Parametric Linkage Analysis and Disequilibrium Methods to Identify Loci for Complex Disease

A two‐step process was used to find loci contributing to the qualitative disease phenotype in the Genetic Analysis Workshop (GAW) 12 simulated data. The first step used parametric linkage analysis with a limited number of dominant and recessive models to detect linkage to chromosomal regions. Subsequently, a subset of the simulated biallelic sequence polymorphisms was used for transmission/disequilibrium tests and to build haplotypes to fine map the disease‐predisposing polymorphism(s). A haplotype, strongly associated with the disease phenotype whose proximal end was within 39 base pairs of the functional allele for simulated major gene 6, was identified in the isolated population. © 2001 Wiley‐Liss, Inc.     

Identifying Susceptibility Genes Using Linkage and Linkage Disequilibrium Analysis in Large Pedigrees

Linkage and linkage disequilibrium tests are powerful tools for mapping complex disease genes. We investigated two approaches to identifying markers associated with disease. One method applied linkage analysis and then linkage disequilibrium tests to markers within linked regions. The other method looked for linkage disequilibrium with disease using all markers. Additionally, we investigated using Simes’ test to combine p‐values from linkage disequilibrium tests for nearby markers. We applied both approaches to all replicates of the Genetic Analysis Workshop 12 problem 2 isolated population data set. We reported results from the 25^th replicate as if it were a real problem and assessed the power of our methods using all replicates. Using all replicates, we found that testing all markers for linkage disequilibrium with disease was more powerful than identifying markers that were in linkage with disease and then testing markers within those regions for linkage disequilibrium with the implementations that we chose. Using Simes’ test to combine p‐values for linkage disequilibrium tests on correlated markers seemed to be of marginal value. © 2001 Wiley‐Liss, Inc.     

Population Based Linkage Disequilibrium Mapping of QTL: An Application to Simulated Data in an Isolated Population

Despite successes in mapping and cloning genes involved in rare Mendelian diseases, genetic dissection of quantitative traits into single Mendelian factors still remains a challenging task. As the dense map of single nucleotide polymorphism (SNP) markers becomes available in the near future, linkage disequilibrium (LD) mapping will become one of major tools for mapping and identifying quantitative trait loci (QTL). In this report, we present a population‐based linkage disequilibrium mapping of QTL. This method unifies the analysis of mapping QTL in humans and in model organisms and can be used for randomly sampled individuals. The proposed method is applied to search for polymorphism sites within the candidate genes 2 and 6, which influence quantitative traits Q1 and Q2 or Q5, in a simulated data set in an isolated population. © 2001 Wiley‐Liss, Inc.     

Linkage Disequilibrium Structure and Its Impact on the Localization of a Candidate Functional Mutation

We have used the unblinded MG1/Q1 Genetic Analysis Workshop 12 simulated data as a model system for investigating the use of linkage disequilibrium structure and simple genotype‐phenotype associations to identify candidate functional mutations within a gene of interest. Analysis of the pattern of pair‐wise linkage disequilibrium indicated three groups of single‐nucleotide polymorphisms for which the linkage disequilibrium was high between sites within a group, but lower between sites of different groups. Using linear regression to predict levels of the trait Q1 showed that the known functional site, 5782, was usually not the best genetic predictor of Q1, but sites belonging to the same group as 5782 (i.e., group 2) were always included in the prediction model. In 49 out of the 50 replicates, the functional site was not the best predictor of the trait. Finally, more detailed analyses demonstrate that the relationship between the adjusted R² for the marker in the prediction model and its disequilibrium with 5782 was linear with the intercept at the origin and terminating at the R² value for the known functional mutation when the disequilibrium is maximal. These data indicate that simple association studies will not identify the functional mutation, but rather will identify candidate functional mutations that are in very tight linkage disequilibrium with the functional mutation. © 2001 Wiley‐Liss, Inc.     

Using Step‐Wise Linear Regression to Detect “Functional” Sequence Variants: Application to Simulated Data

Step‐wise linear regression was used to detect the “functional” sequence variant in gene 6 responsible for phenotypic variation in traits Q1 and Q2. Prior to analysis, single‐nucleotide polymorphisms (SNPs) that were in complete or near complete linkage disequilibrium were binned. In total, we identified 11 separate alleles (or allelic bins). Analyses were performed on all 50 replicates. The “functional” allele variant in gene 6 (at position 5782) accounted for 24% of the variation in Q1 and 11% of the variation in Q2. We detected a significant association between this SNP and Q1 in 90% of the replicates (i.e., in 45 of 50 replicates) and between this SNP and Q2 in 78% of the replicates. Although significant associations were also observed with some nonfunctional SNPs, our results nevertheless suggest that simple step‐wise regression may play a useful role in analyzing sequence data. Some additional extensions to this approach are suggested. © 2001 Wiley‐Liss, Inc.     

Analysis of Single Nucleotide Polymorphisms in Candidate Genes Using the Pedigree Disequilibrium Test

The pedigree disequilibrium test (PDT) has been proposed recently as a test for association in general pedigrees [Martin et al., Am J Hum Genet 67:146–54, 2000]. The Genetic Analysis Workshop (GAW) 12 simulated data, with many extended pedigrees, is an example the type of data to which the PDT is ideally suited. In replicate 42 from the general population the PDT correctly identifies candidate genes 1, 2, and 6 as containing single nucleotide polymorphisms (SNPs) that arc significantly associated with the disease. We also applied the truncated product method (TPM) [Zaykin et al., Genet Epidemiol, in press] to combine p‐values in overlapping windows across the genes. Our results show that the TPM is helpful in identifying significant SNPs as well as removing spurious false positives. Our results indicate that, using the PDT, functional disease‐associated SNPs can be successfully identified with a dense map of moderately polymorphic SNPs. © 2001 Wiley‐Liss, Inc.     

Computational Methods for Single‐Point and Multipoint Analysis of Genetic Variants Associated with a Simulated Complex Disorder in a General Population

Several techniques for association analysis have been applied to simulated genetic data for a general population. We describe and compare the performance of three single‐point methods and two multipoint approaches rooted in machine learning and data mining. © 2001 Wiley‐Liss, Inc.     

Detection of Parent‐of‐Origin Effects for Atopy by Model‐Free and Model‐Based Linkage Analyses

Parent‐of‐origin effects for atopy were investigated by a model‐free affected sib‐pair (ASP) method and two model‐based approaches in the Busselton nuclear families. Among the regions showing potential linkages to atopy by the ASP method, a significant excess of paternal allele sharing as compared with maternal allele sharing was observed for a cluster of three markers on chromosome 13. The two model‐based methods, which specify either sex‐specific recombination rates or different penetrances for heterozygotes according to the parental origin of disease allele (imprinting), led to the same results, both suggesting a paternal effect. Thus, these two ways of modelling parent‐of‐origin effects appear equivalent in nuclear family data. Further simulations arc needed to investigate whether the mechanisms underlying parent‐of‐origin effects can be distinguished in larger pedigree structures. © 2001 Wiley‐Liss, Inc.     

A general and accurate approach for computing the statistical power of the transmission disequilibrium test for complex disease genes.

Transmission disequilibrium test (TDT) is a nuclear family-based analysis that can test linkage in the presence of association. It has gained extensive attention in theoretical investigation and in practical application; in both cases, the accuracy and generality of the power computation of the TDT are crucial. Despite extensive investigations, previous approaches for computing the statistical power of the TDT are neither accurate nor general. In this paper, we develop a general and highly accurate approach to analytically compute the power of the TDT. We compare the results from our approach with those from several other recent papers, all against the results obtained from computer simulations. We show that the results computed from our approach are more accurate than or at least the same as those from other approaches. More importantly, our approach can handle various situations, which include (1) families that consist of one or more children and that have any configuration of affected and nonaffected sibs; (2) families ascertained through the affection status of parent(s); (3) any mixed sample with different types of families in (1) and (2); (4) the marker locus is not a disease susceptibility locus; and (5) existence of allelic heterogeneity. We implement this approach in a user-friendly computer program: TDT Power Calculator. Its applications are demonstrated. The approach and the program developed here should be significant for theoreticians to accurately investigate the statistical power of the TDT in various situations, and for empirical geneticists to plan efficient studies using the TDT.     

On the Detection of Linkage in Multiple Data Sets: A Comparison of Various Statistical Approaches

We contrast the pooling of multiple data sets with the compound HLOD (HLODC) and the posterior probability of linkage (PPL), two approaches that have been shown to have more power in the presence of genetic heterogeneity. We also propose and evaluate several multipoint extensions. © 2001 Wiley‐Liss, Inc.     

Transmission/Disequilibrium Test Analysis of Total Serum IgE Levels in the Hutterite and Collaborative Study on the Genetics of Asthma Data Sets

The Hutterite and Collaborative Study on the Genetics of Asthma data sets provided by Genetic Analysis Workshop 12 were analyzed using a regression‐based transmission/disequilibrium test that assesses linkage between a marker locus and quantitative trait locus when allelic association is present, as proposed by George et al. [Am J Hum Genet 65:236–45, 1999]. Because the same marker set and analytical technique was used, the results from these data sets are amenable for comparison. Statistically significant results common to both data sets were found on chromosomes 1 and 3. A noteworthy result, significant at p < 10^‐4, was detected on chromosome 18 in the Hutterites. © 2001 Wiley‐Liss, Inc.     

Linkage Analysis of Quantitative Traits in Randomly Ascertained Pedigrees: Comparison of Penetrance‐Based and Variance Component Analysis

Penetrance‐based linkage analysis and variance component linkage analysis are two methods that are widely used to localize genes influencing quantitative traits. Using computer programs PAP and SOLAR as representative software implementations, we have conducted an empirical comparison of both methods' power to map quantitative trait loci in extended, randomly ascertained pedigrees, using simulated data. Two‐point linkage analyses were conducted on several quantitative traits of different genetic and environmental etiology using both programs, and the lod scores were compared. The two methods appear to have similar power when the underlying quantitative trait locus is diallelic, with one or the other method being slightly more powerful depending on the characteristics of the quantitative trait and the quantitative trait locus. In the case of a multiallelic quantitative trait locus, however, the variance component approach has much greater power. These findings suggest that one should give careful thought to the likely allelic architecture of the quantitative trait to be analyzed when choosing between these two analytical approaches. It may be the case in general that linkage methods which explicitly or implicitly rely on the assumption of a diallelic trait locus fare poorly when this assumption is incorrect. © 2001 Wiley‐Liss, Inc.     

Family‐Based Tests of Association in the Presence and Absence of Known Linkage

Correlation among sibling marker genotypes may invalidate the results of family‐based tests of association in the presence of linkage. We apply an empirical variance estimation method, which is implemented in the program package FBAT, on Caucasian families with asthma in the presence and absence of linkage and compare the results with those obtained using the TDT (TDTEX‐PAIRS) on the same data sets. Our results indicate that both tests generally perform comparably in either setting. © 2001 Wiley‐Liss, Inc.     

Association and Linkage for Age at Onset of a Common Oligogenic Disease Using Genetic Variance Component Models

The aims of our analysis were: (1) to investigate association of single nucleotide polymorphisms (SNPs) and other covariates with age at onset in the simulated Genetic Analysis Workshop (GAW) 12 general population data, and (2) to use the polygenic random effects estimated during model fitting (sigma squared A random effects) as input to a Haseman‐Elston linkage analysis. The association analyses used genetic variance component models in a generalized linear mixed models framework and were fitted using Gibbs sampling. This method successfully detected the only three sequenced genes that were also major genes. The single‐point linkage analysis used all markers provided. Regions of linkage were found close to all four of the sites of major genes that explained a non‐trivial component of the variance of age at onset. In all four cases the linkage peak fell within 5 cM of the true location. In three cases the peak significance was p < 0.01. © 2001 Wiley‐Liss, Inc.     

iGWAS: Integrative Genome‐Wide Association Studies of Genetic and Genomic Data for Disease Susceptibility Using Mediation Analysis

Genome‐wide association studies (GWAS) have been a standard practice in identifying single nucleotide polymorphisms (SNPs) for disease susceptibility. We propose a new approach, termed integrative GWAS (iGWAS) that exploits the information of gene expressions to investigate the mechanisms of the association of SNPs with a disease phenotype, and to incorporate the family‐based design for genetic association studies. Specifically, the relations among SNPs, gene expression, and disease are modeled within the mediation analysis framework, which allows us to disentangle the genetic effect on a disease phenotype into two parts: an effect mediated through a gene expression (mediation effect, ME) and an effect through other biological mechanisms or environment‐mediated mechanisms (alternative effect, AE). We develop omnibus tests for the ME and AE that are robust to underlying true disease models. Numerical studies show that the iGWAS approach is able to facilitate discovering genetic association mechanisms, and outperforms the SNP‐only method for testing genetic associations. We conduct a family‐based iGWAS of childhood asthma that integrates genetic and genomic data. The iGWAS approach identifies six novel susceptibility genes (MANEA, MRPL53, LYCAT, ST8SIA4, NDFIP1, and PTCH1) using the omnibus test with false discovery rate less than 1%, whereas no gene using SNP‐only analyses survives with the same cut‐off. The iGWAS analyses further characterize that genetic effects of these genes are mostly mediated through their gene expressions. In summary, the iGWAS approach provides a new analytic framework to investigate the mechanism of genetic etiology, and identifies novel susceptibility genes of childhood asthma that were biologically meaningful.     

Flexible and Robust Methods for Rare‐Variant Testing of Quantitative Traits in Trios and Nuclear Families

Most rare‐variant association tests for complex traits are applicable only to population‐based or case‐control resequencing studies. There are fewer rare‐variant association tests for family‐based resequencing studies, which is unfortunate because pedigrees possess many attractive characteristics for such analyses. Family‐based studies can be more powerful than their population‐based counterparts due to increased genetic load and further enable the implementation of rare‐variant association tests that, by design, are robust to confounding due to population stratification. With this in mind, we propose a rare‐variant association test for quantitative traits in families; this test integrates the QTDT approach of Abecasis et al. [Abecasis et al., 2000a ] into the kernel‐based SNP association test KMFAM of Schifano et al. [Schifano et al., 2012 ]. The resulting within‐family test enjoys the many benefits of the kernel framework for rare‐variant association testing, including rapid evaluation of P‐values and preservation of power when a region harbors rare causal variation that acts in different directions on phenotype. Additionally, by design, this within‐family test is robust to confounding due to population stratification. Although within‐family association tests are generally less powerful than their counterparts that use all genetic information, we show that we can recover much of this power (although still ensuring robustness to population stratification) using a straightforward screening procedure. Our method accommodates covariates and allows for missing parental genotype data, and we have written software implementing the approach in R for public use.     

A new measure of the effective number of tests,a practical tool for comparing families of non‐independent significance tests

p‐Values from tests of significance can be combined using the ?idák correction (or the closely related Bonferroni correction) or Fisher's method, but both these methods require that the p‐values combined be independent when all null hypotheses tested are true. In this paper adjustments to these methods are proposed, using a new eigenvalue‐based measure of the effective number of independent tests to which the actual tests performed are equivalent, and are compared with adjustments proposed by previous authors. The adjusted methods are evaluated using a sample of 726 Alzheimer's disease (AD) cases and 707 group‐matched controls, genotyped at 84,975 single‐nucleotide polymorphism loci in 2,000 randomly chosen genes. The tests for genetic association with AD at loci within each gene are combined. The number of loci tested per gene varies from 2 to 994. The adjusted combined p‐values agree well with the significance of the combined p‐values determined empirically by random permutation of the data (?idák correction: r=0.990; Fisher's method: r=0.994). This indicates that the combined p‐values can be used to assess the relative strength of evidence for association of these genes with AD. The adjustment proposed here is a refinement of that of Nyholt ([2004] Am. J. Hum. Genet. 74:765–769), giving improved agreement with the results of random permutation. The improvement obtained is similar to that given by the refinement proposed by Li and Ji ([2005] Heredity 95:221–227). It is concluded that the concept of an effective number of tests is a valid approximation that allows p‐values to be combined in a highly informative way. Genet. Epidemiol. 33:559–568, 2009. © 2009 Wiley‐Liss, Inc.     

Relatedness mapping and tracts of relatedness for genome‐wide data in the presence of linkage disequilibrium

Estimates of relatedness have several applications such as the identification of relatives or in identifying disease related genes through identity by descent (IBD) mapping. Here we present a new method for identifying IBD tracts among individuals from genome‐wide single nucleotide polymorphisms data. We use a continuous time Markov model where the hidden states are the number of alleles shared IBD between pairs of individuals at a given position. In contrast to previous methods, our method accurately accounts for linkage disequilibrium using pairwise haplotype probabilities. The method provides a map of the local relatedness along the genome. We illustrate the potential of the method for mapping disease genes on a real data set, and show that the method has the potential to map causative disease mutations using only a handful of affected individuals. The new IBD mapping method provides considerable improvement in mapping power in natural populations compared to standard association mapping methods. Genet. Epidemiol. 2009. © 2008 Wiley‐Liss, Inc.