An SSR genetic map of Sorghum bicolor (L.) Moench and its comparison to a published genetic map.

Sorghum bicolor (L.) Moench is an important grain and forage crop grown worldwide. We developed a simple sequence repeat (SSR) linkage map for sorghum using 352 publicly available SSR primer pairs and a population of 277 F2 individuals derived from a cross between the Westland A line and PI 550610. A total of 132 SSR loci appeared polymorphic in the mapping population, and 118 SSRs were mapped to 16 linkage groups. These mapped SSR loci were distributed throughout 10 chromosomes of sorghum, and spanned a distance of 997.5 cM. More important, 38 new SSR loci were added to the sorghum genetic map in this study. The mapping result also showed that chromosomes SBI-01, SBI-02, SBI-05, and SBI-06 each had 1 linkage group; the other 6 chromosomes were composed of 2 linkage groups each. Except for 5 closely linked marker flips and 1 locus (Sb6_34), the marker order of this map was collinear to a published sorghum map, and the genetic distances of common marker intervals were similar, with a difference ratio 相似文献   

Two statistical tests for meiotic breakpoint analysis.

Meiotic breakpoint analysis (BPA), a statistical method for ordering genetic markers, is increasing in importance as a method for building genetic maps of human chromosomes. Although BPA does not provide estimates of genetic distances between markers, it efficiently locates new markers on already defined dense maps, when likelihood analysis becomes cumbersome or the sample size is small. However, until now no assessments of statistical significance have been available for evaluating the possibility that the results of a BPA were produced by chance. In this paper, we propose two statistical tests to determine whether the size of a sample and its genetic information content are sufficient to distinguish between "no linkage" and "linkage" of a marker mapped by BPA to a certain region. Both tests are exact and should be conducted after a BPA has assigned the marker to an interval on the map. Applications of the new tests are demonstrated by three examples: (1) a synthetic data set, (2) a data set of five markers on human chromosome 8p, and (3) a data set of four markers on human chromosome 17q.     

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.     

Mapping quantitative trait loci using binned genotypes

Precise mapping of quantitative trait loci(QTLs)is critical for assessing genetic effects and identifying candidate genes for quantitative traits.Interval and composite interval mappings have been the methods of choice for several decades,which have provided tools for identifying genomic regions harboring causal genes for quantitative traits.Historically,the concept was developed on the basis of sparse marker maps where genotypes of loci within intervals could not be observed.Currently,genomes of many organisms have been saturated with markers due to the new sequencing technologies.Genotyping by sequencing usually generates hundreds of thousands of single nucleotide polymorphisms(SNPs),which often include the causal polymorphisms.The concept of interval no longer exists,prompting the necessity of a norm change in QTL mapping technology to make use of the high-volume genomic data.Here we developed a statistical method and a software package to map QTLs by binning markers into haplotype blocks,called bins.The new method detects associations of bins with quantitative traits.It borrows the mixed model methodology with a polygenic control from genome-wide association studies(GWAS)and can handle all kinds of experimental populations under the linear mixed model(LMM)framework.We tested the method using both simulated data and data from populations of rice.The results showed that this method has higher power than the current methods.An R package named binQTL is available from GitHub.     

A recoding scheme for X-linked and pseudoautosomal loci to be used with computer programs for autosomal LOD-score analysis

We present a recoding scheme that allows for a parametric multipoint X-chromosomal linkage analysis of dichotomous traits in the context of a computer program for autosomes that can use trait models with imprinting. Furthermore, with this scheme, it is possible to perform a joint multipoint analysis of X-linked and pseudoautosomal loci. It is required that (1) the marker genotypes of all female nonfounders are available and that (2) there are no male nonfounders who have daughters in the pedigree. The second requirement does not apply if the trait locus is pseudoautosomal. The X-linked marker loci are recorded by adding a dummy allele to the males' hemizygous genotypes. For modelling an X-linked trait locus, five different liability classes are defined, in conjunction with a paternal imprinting model for male nonfounders. The formulation aims at the mapping of a diallelic trait locus relative to an arbitrary number of codominant markers with known genetic distances, in cases where a program for a genuine X-chromosomal analysis is not available.     

A Scale-Corrected Comparison of Linkage Disequilibrium Levels between Genic and Non-Genic Regions

The understanding of non-random association between loci, termed linkage disequilibrium (LD), plays a central role in genomic research. Since causal mutations are generally not included in genomic marker data, LD between those and available markers is essential for capturing the effects of causal loci on localizing genes responsible for traits. Thus, the interpretation of association studies requires a detailed knowledge of LD patterns. It is well known that most LD measures depend on minor allele frequencies (MAF) of the considered loci and the magnitude of LD is influenced by the physical distances between loci. In the present study, a procedure to compare the LD structure between genomic regions comprising several markers each is suggested. The approach accounts for different scaling factors, namely the distribution of MAF, the distribution of pair-wise differences in MAF, and the physical extent of compared regions, reflected by the distribution of pair-wise physical distances. In the first step, genomic regions are matched based on similarity in these scaling factors. In the second step, chromosome- and genome-wide significance tests for differences in medians of LD measures in each pair are performed. The proposed framework was applied to test the hypothesis that the average LD is different in genic and non-genic regions. This was tested with a genome-wide approach with data sets for humans (Homo sapiens), a highly selected chicken line (Gallus gallus domesticus) and the model plant Arabidopsis thaliana. In all three data sets we found a significantly higher level of LD in genic regions compared to non-genic regions. About 31% more LD was detected genome-wide in genic compared to non-genic regions in Arabidopsis thaliana, followed by 13.6% in human and 6% chicken. Chromosome-wide comparison discovered significant differences on all 5 chromosomes in Arabidopsis thaliana and on one third of the human and of the chicken chromosomes.     

Stochastic search gene suggestion: a Bayesian hierarchical model for gene mapping

Mapping the genes for a complex disease, such as diabetes or rheumatoid arthritis (RA), involves finding multiple genetic loci that may contribute to the onset of the disease. Pairwise testing of the loci leads to the problem of multiple testing. Looking at haplotypes, or linear sets of loci, avoids multiple tests but results in a contingency table with sparse counts, especially when using marker loci with multiple alleles. We propose a hierarchical Bayesian model for case-parent triad data that uses a conditional logistic regression likelihood to model the probability of transmission to a diseased child. We define hierarchical prior distributions on the allele main effects to model the genetic dependencies present in the human leukocyte antigen (HLA) region of chromosome 6. First, we add a hierarchical level for model selection that accounts for both locus and allele selection. This allows us to cast the problem of identifying genetic loci relevant to the disease into a problem of Bayesian variable selection. Second, we attempt to include linkage disequilibrium as a covariance structure in the prior for model coefficients. We evaluate the performance of the procedure with some simulated examples and then apply our procedure to identifying genetic markers in the HLA region that influence risk for RA. Our software is available on the website http://www.epigenetic.org/Linkage/ssgs-public/.     

Bayesian analysis of linkage between genetic markers and quantitative trait loci. II. Combining prior knowledge with experimental evidence

Summary A Bayesian method was developed for identifying genetic markers linked to quantitative trait loci (QTL) by analyzing data from daughter or granddaughter designs and single markers or marker pairs. Traditional methods may yield unrealistic results because linkage tests depend on number of markers and QTL gene effects associated with selected markers are overestimated. The Bayesian or posterior probability of linkage combines information from a daughter or granddaughter design with the prior probability of linkage between a marker locus and a QTL. If the posterior probability exceeds a certain quantity, linkage is declared. Upon linkage acceptance, Bayesian estimates of marker-QTL recombination rate and QTL gene effects and frequencies are obtained. The Bayesian estimates of QTL gene effects account for different amounts of information by shrinking information from data toward the mean or mode of a prior exponential distribution of gene effects. Computation of the Bayesian analysis is feasible. Exact results are given for biallelic QTL, and extensions to multiallelic QTL are suggested.     

A genome-wide microsatellite polymorphism database for the indica and japonica rice.

Microsatellite (MS) polymorphism is an important source of genetic diversity, providing support for map-based cloning and molecular breeding. We have developed a new database that contains 52 845 polymorphic MS loci between indica and japonica, composed of ample Class II MS markers, and integrated 18 828 MS loci from IRGSP and genetic markers from RGP. Based on genetic marker positions on the rice genome (http://rise.genomics.org.cn/rice2/index.jsp ), we determined the approximate genetic distances of these MS loci and validated 100 randomly selected markers experimentally with 90% success rate. In addition, we recorded polymorphic MS positions in indica cv. 9311 that is the most important paternal parent of the two-line hybrid rice in China. Our database will undoubtedly facilitate the application of MS markers in genetic researches and marker-assisted breeding. The data set is freely available from www.wigs.zju.edu.cn/achievment/polySSR.     

An integrated SSR map of grapevine based on five mapping populations

A grapevine (mainly Vitis vinifera L., 2n = 38) composite genetic map was constructed with CarthaGene using segregation data from five full-sib populations of 46, 95, 114, 139 and 153 individuals, to determine the relative position of a large set of molecular markers. This consensus map comprised 515 loci (502 SSRs and 13 other type PCR-based markers), amplified using 439 primer pairs (426 SSRs and 13 others) with 50.1% common markers shared by at least two crosses. Out of all loci, 257, 85, 74, 69 and 30 were mapped in 1, 2, 3, 4 and 5 individual mapping populations, respectively. Marker order was generally well conserved between maps of individual populations, with only a few significant differences in the recombination rate of marker pairs between two or more populations. The total length of the integrated map was 1,647 cM Kosambi covering 19 linkage groups, with a mean distance between neighbour loci of 3.3 cM. A framework-integrated map was also built, with marker order supported by a LOD of 2.0. It included 257 loci spanning 1,485 cM Kosambi with a mean inter-locus distance of 6.2 cM over 19 linkage groups. These integrated maps are the most comprehensive SSR-based maps available so far in grapevine and will serve either for choosing markers evenly scattered over the whole genome or for selecting markers that cover particular regions of interest. The framework map is also a useful starting point for the integration of the V. vinifera physical and genetic maps.Electronic Supplementary Material Supplementary material is available for this article at and is accessible for authorized users.     

A transmission/disequilibrium test approach to screen for quantitative trait loci in two selected lines of large white pigs

Pedigree and marker data from a multiple-generation pig selection experiment have been analysed to screen for loci affecting quantitative traits (QTL). Pigs from a base population were selected either for low backfat thickness at fixed live weight (L-line) or high live weight at fixed age (F-line). Selection was based on single-trait own performance and DNA was available on selected individuals only. Genotypes for three marker loci with known positions on chromosome 4 were available. The transmission/disequilibrium test (TDT) was originally described in human genetics to test for linkage between a genetic marker and a disease-susceptibility locus, in the presence of association. Here, we adapt the TDT to test for linkage between a marker and QTL favoured by selection, and for linkage disequilibrium between them in the base population. The a priori unknown distribution of the test statistic under the null hypothesis, no linkage, was obtained via Monte Carlo simulation. Significant TDT statistics were found for markers AFABP and SW818 in the F-line, indicating the presence of a closely linked QTL affecting growth performance. In the L-line, none of the markers studied showed significance. This study emphasizes the potential of the TDT as a quick and simple approach to screen for QTL in situations where marker genotypes are available on selected individuals. The results suggest that previously identified QTL in crosses of genetically diverse breeds may also segregate in commercial selection lines.     

Evaluation of inter-simple sequence repeat analysis for mapping in Citrus and extension of the genetic linkage map

Inter-simple sequence repeat (ISSR) analysis was evaluated for its usefulness in generating markers to extend the genetic linkage map of Citrus using a backcross population previously mapped with restriction fragment length polymorphism (RFLP), random amplified polymorphic DNA (RAPD) and isozyme markers. ISSR markers were obtained through the simple technique of PCR followed by analysis on agarose gels, using simple sequence repeat (SSR) primers. Optimization of reaction conditions was achieved for 50% of the SSR primers screened, and the primers amplified reproducible polymorphic bands in the parents and progeny of the backcross population. Mendelian segregation of the polymorphic bands was demonstrated, with an insignificant number of skewed loci. Most of the SSR primers produced dominant loci; however co-dominance was observed with loci derived from three primers. A new genetic map was produced by combining the segregation data for the ISSR markers and data for the RFLP, RAPD and isozyme markers from the previous map and creating genetic linkages among all the markers using JoinMap 2.0 mapping software. The new map has an improved distribution of markers along the linkage groups with fewer gaps, and marker order showed partial or complete conservation in the linkage groups. The incorporation of ISSR markers into the genetic linkage map demonstrates that ISSR markers are suitable for genetic mapping in Citrus. Received: 3 February 2000 / Accepted: 12 May 2000     

Linkage analysis of "necessary" disease loci versus "susceptibility" loci.

The association of some diseases with specific alleles of certain genetic markers has been difficult to explain. Several explanations have been proposed for the phenomenon of association, e.g. the existence of multiple, interacting genes (epistasis) or a disease locus in linkage disequilibrium with the marker locus. One might suppose that when marker data from families with associated diseases are analyzed for linkage, the existence of the association would assure that linkage will be found, and found at a tight recombination fraction. In fact, however, linkage analyses of some diseases associated with HLA, as well as diseases associated with alleles at other loci located throughout the genome, show significant evidence against linkage, and others show loose linkage, to the puzzlement of many researchers. In part, the puzzlement arises because linkage analysis is ideal for looking for loci that are necessary, even if not sufficient, for disease expression but may be much less useful for finding loci that are neither necessary nor sufficient for disease expression (so-called susceptibility loci). This work explores what happens when one looks for linkage to susceptibility loci. A susceptibility locus in this case means that the allele increases risk but is neither necessary nor sufficient for disease expression. It might be either an allele at the marker locus itself that is increasing susceptibility or an allele at a locus in linkage disequilibrium with the marker. This work uses computer simulation to examine how linkage analyses behave when confronted with data from such a model.(ABSTRACT TRUNCATED AT 250 WORDS)     

Genetic linkage studies of autosomal dominant polycystic kidney disease: search for the second gene in a large Sicilian family

Summary Polycystic kidney disease (PKD) is a common autosomal dominant genetic disorder caused by mutation in at least two different gene loci. The PKD1 gene has been localized on the short arm of chromosome 16. The location of a second genetic locus in the human genome is not yet known. A large PKD kindred, unlinked to chromosome 16, with over 250 members was studied using both DNA and classical markers. In total, 29 informative marker loci on 11 autosomes have been analyzed for linkage with PKD. The data significantly exclude the linkage with disease locus from 17 marker loci and show no evidence of close linkage with the other loci.     

The effect of using approximate gametic variance covariance matrices on marker assisted selection by BLUP

Under additive inheritance, the Henderson mixed model equations (HMME) provide an efficient approach to obtaining genetic evaluations by marker assisted best linear unbiased prediction (MABLUP) given pedigree relationships, trait and marker data. For large pedigrees with many missing markers, however, it is not feasible to calculate the exact gametic variance covariance matrix required to construct HMME. The objective of this study was to investigate the consequences of using approximate gametic variance covariance matrices on response to selection by MABLUP. Two methods were used to generate approximate variance covariance matrices. The first method (Method A) completely discards the marker information for individuals with an unknown linkage phase between two flanking markers. The second method (Method B) makes use of the marker information at only the most polymorphic marker locus for individuals with an unknown linkage phase. Data sets were simulated with and without missing marker data for flanking markers with 2, 4, 6, 8 or 12 alleles. Several missing marker data patterns were considered. The genetic variability explained by marked quantitative trait loci (MQTL) was modeled with one or two MQTL of equal effect. Response to selection by MABLUP using Method A or Method B were compared with that obtained by MABLUP using the exact genetic variance covariance matrix, which was estimated using 15 000 samples from the conditional distribution of genotypic values given the observed marker data. For the simulated conditions, the superiority of MABLUP over BLUP based only on pedigree relationships and trait data varied between 0.1% and 13.5% for Method A, between 1.7% and 23.8% for Method B, and between 7.6% and 28.9% for the exact method. The relative performance of the methods under investigation was not affected by the number of MQTL in the model.     

Detecting susceptibility genes in case-control studies using set association

Complex diseases are generally caused by intricate interactions of multiple genes and environmental factors. Most available linkage and association methods are developed to identify individual susceptibility genes assuming a simple disease model blind to any possible gene - gene and gene - environmental interactions. We used a set association method that uses single-nucleotide polymorphism markers to locate genetic variation responsible for complex diseases in which multiple genes are involved. Here we extended the set association method from bi-allelic to multiallelic markers. In addition, we studied the type I error rates and power for both approaches using simulations based on the coalescent process. Both bi-allelic set association (BSA) and multiallelic set association (MSA) tests have the correct type I error rates. In addition, BSA and MSA can have more power than individual marker analysis when multiple genes are involved in a complex disease. We applied the MSA approach to the simulated data sets from Genetic Analysis Workshop 13. High cholesterol level was used as the definitive phenotype for a disease. MSA failed to detect markers with significant linkage disequilibrium with genes responsible for cholesterol level. This is due to the wide spacing between the markers and the lack of association between the marker loci and the simulated phenotype.     

A mapped set of genetic markers for human chromosome 9

A genetic map of markers for human chromosome 9, spanning a genetic distance of 147 cM in males and 231 cM in females, has been constructed from linkage studies with 19 loci in a large panel of reference families. The markers included four classical systems previously assigned to chromosome 9, and restriction fragment length polymorphisms of two cloned genes, ABL oncogene and argininosuccinase synthetase pseudogene 3 (ASSP3). The remaining 13 marker loci, with an average heterozygosity of 42%, were defined by arbitrary DNA probes newly ascertained from genomic libraries; seven of them were variable number of tandem repeat (VNTR) loci. A subset of 7 of the 19 linked markers is proposed for a primary map that could detect linkage with a genetic defect within the covered region of chromosome 9, provided that at least 45 phase-known meioses were available for study in an affected family.     

Construction of genetic linkage map of the medicinal and ornamental plant <Emphasis Type="Italic">Catharanthus roseus</Emphasis>

An integrated genetic linkage map of the medicinal and ornamental plant Catharanthus roseus, based on different types of molecular and morphological markers was constructed, using a F₂ population of 144 plants. The map defines 14 linkage groups (LGs) and consists of 131 marker loci, including 125 molecular DNA markers (76 RAPD, 3 RAPD combinations; 7 ISSR; 2 EST-SSR from Medicago truncatula and 37 other PCR based DNA markers), selected from a total of 472 primers or primer pairs, and six morphological markers (stem pigmentation, leaf lamina pigmentation and shape, leaf petiole and pod size, and petal colour). The total map length is 1131.9 cM (centiMorgans), giving an average map length and distance between two markers equal to 80.9 cM and 8.6 cM, respectively. The morphological markers/genes were found linked with nearest molecular or morphological markers at distances varying from 0.7 to 11.4 cM. Linkage was observed between the morphological markers concerned with lamina shape and petiole size of leaf on LG1 and leaf, stem and petiole pigmentation and pod size on LG8. This is the first genetic linkage map of C. roseus.     

Twelve loci form a continuous linkage map for human chromosome 18

We have constructed a primary genetic map of human chromosome 18 consisting of 11 DNA markers and one serological marker (JK). Two of these loci define highly polymorphic VNTR systems. The markers define a continuous genetic linkage map of 97 cM in males and 205 cM in females; female genetic distances in a panel of 59 three-generation families were consistently about twice those observed in males. The high odds in support of the linear order of the markers on this recombination map, and the extent of coverage of chromosome 18, indicate that this map will permit efficient linkage studies of human genetic diseases that may be segregating on chromosome 18 and will provide anchor points for development of high-resolution maps for this chromosome.