Powerful regression-based quantitative-trait linkage analysis of general pedigrees

We present a new method of quantitative-trait linkage analysis that combines the simplicity and robustness of regression-based methods and the generality and greater power of variance-components models. The new method is based on a regression of estimated identity-by-descent (IBD) sharing between relative pairs on the squared sums and squared differences of trait values of the relative pairs. The method is applicable to pedigrees of arbitrary structure and to pedigrees selected on the basis of trait value, provided that population parameters of the trait distribution can be correctly specified. Ambiguous IBD sharing (due to incomplete marker information) can be accommodated in the method by appropriate specification of the variance-covariance matrix of IBD sharing between relative pairs. We have implemented this regression-based method and have performed simulation studies to assess, under a range of conditions, estimation accuracy, type I error rate, and power. For normally distributed traits and in large samples, the method is found to give the correct type I error rate and an unbiased estimate of the proportion of trait variance accounted for by the additive effects of the locus-although, in cases where asymptotic theory is doubtful, significance levels should be checked by simulations. In large sibships, the new method is slightly more powerful than variance-components models. The proposed method provides a practical and powerful tool for the linkage analysis of quantitative traits.     

Exact multipoint quantitative-trait linkage analysis in pedigrees by variance components

Methods based on variance components are powerful tools for linkage analysis of quantitative traits, because they allow simultaneous consideration of all pedigree members. The central idea is to identify loci making a significant contribution to the population variance of a trait, by use of allele-sharing probabilities derived from genotyped marker loci. The technique is only as powerful as the methods used to infer these probabilities, but, to date, no implementation has made full use of the inheritance information in mapping data. Here we present a new implementation that uses an exact multipoint algorithm to extract the full probability distribution of allele sharing at every point in a mapped region. At each locus in the region, the program fits a model that partitions total phenotypic variance into components due to environmental factors, a major gene at the locus, and other unlinked genes. Numerical methods are used to derive maximum-likelihood estimates of the variance components, under the assumption of multivariate normality. A likelihood-ratio test is then applied to detect any significant effect of the hypothesized major gene. Simulations show the method to have greater power than does traditional sib-pair analysis. The method is freely available in a new release of the software package GENEHUNTER.     

Multipoint linkage analysis.

A formula is given for the advantage of n-point sampling, which approaches infinity with n. However, 2-point and 3-point analyses extract nearly all the information in such samples and at the same time communicate this information as lods and chi 2, which can be combined with other data by simple addition without reevaluation of the likelihood. When null interference is assumed, map distances and multiple recombination frequencies are inflated, and there is substantial loss of efficiency and of support for the correct order.     

Genomic imprinting and linkage test for quantitative-trait Loci in extended pedigrees

Genomic imprinting is a mechanism in which only one of the two copies of a gene is expressed. Some genes that affect development and behavior in mammals are known to be imprinted. Deregulation of imprinted genes has been found in a number of human diseases. Incorporating imprinting information into linkage analysis results in a more powerful test for linkage. Here, we propose an efficient method to test for linkage and imprinting of quantitative traits in extended pedigrees. We compared the results obtained by using the extended-pedigree-analysis approach proposed in this study with other existing approaches. We found that the proposed method is more powerful and uses extended-pedigree information most efficiently.     

Accounting for epistasis in linkage analysis of general pedigrees

Complex traits are generally believed to be influenced by multiple loci. Identification of loci involved in complex traits is more difficult for interacting than for additive loci. Here we describe an extension of the program lm_twoqtl in the package MORGAN to handle two quantitative trait loci (QTLs) with gene-gene interaction. We investigate whether parametric linkage analysis that accounts for such epistasis improves prospects for linkage detection and accuracy of localization of QTLs. Through use of simulated data we show that analysis that accounts for epistasis provides higher lod scores and better localization than does analysis without epistasis. In addition, we demonstrate that the difference between lod scores in the presence vs. absence of use of an interaction model in analysis is greater in extended than in nuclear pedigrees.     

Multipoint linkage analysis in Menkes disease.

Linkage analyses were performed in 11 families with X-linked Menkes disease. In each family more than one affected patient had been diagnosed. Forty informative meioses were tested using 11 polymorphic DNA markers. From two-point linkage analyses high lod scores are seen for DXS146 (pTAK-8; maximal lod score 3.16 at recombination fraction [theta] = .0), for DXS1 (p-8; maximal lod score 3.44 at theta = .0), for PGK1 (maximal lod score 2.48 at theta = .0), and for DXS3 (p19-2; maximal lod score 2.90 at theta = .0). This indicates linkage to the pericentromeric region. Multilocus linkage analyses of the same data revealed a peak for the location score between DXS146(pTAK-8) and DXYS1X(pDP34). The most likely location is between DXS159 (cpX289) and DXYS1X(pDP34). Odds for this location relative to the second-best-supported region, between DXS146(pTAK-8) and DXS159 (cpX289), are better than 74:1. Visualization of individual recombinant X chromosomes in two of the Menkes families showed the Menkes locus to be situated between DXS159(cpX289) and DXS94(pXG-12). Combination of the present results with the reported absence of Menkes symptoms in male patients with deletions in Xq21 leads to the conclusion that the Menkes locus is proximal to DXSY1X(pDP34) and located in the region Xq12 to Xq13.3.     

Nonparametric simulation-based statistics for detecting linkage in general pedigrees.

We present here four nonparametric statistics for linkage analysis that test whether pairs of affected relatives share marker alleles more often than expected. These statistics are based on simulating the null distribution of a given statistic conditional on the unaffecteds' marker genotypes. Each statistic uses a different measure of marker sharing: the SimAPM statistic uses the simulation-based affected-pedigree-member measure based on identity-by-state (IBS) sharing. The SimKIN (kinship) measure is 1.0 for identity-by-descent (IBD) sharing, 0.0 for no IBD status sharing, and the kinship coefficient when the IBD status is ambiguous. The simulation-based IBD (SimIBD) statistic uses a recursive algorithm to determine the probability of two affecteds sharing a specific allele IBD. The SimISO statistic is identical to SimIBD, except that it also measures marker similarity between unaffected pairs. We evaluated our statistics on data simulated under different two-locus disease models, comparing our results to those obtained with several other nonparametric statistics. Use of IBD information produces dramatic increases in power over the SimAPM method, which uses only IBS information. The power of our best statistic in most cases meets or exceeds the power of the other nonparametric statistics. Furthermore, our statistics perform comparisons between all affected relative pairs within general pedigrees and are not restricted to sib pairs or nuclear families.     

Multipoint linkage analysis. A cautionary note.

Multipoint linkage analysis is commonly used to evaluate linkage of a disease to multiple markers in a small region. Multipoint analysis is particularly powerful when the IBD relations of family members at the trait locus are ambiguous. The increased power arises because, unlike single-marker analyses, multipoint analysis uses haplotype information from several markers to infer the IBD relations. We wish to temper this advantage with a cautionary note: multipoint analysis is sensitive to power loss due to misspecification of intermarker distances. Such misspecification is especially problematic when dealing with closely spaced markers. We present computer simulations comparing the power of single-point and multipoint analyses, both when IBD relations are ambiguous, and when the intermarker distances are misspecified. We conclude that when evaluating markers in a small region to confirm or refute previous findings, a situation in which p values of modest statistical significance are important, single marker analyses may provide more reliable measures of the strength of support for linkage than multipoint statistics.     

Exact genetic linkage computations for general pedigrees

MOTIVATION: Genetic linkage analysis is a useful statistical tool for mapping disease genes and for associating functionality of genes with their location on the chromosome. There is a need for a program that computes multipoint likelihood on general pedigrees with many markers that also deals with two-locus disease models. RESULTS: In this paper we present algorithms for performing exact multipoint likelihood calculations on general pedigrees with a large number of highly polymorphic markers, taking into account a variety of disease models. We have implemented these algorithms in a new computer program called SUPERLINK which outperforms leading linkage software with regards to functionality, speed, memory requirements and extensibility.     

Multipoint linkage analysis with many multiallelic or dense diallelic markers: Markov chain-Monte Carlo provides practical approaches for genome scans on general pedigrees

Computations for genome scans need to adapt to the increasing use of dense diallelic markers as well as of full-chromosome multipoint linkage analysis with either diallelic or multiallelic markers. Whereas suitable exact-computation tools are available for use with small pedigrees, equivalent exact computation for larger pedigrees remains infeasible. Markov chain-Monte Carlo (MCMC)-based methods currently provide the only computationally practical option. To date, no systematic comparison of the performance of MCMC-based programs is available, nor have these programs been systematically evaluated for use with dense diallelic markers. Using simulated data, we evaluate the performance of two MCMC-based linkage-analysis programs--lm_markers from the MORGAN package and SimWalk2--under a variety of analysis conditions. Pedigrees consisted of 14, 52, or 98 individuals in 3, 5, or 6 generations, respectively, with increasing amounts of missing data in larger pedigrees. One hundred replicates of markers and trait data were simulated on a 100-cM chromosome, with up to 10 multiallelic and up to 200 diallelic markers used simultaneously for computation of multipoint LOD scores. Exact computation was available for comparison in most situations, and comparison with a perfectly informative marker or interprogram comparison was available in the remaining situations. Our results confirm the accuracy of both programs in multipoint analysis with multiallelic markers on pedigrees of varied sizes and missing-data patterns, but there are some computational differences. In contrast, for large numbers of dense diallelic markers, only the lm_markers program was able to provide accurate results within a computationally practical time. Thus, programs in the MORGAN package are the first available to provide a computationally practical option for accurate linkage analyses in genome scans with both large numbers of diallelic markers and large pedigrees.     

Multipoint linkage analysis in X-linked Alport syndrome

Summary In order to localize the gene for the X-linked form of Alport syndrome (ATS) more precisely, we performed restriction fragment length polymorphism analysis with nine different X-chromosomal DNA markers in 107 members of twelve Danish families segregating for classic ATS or progressive hereditary nephritis without deafness. Two-point linkage analysis confirmed close linkage to the markers DXS17(S21) (Z _max = 4.44 at = 0.04), DXS94(pXG-12) (Z _max=8.07 at =0.04), and DXS101(cX52.5) (Z _max=6.04 at =0.00), and revealed close linkage to two other markers: DXS88(pG3-1) (Z _max =6.36 at =0.00) and DXS11(p22–33) (z _max=3.45 at =0.00). Multipoint linkage analysis has mapped the gene to the region between the markers DXS17 and DXS94, closely linked to DXS101. By taking into account the consensus map and results from other studies, the most probable order of the loci is: DXYS1(pDP34)-DXS3(p19-2)-DXS17-(ATS, DXS101)-DXS94-DXS11-DXS42(p43-15)-DXS51(52A). DXS88 was found to be located between DXS17 and DXS42, but the order in relation to the ATS locus and the other markers used in this study could not be determined.     

Multipoint linkage analysis in neurofibromatosis type I: an international collaboration.

In addition to reporting, in accompanying papers, their individual analyses of mapping the neurofibromatosis type 1 (NF1) gene on chromosome 17, members of the International Consortium for NF1 Linkage contributed their data for our joint analysis to determine the exact sequence of flanking markers and to obtain precise estimates and confidence limits of the recombination fractions for the closest markers, in anticipation of clinical use. With specimens from 142 families and more than 700 affected persons, eight teams used 31 markers in the pericentric region of chromosome 17 to perform 13,838 genotypings. With the combined data, we used the computer program CRI-MAP to build the most likely sequence of loci by sequentially adding single loci to a fixed pair of loci and separately calculating the likelihood of all permutations of four consecutive loci. The best order is pter-pA10-41-EW301-centromere (p17H8)-pHHH202-NF1-EW206-EW207-EW203++ +-CRI-L581-CRI-L946-HOX2-NGFR-qter. The total genetic distance from pA10-41 to NGFR is 26 cM in males and 56 cM in females, and the overall difference in sex-specific maps is statistically significant (P = .006). The upper 99% confidence limits of the recombination fraction of the closest proximal marker, pHHH202, is 4%, and that for the closest distal marker, EW206, is 9%. These limits should decrease with the use of additional probes and the further evaluation of DNA from the six persons showing multiple recombinations within short genetic distances. Clinical application is technically feasible with currently available markers, although its appropriate use for prenatal and presymptomatic diagnosis requires further discussion and evaluation.     

The effect of linkage disequilibrium on linkage analysis of incomplete pedigrees

Dense SNP maps can be highly informative for linkage studies. But when parental genotypes are missing, multipoint linkage scores can be inflated in regions with substantial marker-marker linkage disequilibrium (LD). Such regions were observed in the Affymetrix SNP genotypes for the Genetic Analysis Workshop 14 (GAW14) Collaborative Study on the Genetics of Alcoholism (COGA) dataset, providing an opportunity to test a novel simulation strategy for studying this problem. First, an inheritance vector (with or without linkage present) is simulated for each replicate, i.e., locations of recombinations and transmission of parental chromosomes are determined for each meiosis. Then, two sets of founder haplotypes are superimposed onto the inheritance vector: one set that is inferred from the actual data and which contains the pattern of LD; and one set created by randomly selecting parental alleles based on the known allele frequencies, with no correlation (LD) between markers. Applying this strategy to a map of 176 SNPs (66 Mb of chromosome 7) for 100 replicates of 116 sibling pairs, significant inflation of multipoint linkage scores was observed in regions of high LD when parental genotypes were set to missing, with no linkage present. Similar inflation was observed in analyses of the COGA data for these affected sib pairs with parental genotypes set to missing, but not after reducing the marker map until r2 between any pair of markers was 相似文献   

Multipoint estimation of identity-by-descent probabilities at arbitrary positions among marker loci on general pedigrees

OBJECTIVES: To describe, implement, and test an efficient algorithm to obtain multipoint identity-by-descent (IBD) probabilities at arbitrary positions among marker loci for general pedigrees. Unlike existing programs, our algorithm can analyze data sets with large numbers of people and markers. The algorithm has been implemented in the SimWalk2 computer package. METHODS: Using a rigorous testing regimen containing five pedigrees of various sizes with realistic marker data, we compared several widely used IBD computation programs: Allegro, Aspex, GeneHunter, MapMaker/Sibs, Mendel, Sage, SimWalk2, and Solar. RESULTS: The testing revealed a few discrepancies, particularly on consanguineous pedigrees, but overall excellent results in the deterministic multipoint packages. SimWalk2 was also found to be in good agreement with the deterministic multipoint programs, usually matching to two decimal places the kinship coefficient that ranges from 0 to 1. However, the packages based on single-point IBD estimation, while consistent with each other, often showed poor results, disagreeing with the multipoint kinship results by as much as 0.5. CONCLUSIONS: Our testing has clearly shown that multipoint IBD estimation is much better than single-point estimation. In addition, our testing has validated our algorithm for estimating IBD probabilities at arbitrary positions on general pedigrees.     

Blocking Gibbs sampling for linkage analysis in large pedigrees with many loops.

We apply the method of "blocking Gibbs" sampling to a problem of great importance and complexity-linkage analysis. Blocking Gibbs sampling combines exact local computations with Gibbs sampling, in a way that complements the strengths of both. The method is able to handle problems with very high complexity, such as linkage analysis in large pedigrees with many loops, a task that no other known method is able to handle. New developments of the method are outlined, and it is applied to a highly complex linkage problem in a human pedigree.     

Multipoint oligogenic analysis of age-at-onset data with applications to Alzheimer disease pedigrees

It is usually difficult to localize genes that cause diseases with late ages at onset. These diseases frequently exhibit complex modes of inheritance, and only recent generations are available to be genotyped and phenotyped. In this situation, multipoint analysis using traditional exact linkage analysis methods, with many markers and full pedigree information, is a computationally intractable problem. Fortunately, Monte Carlo Markov chain sampling provides a tool to address this issue. By treating age at onset as a right-censored quantitative trait, we expand the methods used by Heath (1997) and illustrate them using an Alzheimer disease (AD) data set. This approach estimates the number, sizes, allele frequencies, and positions of quantitative trait loci (QTLs). In this simultaneous multipoint linkage and segregation analysis method, the QTLs are assumed to be diallelic and to interact additively. In the AD data set, we were able to localize correctly, quickly, and accurately two known genes, despite the existence of substantial genetic heterogeneity, thus demonstrating the great promise of these methods for the dissection of late-onset oligogenic diseases.     

Segregation and linkage analysis of serum angiotensin I-converting enzyme levels: evidence for two quantitative-trait loci.

Human serum angiotensin I-converting enzyme (ACE) levels vary substantially between individuals and are highly heritable. Segregation analysis in European families has shown that more than half of the total variability in ACE levels is influenced by quantitative-trait loci (QTL). One of these QTLs is located within or close to the ACE locus itself. Combined segregation/linkage analysis in a series of African Caribbean families from Jamaica shows that the ACE insertion-deletion polymorphism is in moderate linkage disequilibrium with an ACE-linked QTL. Linkage analysis with a highly informative polymorphism at the neighboring growth-hormone gene (GH) shows surprisingly little support for linkage (LOD score [Z] = 0.12). An extended analysis with a two-QTL model, where an ACE-linked QTL interacts additively with an unlinked QTL, significantly improves both the fit of the model (P = .002) and the support for linkage between the ACe-linked QTL interacts additively with an unlinked QTL, significantly improves both the fit of the model (P = .002) and the support for linkage between the ACe-linked QTL and GH polymorphism (Z = 5.0). We conclude that two QTLs jointly influence serum ACE levels in this population. One QTL is located within or close to the ACE locus and explains 27% of the total variability; the second QTL is unlinked to the ACE locus and explains 52% of the variability. The identification of the molecular mechanisms underlying both QTLs is necessary in order to interpret the role of ACE in cardiovascular disease.     

Multipoint linkage analysis in X-linked ocular albinism of the Nettleship-Falls type

Summary The genes that encode the alpha 1 (VI) and alpha 2 (VI) collagen chains, designated COL6A1 and COL6A2, map to human chromosomal band 21q22.3. Using pulsed-field gel electrophoresis and somatic cell hybrids, we found that COL6A1 and COL6A2 form a gene cluster on the most distal part of chromosome 21. Furthermore, we detected several DNA polymorphisms (both restriction site and VNTRs) associated with these loci. These polymorphisms make the COL6A1 and COL6A2 genes among the most informative markers on human chromosome 21.