Detecting susceptibility genes in case-control studies using set association

Complex diseases are generally caused by intricate interactions of multiple genes and environmental factors. Most available linkage and association methods are developed to identify individual susceptibility genes assuming a simple disease model blind to any possible gene - gene and gene - environmental interactions. We used a set association method that uses single-nucleotide polymorphism markers to locate genetic variation responsible for complex diseases in which multiple genes are involved. Here we extended the set association method from bi-allelic to multiallelic markers. In addition, we studied the type I error rates and power for both approaches using simulations based on the coalescent process. Both bi-allelic set association (BSA) and multiallelic set association (MSA) tests have the correct type I error rates. In addition, BSA and MSA can have more power than individual marker analysis when multiple genes are involved in a complex disease. We applied the MSA approach to the simulated data sets from Genetic Analysis Workshop 13. High cholesterol level was used as the definitive phenotype for a disease. MSA failed to detect markers with significant linkage disequilibrium with genes responsible for cholesterol level. This is due to the wide spacing between the markers and the lack of association between the marker loci and the simulated phenotype.     

Joint linkage and linkage disequilibrium mapping in natural populations

A new strategy for studying the genome structure and organization of natural populations is proposed on the basis of a combined analysis of linkage and linkage disequilibrium using known polymorphic markers. This strategy exploits a random sample drawn from a panmictic natural population and the open-pollinated progeny of the sample. It is established on the principle of gene transmission from the parental to progeny generation during which the linkage between different markers is broken down due to meiotic recombination. The strategy has power to simultaneously capture the information about the linkage of the markers (as measured by recombination fraction) and the degree of their linkage disequilibrium created at a historic time. Simulation studies indicate that the statistical method implemented by the Fisher-scoring algorithm can provide accurate and precise estimates for the allele frequencies, recombination fractions, and linkage disequilibria between different markers. The strategy has great implications for constructing a dense linkage disequilibrium map that can facilitate the identification and positional cloning of the genes underlying both simple and complex traits.     

An analysis of linkage disequilibrium in the interleukin-1 gene cluster, using a novel grouping method for multiallelic markers.

In population- and family-based association studies, it is useful to have some knowledge of the patterns of linkage disequilibrium that exist between markers in candidate regions. When such studies are carried out with multiallelic markers, it is often convenient to group the alleles into a biallelic system, for analysis. In this study, we specifically examined the interleukin-1 (IL-1) gene cluster on chromosome 2, a region containing candidates for many inflammatory and autoimmune disorders. Data were collected on eight markers, four of which were multiallelic. Using these data, we investigated the effect of three allele-grouping strategies, including a novel method, on the detection of linkage disequilibrium. The novel approach, termed the "delta method," measures the deviation from the expected haplotype frequencies under linkage equilibrium, for each allelic combination. This information is then used to group the alleles, in an attempt to avoid the grouping together of alleles at one locus that are in opposite disequilibrium with the same allele at the second locus. The estimate haplotype frequencies (EH) program was used to estimate haplotype frequencies and the disequilibrium measure. In our data it was found that the delta method compared well with the other two strategies. Using this method, we found that there was a reasonable correlation between disequilibrium and physical distance in the region (r=-.540, P=.001, one-tailed). We also identified a common, eight-locus haplotype of the IL-1 gene cluster.     

Selecting tagging SNPs for association studies using power calculations from genotype data

Recent studies have indicated that linkage disequilibrium (LD) between single nucleotide polymorphism (SNP) markers can be used to derive a reduced set of tagging SNPs (tSNPs) for genetic association studies. Previous strategies for identifying tSNPs have focused on LD measures or haplotype diversity, but the statistical power to detect disease-associated variants using tSNPs in genetic studies has not been fully characterized. We propose a new approach of selecting tSNPs based on determining the set of SNPs with the highest power to detect association. Two-locus genotype frequencies are used in the power calculations. To show utility, we applied this power method to a large number of SNPs that had been genotyped in Caucasian samples. We demonstrate that a significant reduction in genotyping efforts can be achieved although the reduction depends on genotypic relative risk, inheritance mode and the prevalence of disease in the human population. The tSNP sets identified by our method are remarkably robust to changes in the disease model when small relative risk and additive mode of inheritance are employed. We have also evaluated the ability of the method to detect unidentified SNPs. Our findings have important implications in applying tSNPs from different data sources in association studies.     

Extent and distribution of linkage disequilibrium in three genomic regions

The positional cloning of genes underlying common complex diseases relies on the identification of linkage disequilibrium (LD) between genetic markers and disease. We have examined 127 polymorphisms in three genomic regions in a sample of 575 chromosomes from unrelated individuals of British ancestry. To establish phase, 800 individuals were genotyped in 160 families. The fine structure of LD was found to be highly irregular. Forty-five percent of the variation in disequilibrium measures could be explained by physical distance. Additional factors, such as allele frequency, type of polymorphism, and genomic location, explained <5% of the variation. Nevertheless, disequilibrium was occasionally detectable at 500 kb and was present for over one-half of marker pairs separated by <50 kb. Although these findings are encouraging for the prospects of a genomewide LD map, they suggest caution in interpreting localization due to allelic association.     

The detection of linkage disequilibrium between closely linked markers: RFLPs at the AI-CIII apolipoprotein genes.

Study of very closely linked DNA variants at various loci has frequently shown linkage disequilibrium. We studied three closely linked RFLPs at the apolipoprotein AI-CIII locus. Two variants detected by MspI and SstI were in strong linkage disequilibrium; but when conventional statistical tests were used, a third variant (PstI), located between the MspI and SstI markers, appeared to be in linkage equilibrium with these two "outside" markers. Similar discrepancies from the expected monotone relationship between physical distance and linkage disequilibrium have been reported by others. To investigate these discrepancies, the power to detect linkage disequilibrium was calculated. It could be shown that, for the gene frequencies encountered, very large sample sizes would be required to demonstrate negative (i.e., repulsion-phase) linkage disequilibrium. Such numbers are usually very difficult to attain in human studies. Failure to demonstrate linkage disequilibrium by conventional methods therefore does not imply its absence. Appropriate nomograms and tables are provided.     

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.     

Generalized T2 test for genome association studies

Recent progress in the development of single-nucleotide polymorphism (SNP) maps within genes and across the genome provides a valuable tool for fine-mapping and has led to the suggestion of genomewide association studies to search for susceptibility loci for complex traits. Test statistics for genome association studies that consider a single marker at a time, ignoring the linkage disequilibrium between markers, are inefficient. In this study, we present a generalized T2 statistic for association studies of complex traits, which can utilize multiple SNP markers simultaneously and considers the effects of multiple disease-susceptibility loci. This generalized T2 statistic is a corollary to that originally developed for multivariate analysis and has a close relationship to discriminant analysis and common measure of genetic distance. We evaluate the power of the generalized T2 statistic and show that power to be greater than or equal to those of the traditional chi2 test of association and a similar haplotype-test statistic. Finally, examples are given to evaluate the performance of the proposed T2 statistic for association studies using simulated and real data.     

A sib-pair regression model of linkage disequilibrium for quantitative traits

A multiple-regression model is described for the detection of linkage disequilibrium in quantitative trait loci. The model is developed for application to large numbers of single nucleotide polymorphism (SNP) markers genotyped on small nuclear families. Parental data are not required by the method, although it provides a direct means to test quantitative trait locus-marker allele association and to determine whether any such association is attributable to linkage disequilibrium or population admixture. Analytical expectations for the regression coefficients are derived, allowing direct interpretation of the parameter estimates. Simulation studies indicate a substantial improvement in power over classical linkage studies of sibling pairs and show the effects of population admixture on the model outcomes.     

Study of human SP-A,SP-B and SP-D loci: allele frequencies,linkage disequilibrium and heterozygosity in different races and ethnic groups

Background  

SP-A, SP-B, and SP-D are pulmonary surfactant proteins. Several linkage and association studies have been done using these genes as markers to locate pulmonary disease susceptibility genes, but few have studied the markers systematically in different ethnic groups. Here we studied eight markers in SP-A, SP-B, and SP-D genes in seven ethnic groups from three races (Caucasian, Black and Hispanic). We measured the similarity of the marker distribution among the ethnic groups in order to see whether people in different ethnic groups or races could be mixed together for linkage and association studies. To evaluate the usefulness of these markers, we estimated the informativeness of each marker loci in the seven ethnic groups by assessing their heterozygosity and PIC values. We also conducted linkage disequilibrium (LD) analysis to identify associated marker loci and to estimate the haplotype frequencies in each of the seven ethnic groups in an attempt to find valuable haplotypes so that the level of polymorphism of the "markers" could be increased.     

Joint linkage and linkage disequilibrium mapping of quantitative trait loci in natural populations

Linkage analysis and allelic association (also referred to as linkage disequilibrium) studies are two major approaches for mapping genes that control simple or complex traits in plants, animals, and humans. But these two approaches have limited utility when used alone, because they use only part of the information that is available for a mapping population. More recently, a new mapping strategy has been designed to integrate the advantages of linkage analysis and linkage disequilibrium analysis for genome mapping in outcrossing populations. The new strategy makes use of a random sample from a panmictic population and the open-pollinated progeny of the sample. In this article, we extend the new strategy to map quantitative trait loci (QTL), using molecular markers within the EM-implemented maximum-likelihood framework. The most significant advantage of this extension is that both linkage and linkage disequilibrium between a marker and QTL can be estimated simultaneously, thus increasing the efficiency and effectiveness of genome mapping for recalcitrant outcrossing species. Simulation studies are performed to test the statistical properties of the MLEs of genetic and genomic parameters including QTL allele frequency, QTL effects, QTL position, and the linkage disequilibrium of the QTL and a marker. The potential utility of our mapping strategy is discussed.     

Evaluating statistical significance in two-stage genomewide association studies

Genomewide association studies are being conducted to unravel the genetic etiology of complex human diseases. Because of cost constraints, these studies typically employ a two-stage design, under which a large panel of markers is examined in a subsample of subjects, and the most-promising markers are then examined in all subjects. This report describes a simple and efficient method to evaluate statistical significance for such genome studies. The proposed method, which properly accounts for the correlated nature of polymorphism data, provides accurate control of the overall false-positive rate and is substantially more powerful than the standard Bonferroni correction, especially when the markers are in strong linkage disequilibrium.     

Detection of genes for ordinal traits in nuclear families and a unified approach for association studies

There is growing interest in genomewide association analysis using single-nucleotide polymorphisms (SNPs), because traditional linkage studies are not as powerful in identifying genes for common, complex diseases. Tests for linkage disequilibrium have been developed for binary and quantitative traits. However, since many human conditions and diseases are measured in an ordinal scale, methods need to be developed to investigate the association of genes and ordinal traits. Thus, in the current report we propose and derive a score test statistic that identifies genes that are associated with ordinal traits when gametic disequilibrium between a marker and trait loci exists. Through simulation, the performance of this new test is examined for both ordinal traits and quantitative traits. The proposed statistic not only accommodates and is more powerful for ordinal traits, but also has similar power to that of existing tests when the trait is quantitative. Therefore, our proposed statistic has the potential to serve as a unified approach to identifying genes that are associated with any trait, regardless of how the trait is measured. We further demonstrated the advantage of our test by revealing a significant association (P = 0.00067) between alcohol dependence and a SNP in the growth-associated protein 43.     

General equations for Pt, Ps, and the power of the TDT and the affected-sib-pair test

Several equations are highlighted here, whose algebraic symmetries and generality make them very useful for understanding and comparing the properties of the transmission disequilibrium test (TDT) and affected sib-pair test. Methods using the equations are also presented that yield precise estimates of sample sizes needed for genome scans or for testing a single candidate gene, and these power methods are shown to compare favorably with alternative approaches recently described by Knapp (1999) and by Tu and Whittemore (1999). Simple relationships are also noted that summarize the relative sample sizes required for equivalent power to detect association by the TDT or case-control designs. As single-nucleotide polymorphism (SNP) maps revolutionize the search for disease-causing genes, the equations should prove useful for planning and evaluating studies of linkage and association across a broad range of possible disease models and relationships between markers and linked disease loci.     

Is there still a need for candidate gene approaches in the era of genome-wide association studies?

Most genetic variants associated with complex diseases in humans are believed to have a small impact on risk. With traditional candidate gene/pathway approaches several associations with disease risk could be identified. However, now that genome-wide association studies are feasible, the question arises if there is still a need for these approaches. By using HapMap data, we evaluated to which extent commercially available microarrays cover, through linkage disequilibrium, all currently known genes and biological processes in different populations. Furthermore, we estimated the power to detect an association with any specific SNP. Our study shows that coverage of individual genes and pathways by current commercial genotyping platforms is satisfactory for the vast majority of RefSeq gene regions. However, depending on the gene or the population, there may still be a need for candidate gene approaches, especially when looking at polymorphisms with low allele frequencies.     

Estimation of single nucleotide polymorphism allele frequency in DNA pools by using Pyrosequencing

Positional cloning of genes underlying complex diseases, such as type 2 diabetes mellitus (T2DM), typically follows a two-tiered process in which a chromosomal region is first identified by genome-wide linkage scanning, followed by association analyses using densely spaced single nucleotide polymorphic markers to identify the causal variant(s). The success of genome-wide single nucleotide polymorphism (SNP) detection has resulted in a vast number of potential markers available for use in the construction of such dense SNP maps. However, the cost of genotyping large numbers of SNPs in appropriately sized samples is nearly prohibitive. We have explored pooled DNA genotyping as a means of identifying differences in allele frequency between pools of individuals with T2DM and unaffected controls by using Pyrosequencing technology. We found that allele frequencies in pooled DNA were strongly correlated with those in individuals (r=0.99, P<0.0001) across a wide range of allele frequencies (0.02-0.50). We further investigated the sensitivity of this method to detect allele frequency differences between contrived pools, also over a wide range of allele frequencies. We found that Pyrosequencing was able to detect an allele frequency difference of less than 2% between pools, indicating that this method may be sensitive enough for use in association studies involving complex diseases where a small difference in allele frequency between cases and controls is expected.     

Normative genetic profiles of RAAS pathway gene polymorphisms in North Indian and South Indian populations

Population-based genetic association studies, popularly known as case-control studies, have continued to be the most preferred method for deciphering the genetic basis of various complex diseases, even in the post-human genome sequencing era. However, interpopulation differences in allele, genotype, and haplotype frequencies and linkage disequilibrium patterns lead to inconsistent results in candidate gene association studies. Therefore, for any meaningful disease association study, knowledge of the normative genetic background of the baseline population is a prerequisite. In addition, such genetic variation data also provide a ready-made menu of allele frequencies and linkage disequilibrium patterns of various polymorphisms in specific candidate genes in a particular population, which is a useful reference for further genetic association studies. Such genetic variation data are lacking for the Indian population, which represents about one-sixth of the world's population. In the present study we have reported the allele, genotype, and haplotype frequencies, Hardy-Weinberg equilibrium status, and linkage disequilibrium patterns of 12 polymorphisms in six candidate genes from the renin-angiotensin-aldosterone system among Indians. Because of their different history of origin, the Indian population is broadly divided into two subpopulations: North Indians (Caucasian Europeans) and South Indians (Dravidians). Considering this well-documented difference in gene pools, we have presented a comparative account of the normative genetic data of North Indian and South Indian populations with at least four individuals of urban and suburban origin from each of the representative states of northern and southern India.     

Designing Genome-Wide Association Studies: Sample Size, Power, Imputation, and the Choice of Genotyping Chip

Genome-wide association studies are revolutionizing the search for the genes underlying human complex diseases. The main decisions to be made at the design stage of these studies are the choice of the commercial genotyping chip to be used and the numbers of case and control samples to be genotyped. The most common method of comparing different chips is using a measure of coverage, but this fails to properly account for the effects of sample size, the genetic model of the disease, and linkage disequilibrium between SNPs. In this paper, we argue that the statistical power to detect a causative variant should be the major criterion in study design. Because of the complicated pattern of linkage disequilibrium (LD) in the human genome, power cannot be calculated analytically and must instead be assessed by simulation. We describe in detail a method of simulating case-control samples at a set of linked SNPs that replicates the patterns of LD in human populations, and we used it to assess power for a comprehensive set of available genotyping chips. Our results allow us to compare the performance of the chips to detect variants with different effect sizes and allele frequencies, look at how power changes with sample size in different populations or when using multi-marker tags and genotype imputation approaches, and how performance compares to a hypothetical chip that contains every SNP in HapMap. A main conclusion of this study is that marked differences in genome coverage may not translate into appreciable differences in power and that, when taking budgetary considerations into account, the most powerful design may not always correspond to the chip with the highest coverage. We also show that genotype imputation can be used to boost the power of many chips up to the level obtained from a hypothetical “complete” chip containing all the SNPs in HapMap. Our results have been encapsulated into an R software package that allows users to design future association studies and our methods provide a framework with which new chip sets can be evaluated.     

The HLA system and the analysis of multifactorial genetic disease

The human leukocyte antigen (HLA) system comprises closely linked genes controlling highly polymorphic proteins involved in the presentation of peptides to the T-cell receptor. Specific alleles at HLA loci are associated with diseases, often those suspected to be of autoimmune aetiology. Many of these associations result from linkage disequilibrium between the HLA gene studied and other HLA genes or non-HLA gebes close by. Owing to its high level of polymorphism and its candidate role in many diseases, HLA was the first system used in many techniques of genetic mapping, such as affected-sib-pair analysis and association (linkage disequilibrium) studies. Much remains unknown about the reasons why diseases are associated with HLA. Experience gained from HLA has, however, shown how other loci involved in complex traits can be identified by studying families with multiple affected cases or sib pairs, followed by linkage-disequilibrium mapping and then analysis of candidate genes.     

Will the real disease gene please stand up?

A common dilemma arising in linkage studies of complex genetic diseases is the selection of positive signals, their follow-up with association studies and discrimination between true and false positive results. Several strategies for overcoming these issues have been devised. Using the Genetic Analysis Workshop 14 simulated dataset, we aimed to apply different analytical approaches and evaluate their performance in discerning real associations. We considered a) haplotype analyses, b) different methods adjusting for multiple testing, c) replication in a second dataset, and d) exhaustive genotyping of all markers in a sufficiently powered, large sample group. We found that haplotype-based analyses did not substantially improve over single-point analysis, although this may reflect the low levels of linkage disequilibrium simulated in the datasets provided. Multiple testing correction methods were in general found to be over-conservative. Replication of nominally positive results in a second dataset appears to be less stringent, resulting in the follow-up of false positives. Performing a comprehensive assay of all markers in a large, well-powered dataset appears to be the most effective strategy for complex disease gene identification.