A genetic map of chromosome 1: comparison of different data sets and linkage programs

We have used 22 chromosome 1 loci to construct a genetic linkage map of this autosome using the Venezuelan Reference Pedigree. These markers formed two linkage groups separated by an interval of more than 30 cM. Linkage maps were constructed separately using the computer programs LINKAGE and MAPMAKER to determine their relative speed, efficiency, and accuracy. We found that both programs generated maps with the same order and distances, although the LINKAGE program derived more information from the data, allowing placement of one additional marker. Many of the probes have previously been mapped using the CEPH pedigrees. However, the current map is generated from a different data set and so can be used to increase the certainty of locus order and map position. Ultimately, the generation and confirmation of a 1-cM map of this chromosome will require such multiple data sets.     

Construction of dense linkage maps “on the fly” using early generation wheat breeding populations

In plant species, construction of framework linkage maps to facilitate quantitative trait loci mapping and molecular breeding has been confined to experimental mapping populations. However, development and evaluation of these populations is detached from breeding efforts for cultivar development. In this study, we demonstrate that dense and reliable linkage maps can be constructed using extant breeding populations derived from a large number of crosses, thus eliminating the need for extraneous population development. Using 565 segregating F₁ progeny from 28 four-way cross breeding populations, a linkage map of the hexaploid wheat genome consisting of 3,785 single nucleotide polymorphism (SNP) loci and 22 simple sequence repeat loci was developed. Map estimation was facilitated by application of mapping algorithms for general pedigrees implemented in the software package CRI-MAP. The developed linkage maps showed high rank-order concordance with a SNP consensus map developed from seven mapping studies. Therefore, the linkage mapping methodology presented here represents a resource efficient approach for plant breeding programs that enables development of dense linkage maps “on the fly” to support molecular breeding efforts.     

A novel Markov chain monte carlo approach for constructing accurate meiotic maps

Mapping markers from linkage data continues to be a task performed in many genetic epidemiological studies. Data collected in a study may be used to refine published map estimates and a study may use markers that do not appear in any published map. Furthermore, inaccuracies in meiotic maps can seriously bias linkage findings. To make best use of the available marker information, multilocus linkage analyses are performed. However, two computational issues greatly limit the number of markers currently mapped jointly; the number of candidate marker orders increases exponentially with marker number and computing exact multilocus likelihoods on general pedigrees is computationally demanding. In this article, a new Markov chain Monte Carlo (MCMC) approach that solves both these computational problems is presented. The MCMC approach allows many markers to be mapped jointly, using data observed on general pedigrees with unobserved individuals. The performance of the new mapping procedure is demonstrated through the analysis of simulated and real data. The MCMC procedure performs extremely well, even when there are millions of candidate orders, and gives results superior to those of CRI-MAP.     

A 2-cM genetic linkage map of human chromosome 7p that includes 47 loci.

A new high-resolution genetic linkage map for human chromosome 7p has been constructed. The map is composed of 47 loci (54 polymorphic systems), 19 of which are uniquely placed with odds of at least 1000:1. Four genes are represented, including glucokinase (GCK, ATP:D-hexose-6-phosphotransferase, EC 2.7.1.2) which was mapped via a (CA)n dinucleotide repeat polymorphism. The sex-average map measures 94.4 cM and the male and female maps measure 73.2 and 116.1 cM, respectively. We believe that the genetic map extends nearly the full length of the short arm of chromosome 7 since a centromere marker has been incorporated, and the most distal marker, D7S21, has been cytogenetically localized by in situ hybridization to 7p22-pter. The average marker spacing is 2 cM, and the largest interval between uniquely placed markers is 13 cM (sex-average map). Overall, female recombination was observed to be about 1.5 times that of males, and a statistically significant sex-specific recombination frequency was found for a single interval. The map is based on genotypic data gathered from 40 CEPH reference pedigrees and was constructed using the CRI-MAP program package. The map presented here represents a combined and substantially expanded dataset compared to previously published chromosome 7 maps, and it will serve as a "baseline" genetic map that should prove useful for future efforts to develop a 1-cM map and for construction of a contiguous clone-based physical map for this chromosome.     

Closure of a genetic linkage map of human chromosome 7q with centromere and telomere polymorphisms.

We have constructed a 2.4-cM resolution genetic linkage map for chromosome 7q that is bounded by centromere and telomere polymorphisms and contains 66 loci (88 polymorphic systems), 38 of which are uniquely placed with odds for order of at least 1000:1. Ten genes are included in the map and 11 markers have heterozygosities of at least 70%. This map is the first to incorporate several highly informative markers derived from a telomere YAC clone HTY146 (locus D7S427), including HTY146c3 (HET 92%). The telomere locus markers span at least 200 kb of the 7q terminus and no crossovers within the physical confines of the locus were observed in approximately 240 jointly informative meioses. The sex-equal map length is 158 cM and the largest genetic interval between uniquely localized markers in this map is 11 cM. The female and male map lengths are 181 and 133 cM, respectively. The map is based on the CEPH reference pedigrees and includes over 4000 new genotypes, our previously reported data plus 29 allele systems from the published CEPH version 5 database, and was constructed using the program package CRI-MAP. This genetic linkage map can be considered a baseline map for 7q, and will be useful for defining the extent of chromosome deletions previously reported for breast and prostate cancers, for developing additional genetic maps such as index marker and 1-cM maps, and ultimately for developing a fully integrated genetic and physical map for this chromosome.     

convert: A user‐friendly program to reformat diploid genotypic data for commonly used population genetic software packages

convert is a user‐friendly, 32‐bit Windows program that facilitates ready transfer of codominant, diploid genotypic data amongst commonly used population genetic software packages. convert reads input files in its own ‘standard’ data format, easily produced from an excel file of diploid, codominant marker data, and can convert these to the input formats of the following programs: gda , genepop , arlequin , popgene , microsat , phylip , and structure . convert can also read input files in genepop format. In addition, convert can produce a summary table of allele frequencies in which private alleles and the sample sizes at each locus are indicated.     

MAP-O-MAT: internet-based linkage mapping

SUMMARY: MAP-O-MAT is a web-based server for automated linkage mapping of human polymorphic DNA markers. MAP-O-MAT facilitates the verification of order and map distances for custom mapping sets using genotype data from the CEPH database, and from the Marshfield, SNP Consortium and Rutgers linkage maps (exclusive to the deCODE genotyping data). The CRI-MAP program is used for likelihood calculations and some mapping algorithms, and physical map positions are provided from the human genome assembly. AVAILABILITY: MAP-O-MAT is located at http://compgen.rutgers.edu/mapomat/ CONTACT: matise@biology.rutgers.edu.     

MAPMAKER: an interactive computer package for constructing primary genetic linkage maps of experimental and natural populations

With the advent of RFLPs, genetic linkage maps are now being assembled for a number of organisms including both inbred experimental populations such as maize and outbred natural populations such as humans. Accurate construction of such genetic maps requires multipoint linkage analysis of particular types of pedigrees. We describe here a computer package, called MAPMAKER, designed specifically for this purpose. The program uses an efficient algorithm that allows simultaneous multipoint analysis of any number of loci. MAPMAKER also includes an interactive command language that makes it easy for a geneticist to explore linkage data. MAPMAKER has been applied to the construction of linkage maps in a number of organisms, including the human and several plants, and we outline the mapping strategies that have been used.     

SMOOTH: a statistical method for successful removal of genotyping errors from high-density genetic linkage data

High-density genetic linkage maps can be used for purposes such as fine-scale targeted gene cloning and anchoring of physical maps. However, their construction is significantly complicated by even relatively small amounts of scoring errors. Currently available software is not able to solve the ordering ambiguities in marker clusters, which inhibits the application of high-density maps. A statistical method named SMOOTH was developed to remove genotyping errors from genetic linkage data during the mapping process. The program SMOOTH calculates the difference between the observed and predicted values of data points based on data points of neighbouring loci in a given marker order. Highly improbable data points are removed by the program in an iterative process with a mapping algorithm that recalculates the map after cleaning. SMOOTH has been tested with simulated data and experimental mapping data from potato. The simulations prove that this method is able to detect a high amount of scoring errors and demonstrates that the program enables mapping software to successfully construct a very accurate high-density map. In potato the application of the program resulted in a reliable placement of nearly 1,000 markers in one linkage group.     

Linkage analysis of the GAW14 simulated dataset with microsatellite and single-nucleotide polymorphism markers in large pedigrees

Recent studies have suggested that a high-density single nucleotide polymorphism (SNP) marker set could provide equivalent or even superior information compared with currently used microsatellite (STR) marker sets for gene mapping by linkage. The focus of this study was to compare results obtained from linkage analyses involving extended pedigrees with STR and single-nucleotide polymorphism (SNP) marker sets. We also wanted to compare the performance of current linkage programs in the presence of high marker density and extended pedigree structures. One replicate of the Genetic Analysis Workshop 14 (GAW14) simulated extended pedigrees (n = 50) from New York City was analyzed to identify the major gene D2. Four marker sets with varying information content and density on chromosome 3 (STR [7.5 cM]; SNP [3 cM, 1 cM, 0.3 cM]) were analyzed to detect two traits, the original affection status, and a redefined trait more closely correlated with D2. Multipoint parametric and nonparametric linkage analyses (NPL) were performed using programs GENEHUNTER, MERLIN, SIMWALK2, and S.A.G.E. SIBPAL. Our results suggested that the densest SNP map (0.3 cM) had the greatest power to detect linkage for the original trait (genetic heterogeneity), with the highest LOD score/NPL score and mapping precision. However, no significant improvement in linkage signals was observed with the densest SNP map compared with STR or SNP-1 cM maps for the redefined affection status (genetic homogeneity), possibly due to the extremely high information contents for all maps. Finally, our results suggested that each linkage program had limitations in handling the large, complex pedigrees as well as a high-density SNP marker set.     

A user-friendly Hypercard interface for human linkage analysis

The availability of a large number of highly informative geneticmarkers has made human linkage analysis faster and easier toperform. However, current linkage analysis software does notprovide an organizational database into which a large body oflinkage data can be easily stored and manipulated. This manualentry and editing of linkage data is often time consuming andprone to typing errors. In addition, the large number of allelesin many of these markers must be reduced in order to performlinkage analysis with multiple loci across large genetic distances.This reduction in allele number is often difficult and confusing,especially in large pedigrees. We have taken advantage of theMacintosh-based Hypercard program to develop an interface withwhich linkage data can be easily stored, retrieved and edited.For each family, the components of the pedigree, including IDnumbers, sex and affection status, only need to be entered once.The program (Linkage Interface) retrieves this information eachtime the data from a new polymorphic marker is entered. LinkageInterface has flexible editing capabilities that allow the userto change any portion of the pedigree, including the additionor deletion of family members, without affecting previouslyentered genotype data. Linkage Interface can also analyze boththe pedigree and marker data and will detect any inconsistenciesin inheritance patterns. In addition, the program can reducethe number of alleles for a polynwrphic marker. Linkage Interfacewill then compare the ‘reduced’ data to the originalmarker data and assists in maintaining all informative meiosesby pointing out which meioses have become non-informative. Oncepolymorphic marker data are entered, the pedigree data, includingthe marker genotypes, are easily exported to a text file. Thistext file can be transferred to an IBM-compatible computer fordirect use with DOS-based linkage programs.     

Consensus and comprehensive linkage maps of bovine chromosome 7

The objective of this project was to integrate the currently available linkage maps for bovine chromosome 7 (BTA7) by combining data sets from eight research groups. A total of 54 unique markers were typed in eight pedigrees. Multilocus linkage analysis with CRI-MAP produced a bovine chromosome 7 consensus framework map of 27 loci ordered with odds greater than 1000:1. Furthermore, we present a bovine chromosome 7 comprehensive map integrating 54 loci. The locus order is in general agreement with the recently published linkage maps except for one discrepancy. The order of loci BM9289, BMS713, and ILSTS001 was reversed in the consensus framework map relative to the published USDA-MARC bovine chromosome 7 linkage map.     

MapDraw，在Excel中绘制遗传连锁图的宏

MAPMAKER是现今广泛使用的遗传连锁数据分析软件,然而其广泛使用的DOS版本却不具有连锁图绘制功能,给连锁作图工作带来了相当大的麻烦。为了解决这一问题,我们以大家广泛使用的数据处理软件Microsoft Excel为平台,编写了一个Excel宏——MapDraw来在轻松的操作中实现遗传连锁图的绘制。 Abstract:MAPMAKER is one of the most widely used computer software package for constructing genetic linkage maps.However,the PC version,MAPMAKER 3.0 for PC,could not draw the genetic linkage maps that its Macintosh version,MAPMAKER 3.0 for Macintosh,was able to do.Especially in recent years,Macintosh computer is much less popular than PC.Most of the geneticists use PC to analyze their genetic linkage data.So a new computer software to draw the same genetic linkage maps on PC as the MAPMAKER for Macintosh to do on Macintosh has been crying for.Microsoft Excel,one component of Microsoft Office package,is one of the most popular software in laboratory data processing.Microsoft Visual Basic for Applications (VBA) is one of the most powerful functions of Microsoft Excel.Using this program language,we can take creative control of Excel,including genetic linkage map construction,automatic data processing and more.In this paper,a Microsoft Excel macro called MapDraw is constructed to draw genetic linkage maps on PC computer based on given genetic linkage data.Use this software,you can freely construct beautiful genetic linkage map in Excel and freely edit and copy it to Word or other application.This software is just an Excel format file.You can freely copy it from ftp://211.69.140.177 or ftp://brassica.hzau.edu.cn and the source code can be found in Excel′s Visual Basic Editor.     

Efficient computations in multilocus linkage analysis.

This paper describes efficient methods for likelihood calculations and maximum-likelihood estimation in multilocus linkage analysis of reference families and general disease pedigrees, and it documents their performance as implemented in the LINKAGE programs. This information should be of considerable value in determining computing needs for linkage investigations, and in evaluating the merits of alternative algorithms.     

A detailed multipoint map of human chromosome 4 provides evidence for linkage heterogeneity and position-specific recombination rates

Utilizing the CEPH reference panel and genotypic data for 53 markers, we have constructed a 20-locus multipoint genetic map of human chromosome 4. New RFLPs are reported for four loci. The map integrates a high-resolution genetic map of 4p16 into a continuous map extending to 4q31 and an unlinked cluster of three loci at 4q35. The 20 linked markers form a continuous linkage group of 152 cM in males and 202 cM in females. Likely genetic locations are provided for 25 polymorphic anonymous sequences and 28 gene-specific RFLPs. The map was constructed employing the LINKAGE and CRIMAP computational methodologies to build the multipoint map via a stepwise algorithm. A detailed 10-point map of the 4p16 region constructed from the CEPH panel provides evidence for heterogeneity in the linkage maps constructed from families segregating for Huntington disease (HD). It additionally provides evidence for position-specific recombination frequencies in the telomeric region of 4p.     

Characterization of a minimal screening set of 172 microsatellite markers for genome-wide screens of the canine genome

We have characterized a subset of 172 microsatellite markers from the canine map, termed 'Minimal Screening Set 1' (Canine MSS-1), which we propose be used for initial genome-wide genetic linkage studies. Three hierarchical criteria were used to select markers from the current meiotic linkage and radiation hybrid maps for MSS-1. Markers were selected that (1) provided as complete coverage as possible of the canine genome, (2) were highly informative, and (3) have been ordered in linkage groups with a high degree of statistical support. This resulting screening set spans all reported meiotic linkage and RH groups, leaving only 10 known gaps > or = 20 cM. The average polymorphic information content (PIC) value of markers tested is 0.74. Coverage estimates suggest 42% of the genome is within 5 cM of at least one marker in the minimal screening set, 77% of the genome is within 10 cM. This minimal mapping set therefore provides an efficient and cost effective way to begin screening pedigrees of interest for genetic linkage.     

Improving linkage analysis in outcrossed forest trees - an example from Acacia mangium

Mapping in forest trees generally relies on outbred pedigrees in which genetic segregation is the result of meiotic recombination from both parents. The currently available mapping packages are not optimal for outcrossed pedigrees as they either cannot order phase-ambiguous data or only use pairwise information when ordering loci within linkage groups. A new package, OUTMAP, has been developed for mapping codominant loci in outcrossed trees. A comparison of maps produced using linkage data from two pedigrees of Acacia mangium Willd demonstrated that the marker orders produced using OUTMAP were consistently of higher likelihood than those produced by JOINMAP. In addition, the maps were produced more efficiently, without the need for recoding data or the detailed investigation of pairwise recombination fractions which was necessary to select the optimal marker order using JOINMAP. Distances between markers often varied from those calculated by JOINMAP, resulting in an increase in the estimated genome length. OUTMAP can be used with all segregation types to determine phase and to calculate the likelihood of alternative marker orders, with a choice of three optimisation methods.     

SimPed: a simulation program to generate haplotype and genotype data for pedigree structures

With the widespread availability of SNP genotype data, there is great interest in analyzing pedigree haplotype data. Intermarker linkage disequilibrium for microsatellite markers is usually low due to their physical distance; however, for dense maps of SNP markers, there can be strong linkage disequilibrium between marker loci. Linkage analysis (parametric and nonparametric) and family-based association studies are currently being carried out using dense maps of SNP marker loci. Monte Carlo methods are often used for both linkage and association studies; however, to date there are no programs available which can generate haplotype and/or genotype data consisting of a large number of loci for pedigree structures. SimPed is a program that quickly generates haplotype and/or genotype data for pedigrees of virtually any size and complexity. Marker data either in linkage disequilibrium or equilibrium can be generated for greater than 20,000 diallelic or multiallelic marker loci. Haplotypes and/or genotypes are generated for pedigree structures using specified genetic map distances and haplotype and/or allele frequencies. The simulated data generated by SimPed is useful for a variety of purposes, including evaluating methods that estimate haplotype frequencies for pedigree data, evaluating type I error due to intermarker linkage disequilibrium and estimating empirical p values for linkage and family-based association studies.     

Efficient and accurate construction of genetic linkage maps from the minimum spanning tree of a graph

Genetic linkage maps are cornerstones of a wide spectrum of biotechnology applications, including map-assisted breeding, association genetics, and map-assisted gene cloning. During the past several years, the adoption of high-throughput genotyping technologies has been paralleled by a substantial increase in the density and diversity of genetic markers. New genetic mapping algorithms are needed in order to efficiently process these large datasets and accurately construct high-density genetic maps. In this paper, we introduce a novel algorithm to order markers on a genetic linkage map. Our method is based on a simple yet fundamental mathematical property that we prove under rather general assumptions. The validity of this property allows one to determine efficiently the correct order of markers by computing the minimum spanning tree of an associated graph. Our empirical studies obtained on genotyping data for three mapping populations of barley (Hordeum vulgare), as well as extensive simulations on synthetic data, show that our algorithm consistently outperforms the best available methods in the literature, particularly when the input data are noisy or incomplete. The software implementing our algorithm is available in the public domain as a web tool under the name MSTmap.