Combined association,segregation and aggregation analysis on case-control family data

Recently genetic epidemiologists have begun using case-control family study designs to investigate the role of genetic and environmental risk factors in disease etiology. The objective of these studies is to assess the association of environmental factors with the disease trait; to characterize the disease genes using segregation analysis; and to quantify the residual familial aggregation after controlling for environmental and genetic factors. Typically these objectives are achieved by conducting separate studies and analysis. This paper describes an estimating equation based approach for a combined association, segregation and aggregation analysis on data from case-control family studies. Simulations indicate that the method performs well in a variety of settings. The method is illustrated using simulated family history data made available to participants in a recent Genetic Analysis Workshop.     

A Novel Approach for Identifying Causal Models of Complex Diseases from Family Data

Causal models including genetic factors are important for understanding the presentation mechanisms of complex diseases. Familial aggregation and segregation analyses based on polygenic threshold models have been the primary approach to fitting genetic models to the family data of complex diseases. In the current study, an advanced approach to obtaining appropriate causal models for complex diseases based on the sufficient component cause (SCC) model involving combinations of traditional genetics principles was proposed. The probabilities for the entire population, i.e., normal–normal, normal–disease, and disease–disease, were considered for each model for the appropriate handling of common complex diseases. The causal model in the current study included the genetic effects from single genes involving epistasis, complementary gene interactions, gene–environment interactions, and environmental effects. Bayesian inference using a Markov chain Monte Carlo algorithm (MCMC) was used to assess of the proportions of each component for a given population lifetime incidence. This approach is flexible, allowing both common and rare variants within a gene and across multiple genes. An application to schizophrenia data confirmed the complexity of the causal factors. An analysis of diabetes data demonstrated that environmental factors and gene–environment interactions are the main causal factors for type II diabetes. The proposed method is effective and useful for identifying causal models, which can accelerate the development of efficient strategies for identifying causal factors of complex diseases.     

Family-based designs in the age of large-scale gene-association studies

Both population-based and family-based designs are commonly used in genetic association studies to locate genes that underlie complex diseases. The simplest version of the family-based design--the transmission disequilibrium test--is well known, but the numerous extensions that broaden its scope and power are less widely appreciated. Family-based designs have unique advantages over population-based designs, as they are robust against population admixture and stratification, allow both linkage and association to be tested for and offer a solution to the problem of model building. Furthermore, the fact that family-based designs contain both within- and between-family information has substantial benefits in terms of multiple-hypothesis testing, especially in the context of whole-genome association studies.     

Efficient study designs for test of genetic association using sibship data and unrelated cases and controls

Linkage mapping of complex diseases is often followed by association studies between phenotypes and marker genotypes through use of case-control or family-based designs. Given fixed genotyping resources, it is important to know which study designs are the most efficient. To address this problem, we extended the likelihood-based method of Li et al., which assesses whether there is linkage disequilibrium between a disease locus and a SNP, to accommodate sibships of arbitrary size and disease-phenotype configuration. A key advantage of our method is the ability to combine data from different family structures. We consider scenarios for which genotypes are available for unrelated cases, affected sib pairs (ASPs), or only one sibling per ASP. We construct designs that use cases only and others that use unaffected siblings or unrelated unaffected individuals as controls. Different combinations of cases and controls result in seven study designs. We compare the efficiency of these designs when the number of individuals to be genotyped is fixed. Our results suggest that (1) when the disease is influenced by a single gene, the one sibling per ASP-control design is the most efficient, followed by the ASP-control design, and familial cases contribute more association information than singleton cases; (2) when the disease is influenced by multiple genes, familial cases provide more association information than singleton cases, unless the effect of the locus being tested is much smaller than at least one other untested disease locus; and (3) the case-control design can be useful for detecting genes with small effect in the presence of genes with much larger effect. Our findings will be helpful for researchers designing and analyzing complex disease-association studies and will facilitate genotyping resource allocation.     

A Frailty‐Model‐Based Approach to Estimating the Age‐Dependent Penetrance Function of Candidate Genes Using Population‐Based Case‐Control Study Designs: An Application to Data on the BRCA1 Gene

Summary The population‐based case–control study design is perhaps one of, if not the most, commonly used designs for investigating the genetic and environmental contributions to disease risk in epidemiological studies. Ages at onset and disease status of family members are routinely and systematically collected from the participants in this design. Considering age at onset in relatives as an outcome, this article is focused on using the family history information to obtain the hazard function, i.e., age‐dependent penetrance function, of candidate genes from case–control studies. A frailty‐model‐based approach is proposed to accommodate the shared risk among family members that is not accounted for by observed risk factors. This approach is further extended to accommodate missing genotypes in family members and a two‐phase case–control sampling design. Simulation results show that the proposed method performs well in realistic settings. Finally, a population‐based two‐phase case–control breast cancer study of the BRCA1 gene is used to illustrate the method.     

复杂疾病全基因组关联研究进展——遗传统计分析

2005年, Science杂志首次报道了有关人类年龄相关性黄斑变性的全基因组关联研究, 此后有关肥胖、2型糖尿病、冠心病、阿尔茨海默病等一系列复杂疾病的全基因组关联研究被陆续报道, 这一阶段被称为人类全基因组关联研究的第一次浪潮。文章分别介绍了全基因组关联研究统计分析的方法、软件和应用实例; 比较了关联分析中多重检验的P值调整方法, 包括Bonferroni、递减的Bonferroni校正法、模拟运算法和控制错误发现率的方法; 还讨论了人群混杂对关联分析结果可能产生的影响及原理, 以及全基因组关联研究中控制人群混杂的方法的研究进展和应用实例。在全基因组关联研究的第一次浪潮中, 应用经典的遗传统计方法发现了许多基因-表型之间的关联并且能够对这些关联做出解释, 其中包括许多基因组中的未知基因和染色体区域。然而, 全基因组关联研究的继续发展需要进一步阐述基因组内基因之间相互作用、基因-基因之间的复杂作用网络与环境因素的相互作用在复杂疾病发生中的作用, 现有的统计分析方法肯定不能满足需要, 开发更为高级的统计分析方法势在必行。最后, 文章还给出了全基因组关联研究统计分析软件的相关网站信息。     

Localization and identification of human quantitative trait loci: king harvest has surely come

The scientific process of localization and subsequent identification of genes influencing risk of common diseases is still in its infancy. Initial localization of disease-related loci has traditionally been performed using family-based linkage methods to scan the genome. Early pronouncements of the failure of this approach for common diseases were premature and based on comparing suboptimal linkage designs with overly optimistic and empirically unproven association-based designs. On the contrary, substantial recent progress in the positional cloning of genes influencing such complex phenotypes suggests that modern approaches based around a family-based linkage paradigm will be successful. In particular, the rapidly growing emphasis on the analysis of the genetic basis of quantitative correlates of disease risk represents a novel and promising approach in which initial localization is performed using linkage and subsequent identification utilizes association approaches in positional candidate genes.     

Isolated populations and complex disease gene identification

The utility of genetically isolated populations (population isolates) in the mapping and identification of genes is not only limited to the study of rare diseases; isolated populations also provide a useful resource for studies aimed at improved understanding of the biology underlying common diseases and their component traits. Well characterized human populations provide excellent study samples for many different genetic investigations, ranging from genome-wide association studies to the characterization of interactions between genes and the environment.     

Score tests of genetic association in the presence of linkage based on the additive genetic gamma frailty model

Nuclear families with multiple affected sibs are often collected for genetic linkage analysis of complex diseases. Once linkage evidence is established, dense markers are often typed in the linked region for genetic association analysis based on linkage disequilibrium (LD). Detection of association in the presence of linkage localizes disease genes more accurately than the methods that rely on linkage alone. However, test of association due to LD in the linked region needs to account for dependency of the allele transmissions to different sibs within a family. In this paper, we define a joint model for genetic linkage and association and derive the corresponding joint survival function of age of onset for the sibs within a sibship. The joint survival function is a function of both the inheritance vector and the genotypes at the candidate marker locus. Based on this joint survival function, we derive score tests for genetic association. The proposed methods utilize the phenotype data of all the sibs and have the advantages of family-based designs which can avoid the potential spurious association caused by population admixture. In addition, the methods can account for variable age of onset or age at censoring and possible covariate effects, and therefore provide important tools for modelling disease heterogeneity. Simulation studies and application to the data sets from the 12th Genetic Analysis Workshop indicate that the proposed methods have correct type 1 error rates and increased power over other existing methods for testing allelic association.     

Genetic relationships between resistances to Fusarium head blight and crown rot in bread wheat (Triticum aestivum L.)

Fusarium head blight (FHB) and crown rot (CR) are two wheat diseases caused by the same Fusarium pathogens. Progress towards CR resistance could benefit from FHB-resistant germplasm if the same genes are involved in resistance to these two different diseases. Two independent studies were conducted to investigate the relationship between host resistances to these two diseases. In the first study 32 genotypes were assessed and no significant correlation between their reactions to FHB and CR was detected. The second study was based on a QTL analysis of a doubled haploid population derived from a variety with resistance to both diseases. Results from this study showed that loci conferring resistance to FHB and CR are located on different chromosomes. Together, these results suggest that, despite a common aetiology, different host genes are involved in the resistance against FHB and CR in wheat. Thus, although it is possible that genes affecting both diseases may exist in other germplasm or under different conditions, separate screening seems to be needed in identifying sources of CR resistance.     

A unified association analysis approach for family and unrelated samples correcting for stratification

There are two common designs for association mapping of complex diseases: case-control and family-based designs. A case-control sample is more powerful to detect genetic effects than a family-based sample that contains the same numbers of affected and unaffected persons, although additional markers may be required to control for spurious association. When family and unrelated samples are available, statistical analyses are often performed in the family and unrelated samples separately, conditioning on parental information for the former, thus resulting in reduced power. In this report, we propose a unified approach that can incorporate both family and case-control samples and, provided the additional markers are available, at the same time corrects for population stratification. We apply the principal components of a marker matrix to adjust for the effect of population stratification. This unified approach makes it unnecessary to perform a conditional analysis of the family data and is more powerful than the separate analyses of unrelated and family samples, or a meta-analysis performed by combining the results of the usual separate analyses. This property is demonstrated in both a variety of simulation models and empirical data. The proposed approach can be equally applied to the analysis of both qualitative and quantitative traits.     

Genetic polymorphisms: impact on the risk of fetal alcohol spectrum disorders

Clinical reports on monozygotic and dizygotic twins provided the initial evidence for the involvement of genetic factors in risk vulnerability for fetal alcohol spectrum disorders (FASD) including fetal alcohol syndrome (FAS). Research with selectively bred and inbred rodents, genetic crosses of these lines and strains, and embryo culture studies have further clarified the role of both maternal and fetal genetics in the development of FASD. Research to identify specific polymorphisms contributing to FASD is still at an early stage. To date, polymorphisms of only one of the genes for the alcohol dehydrogenase enzyme family, the ADH1B, have been demonstrated to contribute to FASD vulnerability. In comparison with ADH1B*1, both maternal and fetal ADH1B*2 have been shown to reduce risk for FAS in a mixed ancestry South African population. ADH1B*3 appears to afford protection for FASD outcomes in African-American populations. Other candidate genes should be examined with respect to FASD risk, including those for the enzymes of serotonin metabolism, in particular the serotonin transporter. By its very nature, alcohol teratogenesis is the expression of the interaction of genes with environment. The study of genetic factors in FASD falls within the new field of ecogenetics. Understanding of the array of genetic factors in FASD will be enhanced by future genetic investigations, including case-control, family association, and linkage studies.     

Genetic markers of osteoarticular disorders: facts and hopes

Osteoarthritis and osteoporosis are the two most common age-related chronic disorders of articular joints and skeleton, representing a major public health problem in most developed countries. Apart from being influenced by environmental factors, both disorders have a strong genetic component, and there is now considerable evidence from large population studies that these two disorders are inversely related. Thus, an accurate analysis of the genetic component of one of these two multifactorial diseases may provide data of interest for the other. However, the existence of confounding factors must always be borne in mind in interpreting the genetic analysis. In addition, each patient must be given an accurate clinical evaluation, including family history, history of drug treatments, lifestyle, and environment, in order to reduce the background bias. Here, we review the impact of recent work in molecular genetics suggesting that powerful molecular biology techniques will soon make possible both a rapid accumulation of data on the genetics of both disorders and the development of novel diagnostic, prognostic, and therapeutic approaches.     

高血压易感基因的分子进化

利用家系连锁分析、候选基因法及全基因组关联研究均未能有效发现普通人群的高血压易感基因或位点。遗传学研究表明, 人类许多疾病易感性的形成与走出非洲时的环境适应性进化密切相关, 这为高血压遗传学研究提供了新思路。文章系统综述了高血压易感基因分子进化研究的理论基础和最新进展, 介绍了本研究小组运用分子进化思路在中国汉族人群高血压遗传学研究中的发现, 对未来的研究方向进行了展望, 以期为高血压和其他疾病的遗传学研究提供参考。     

Pathway analysis of seven common diseases assessed by genome-wide association

Recent genome-wide association studies (GWAS) have identified DNA sequence variations that exhibit unequivocal statistical associations with many common chronic diseases. However, the vast majority of these studies identified variations that explain only a very small fraction of disease burden in the population at large, suggesting that other factors, such as multiple rare or low-penetrance variations and interacting environmental factors, are major contributors to disease susceptibility. Identifying multiple low-penetrance variations (or "polygenes") contributing to disease susceptibility will be difficult. We present a pathway analysis approach to characterizing the likely polygenic basis of seven common diseases using the Wellcome Trust Case Control Consortium (WTCCC) GWAS results. We identify numerous pathways implicated in disease predisposition that would have not been revealed using standard single-locus GWAS statistical analysis criteria. Many of these pathways have long been assumed to contain polymorphic genes that lead to disease predisposition. Additionally, we analyze the genetic relationships between the seven diseases, and based upon similarities with respect to the associated genes and pathways affected in each, propose a new way of categorizing the diseases.     

基于功能一致性和网络拓扑属性预测冠心病致病基因

基于功能一致性利用蛋白质互作网络挖掘潜在的疾病致病基因,对于了解疾病致病机理和改进临床治疗至关重要．基于基因功能一致性和其在蛋白质互作网络中的拓扑属性将基因与疾病之间建立关联,对疾病风险位点内的基因进行了致病风险预测,并通过GO及KEGG功能富集分析方法进一步筛选,预测出新的致病基因．预测出了51个新的冠心病致病基因,分析发现大部分基因参与了冠心病的致病过程．为疾病基因的挖掘提出一个新的思路,从而有助于复杂疾病致病机理的研究．     

Group sequential study designs in genetic-epidemiological case-control studies

OBJECTIVE: In past years, the focus of genetic-epidemiological studies has shifted to analyzing complex diseases. Here, single genes often contribute only little to the manifestation of traits so that many probands have to be included in a study to reliably detect small effects. To reduce the number of required phenotypings and genotypings and thus facilitate analyzing complex traits, sequential study designs can be applied. METHODS: For sequential analyses of complex diseases in association studies, we compare the procedure by Sobell et al. (Am J Med Genet 1993;48:28-35) with the adaptation of formal group sequential study designs by Pampallona and Tsiatis (J Stat Plan Inf 1994;42:19-35). Error rates and average sample sizes are investigated by Monte-Carlo simulations. RESULTS: Formal sequential designs have a higher power regardless of underlying genetic effects. In addition, compared with conventional designs with fixed samples, average sample sizes are reduced considerably; under the null hypothesis of no association, up to 50% of the required sample size can be spared. CONCLUSIONS: To increase the efficiency of genetic-epidemiological case-control studies, we recommend using formal group sequential study designs. The tremendous savings in average sample sizes are expected to affect both cost and time spent on large-scale studies.     

Genome-wide identification and expression profiling of ankyrin-repeat gene family in maize

Members of the ankyrin repeats (ANK) gene family encode ANK domain that are common in diverse organisms and play important roles in cell growth and development, such as cell-cell signal transduction and cell cycle regulation. Recently, genome-wide identification and evolutionary analyses of the ANK gene family have been carried out in Arabidopsis and rice. However, little is known regarding the ANK genes in the entire maize genome. In this study, we described the identification and structural characterization of 71 ANK genes in maize (ZmANK). Then, comprehensive bioinformatics analyses of ZmANK genes family were performed including phylogenetic, domain and motif analysis, chromosomal localization, intron/exon structural patterns, gene duplications and expression profiling. Domain composition analyses showed that ZmANK genes formed ten subfamilies. Five tandem duplications and 14 segmental duplications were identified in ZmANK genes. Furthermore, we took comparative analysis of the total ANK gene family in Arabidopsis, rice and maize, ZmANKs were more closely paired with OsANKs than with AtANKs. At last, expression profile analyses were performed. Forty-one members of ZmANK genes held EST sequences records. Semi-quantitative expression and microarray data analysis of these 41 ZmANK genes demonstrated that ZmANK genes exhibit a various expression pattern, suggesting that functional diversification of ZmANK genes family. The results will present significant insights to explore ANK genes expression and function in future studies in maize.     

Genetic analysis of the structure of the predisposition to diabetes mellitus. I. Genetic epidemiological approaches to an analysis of age-dependent multifactorial diseases

The statistical methods for genetic analysis of multifactorial diseases (MFD) with variable age-at-onset are developed. It is emphasized that the index numbers used in the genetical-epidemiological studies, such as frequency of MFD in the population (qg--incidence) and that among family members (PR), should be independent on the demographic structure of both the population and the families. The mathematical grounds for the estimating methods as well as biological meaning for such index numbers are given. The approach suggested will be used later in analysis of the structure of predisposition to diabetes mellitus.