Genetic polymorphisms, lipoproteins and coronary artery disease risk

Association studies between gene variants (polymorphisms) and measured intermediate phenotypes, such as lipid/lipoprotein levels, or disease endpoints such as coronary artery disease, are commonplace in the literature. But have we learnt anything from the shortcomings in study design and analytical strategies that have resulted in much controversy in this field over the last few years? This review highlights some of these problems. Using the lipoprotein lipase gene as an example, we evaluate new approaches to identifying polymorphisms that will stand up to linkage disequilibrium/association studies with complex disorders in this post Human Genome Project age, and emphasize the importance of gene-environment interaction in assessing the impact of gene variants.     

A Perspective on Epistasis: Limits of Models Displaying No Main Effect

The completion of a draft sequence of the human genome and the promise of rapid single-nucleotide-polymorphism-genotyping technologies have resulted in a call for the abandonment of linkage studies in favor of genome scans for association. However, there exists a large class of genetic models for which this approach will fail: purely epistatic models with no additive or dominance variation at any of the susceptibility loci. As a result, traditional association methods (such as case/control, measured genotype, and transmission/disequilibrium test [TDT]) will have no power if the loci are examined individually. In this article, we examine this class of models, delimiting the range of genetic determination and recurrence risks for two-, three-, and four-locus purely epistatic models. Our study reveals that these models, although giving rise to no additive or dominance variation, do give rise to increased allele sharing between affected sibs. Thus, a genome scan for linkage could detect genomic subregions harboring susceptibility loci. We also discuss some simple multilocus extensions of single-locus analysis methods, including a conditional form of the TDT.     

Vitiligo: pathomechanisms and genetic polymorphism of susceptible genes

Vitiligo is a depigmenting disorder resulting from the loss of melanocytes in the skin and affects 1-4% of the world population. Incidence of vitiligo is found to be 0.5-2.5% in India with a high prevalence of 8.8% in Gujarat and Rajasthan states. The cellular and molecular mechanisms that lead to melanocyte destruction in this disorder are not yet been fully elucidated. Genetic factors, neural factors, toxic ROS metabolites, autoantibodies and autoreactive T lymphocytes may be the causative agents for the selective destruction of melanocytes. Three major hypotheses of pathogenesis of vitiligo are neural, autoimmune and oxidative stress hypotheses, however none of them explains the pathogenesis of vitiligo in toto. Genetics of vitiligo is characterized by incomplete penetrance, multiple susceptibility loci and genetic heterogeneity. Recent advances in this field are linkage and association of candidate gene studies. The linkage and association studies provide a strong evidence for the presence of multiple vitiligo susceptibility genes on different chromosomes. Several candidate genes for vitiligo are identified from different populations. In this review, we have provide an overview of different hypotheses of vitiligo pathogenesis, and discuss the recent advances in this field with special reference to linkage, association and candidate gene approach.     

Analysis of binary traits: testing association in the presence of linkage

Most methods for testing association in the presence of linkage, using family-based studies, have been developed for continuous traits. FBAT (family-based association tests) is one of few methods appropriate for discrete outcomes. In this article we describe a new test of association in the presence of linkage for binary traits. We use a gamma random effects model in which association and linkage are modelled as fixed effects and random effects, respectively. We have compared the gamma random effects model to an FBAT and a generalized estimating equation-based alternative, using two regions in the Genetic Analysis Workshop 14 simulated data. One of these regions contained haplotypes associated with disease, and the other did not.     

The SERPINE2 gene is associated with chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is a complex human disease likely influenced by multiple genes, cigarette smoking, and gene-by-smoking interactions, but only severe alpha 1-antitrypsin deficiency is a proven genetic risk factor for COPD. Prior linkage analyses in the Boston Early-Onset COPD Study have demonstrated significant linkage to a key intermediate phenotype of COPD on chromosome 2q. We integrated results from murine lung development and human COPD gene-expression microarray studies with human COPD linkage results on chromosome 2q to prioritize candidate-gene selection, thus identifying SERPINE2 as a positional candidate susceptibility gene for COPD. Immunohistochemistry demonstrated expression of serpine2 protein in mouse and human adult lung tissue. In family-based association testing of 127 severe, early-onset COPD pedigrees from the Boston Early-Onset COPD Study, we observed significant association with COPD phenotypes and 18 single-nucleotide polymorphisms (SNPs) in the SERPINE2 gene. Association of five of these SNPs with COPD was replicated in a case-control analysis, with cases from the National Emphysema Treatment Trial and controls from the Normative Aging Study. Family-based and case-control haplotype analyses supported similar regions of association within the SERPINE2 gene. When significantly associated SNPs in these haplotypic regions were included as covariates in linkage models, LOD score attenuation was observed most markedly in a smokers-only linkage model (LOD 4.41, attenuated to 1.74). After the integration of murine and human microarray data to inform candidate-gene selection, we observed significant family-based association and independent replication of association in a case-control study, suggesting that SERPINE2 is a COPD-susceptibility gene and is likely influenced by gene-by-smoking interaction.     

No evidence of association of the osteocalcin gene HindIII polymorphism with bone mineral density in Chinese women

Osteoporosis is a major health problem, mainly characterized by low bone mineral density (BMD). Osteocalcin (also known as BGP, for bone Gla protein) is a significant biomarker of bone turnover and thus the BGP gene has been considered as an important candidate gene for osteoporosis. A few studies on the relationship between variants of the BGP gene and BMD variation, via traditional association and/or linkage methods, have yielded conflicting results. In the present study, we simultaneously tested linkage and/or association of the BGP HindIII polymorphism with BMD in a large cohort of pre-menopausal Chinese women. A total of 1,263 subjects from 402 Chinese nuclear families were examined. Each family consists of both parents and at least one daughter aged between 20-45 years. BMDs at the lumbar spine and hip were measured by dual-energy X-ray absorptiometry (DXA). Using the QTDT (quantitative transmission disequilibrium test) program, we did not detect significant evidence of linkage or association between the BGP HindIII polymorphisms and the BMD variation at any skeletal site. Our data do not support the BGP gene having a major effect on BMD variation in pre-menopausal Chinese women.     

The problems of using the transmission/disequilibrium test to infer tight linkage

Family-based association methods such as the transmission/disequilibrium test (TDT) have become very popular during the past few years, often being preferred to case-control studies because family-based approaches avoid the difficulties of ascertainment of appropriate populations of cases and controls for case-control studies. Significant TDT results indicate both linkage and allelic association. However, significant TDT results are often interpreted as implying tight linkage of marker and disease locus, and we shall argue here that, in general, this interpretation is not justified.     

Genetics of LDL particle heterogeneity: from genetic epidemiology to DNA-based variations

Substantial evidence exists suggesting that small, dense LDL particles are associated with an increased risk of coronary heart disease. This disease-related risk factor is recognized to be under both genetic and environmental influences. Several studies have been conducted to elucidate the genetic architecture underlying this trait, and a review of this literature seems timely. The methods and strategies used to determine its genetic component and to identify the genes have greatly changed throughout the years owing to the progress made in genetic epidemiology and the influence of the Human Genome Project. Heritability studies, complex segregation analyses, candidate gene linkage and association studies, genome-wide linkage scans, and animal models are all part of the arsenal to determine the susceptibility genes. The compilation of these studies clearly revealed the complex genetic nature of LDL particles. This work is an attempt to summarize the growing evidence of genetic control on LDL particle heterogeneity with the aim of providing a concise overview in one read.     

Mapping genes for polygenic disorders: considerations for study design in the complex trait of inflammatory bowel disease

While the methodology for the mapping of Mendelian disorders is well established, the practical and theoretical steps required for successful gene identification in a complex trait are still difficult to predict. A number of analytical models and simulations based on repetitive drawings from predefined statistical distributions are available. To supplement these analytical models, we developed an integrated simulation approach by directly simulating entire populations under a disease model based on epidemiological data. Random mating, nonoverlapping populations and the absence of differential fitness were assumed. Samples were drawn from these homogeneous and heterogeneous populations and analyzed with established analysis tools. We investigated the properties of linkage and association studies in inflammatory bowel disease - modeled as a six-locus polygenic disorder - as an example of this approach. In nonparametric linkage studies, lod scores varied widely, with the median required sample size depending on the locus-specific relative sibling risk. A fine mapping resolution <4 cM was found to require nonparametric lod scores >10. Family-based association studies (TDT test) and case-control studies showed a similar sensitivity and can identify risk loci in populations with moderate levels of linkage disequilibrium in sample sizes of 500-800 triplets. Case-control association studies were prone to false-positive results if applied in heterogeneous populations, with the false-positive rate increasing with sample size because population heterogeneity is detected with increasing power.     

Genetic variation at the matrix metalloproteinase-9 locus on chromosome 20q12.2–13.1

Allelic association methods are better suited than linkage analysis for mapping of susceptibility genes that confer modest increases in risk in complex diseases. In both family- and population-based association studies, it is very useful to have prior knowledge of all sequence variants and the degree of linkage disequilibrium in a candidate gene region. In this study, we scanned sequence variants in a 2.2-kb promoter sequence and all 13 exons (totalling 3.3 kb) of the matrix metalloproteinase-9 gene, which is associated with coronary heart disease and a candidate for other diseases involving connective tissue remodelling, such as cancer metastasis. The sequences had a total of ten variable sites, four in the promoter, five in the coding region (three of which alter the amino acid encoded) and one in the 3' untranslated sequence. Sequence inspection suggests that some of the variants will have a functional impact on either level of expression or enzymatic activity. Tight linkage disequilibrium was detected between variants across the entire length of the gene (approximately 9 kb), and frequencies of different haplotypes were determined. The data provide an essential tool for studies of the possible contribution of genetic variation at the matrix metalloproteinase-9 locus to genetically determined susceptibility to a number of important diseases. The results also provide experimental data on the extent of linkage disequilibrium in the general population, which is yet to be resolved.     

Assessing whether an allele can account in part for a linkage signal: the Genotype-IBD Sharing Test (GIST)

To fine map genes, investigators often test for disease-marker association in chromosomal regions with evidence for linkage. Given a marker allele tentatively associated with disease, one would ask if this allele, or one in linkage disequilibrium (LD) with it, could account in part for the observed linkage signal. This question can be addressed by determining if families selected on the basis of the presence of the tentatively associated allele show stronger evidence of linkage as measured by increased allele sharing identical by descent (IBD) by affected family members. However, common selection strategies can be biased for or against linkage in the marker region, even given no disease-marker association. We define unbiased selection schemes and extend the definition to allow weighted selection on the basis of all genotyped family members. For affected-sibship data, we describe three genotype-based weight variables, corresponding to dominant, recessive, and additive models. We then introduce a test for association of a family weight variable with excess IBD sharing. This test allows us to determine if the linkage signal in a region can be attributed in part to the presence of a marker allele, either because of direct involvement in disease etiology or because of LD with a predisposing genetic variant. For samples of 500 affected sib pairs, the tests are powerful in detection of genotype-IBD sharing association, even for disease models with sib relative risk as low as lambda S=1.1, or when evidence for linkage is absent because of sampling variation. This makes our method a new tool for detecting linkage as well as association, especially in regions harboring a candidate gene. We have implemented these methods in the software package GIST (Genotype-IBD Sharing Test).     

Functional linear models for region-based association analysis

Regional association analysis is one of the most powerful tools for gene mapping because instead analysis of individual variants it simultaneously considers all variants in the region. Recent development of the models for regional association analysis involves functional data analysis approach. In the framework of this approach, genotypes of variants within region as well as their effects are described by continuous functions. Such approach allows us to use information about both linkage and linkage disequilibrium and reduce the influence of noise and/or observation errors. Here we define a functional linear mixed model to test association on independent and structured samples. We demonstrate how to test fixed and random effects of a set of genetic variants in the region on quantitative trait. Estimation of statistical properties of new methods shows that type I errors are in accordance with declared values and power is high especially for models with fixed effects of genotypes. We suppose that new functional regression linear models facilitate identification of rare genetic variants controlling complex human and animal traits. New methods are implemented in computer software FREGAT which is available for free download at http://mga.bionet.nsc.ru/soft/FREGAT/.     

Transmission test for linkage disequilibrium: the insulin gene region and insulin-dependent diabetes mellitus (IDDM).

A population association has consistently been observed between insulin-dependent diabetes mellitus (IDDM) and the "class 1" alleles of the region of tandem-repeat DNA (5'' flanking polymorphism [5''FP]) adjacent to the insulin gene on chromosome 11p. This finding suggests that the insulin gene region contains a gene or genes contributing to IDDM susceptibility. However, several studies that have sought to show linkage with IDDM by testing for cosegregation in affected sib pairs have failed to find evidence for linkage. As means for identifying genes for complex diseases, both the association and the affected-sib-pairs approaches have limitations. It is well known that population association between a disease and a genetic marker can arise as an artifact of population structure, even in the absence of linkage. On the other hand, linkage studies with modest numbers of affected sib pairs may fail to detect linkage, especially if there is linkage heterogeneity. We consider an alternative method to test for linkage with a genetic marker when population association has been found. Using data from families with at least one affected child, we evaluate the transmission of the associated marker allele from a heterozygous parent to an affected offspring. This approach has been used by several investigators, but the statistical properties of the method as a test for linkage have not been investigated. In the present paper we describe the statistical basis for this "transmission test for linkage disequilibrium" (transmission/disequilibrium test [TDT]). We then show the relationship of this test to tests of cosegregation that are based on the proportion of haplotypes or genes identical by descent in affected sibs. The TDT provides strong evidence for linkage between the 5''FP and susceptibility to IDDM. The conclusions from this analysis apply in general to the study of disease associations, where genetic markers are usually closely linked to candidate genes. When a disease is found to be associated with such a marker, the TDT may detect linkage even when haplotype-sharing tests do not.     

Two-trait-locus linkage analysis: a powerful strategy for mapping complex genetic traits.

Recent advances in molecular biology have provided geneticists with ever-increasing numbers of highly polymorphic genetic markers that have made possible linkage mapping of loci responsible for many human diseases. However, nearly all diseases mapped to date follow clear Mendelian, single-locus segregation patterns. In contrast, many common familial diseases such as diabetes, psoriasis, several forms of cancer, and schizophrenia are familial and appear to have a genetic component but do not exhibit simple Mendelian transmission. More complex models are required to explain the genetics of these important diseases. In this paper, we explore two-trait-locus, two-marker-locus linkage analysis in which two trait loci are mapped simultaneously to separate genetic markers. We compare the utility of this approach to standard one-trait-locus, one-marker-locus linkage analysis with and without allowance for heterogeneity. We also compare the utility of the two-trait-locus, two-marker-locus analysis to two-trait-locus, one-marker-locus linkage analysis. For common diseases, pedigrees are often bilineal, with disease genes entering via two or more unrelated pedigree members. Since such pedigrees often are avoided in linkage studies, we also investigate the relative information content of unilineal and bilineal pedigrees. For the dominant-or-recessive and threshold models that we consider, we find that two-trait-locus, two-marker-locus linkage analysis can provide substantially more linkage information, as measured by expected maximum lod score, than standard one-trait-locus, one-marker-locus methods, even allowing for heterogeneity, while, for a dominant-or-dominant generating model, one-locus models that allow for heterogeneity extract essentially as much information as the two-trait-locus methods. For these three models, we also find that bilineal pedigrees provide sufficient linkage information to warrant their inclusion in such studies. We also discuss strategies for assessing the significance of the two linkages assumed in two-trait-locus, two-marker-locus models.     

Convergent functional genomics: a Bayesian candidate gene identification approach for complex disorders

Identifying genes involved in complex neuropsychiatric disorders through classic human genetic approaches has proven difficult. To overcome that barrier, we have developed a translational approach called Convergent Functional Genomics (CFG), which cross-matches animal model microarray gene expression data with human genetic linkage data as well as human postmortem brain data and biological role data, as a Bayesian way of cross-validating findings and reducing uncertainty. Our approach produces a short list of high probability candidate genes out of the hundreds of genes changed in microarray datasets and the hundreds of genes present in a linkage peak chromosomal area. These genes can then be prioritized, pursued, and validated in an individual fashion using: (1) human candidate gene association studies and (2) cell culture and mouse transgenic models. Further bioinformatics analysis of groups of genes identified through CFG leads to insights into pathways and mechanisms that may be involved in the pathophysiology of the illness studied. This simple but powerful approach is likely generalizable to other complex, non-neuropsychiatric disorders, for which good animal models, as well as good human genetic linkage datasets and human target tissue gene expression datasets exist.     

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.     

Gene set analysis of genome-wide association studies: methodological issues and perspectives

Recent studies have demonstrated that gene set analysis, which tests disease association with genetic variants in a group of functionally related genes, is a promising approach for analyzing and interpreting genome-wide association studies (GWAS) data. These approaches aim to increase power by combining association signals from multiple genes in the same gene set. In addition, gene set analysis can also shed more light on the biological processes underlying complex diseases. However, current approaches for gene set analysis are still in an early stage of development in that analysis results are often prone to sources of bias, including gene set size and gene length, linkage disequilibrium patterns and the presence of overlapping genes. In this paper, we provide an in-depth review of the gene set analysis procedures, along with parameter choices and the particular methodology challenges at each stage. In addition to providing a survey of recently developed tools, we also classify the analysis methods into larger categories and discuss their strengths and limitations. In the last section, we outline several important areas for improving the analytical strategies in gene set analysis.     

Evaluation of candidate genes in the absence of positional information: a poor bet on a blind dog!

More than 350 inherited diseases have been reported in dogs and at least 50% of them have human counterparts. To remove the diseases from dog breeds and to identify canine models for human diseases, it is necessary to find the mutations underlying them. To this end, two methods have been used: the functional candidate gene approach and linkage analysis. Here we present an evaluation of these in canine retinal diseases, which have been the subject of a large number of molecular genetic studies, and we show the contrasting outcomes of these approaches when dealing with genetically heterogeneous diseases. The candidate gene approach has led to 377 published results with 23 genes. Most of the results (66.6%) excluded the presence of a mutation in a gene or its coding region, while only 3.4% of the results identified the mutation causing the disease. On the other hand, five linkage analysis studies have been done on retinal diseases, resulting in three identified mutations and two mapped disease loci. Mapping studies have relied on dog research colonies. If this favorable application of linkage analysis can be extended to dogs in the pet population, success in identifying canine mutations could increase, with advantages to veterinary and human medicine.     

Polymorphic GGC repeats in the androgen receptor gene are associated with hereditary and sporadic prostate cancer risk

Androgen receptor (AR) has long been hypothesized to play an important role in prostate cancer etiology. Two trinucleotide repeat polymorphisms (CAG and GGC repeats in exon 1 of the AR gene) have been investigated as risk factors for prostate cancer in several studies. However, the results are inconclusive, probably because of the variations of study designs, characteristics of study samples, and choices of analytical methods. In this study, we evaluated evidence for linkage and association between the two AR repeats and prostate cancer by using the following comprehensive approaches: (1) a combination of linkage and association studies, (2) a test for linkage by parametric analysis and the male-limited X-linked transmission/disequilibrium test (XLRC-TDT), (3) a test for association by using both population-based and family-based tests, and (4) a study of both hereditary and sporadic cases. A positive but weak linkage score (HLOD=0.49, P=0.12) was identified in the AR region by parametric analysis; however, stronger evidence for linkage in the region, especially at the GGC locus, was observed in the subset of families whose proband had < or = 16 GGC repeats (HLOD=0.70, P=0.07) or by using XLRC-TDT ( z'=2.65, P=0.008). Significantly increased frequencies of the < or = 16 GGC repeat alleles in 159 independent hereditary cases (71%) and 245 sporadic cases (68%) cases compared with 211 controls (59%) suggested that GGC repeats were associated with prostate cancer ( P=0.02). Evidence for the association between the < or = 16 GGC repeats and prostate cancer risk was stronger with XLRC-TDT ( z'=2.66, P=0.007). No evidence for association between the CAG repeats and prostate cancer risk was observed. The consistent results from both linkage and association studies strongly implicate the GGC repeats in the AR as a prostate cancer susceptibility gene. Further studies on this polymorphism in other independent data sets and functional analysis of the GGC repeat length on AR activity are warranted.