determined using Transmission Disequilibrium Test (TDT) and Family Based Association Test (FBAT). Significant associations (p ≤ 0.01) were observed for alleles of SP-B and SP-B-linked microsatellite markers, and haplotypes of SP-A, SP-D, and SP-B. Specifically, allele B-18_C associated with susceptibility in BPD_36W. Microsatellite marker AAGG_6 associated with susceptibility in BPD_28D/36W group. Haplotype analysis revealed ten susceptibility and one protective haplotypes for SP-B and SP-B-linked microsatellite markers and two SP-A-SP-D protective haplotypes. The data indicate that SP loci are linked to BPD. Studies in different study groups and/or of larger sample size are warranted to confirm these observations and delineate genetic background of BPD subgroups.

Next generation analytic tools for large scale genetic epidemiology studies of complex diseases.

Over the past several years, genome‐wide association studies (GWAS) have succeeded in identifying hundreds of genetic markers associated with common diseases. However, most of these markers confer relatively small increments of risk and explain only a small proportion of familial clustering. To identify obstacles to future progress in genetic epidemiology research and provide recommendations to NIH for overcoming these barriers, the National Cancer Institute sponsored a workshop entitled “Next Generation Analytic Tools for Large‐Scale Genetic Epidemiology Studies of Complex Diseases” on September 15–16, 2010. The goal of the workshop was to facilitate discussions on (1) statistical strategies and methods to efficiently identify genetic and environmental factors contributing to the risk of complex disease; and (2) how to develop, apply, and evaluate these strategies for the design, analysis, and interpretation of large‐scale complex disease association studies in order to guide NIH in setting the future agenda in this area of research. The workshop was organized as a series of short presentations covering scientific (gene‐gene and gene‐environment interaction, complex phenotypes, and rare variants and next generation sequencing) and methodological (simulation modeling and computational resources and data management) topic areas. Specific needs to advance the field were identified during each session and are summarized. Genet. Epidemiol. 36 : 22–35, 2012.

Pleiotropy Analysis of Quantitative Traits at Gene Level by Multivariate Functional Linear Models

In genetics, pleiotropy describes the genetic effect of a single gene on multiple phenotypic traits. A common approach is to analyze the phenotypic traits separately using univariate analyses and combine the test results through multiple comparisons. This approach may lead to low power. Multivariate functional linear models are developed to connect genetic variant data to multiple quantitative traits adjusting for covariates for a unified analysis. Three types of approximate F‐distribution tests based on Pillai–Bartlett trace, Hotelling–Lawley trace, and Wilkss Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants in one genetic region. The approximate F‐distribution tests provide much more significant results than those of F‐tests of univariate analysis and optimal sequence kernel association test (SKAT‐O). Extensive simulations were performed to evaluate the false positive rates and power performance of the proposed models and tests. We show that the approximate F‐distribution tests control the type I error rates very well. Overall, simultaneous analysis of multiple traits can increase power performance compared to an individual test of each trait. The proposed methods were applied to analyze (1) four lipid traits in eight European cohorts, and (2) three biochemical traits in the Trinity Students Study. The approximate F‐distribution tests provide much more significant results than those of F‐tests of univariate analysis and SKAT‐O for the three biochemical traits. The approximate F‐distribution tests of the proposed functional linear models are more sensitive than those of the traditional multivariate linear models that in turn are more sensitive than SKAT‐O in the univariate case. The analysis of the four lipid traits and the three biochemical traits detects more association than SKAT‐O in the univariate case.

Entropy‐based information gain approaches to detect and to characterize gene‐gene and gene‐environment interactions/correlations of complex diseases

For complex diseases, the relationship between genotypes, environment factors, and phenotype is usually complex and nonlinear. Our understanding of the genetic architecture of diseases has considerably increased over the last years. However, both conceptually and methodologically, detecting gene‐gene and gene‐environment interactions remains a challenge, despite the existence of a number of efficient methods. One method that offers great promises but has not yet been widely applied to genomic data is the entropy‐based approach of information theory. In this article, we first develop entropy‐based test statistics to identify two‐way and higher order gene‐gene and gene‐environment interactions. We then apply these methods to a bladder cancer data set and thereby test their power and identify strengths and weaknesses. For two‐way interactions, we propose an information gain (IG) approach based on mutual information. For three‐ways and higher order interactions, an interaction IG approach is used. In both cases, we develop one‐dimensional test statistics to analyze sparse data. Compared to the naive chi‐square test, the test statistics we develop have similar or higher power and is robust. Applying it to the bladder cancer data set allowed to investigate the complex interactions between DNA repair gene single nucleotide polymorphisms, smoking status, and bladder cancer susceptibility. Although not yet widely applied, entropy‐based approaches appear as a useful tool for detecting gene‐gene and gene‐environment interactions. The test statistics we develop add to a growing body methodologies that will gradually shed light on the complex architecture of common diseases. Genet. Epidemiol. 35:706–721, 2011.

Functional linear models for association analysis of quantitative traits.

Functional linear models are developed in this paper for testing associations between quantitative traits and genetic variants, which can be rare variants or common variants or the combination of the two. By treating multiple genetic variants of an individual in a human population as a realization of a stochastic process, the genome of an individual in a chromosome region is a continuum of sequence data rather than discrete observations. The genome of an individual is viewed as a stochastic function that contains both linkage and linkage disequilibrium (LD) information of the genetic markers. By using techniques of functional data analysis, both fixed and mixed effect functional linear models are built to test the association between quantitative traits and genetic variants adjusting for covariates. After extensive simulation analysis, it is shown that the F‐distributed tests of the proposed fixed effect functional linear models have higher power than that of sequence kernel association test (SKAT) and its optimal unified test (SKAT‐O) for three scenarios in most cases: (1) the causal variants are all rare, (2) the causal variants are both rare and common, and (3) the causal variants are common. The superior performance of the fixed effect functional linear models is most likely due to its optimal utilization of both genetic linkage and LD information of multiple genetic variants in a genome and similarity among different individuals, while SKAT and SKAT‐O only model the similarities and pairwise LD but do not model linkage and higher order LD information sufficiently. In addition, the proposed fixed effect models generate accurate type I error rates in simulation studies. We also show that the functional kernel score tests of the proposed mixed effect functional linear models are preferable in candidate gene analysis and small sample problems. The methods are applied to analyze three biochemical traits in data from the Trinity Students Study.

Transmission of surfactant protein variants and haplotypes in children hospitalized with respiratory syncytial virus.

Severity of lung injury with respiratory syncytial virus (RSV) infection is variable and may be related to genetic variations. This preliminary report describes a prospective, family-based association study of children hospitalized secondary to RSV, aimed to determine whether intragenic and other haplotypes of surfactant proteins (SP)-A and SP-D are transmitted disproportionately from parents to offspring with RSV disease. Genomic DNA was genotyped for several SP-A and SP-D single nucleotide polymorphisms (SNPs). Transmission disequilibrium test analysis was used to determine transmission of variants and haplotypes from parents to affected offspring. Three hundred seventy-five individuals were studied, including 148 children with active RSV disease and one or both parents. The SP-A2 intragenic haplotype 1A2 was found to be protective (p = 0.013). The SP-D SNP DA160_A may possibly be an “at-risk” marker (p = 0.0058). Additional two- and three-marker haplotypes were associated with severe RSV disease, with two being protective (DA11_T/DA160_G and DA160_G/SP-A2 1A0/SP-A1 6A2). We conclude that there may be associations between SP-A and SP-D and RSV disease. Further study is required to determine whether these variants can be used to target a high-risk patient population in clinical trials aimed at reducing either the symptoms of acute infection or long-term pulmonary sequelae.

Haplotypes of the surfactant protein genes A and D as susceptibility factors for the development of respiratory distress syndrome.

Aims: Polymorphisms of genes are transmitted together in haplotypes, which can be used in the study of the development of complex diseases such as respiratory distress syndrome (RDS). The surfactant proteins (SPs) play important roles in lung function, and genetic variants of these proteins have been linked with lung diseases, including RDS. To determine whether haplotypes of SP‐A and SP‐D are transmitted disproportionately from parents to offspring with RDS, we hypothesized that previously unstudied genetic haplotypes of these SP genes are associated with the development of RDS.

Surfactant Protein A and B Genetic Variants and Respiratory Distress Syndrome: Allele Interactions

The contribution of multiple genetic components in disease pathogenesis is relevant to both diseases of multifactorial and/or multigenic etiology such as the respiratory distress syndrome (RDS) and to diseases where a single gene has been identified as the disease-causing gene. An example of the latter is cystic fibrosis (CF) where the disease-causing gene has been clearly identified as the CF transmembrane conductance regulator gene, but genetic variants of the mannose binding protein and surfactant protein A have been associated with disease severity in CF. The overall rationale for considering genetic contribution to disease pathogenesis is based on the premise that all diseases or deaths (except perhaps those resulting from trauma) have a genetic component. The difference in genetic contribution among various diseases is the percent contribution and the number of factors that make this contribution. Therefore, if the number of genetic contributors is small and the percentage of genetic contribution is high it may be less challenging to identify such factors. In this paper we summarize allele associations and discuss allele interaction of the surfactant protein genes in relation to RDS (the term allele and genetic variant will be used interchangeably).

Longitudinal Association Analysis of Quantitative Traits

Longitudinal genetic studies provide a valuable resource for exploring key genetic and environmental factors that affect complex traits over time. Genetic analysis of longitudinal data that incorporate temporal variations is important for understanding genetic architecture and biological variations of common complex diseases. Although they are important, there is a paucity of statistical methods to analyze longitudinal human genetic data. In this article, longitudinal methods are developed for temporal association mapping to analyze population longitudinal data. Both parametric and nonparametric models are proposed. The models can be applied to multiple diallelic genetic markers such as single‐nucleotide polymorphisms and multiallelic markers such as microsatellites. By analytical formulae, we show that the models take both the linkage disequilibrium and temporal trends into account simultaneously. Variance‐covariance structure is constructed to model the single measurement variation and multiple measurement correlations of an individual based on the theory of stochastic processes. Novel penalized spline models are used to estimate the time‐dependent mean functions and regression coefficients. The methods were applied to analyze Framingham Heart Study data of Genetic Analysis Workshop (GAW) 13 and GAW 16. The temporal trends and genetic effects of the systolic blood pressure are successfully detected by the proposed approaches. Simulation studies were performed to find out that the nonparametric penalized linear model is the best choice in fitting real data. The research sheds light on the important area of longitudinal genetic analysis, and it provides a basis for future methodological investigations and practical applications.