Genetic comparison of a Croatian isolate and CEPH European founders

Human isolates have been postulated as a good resource for the identification of QTL due to reduced genetic diversity and a more homogeneous environment. Isolates may also have increased linkage disequilibrium (LD) due to small effective population size and, either loss or increase in frequency of alleles that are rare in the general population from which they originate. Here we investigate the difference in allele and genotype frequencies, LD and homozygous tracts between an isolate—several villages from the island of Vis in Croatia—and an outbred population of European origin: the Hapmap CEPH founders. Using the HumanHap300 v1 Genotyping BeadChip, we show that our population does not differ greatly from the reference CEU outbred population despite having a slightly higher proportion of monomorphic loci, a slightly higher long‐range LD, and a greater proportion of individuals with long homozygous tracts. We conclude that genotyping arrays should perform equally well in our isolate as in outbred European populations for disease mapping studies and that SNP–trait associations discovered in our well‐characterized Croatian isolate should be valid in the general European population from which they descend. Genet. Epidemiol. 34: 140–145, 2010. © 2009 Wiley‐Liss, Inc.     

Shades of gray: a comparison of linkage disequilibrium between Hutterites and Europeans

Founder or isolated populations have advantages for genetic studies due to decreased genetic and environmental heterogeneity. However, whereas longer‐range linkage disequilibrium (LD) in these populations is expected to facilitate gene localization, extensive LD may actually limit the ability for gene discovery. The North American Hutterite population is one of the best characterized young founder populations and members of this isolate have been the subjects of our studies of complex traits, including fertility, asthma and cardiovascular disease, for >20 years. Here, we directly assess the patterns and extent of global LD using single nucleotide polymorphism genotypes with minor allele frequencies (MAFs) ≥5% from the Affymetrix GeneChip^® Mapping 500 K array in 60 relatively unrelated Hutterites and 60 unrelated Europeans (HapMap CEU). Although LD among some marker pairs extends further in the Hutterites than in Europeans, the pattern of LD and MAF are surprisingly similar. These results indicate that (1) identifying disease genes should be no more difficult in the Hutterites than in outbred European populations, (2) the same common susceptibility alleles for complex diseases should be present in the Hutterites and outbred European populations, and (3) imputation algorithms based on HapMap CEU should be applicable to the Hutterites. Genet. Epidemiol. 34: 133–139, 2010. © 2009 Wiley‐Liss, Inc.     

Introduction: transmission/disequilibrium test/association group.

A range of study designs, using unrelated or family controls, were used to investigate the pattern of association with disease of single nucleotide polymorphisms (SNPs) within candidate gene 1 (simulated data). Strong evidence of disease association at the functional locus was detected using all study designs, and in the "general" but not the "isolated" population the functional polymorphism displayed considerably higher association than surrounding SNPs. There was much variation in the strength of association of SNPs with disease, up to 70% of which was explained by SNP allele frequency and distance from the functional polymorphism. Some common polymorphisms very close to the functional locus however showed no association with disease. Analysis of short haplotypes of SNPs reduced but did not totally remove this feature.     

Genetic Analysis Workshop II: results of incorporating a linkage disequilibrium parameter

The corporation of a linkage disequilibrium parameter, delta, into linkage analysis is illustrated for data from Genetic Analysis Workshop II. Points from a joint likelihood surface are calculated and displayed on a recombination fraction-linkage disequilibrium grid using a simple modification of LIPED. The approach is shown to increase the power of linkage analysis and the power of tests for heterogeneity of linkage for the simulated examples.     

The haplotype runs test: the parent-parent-affected offspring trio design

The increasing availability of maps of dense polymorphic markers makes use of haplotype data in family-based association analyses an attractive alternative to single marker association tests. We describe a novel class of statistics designed to test for an association between marker haplotypes and a qualitative trait using the parent-parent-affected-offspring trio design. Our haplotype runs test (HRT) is based on consecutive allele-sharing between pairs of haplotypes. We assign weights according to the relative frequencies of the alleles for which the two haplotypes match. Herein, we compare the HRT to the maximum-identity-length-contrast (MILC) statistic, the single-locus transmission/disequilibrium test (TDT), and the generalized test of transmission disequilibrium for haplotype data, as implemented in the software TRANSMIT, using both simulated data and published haplotype data from the recessive disorder ataxia-telangiectasia. Our simulation results suggest that the HRT outperforms the MILC and that the HRT provides comparable power to the TDT and TRANSMIT when the number of distinct founder haplotypes with a disease susceptibility allele is small but substantially outperforms the TDT and TRANSMIT when the number of distinct founder haplotypes with a disease susceptibility allele is even of modest size.     

Use of unphased multilocus genotype data in indirect association studies

It is usually assumed that detection of a disease susceptability gene via marker polymorphisms in linkage disequilibrium with it is facilitated by consideration of marker haplotypes. However, capture of the marker haplotype information requires resolution of gametic phase, and this must usually be inferred statistically. Recently, we questioned the value of the marker haplotype information, and suggested that certain analyses of multivariate marker data, not based on haplotypes explicitly and not requiring resolution of gametic phase, are often more powerful than analyses based on haplotypes. Here, we review this work and assess more carefully the situations in which our conclusions might apply. We also relate these analyses to alternative approaches to haplotype analysis, namely those based on haplotype similarity and those inspired by cladistics.     

Description of Three Data Sets: Collaborative Study on the Genetics of Asthma (CSGA), the German Affected‐Sib‐Pair Study,and the Hutterites of South Dakota

Three different data sets with clinical data and markers from genome‐wide screens were submitted for analysis at Genetic Analysis Workshop 12. In each study, participants were carefully characterized for asthma and related phenotypes. Testing for bronchial hyper‐responsiveness using methacholine and standardized protocols was performed. Total serum IgE levels were measured using standardized techniques. In addition, similar questionnaire data on symptoms and relevant environmental exposures were obtained. Relevant clinical data and genotypes for the polymorphic markers used for each genome‐wide screen were submitted. The data set from the United States Collaborative Study on the Genetics of Asthma represents a heterogeneous population consisting of both Caucasian and African American families ascertained through two siblings with clinical asthma from multiple centers. Likewise, the families from the German Asthma Genetics Group were also ascertained through two siblings with asthma at multiple centers. In a contrast to these data sets, Dr. Carole Ober and her collaborators submitted data from the inbred Hutterite population in South Dakota. © 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.     

Principal component analysis for selection of optimal SNP-sets that capture intragenic genetic variation

Candidate gene association studies often utilize one single nucleotide polymorphism (SNP) for analysis, with an initial report typically not being replicated by subsequent studies. The failure to replicate may result from incomplete or poor identification of disease-related variants or haplotypes, possibly due to naive SNP selection. A method for identification of linkage disequilibrium (LD) groups and selection of SNPs that capture sufficient intra-genic genetic diversity is described. We assume all SNPs with minor allele frequency above a pre-determined frequency have been identified. Principal component analysis (PCA) is applied to evaluate multivariate SNP correlations to infer groups of SNPs in LD (LD-groups) and to establish an optimal set of group-tagging SNPs (gtSNPs) that provide the most comprehensive coverage of intra-genic diversity while minimizing the resources necessary to perform an informative association analysis. This PCA method differs from haplotype block (HB) and haplotype-tagging SNP (htSNP) methods, in that an LD-group of SNPs need not be a contiguous DNA fragment. Results of the PCA method compared well with existing htSNP methods while also providing advantages over those methods, including an indication of the optimal number of SNPs needed. Further, evaluation of the method over multiple replicates of simulated data indicated PCA to be a robust method for SNP selection. Our findings suggest that PCA may be a powerful tool for establishing an optimal SNP set that maximizes the amount of genetic variation captured for a candidate gene using a minimal number of SNPs.     

Genetic Analysis Workshop II: study of Problem 2 linkage relationships by different methods

Segregation and linkage analyse were performed on Problem 2 simulated data. Segregation analysis showed evidence for nearly recessive major susceptibility locus with parameter estimates close to the simulation input values. Linkage between this susceptibility locus (X) and the ten marker loci (A to J) led us to propose the following map: X-G-F-B.     

Linkage analysis of a complex disease: Application to familial Alzheimer's disease

Evidence for linkage of the Alzheimer's gene to markers on chromosomes 19 and 21 was assessed using single-locus and two-locus models of inheritance. Families were divided into groups determined by their average age at onset. The youngest group produced higher lod scores for markers on chromosome 21 while an older group showed evidence for linkage to markers on chromosome 19. Two-locus models of disease were used to analyze the youngest group for linkage to pairs of markers on chromosome 21 and an older group with markers on chromosome 19. © 1993 Wiley-Liss, Inc.     

Etiological heterogeneity in Hodgkin's disease: HLA linked and unlinked determinants of susceptibility independent of histological concordance

Forty-one multiplex families, from published sources and new data from the National Cancer Institute, segregating for Hodgkin's disease and HLA, have been studied. A reanalysis of these data strongly suggests a recessive mode of inheritance for susceptibility to Hodgkin's disease. The HLA haplotype sharing data between affected relatives demonstrate that approximately 60% of cases in multiplex families are due to an HLA-linked susceptibility gene, the remaining 40% being due to other familial factors. The data clearly support the hypothesis of etiological heterogeneity for Hodgkin's disease, with both HLA-linked and HLA-unlinked factors being responsible. Finally, there is an increased concordance of histological types between affected relatives, but this concordance seems independent of HLA sharing.     

The Importance of Connections: Joining Components of the Hutterite Pedigree

We analyzed a quantitative trait (serum IgE levels), and a binary trait (asthma), in four Hutterite sub‐pedigrees. A genome screen for asthma was performed using GENEHUNTER, and interesting regions were followed up using extended pedigrees and the FASTLINK package. Markov chain Monte Carlo (MCMC) methods were used to assess haplotype sharing among affected individuals (MORGAN/AUTOZYG), and to perform a combined oligogenic segregation and linkage analysis (LOKI) for log₁₀(IgE). We found evidence for at least two susceptibility loci for asthma on chromosome 5, and a QTL for log₁₀(IgE) on chromosome 1. Our analyses demonstrate that using the most complete pedigree structure possible is advisable, with attention to the possibility of heterogeneity among subunits of a very large pedigree. © 2001 Wiley‐Liss, Inc.     

Haplotype frequency estimation in patient populations: the effect of departures from Hardy-Weinberg proportions and collapsing over a locus in the HLA region.

Haplotype analyses are an important area in the study of the genetic components of human disease. Associations between markers and disease loci that are not evident with a single marker locus may be identified in multi-locus marker analyses using estimated haplotype frequencies (HFs). Procedures that make use of the expectation-maximization (EM) algorithm to estimate HFs from unphased genotype data are in common use in genetic studies. The EM algorithm uses these unphased genotype frequencies along with the assumption of Hardy-Weinberg proportions (HWP) to converge on HF estimates. In this paper, we assess the accuracy of EM estimates of HFs in patients with type I diabetes for whom the true haplotypes are known, but the data are analyzed ignoring family information to allow comparison between estimated and true frequencies. The data consist of six HLA loci with high levels of polymorphism and a range of departures from HWP and linkage equilibrium. While the overall accuracy of the EM estimates is good, there can be large over- and underestimates of particular HFs, even for common haplotypes, especially when the loci involved deviate significantly from HWP. Estimating HFs for three or more loci and then collapsing over loci so as to generate two locus haplotypes can improve the accuracy of the estimation. The collapsing procedure is most beneficial when one of the loci in the two-locus haplotype of interest deviates significantly from HWP and the locus collapsed over is in linkage disequilibrium with the other loci.     

The limits of fine‐scale mapping

When a novel genetic trait arises in a population, it introduces a signal in the haplotype distribution of that population. Through recombination that signal's history becomes differentiated from the DNA distant to it, but remains similar to the DNA close by. Fine‐scale mapping techniques rely on this differentiation to pinpoint trait loci. In this study, we analyzed the differentiation itself to better understand how much information is available to these techniques. Simulated alleles on known recombinant coalescent trees show the upper limit for fine‐scale mapping. Varying characteristics of the population being studied increase or decrease this limit. The initial uncertainty in map position has the most direct influence on the final precision of the estimate, with wider initial areas resulting in wider final estimates, though the increase is sigmoidal rather than linear. The Θ of the trait (4Nμ) is also important, with lower values for Θ resulting in greater precision of trait placement up to a point—the increase is sigmoidal as Θ decreases. Collecting data from more individuals can increase precision, though only logarithmically with the total number of individuals, so that each added individual contributes less to the final precision. However, a case/control analysis has the potential to greatly increase the effective number of individuals, as the bulk of the information lies in the differential between affected and unaffected genotypes. If haplotypes are unknown due to incomplete penetrance, much information is lost, with more information lost the less indicative phenotype is of the underlying genotype. Genet. Epidemiol. 2009. © 2008 Wiley‐Liss, Inc.     

Phylogenetic analysis of the polyprotein coding region of an infectious South African bursal disease virus (IBDV) strain

Infectious bursal disease virus (IBDV) causes Gumboro disease, which is highly contagious and immunosuppressive in young chickens. A virulent form of IBDV reached South Africa in 1989 and to date there has been little molecular information available for this strain. In this study, the polyprotein coding region of the South African strain SA-KZN95 was sequenced and analysed along with 52 representative sequences of other serotype I and II strains. We explored the relative impact of recombination on phylogenetic reconstruction using a multidimensional scaling approach. Phylogenetic analyses consistently placed the South African isolate within the very virulent IBDV clade. Selection analyses were also conducted to identify evolutionarily relevant amino acid residues. Previously, 19 residues in the polyprotein were shown to be potentially diagnostic for the different IBDV pathotypes. This study identified an additional two unique residues in the polyprotein which may be used as genetic signatures in future viral identifications. Better strain identification would aid in the development and application of vaccines.     

Evidence for linkage between Wilson disease and esterase D in three kindreds: detection of linkage for an autosomal recessive disorder by the family study method

Wilson disease (WD) is an inherited disorder of copper metabolism that affects the brain, liver, and other organs. Our group recently reported close linkage between the locus for WD and a polymorphic red cell enzyme, esterase D (EsD), in a large inbred Israeli-Arab lineage. We have subsequently studied two unrelated Druze kindreds in order to confirm this linkage and more precisely define the distance between the two loci. The maximum likelihood estimate of recombination was determined to be zero with lod scores of 1.48 and 1.06 in each Druze family, respectively. The combined maximum lod score based on pooled results from the Israeli-Arab and Druze kindreds is 5.49 at theta = 0.03. WD is one of a few autosomal recessive disorders that has been mapped by classical family study methods. In this paper, the merits for using large, inbred families in linkage studies of rare recessive disorders are discussed. Major considerations for pedigree selection are size and number of constituent nuclear families, number and distribution of affected individuals, and pedigree structure that may provide information for determination of phase between the disease and marker loci.     

Robust genetic linkage analysis based on a score test of homogeneity: The weighted pairwise correlation statistic

In testing genetic linkage using large or complex pedigrees, robust methods may be preferred to the Lod-score method. The affected-pedigree-member method is robust but does not use the information available in nonaffected subjects, which results in a loss of power. We propose a new test of genetic linkage based on a score test of homogeneity derived from a random effect model. The statistic is simple and uses only pairs of subjects. In contrast with the affected-pedigree-method, it uses all the available pairs and does not depend on the marker alleles frequencies. A rank version of this weighted pairwise correlation statistic is proposed, its exact first and second moment derived. We show that for age-dependent diseases, it provides a simple nonparametric test. © 1994 Wiley-Liss, Inc.     

The detection of major loci by segregation and linkage analysis: A simulation study

Simulated multigenerational pedigrees were analyzed using the program GENPED and POINTER to examine the 1) limits of segregation analysis for detecting single locus, two-allele transmission of a dichotomous trait and 2) accuracy of the parameter estimates. Ten data sets of 30 pedigrees each (approximately 25 persons per pedigree) were simulated. The genotypic penetrance values were varied but the population prevalence of the trait was kept constant at 2%. For some data sets a linked marker locus was also simulated. Previous results had shown that a single major locus could be easily detected when the heterozygote penetrance (f₁) was high or midway between the two homozygote penetrances. In this study, we found a single major locus could not be consistently detected by either method of segregation analysis when f₁ was “low” to “intermediate.” Accuracy of the parameter estimates depended on assumptions about the population prevalence. In those cases where the major locus could not be detected by segregation analysis, linkage to a marker locus could be detected as long as the marker was closely linked and there were not phenocopies in the population. Owing to the limited number of simulations in this study, we cannot generalize these findings. However, they provide a basis for further testing of methods of segregation analysis when factors such as the parameter values, family structure, and ascertainment scheme are varied.     

Maximum likelihood estimation of haplotype effects and haplotype-environment interactions in association studies

The associations between haplotypes and disease phenotypes offer valuable clues about the genetic determinants of complex diseases. It is highly challenging to make statistical inferences about these associations because of the unknown gametic phase in genotype data. We describe a general likelihood-based approach to inferring haplotype-disease associations in studies of unrelated individuals. We consider all possible phenotypes (including disease indicator, quantitative trait, and potentially censored age at onset of disease) and all commonly used study designs (including cross-sectional, case-control, cohort, nested case-control, and case-cohort). The effects of haplotypes on phenotype are characterized by appropriate regression models, which allow various genetic mechanisms and gene-environment interactions. We present the likelihood functions for all study designs and disease phenotypes under Hardy-Weinberg disequilibrium. The corresponding maximum likelihood estimators are approximately unbiased, normally distributed, and statistically efficient. We provide simple and efficient numerical algorithms to calculate the maximum likelihood estimators and their variances, and implement these algorithms in a freely available computer program. Extensive simulation studies demonstrate that the proposed methods perform well in realistic situations. An application to the Carolina Breast Cancer Study reveals significant haplotype effects and haplotype-smoking interactions in the development of breast cancer.