Classifying disease chromosomes arising from multiple founders, with application to fine-scale haplotype mapping

Expanding the Spectrum of Founder Mutations Causing Isolated Gonadotropin-Releasing Hormone Deficiency

CONTEXT

Loss of function (LoF) mutations in more than 20 genes are now known to cause isolated GnRH deficiency (IGD) in humans. Most causal IGD mutations are typically private, ie, limited to a single individual/pedigree. However, somewhat paradoxically, four IGD genes (GNRH1, TAC3, PROKR2, and GNRHR) have been shown to harbor LoF founder mutations that are shared by multiple unrelated individuals. It is not known whether similar founder mutations occur in other IGD genes.

OBJECTIVE

The objective of the study was to determine whether shared deleterious mutations in IGD-associated genes represent founder alleles.

SETTING

This study was an international collaboration among academic medical centers.

METHODS

IGD patients with shared mutations, defined as those documented in three or more unrelated probands in 14 IGD-associated genes, were identified from various academic institutions, the Human Gene Mutation Database, and literature reports by other international investigators. Haplotypes of single-nucleotide polymorphisms and short tandem repeats surrounding the mutations were constructed to assess genetic ancestry.

RESULTS

A total of eight founder mutations in five genes, GNRHR (Q106R, R262Q, R139H), TACR3 (W275X), PROKR2 (R85H), FGFR1 (R250Q, G687R), and HS6ST1 (R382W) were identified. Most founder alleles were present at low frequency in the general population. The estimated age of these mutant alleles ranged from 1925 to 5600 years and corresponded to the time of rapid human population expansion.

CONCLUSIONS

We have expanded the spectrum of founder alleles associated with IGD to a total of eight founder mutations. In contrast to the approximately 9000-year-old PROKR2 founder allele that may confer a heterozygote advantage, the rest of the founder alleles are relatively more recent in origin, in keeping with the timing of recent human population expansion and any selective heterozygote advantage of these alleles requires further evaluation.

Efficient identification of context dependent subgroups of risk from genome-wide association studies

We have developed a modified Patient Rule-Induction Method (PRIM) as an alternative strategy for analyzing representative samples of non-experimental human data to estimate and test the role of genomic variations as predictors of disease risk in etiologically heterogeneous sub-samples. A computational limit of the proposed strategy is encountered when the number of genomic variations (predictor variables) under study is large (>500) because permutations are used to generate a null distribution to test the significance of a term (defined by values of particular variables) that characterizes a sub-sample of individuals through the peeling and pasting processes. As an alternative, in this paper we introduce a theoretical strategy that facilitates the quick calculation of Type I and Type II errors in the evaluation of terms in the peeling and pasting processes carried out in the execution of a PRIM analysis that are under-estimated and non-existent, respectively, when a permutation-based hypothesis test is employed. The resultant savings in computational time makes possible the consideration of larger numbers of genomic variations (an example genome-wide association study is given) in the selection of statistically significant terms in the formulation of PRIM prediction models.

Association mapping by generalized linear regression with density-based haplotype clustering

Haplotypes of closely linked single-nucleotide polymorphisms (SNPs) potentially offer greater power than individual SNPs to detect association between genetic variants and disease. We present a novel approach for association mapping in which density-based clustering of haplotypes reduces the dimensionality of the general linear model (GLM)-based score test of association implemented in the HaploStats software (Schaid et al. [2002] Am. J. Hum. Genet. 70:425-434). A flexible haplotype similarity score, a generalization of previously used measures, forms the basis, for grouping haplotypes of probable recent common ancestry. All haplotypes within a cluster are assigned the same regression coefficient within the GLM, and evidence for association is assessed with a score statistic. The approach is applicable to both binary and continuous trait data, and does not require prior phase information. Results of simulation studies demonstrated that clustering enhanced the power of the score test to detect association, under a variety of conditions, while preserving valid Type-I error. Improvement in performance was most dramatic in the presence of extreme haplotype diversity, while a slight improvement was observed even at low diversity. Our method also offers, for binary traits, a slight advantage in power over a similar approach based on an evolutionary model (Tzeng et al. [2006] Am. J. Hum. Genet. 78:231-242).

Genetic association mapping via evolution-based clustering of haplotypes

Multilocus analysis of single nucleotide polymorphism haplotypes is a promising approach to dissecting the genetic basis of complex diseases. We propose a coalescent-based model for association mapping that potentially increases the power to detect disease-susceptibility variants in genetic association studies. The approach uses Bayesian partition modelling to cluster haplotypes with similar disease risks by exploiting evolutionary information. We focus on candidate gene regions with densely spaced markers and model chromosomal segments in high linkage disequilibrium therein assuming a perfect phylogeny. To make this assumption more realistic, we split the chromosomal region of interest into sub-regions or windows of high linkage disequilibrium. The haplotype space is then partitioned into disjoint clusters, within which the phenotype-haplotype association is assumed to be the same. For example, in case-control studies, we expect chromosomal segments bearing the causal variant on a common ancestral background to be more frequent among cases than controls, giving rise to two separate haplotype clusters. The novelty of our approach arises from the fact that the distance used for clustering haplotypes has an evolutionary interpretation, as haplotypes are clustered according to the time to their most recent common ancestor. Our approach is fully Bayesian and we develop a Markov Chain Monte Carlo algorithm to sample efficiently over the space of possible partitions. We compare the proposed approach to both single-marker analyses and recently proposed multi-marker methods and show that the Bayesian partition modelling performs similarly in localizing the causal allele while yielding lower false-positive rates. Also, the method is computationally quicker than other multi-marker approaches. We present an application to real genotype data from the CYP2D6 gene region, which has a confirmed role in drug metabolism, where we succeed in mapping the location of the susceptibility variant within a small error.

Characterization of LD structures and the utility of HapMap in genetic association studies

Observed distribution of and variation in linkage disequilibrium (LD) with respect to the evolution history and disease transmission in a population is the driving force behind the current wave of genome-wide association (GWA) studies of complex human diseases. An extensive literature covers topics from haplotype analysis that utilizes local LD structures in candidate genes and regions to genome-wide organization of LD blocks (neighborhood) that led to the development of International HapMap Project and panels of "tagSNPs" used by current GWA studies. In this chapter, we examine the scenarios where each of the major types of analysis methods may be applicable and where the current popular genotyping platforms for GWA might come short. We discuss current association analysis methods by emphasizing their reliance on the local LD structures or the global organization of the LD structures, and highlight the need to consider individual marker information content in large-scale association mapping.

Comparison of measures for haplotype similarity

Measuring the association of haplotype similarities with phenotype similarities has been used to develop statistical tests of genetic association. Previously, we applied the general approach of Mantel statistics to correlate genetic and phenotype similarity, where genetic similarity was defined by the number of intervals flanked by markers identical by state for pairs of haplotypes. Here we investigated in the case-control study design the effect on power of the Mantel statistics for five different measures of genetic similarity based on haplotypes: 1) the number of shared intervals, 2) the physical length of the shared intervals, 3) the genetic length of the shared intervals in centimorgans, 4) the genetic length of the shared intervals in linkage disequilibrium units (LDU) and 5) Yu's measure that attaches more weight to the sharing of rare than common alleles. With prior knowledge of the answers of Genetic Analysis Workshop 15 Problem 3, we analyzed the simulated data sets in two genomic regions surrounding the disease loci on chromosomes 6 and 18. For the dense map on chromosome 6, all methods showed a very high power of comparable magnitude. For chromosome 18, we observed a power between 19% and 99% at the pointwise 5% significance level using 1000 cases and 1000 controls for all methods except Yu's measure. While it yielded a much lower power, Yu's measure had 80% power around the disease locus.

Sequential haplotype scan methods for association analysis

Multi-locus association analyses, including haplotype-based analyses, can sometimes provide greater power than single-locus analyses for detecting disease susceptibility loci. This potential gain, however, can be compromised by the large number of degrees of freedom caused by irrelevant markers. Exhaustive search for the optimal set of markers might be possible for a small number of markers, yet it is computationally inefficient. In this paper, we present a sequential haplotype scan method to search for combinations of adjacent markers that are jointly associated with disease status. When evaluating each marker, we add markers close to it in a sequential manner: a marker is added if its contribution to the haplotype association with disease is warranted, conditional on current haplotypes. This conditional evaluation is based on the well-known Mantel-Haenszel statistic. We propose two permutation based methods to evaluate the growing haplotypes: a haplotype method for the combined markers, and a summary method that sums conditional statistics. We compared our proposed methods, the single-locus method, and a sliding window method using simulated data. We also applied our sequential haplotype scan algorithm to experimental data for CYP2D6. The results indicate that the sequential scan procedure can identify a set of adjacent markers whose haplotypes might have strong genetic effects or be in linkage disequilibrium with disease predisposing variants. As a result, our methods can achieve greater power than the single-locus method, yet is much more computationally efficient than sliding window methods.

An application of the patient rule-induction method for evaluating the contribution of the Apolipoprotein E and Lipoprotein Lipase genes to predicting ischemic heart disease

Different combinations of genetic and environmental risk factors are known to contribute to the complex etiology of ischemic heart disease (IHD) in different subsets of individuals. We employed the Patient Rule-Induction Method (PRIM) to select the combination of risk factors and risk factor values that identified each of 16 mutually exclusive partitions of individuals having significantly different levels of risk of IHD. PRIM balances two competing objectives: (1) finding partitions where the risk of IHD is high and (2) maximizing the number of IHD cases explained by the partitions. A sequential PRIM analysis was applied to data on the incidence of IHD collected over 8 years for a sample of 5,455 unrelated individuals from the Copenhagen City Heart Study (CCHS) to assess the added value of variation in two candidate susceptibility genes beyond the traditional, lipid and body mass index risk factors for IHD. An independent sample of 362 unrelated individuals also from the city of Copenhagen was used to test the model obtained for each of the hypothesized partitions.

Incorporating single-locus tests into haplotype cladistic analysis in case-control studies

In case-control studies, genetic associations for complex diseases may be probed either with single-locus tests or with haplotype-based tests. Although there are different views on the relative merits and preferences of the two test strategies, haplotype-based analyses are generally believed to be more powerful to detect genes with modest effects. However, a main drawback of haplotype-based association tests is the large number of distinct haplotypes, which increases the degrees of freedom for corresponding test statistics and thus reduces the statistical power. To decrease the degrees of freedom and enhance the efficiency and power of haplotype analysis, we propose an improved haplotype clustering method that is based on the haplotype cladistic analysis developed by Durrant et al. In our method, we attempt to combine the strengths of single-locus analysis and haplotype-based analysis into one single test framework. Novel in our method is that we develop a more informative haplotype similarity measurement by using p-values obtained from single-locus association tests to construct a measure of weight, which to some extent incorporates the information of disease outcomes. The weights are then used in computation of similarity measures to construct distance metrics between haplotype pairs in haplotype cladistic analysis. To assess our proposed new method, we performed simulation analyses to compare the relative performances of (1) conventional haplotype-based analysis using original haplotype, (2) single-locus allele-based analysis, (3) original haplotype cladistic analysis (CLADHC) by Durrant et al., and (4) our weighted haplotype cladistic analysis method, under different scenarios. Our weighted cladistic analysis method shows an increased statistical power and robustness, compared with the methods of haplotype cladistic analysis, single-locus test, and the traditional haplotype-based analyses. The real data analyses also show that our proposed method has practical significance in the human genetics field.

Methods of haplotype-based analysis and single-locus analysis are widely used in genetic association studies. There is no consensus as to the best strategy for the performance of the two methods. Although haplotype-based analysis is a powerful tool, the large number of distinct haplotypes may reduce its efficiency. Haplotype clustering analysis is a promising way of decreasing haplotype dimensionality. A potential limitation of many existing clustering methods is that they do not allow the clustering to adapt to the position of the underlying trait locus. In this study, we proposed a weighted haplotype cladistic analysis method by incorporating a single-locus test into haplotype clustering. Under this framework, relationships between single loci and the disease outcomes can be considered when creating the hierarchical tree of haplotypes. The extensive simulations show that our method is robust against varied simulation conditions and is more powerful than either the original unweighted cladistic analysis method or single-locus analysis methods in case-control studies. Our hybrid method combining haplotype-based and single-locus analyses can be readily extended to whole genome association studies.

Association mapping of complex trait loci with context-dependent effects and unknown context variable

A novel method for Bayesian analysis of genetic heterogeneity and multilocus association in random population samples is presented. The method is valid for quantitative and binary traits as well as for multiallelic markers. In the method, individuals are stochastically assigned into two etiological groups that can have both their own, and possibly different, subsets of trait-associated (disease-predisposing) loci or alleles. The method is favorable especially in situations when etiological models are stratified by the factors that are unknown or went unmeasured, that is, if genetic heterogeneity is due to, for example, unknown genes x environment or genes x gene interactions. Additionally, a heterogeneity structure for the phenotype does not need to follow the structure of the general population; it can have a distinct selection history. The performance of the method is illustrated with simulated example of genes x environment interaction (quantitative trait with loosely linked markers) and compared to the results of single-group analysis in the presence of missing data. Additionally, example analyses with previously analyzed cystic fibrosis and type 2 diabetes data sets (binary traits with closely linked markers) are presented. The implementation (written in WinBUGS) is freely available for research purposes from http://www.rni.helsinki.fi/ approximately mjs/.

A haplotype similarity based transmission/disequilibrium test under founder heterogeneity

Taking advantage of increasingly available high-density single nucleotide polymorphisms (SNP) markers across the genome, various types of transmission/disequilibrium tests (TDT) using haplotype information have been developed. A practical challenge arising in such studies is the possibility that transmitted haplotypes have inherited disease-causing mutations from different ancestral chromosomes, or do not bear any disease-causing mutations (founder heterogeneity). To reduce the loss of signal strength due to founder heterogeneity, we propose an SP-TDT test that combines a sequential peeling procedure with the haplotype similarity based TDT method. The proposed SP-TDT method is applicable to any size of nuclear family with or without ambiguous phase information. Simulation studies suggest that the SP-TDT method has the correct type I error rate in stratified populations, and enhanced power compared with some existing haplotype similarity based TDT methods. Finally, we apply the proposed method to study the association of the leptin gene with obesity from the National Heart, Lung, and Blood Institute Family Heart Study.

Fine mapping of disease genes via haplotype clustering

We propose an algorithm for analysing SNP-based population association studies, which is a development of that introduced by Molitor et al. [2003: Am J Hum Genet 73:1368-1384]. It uses clustering of haplotypes to overcome the major limitations of many current haplotype-based approaches. We define a between-haplotype score that is simple, yet appears to capture much of the information about evolutionary relatedness of the haplotypes in the vicinity of a (unobserved) putative causal locus. Haplotype clusters can then be defined via a putative ancestral haplotype and a cut-off distance. The number of an individual's two haplotypes that lie within the cluster predicts the individual's genotype at the causal locus. This predicted genotype can then be investigated for association with the phenotype of interest. We implement our approach within a Markov-chain Monte Carlo algorithm that, in effect, searches over locations and ancestral haplotypes to identify large, case-rich clusters. The algorithm successfully fine-maps a causal mutation in a test analysis using real data, and achieves almost 98% accuracy in predicting the genotype at the causal locus. A simulation study indicates that the new algorithm is substantially superior to alternative approaches, and it also allows us to identify situations in which multi-point approaches can substantially improve over single-SNP analyses. Our algorithm runs quickly and there is scope for extension to a wide range of disease models and genomic scales.

Haplotype sharing correlation of alcohol dependence on chromosomes 1-6 in 93 nuclear families

Haplotype data contain signatures of ancestral alleles and increased information for mapping genes associated with complex traits. The motivation of this paper is to test the feasibility of a recently developed haplotype reconstruction algorithm and to perform haplotype-sharing correlation (HSC) analysis in nuclear families using data provided by the Genetic Analysis Workshop 14 and the Collaborative Study of the Genetics of Alcoholism. As an exemplary analysis, haplotype data on chromosomes 1–6 were reconstructed from genotype data in 93 nuclear families by minimizing both the recombinants in within-family haplotypes and the tree distance in between-family haplotypes. HSC analysis was performed using the best set of reconstructed haplotypes, and chromosome-wide significance was evaluated using a permutation procedure. Three markers were found to have significant haplotype associations with DSM-IV alcohol dependence that exceeded the 0.05 level of chromosome-wide significance: marker rs895941 at 36.7 cM on chromosome 3 (p = 0.03), marker rs1631833 at 109.1 cM on chromosome 4 (p = 0.008), and marker rs953887 at 74.2 cM on chromosome 6 (p = 0.02). These results indicated the usefulness of HSC analysis and provided further evidence on chromosome regions associated with alcohol dependence.

Using tree-based recursive partitioning methods to group haplotypes for increased power in association studies

Motivated by the increasing availability of high-density single nucleotide polymorphism (SNP) markers across the genome, various haplotype-based methods have been developed for candidate gene association studies, and even for genome-wide association studies. Although haplotype approaches dramatically reduce the multiple comparisons problem (as compared to single SNP analysis), even the number of existing haplotypes is relatively large, which increases the degrees of freedom and decreases the power for the corresponding test statistic. Grouping haplotypes is a way to reduce the degrees of freedom. We propose a procedure that uses a tree-based recursive partitioning algorithm to group haplotypes into a small number of clusters, and conducts the association test based on groups of haplotypes instead of individual haplotypes. The method can be used for both population-based and family-based association studies, with known or ambiguous phase information. Simulation studies suggest that the proposed method has the right type I error rate, and is more powerful than some existing haplotype-based tests.

Global transmission/disequilibrium tests based on haplotype sharing in multiple candidate genes

It is well recognized that multiple genes are likely contributing to the susceptibility of most common complex diseases. Studying one gene at a time might reduce our chance to identify disease susceptibility genes with relatively small effect sizes. Therefore, it is crucial to develop statistical methods that can assess the effect of multiple genes collectively. Motivated by the increasingly available high-density markers across the whole human genome, we propose a class of TDT-type methods that can jointly analyze haplotypes from multiple candidate genes (linked or unlinked). Our approach first uses a linear signed rank statistic to compare at an individual gene level the structural similarity among transmitted haplotypes against that among non-transmitted haplotypes. The results of the ranked comparisons from all considered genes are subsequently combined into global statistics, which can simultaneously test the association of the set of genes with the disease. Using simulation studies, we find that the proposed tests yield correct type I error rates in stratified populations. Compared with the gene-by-gene test, the new global tests appear to be more powerful in situations where all candidate genes are associated with the disease.