DNA markers in plant improvement: an overview

The progress made in DNA marker technology has been tremendous and exciting. DNA markers have provided valuable tools in various analyses ranging from phylogenetic analysis to the positional cloning of genes. The development of high-density molecular maps which has been facilitated by PCR-based markers, have made the mapping and tagging of almost any trait possible. Marker-assisted selection has the potential to deploy favorable gene combinations for disease control. Comparative studies between incompatible species using these markers has resulted in synteny maps which are useful not only in predicting genome organization and evolution but also have practical application in plant breeding. DNA marker technology has found application in fingerprinting genotypes, in determining seed purity, in systematic sampling of germplasm, and in phylogenetic analysis. This review discusses the use of this technology for the genetic improvement of plants.     

Positional cloning by linkage disequilibrium

Recently, metric linkage disequilibrium (LD) maps that assign an LD unit (LDU) location for each marker have been developed (Maniatis et al. 2002). Here we present a multiple pairwise method for positional cloning by LD within a composite likelihood framework and investigate the operating characteristics of maps in physical units (kb) and LDU for two bodies of data (Daly et al. 2001; Jeffreys et al. 2001) on which current ideas of blocks are based. False-negative indications of a disease locus (type II error) were examined by selecting one single-nucleotide polymorphism (SNP) at a time as causal and taking its allelic count (0, 1, or 2, for the three genotypes) as a pseudophenotype, Y. By use of regression and correlation, association between every pseudophenotype and the allelic count of each SNP locus (X) was based on an adaptation of the Malecot model, which includes a parameter for location of the putative gene. By expressing locations in kb or LDU, greater power for localization was observed when the LDU map was fitted. The efficiency of the kb map, relative to the LDU map, to describe LD varied from a maximum of 0.87 to a minimum of 0.36, with a mean of 0.62. False-positive indications of a disease locus (type I error) were examined by simulating an unlinked causal SNP and the allele count was used as a pseudophenotype. The type I error was in good agreement with Wald's likelihood theorem for both metrics and all models that were tested. Unlike tests that select only the most significant marker, haplotype, or haploset, these methods are robust to large numbers of markers in a candidate region. Contrary to predictions from tagging SNPs that retain haplotype diversity, the sample with smaller size but greater SNP density gave less error. The locations of causal SNPs were estimated with the same precision in blocks and steps, suggesting that block definition may be less useful than anticipated for mapping a causal SNP. These results provide a guide to efficient positional cloning by SNPs and a benchmark against which the power of positional cloning by haplotype-based alternatives may be measured.     

Linkage disequilibrium and allele-frequency distributions for 114 single-nucleotide polymorphisms in five populations

Single-nucleotide polymorphisms (SNPs) may be extremely important for deciphering the impact of genetic variation on complex human diseases. The ultimate value of SNPs for linkage and association mapping studies depends in part on the distribution of SNP allele frequencies and intermarker linkage disequilibrium (LD) across populations. Limited information is available about these distributions on a genomewide scale, particularly for LD. Using 114 SNPs from 33 genes, we compared these distributions in five American populations (727 individuals) of African, European, Chinese, Hispanic, and Japanese descent. The allele frequencies were highly correlated across populations but differed by >20% for at least one pair of populations in 35% of SNPs. The correlation in LD was high for some pairs of populations but not for others (e.g., Chinese American or Japanese American vs. any other population). Regardless of population, average minor-allele frequencies were significantly higher for SNPs in noncoding regions (20%-25%) than for SNPs in coding regions (12%-16%). Interestingly, we found that intermarker LD may be strongest with pairs of SNPs in which both markers are nonconservative substitutions, compared to pairs of SNPs where at least one marker is a conservative substitution. These results suggest that population differences and marker location within the gene may be important factors in the selection of SNPs for use in the study of complex disease with linkage or association mapping methods.     

Linkage mapping of gene-associated SNPs to pig chromosome 11

Single nucleotide polymorphisms (SNPs) were discovered in porcine expressed sequence tags (ESTs) orthologous to genes from human chromosome 13 (HSA13) and predicted to be located on pig chromosome 11 (SSC11). The SNPs were identified as sequence variants in clusters of EST sequences from pig cDNA libraries constructed in the Sino-Danish pig genome project. In total, 312 human gene sequences from HSA13 were used for similarity searches in our pig EST database. Pig ESTs showing significant similarity with HSA13 genes were clustered and candidate SNPs were identified. Allele frequencies for 26 SNPs were estimated in a group of 80 unrelated pigs from Danish commercial pig breeds: Duroc, Hampshire, Landrace and Large White. Eighteen of the 26 SNPs genotyped in the PiGMaP Reference Families were mapped by linkage analysis to SSC11. The EST-based SNPs published here are new genetic markers useful for linkage and association studies in commercial and experimental pig populations. This study represents the first gene-associated SNP linkage map of pig chromosome 11 and adds new comparative mapping information between SSC11 and HSA13. Furthermore, our data facilitate future studies aimed at the identification of interesting regions on pig chromosome 11, positional cloning and fine mapping of quantitative trait loci in pig.     

The impact of missing and erroneous genotypes on tagging SNP selection and power of subsequent association tests

OBJECTIVE: Single nucleotide polymorphisms (SNPs) serve as effective markers for localizing disease susceptibility genes, but current genotyping technologies are inadequate for genotyping all available SNP markers in a typical linkage/association study. Much attention has recently been paid to methods for selecting the minimal informative subset of SNPs in identifying haplotypes, but there has been little investigation of the effect of missing or erroneous genotypes on the performance of these SNP selection algorithms and subsequent association tests using the selected tagging SNPs. The purpose of this study is to explore the effect of missing genotype or genotyping error on tagging SNP selection and subsequent single marker and haplotype association tests using the selected tagging SNPs. METHODS: Through two sets of simulations, we evaluated the performance of three tagging SNP selection programs in the presence of missing or erroneous genotypes: Clayton's diversity based program htstep, Carlson's linkage disequilibrium (LD) based program ldSelect, and Stram's coefficient of determination based program tagsnp.exe. RESULTS: When randomly selected known loci were relabeled as 'missing', we found that the average number of tagging SNPs selected by all three algorithms changed very little and the power of subsequent single marker and haplotype association tests using the selected tagging SNPs remained close to the power of these tests in the absence of missing genotype. When random genotyping errors were introduced, we found that the average number of tagging SNPs selected by all three algorithms increased. In data sets simulated according to the haplotype frequecies in the CYP19 region, Stram's program had larger increase than Carlson's and Clayton's programs. In data sets simulated under the coalescent model, Carlson's program had the largest increase and Clayton's program had the smallest increase. In both sets of simulations, with the presence of genotyping errors, the power of the haplotype tests from all three programs decreased quickly, but there was not much reduction in power of the single marker tests. CONCLUSIONS: Missing genotypes do not seem to have much impact on tagging SNP selection and subsequent single marker and haplotype association tests. In contrast, genotyping errors could have severe impact on tagging SNP selection and haplotype tests, but not on single marker tests.     

A comprehensive SNP-based genetic analysis of inbred mouse strains

Dense genetic maps of mammalian genomes facilitate a variety of biological studies including the mapping of polygenic traits, positional cloning of monogenic traits, mapping of quantitative or qualitative trait loci, marker association, allelic imbalance, speed congenic construction, and evolutionary or phylogenetic comparison. In particular, single nucleotide polymorphisms (SNPs) have proved useful because of their abundance and compatibility with multiple high-throughput technology platforms. SNP genotyping is especially suited for the genetic analysis of model organisms such as the mouse because biallelic markers remain fully informative when used to characterize crosses between inbred strains. Here we report the mapping and genotyping of 673 SNPs (including 519 novel SNPs) in 55 of the most commonly used mouse strains. These data have allowed us to construct a phylogenetic tree that correlates and expands known genealogical relationships and clarifies the origin of strains previously having an uncertain ancestry. All 55 inbred strains are distinguishable genetically using this SNP panel. Our data reveal an uneven SNP distribution consistent with a mosaic pattern of inheritance and provide some insight into the changing dynamics of the physical architecture of the genome. Furthermore, these data represent a valuable resource for the selection of markers and the design of experiments that require the genetic distinction of any pair of mouse inbred strains such as the generation of congenic mice, positional cloning, and the mapping of quantitative or qualitative trait loci.The content of this publication does not necessarily reflect the view or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government.     

The application of next-generation sequencing in the autozygosity mapping of human recessive diseases

Autozygosity, or the inheritance of two copies of an ancestral allele, has the potential to not only reveal phenotypes caused by biallelic mutations in autosomal recessive genes, but to also facilitate the mapping of such mutations by flagging the surrounding haplotypes as tractable runs of homozygosity (ROH), a process known as autozygosity mapping. Since SNPs replaced microsatellites as markers for the purpose of genomewide identification of ROH, autozygosity mapping of Mendelian genes has witnessed a significant acceleration. Historically, successful mapping traditionally required favorable family structure that permits the identification of an autozygous interval that is amenable to candidate gene selection and confirmation by Sanger sequencing. This requirement presented a major bottleneck that hindered the utilization of simplex cases and many multiplex families with autosomal recessive phenotypes. However, the advent of next-generation sequencing that enables massively parallel sequencing of DNA has largely bypassed this bottleneck and thus ushered in an era of unprecedented pace of Mendelian disease gene discovery. The ability to identify a single causal mutation among a massive number of variants that are uncovered by next-generation sequencing can be challenging, but applying autozygosity as a filter can greatly enhance the enrichment process and its throughput. This review will discuss the power of combining the best of both techniques in the mapping of recessive disease genes and offer some tips to troubleshoot potential limitations.     

Limits of resolution of genetic linkage studies: implications for the positional cloning of human disease genes.

Positional cloning studies to identify disease genes are being carried out for many human genetic diseases. Such studies often include a genome-scan linkage analysis to identify the rough chromosomal location of a disease gene, fine structure genetic mapping to define and narrow the chromosomal interval in which the disease gene may be located, and physical mapping and gene identification in the genetically defined interval to clone the disease gene. During the planning of a positional cloning study, it is important to know that, if linkage is found, the genetic interval identified is likely to be sufficiently narrow to be dissected efficiently by methods of physical mapping and gene identification. Thus, we wish to know the limits of resolution of a genetic linkage study. In this paper, I determine for Mendelian diseases the distributions and moments of three measures of linkage resolution: (1) in a set of N chromosomes, the distance between the nearest crossovers that flank a disease locus, (2) the distance between the nearest genetic markers that flank the pair of flanking crossovers after a genome scan, and (3) the distance between the nearest flanking markers after additional randomly placed markers are generated and typed in an identified interval. These results provide explicit sample-size guidelines for future positional cloning studies of Mendelian diseases and make possible a more objective evaluation of whether a proposed positional cloning study is likely to be successful. I also briefly discuss the more difficult problem of linkage resolution for complex genetic diseases.     

Physical mapping of 14 new DNA markers isolated from the human distal Xp region.

We have isolated 14 new DNA markers from the human Xpter-Xp21 region distal to the Duchenne muscular dystrophy gene by targeted cloning, employing two somatic cell hybrids containing this region as their sole human material. High-resolution physical localization of these markers within this region was obtained by hybridization to two mapping panels consisting of DNA from patients carrying various translocations and deletions in distal Xp. Five markers were assigned to the pseudoautosomal region where their position on the long-range map of this region was further determined by pulsed-field gel electrophoresis. The other nine markers map to the X-specific region. Informative TaqI restriction fragment length polymorphisms were observed for four loci. One of these represents a region-specific low-copy repeated element. These 14 new markers represent useful tools for the understanding of distal Xp deletion and translocation mechanisms and for the positional cloning of disease genes in the region.     

Comparative assessment of the association information captured by SNP tagging

Exploiting the association between single nucleotide polymorphisms (SNP) can potentially reduce the costs of association mapping of common disease genes. Different methods have been proposed for defining subsets of SNPs as proxies (or tagSNPs) for other SNPs, some of which rely upon a model of haplotype blocks. Other approaches only consider the pair-wise correlation between markers as a basis for selecting tagSNPs. Yet another, recently proposed model-based method takes marker heterozygosity and genetic distance into account in order to maximize the expected utility of a marker set to map frequent, but unobserved genetic variants. We compared these tagging approaches with regard to their ability to correlate tagSNPs and bi-allelic, potentially disease-causing genetic variants. We used the CEU sample of chromosome 19 from the HapMap project for an initial comparison, and demonstrated a comparable performance of both approaches but a difference in terms of tagSNPs selected and variants captured. In any case, we conclude that a considerable loss of information appears to be inherent to any type of SNP tagging, even when dense marker sets are available for SNP selection.     

Efficient selective screening of haplotype tag SNPs

Haplotypes defined by common single nucleotide polymorphisms (SNPs) have important implications for mapping of disease genes and human traits. Often only a small subset of the SNPs is sufficient to capture the full haplotype information. Such subsets of markers are called haplotype tagging SNPs (htSNPs). Although htSNPs can be identified by eye, efficient computer algorithms and flexible interactive software tools are required for large datasets such as the human genome haplotype map. We describe a java-based program, SNPtagger, which screens for minimal sets of SNP markers to represent given haplotypes according to various user requirements. The program offers several options for inclusion/exclusion of specific markers and presents alternative panels for final selection. AVAILABILITY: The www-based program is available at http://www.well.ox.ac.uk/~xiayi/haplotype/index.html.     

Development of PCR-based SNP markers for rice blast resistance genes at the<Emphasis Type="Italic"> Piz</Emphasis> locus

We assessed the utility of single-nucleotide polymorphisms (SNPs) and small insertion/deletion polymorphisms (InDels) as DNA markers in genetic analysis and breeding of rice. Toward this end, we surveyed SNPs and InDels in the chromosomal region containing the Piz and Piz-t rice blast resistance genes and developed PCR-based markers for typing the SNPs. Analysis of sequences from a blast-susceptible Japanese cultivar and two cultivars each containing one of these genes revealed that SNPs are abundant in the Piz and Piz-t regions (on average, one SNP every 248 bp), but the number of InDels was much lower. The dense distribution of SNPs facilitated the generation of SNP markers in the vicinity of the genes. For typing these SNPs, we used a modified allele-specific PCR method. Of the 49 candidate allele-specific markers, 33 unambiguously and reproducibly discriminated between the two alleles. We used the markers for mapping the Piz and Piz-t genes and evaluating the size of DNA segments introgressed from the Piz donor cultivar in Japanese near-isogenic lines containing Piz. Our findings suggest that, because of its ability to generate numerous markers within a target region and its simplicity in assaying genotypes, SNP genotyping with allele-specific PCR is a valuable tool for gene mapping, map-based cloning, and marker-assisted selection in crops, especially rice.Communicated by D.J. Mackill     

Towards marker assisted selection in livestock

In recent years, genomic tools have become available for most livestock species and are now being used routinely to map quantitative trait loci underlying the genetic variance for numerous economically important traits. Fine-mapping methods are being devised to refine the initially coarse map positions of the quantitative trait loci to the point required for marker assisted selection and, eventually, the positional cloning of the underlying genes. Mapping information on QTL is beginning to be used to increase genetic response by enhancing genetic variance, selection accuracy, selection intensity and by reducing the generation interval. Optimal use of MAS will require the development of more robust methods for the routine genotyping of preimplantation embryos for multiple markers.     

Identifying the susceptibility gene(s) in a set of trait-linked genes using genotype data

There are generally three steps to isolate a disease linkage-susceptibility gene: genome-wide scan, fine mapping, and, last, positional cloning. The last step is time consuming and involves intensive laboratory work. In some cases, fine mapping cannot proceed further on a set of markers because they are tightly linked. For years, genetic statisticians have been trying different ways to narrow the fine-mapping results to provide some guidance for the next step of laboratory work. Although these methods are practical and efficient, most of them are based on IBD data, which usually can be inferred only from the genotype data with some uncertainty. The corresponding methods thus have no greater power than one using genotype data directly. Also, IBD-based methods apply only to relative pair data. Here, using genotype data, we have developed a statistical hypothesis-testing method to pinpoint a SNP, or SNPs, suspected of responsibility for a disease trait linkage among a set of SNPs tightly linked in a region. Our method uses genotype data of affected individuals or case-control studies, which are widely available in the laboratory. The testing statistic can be constructed using any genotype-based disease-marker disequilibrium measure and is asymptotically distributed as a chi-square mixture. This method can be used for singleton data, relative pair data, or general pedigree data. We have applied the method to simulated data as well as a real data set; it gives satisfactory results.     

Molecular mapping of Or, a gene inducing beta-carotene accumulation in cauliflower (Brassica oleracea L. var. botrytis).

The cauliflower (Brassica oleracea L. var. botrytis) Or gene is a semi-dominant, single-locus mutation that induces the accumulation of high levels of beta-carotene in various tissues of the plant, turning them orange. As part of a map-based cloning strategy, molecular mapping of the Or gene in the cauliflower genome was undertaken in a mapping population consisting of 195 F2 individuals. By using amplified fragment length polymorphism (AFLP) in conjunction with bulked segregant analysis, we identified 10 AFLP markers closely linked to the Or gene. Four of the most closely linked flanking markers were converted into restriction fragment length polymorphism (RFLP) markers. Mapping of these markers in the mapping population placed two of them at 0.5 cM from the Or locus on one side, while another marker flanked the Or gene at 1.6 cM on the other side. Three of these markers were also successfully converted into sequence-characterized amplified region (SCAR) markers. These PCR-based markers will be useful for a large-scale application in facilitating the positional cloning of the Or gene.     

几种农作物细胞质雄性不育恢复基因的定位和分子标记研究进展

利用杂种优势提高作物产量时, 生产杂交种的主要授粉控制系统是细胞质雄性不育及其恢复系统。在杂交品种的选育过程中, 优良恢复系选育至关重要。为了高效并准确地鉴定选择恢复材料, 同时更深入地研究恢复基因的作用机理, 近年来植物细胞质雄性不育恢复基因分子标记研究受到了广泛重视。本文综述了主要农作物水稻、油菜、小麦、棉花和玉米等细胞质雄性不育类型恢复基因的定位和分子标记研究进展, 并讨论了恢复基因的精确定位和分子标记鉴定在基因克隆和分子标记辅助选择育种中的意义和应用前景。     

Comparative genomic analysis identifies an ADP-ribosylation factor-like gene as the cause of Bardet-Biedl syndrome (BBS3)

Bardet-Biedl syndrome (BBS) is a genetically heterogeneous, pleiotropic human disorder characterized by obesity, retinopathy, polydactyly, renal and cardiac malformations, learning disabilities, and hypogenitalism. Eight BBS loci have been mapped, and seven genes have been identified. BBS3 was previously mapped to chromosome 3 by linkage analysis in a large Israeli Bedouin kindred. The rarity of other families mapping to the BBS3 locus has made it difficult to narrow the disease interval sufficiently to identify the gene by positional cloning. We hypothesized that the genomes of model organisms that contained the orthologues to known BBS genes would also likely contain a BBS3 orthologue. Therefore, comparative genomic analysis was performed to prioritize BBS candidate genes for mutation screening. Known BBS proteins were compared with the translated genomes of model organisms to identify a subset of organisms in which these proteins were conserved. By including multiple organisms that have relatively small genome sizes in the analysis, the number of candidate genes was reduced, and a few genes mapping to the BBS3 interval emerged as the best candidates for this disorder. One of these genes, ADP-ribosylation factor-like 6 (ARL6), contains a homozygous stop mutation that segregates completely with the disease in the Bedouin kindred originally used to map the BBS3 locus, identifying this gene as the BBS3 gene. These data illustrate the power of comparative genomic analysis for the study of human disease and identifies a novel BBS gene.     

70个水稻微卫星标记染色体位置的更正

微卫星标记(SSR)因其操作简单和稳定可靠的特点而成为一种重要的分子标记,被广泛应用于遗传作图和种质鉴定等方面。但其在染色体上位置的正确性将直接影响到基因定位的正确性和后续研究的方向。利用美国国家生物信息技术中心(NCBI)网站的Blast程序,将2740个SSR标记的前后引物序列与水稻粳稻品种日本晴基因组进行比对,共发现70个标记位于另一条染色体,对这70个标记重新锚定的染色体进行了更正。这将有助于今后水稻分子标记遗传连锁图的正确构建。     

QTL: their place in engineering tolerance of rice to salinity

Secondary salinization and its relationship to irrigation are strong incentives to improve the tolerance of crops to salinity and to drought. Achieving this through the pyramiding of physiological traits (phenotypic selection without knowledge of genotype) is feasible. However, wide application of this approach is limited by the practicalities of assessing not only the parents, but also large numbers of individuals and families in segregating generations. Genotypic information is required in the form of markers for any quantitative trait loci involved (marker-assisted selection) or of direct knowledge of the genes. In the absence of adequate candidate genes for salt tolerance, a quantitative trait locus/marker-assisted selection approach has been used here. Putative markers for ion transport and selectivity, identified from analysis of amplified fragment length polymorphism, had been discovered within a custom-made mapping population of rice. Here it is reported that none of these markers showed any association with similar traits in a closely related population of recombinant inbred lines or in selections of a cultivar. Whilst markers will be of value in using élite lines from the mapping population in backcrossing, this has to be considered alongside the effort required to develop and map any given population. This result cautions against any expectation of a general applicability of markers for physiological traits. It is concluded that direct knowledge of the genes involved is needed. This cannot be achieved at present by positional cloning. The elucidation of candidate genes is required. Here the problem lies not in the analysis of gene expression but in devising protocols in which only those genes of interest are differentially affected by the experimental treatments.