Fast and Accurate Construction of Ultra-Dense Consensus Genetic Maps Using Evolution Strategy Optimization

Our aim was to develop a fast and accurate algorithm for constructing consensus genetic maps for chip-based SNP genotyping data with a high proportion of shared markers between mapping populations. Chip-based genotyping of SNP markers allows producing high-density genetic maps with a relatively standardized set of marker loci for different mapping populations. The availability of a standard high-throughput mapping platform simplifies consensus analysis by ignoring unique markers at the stage of consensus mapping thereby reducing mathematical complicity of the problem and in turn analyzing bigger size mapping data using global optimization criteria instead of local ones. Our three-phase analytical scheme includes automatic selection of ~100-300 of the most informative (resolvable by recombination) markers per linkage group, building a stable skeletal marker order for each data set and its verification using jackknife re-sampling, and consensus mapping analysis based on global optimization criterion. A novel Evolution Strategy optimization algorithm with a global optimization criterion presented in this paper is able to generate high quality, ultra-dense consensus maps, with many thousands of markers per genome. This algorithm utilizes "potentially good orders" in the initial solution and in the new mutation procedures that generate trial solutions, enabling to obtain a consensus order in reasonable time. The developed algorithm, tested on a wide range of simulated data and real world data (Arabidopsis), outperformed two tested state-of-the-art algorithms by mapping accuracy and computation time.     

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.     

iDArTs: increasing the value of genomic resources at no cost

For many years, genetic markers have been the building blocks in assembling genomic knowledge. Improved technology and methods for collecting marker data have increased accuracy, increased throughput, and reduced cost. However, common genotyping technology still produces far fewer markers in plant species than in animals and humans. We propose a new type of genetic marker based on the Diversity Arrays Technology (DArT) genotyping system for organisms lacking a reference genetic sequence. These markers are based directly on microarray probe intensity profiles and hence are called iDArTs. They require no additional genotyping beyond screening with a DArT array. Since standard methods of genetic analysis cannot be used with these continuous markers, we develop novel methods for the common bi-parental experimental designs doubled haploids, recombinant inbred lines, and backcrosses. These enable the augmentation of genetic maps with iDArTs and permit quantitative trait locus mapping with both discrete and continuous markers. We use simulation to demonstrate the power of this approach for marker mapping. In addition, we construct maps and perform linkage analysis for these DArT genotypes using the doubled haploid progeny lines from a cross between the wheat cultivars Chara and Glenlea. These methods allow access to a previously untapped genetic resource by extracting additional information from the raw data. With no additional genotyping cost, we are able to double the number of markers mapped and thereby increase genome coverage.     

The first SSR-based genetic linkage map for cultivated groundnut (Arachis hypogaea L.)

Molecular markers and genetic linkage maps are pre-requisites for molecular breeding in any crop species. In case of peanut or groundnut (Arachis hypogaea L.), an amphidiploid (4X) species, not a single genetic map is, however, available based on a mapping population derived from cultivated genotypes. In order to develop a genetic linkage map for tetraploid cultivated groundnut, a total of 1,145 microsatellite or simple sequence repeat (SSR) markers available in public domain as well as unpublished markers from several sources were screened on two genotypes, TAG 24 and ICGV 86031 that are parents of a recombinant inbred line mapping population. As a result, 144 (12.6%) polymorphic markers were identified and these amplified a total of 150 loci. A total of 135 SSR loci could be mapped into 22 linkage groups (LGs). While six LGs had only two SSR loci, the other LGs contained 3 (LG_AhXV) to 15 (LG_AhVIII) loci. As the mapping population used for developing the genetic map segregates for drought tolerance traits, phenotyping data obtained for transpiration, transpiration efficiency, specific leaf area and SPAD chlorophyll meter reading (SCMR) for 2 years were analyzed together with genotyping data. Although, 2–5 QTLs for each trait mentioned above were identified, the phenotypic variation explained by these QTLs was in the range of 3.5–14.1%. In addition, alignment of two linkage groups (LGs) (LG_AhIII and LG_AhVI) of the developed genetic map was shown with available genetic maps of AA diploid genome of groundnut and Lotus and Medicago. The present study reports the construction of the first genetic map for cultivated groundnut and demonstrates its utility for molecular mapping of QTLs controlling drought tolerance related traits as well as establishing relationships with diploid AA genome of groundnut and model legume genome species. Therefore, the map should be useful for the community for a variety of applications. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

A Multi-Population Consensus Genetic Map Reveals Inconsistent Marker Order among Maps Likely Attributed to Structural Variations in the Apple Genome

Genetic maps serve as frameworks for determining the genetic architecture of quantitative traits, assessing structure of a genome, as well as aid in pursuing association mapping and comparative genetic studies. In this study, a dense genetic map was constructed using a high-throughput 1,536 EST-derived SNP GoldenGate genotyping platform and a global consensus map established by combining the new genetic map with four existing reliable genetic maps of apple. The consensus map identified markers with both major and minor conflicts in positioning across all five maps. These major inconsistencies among marker positions were attributed either to structural variations within the apple genome, or among mapping populations, or genotyping technical errors. These also highlighted problems in assembly and anchorage of the reference draft apple genome sequence in regions with known segmental duplications. Markers common across all five apple genetic maps resulted in successful positioning of 2875 markers, consisting of 2033 SNPs and 843 SSRs as well as other specific markers, on the global consensus map. These markers were distributed across all 17 linkage groups, with an average of 169±33 marker per linkage group and with an average distance of 0.70±0.14 cM between markers. The total length of the consensus map was 1991.38 cM with an average length of 117.14±24.43 cM per linkage group. A total of 569 SNPs were mapped onto the genetic map, consisting of 140 recombinant individuals, from our recently developed apple Oligonucleotide pool assays (OPA). The new functional SNPs, along with the dense consensus genetic map, will be useful for high resolution QTL mapping of important traits in apple and for pursuing comparative genetic studies in Rosaceae.     

SNP Discovery and Linkage Map Construction in Cultivated Tomato

Few intraspecific genetic linkage maps have been reported for cultivated tomato, mainly because genetic diversity within Solanum lycopersicum is much less than that between tomato species. Single nucleotide polymorphisms (SNPs), the most abundant source of genomic variation, are the most promising source of polymorphisms for the construction of linkage maps for closely related intraspecific lines. In this study, we developed SNP markers based on expressed sequence tags for the construction of intraspecific linkage maps in tomato. Out of the 5607 SNP positions detected through in silico analysis, 1536 were selected for high-throughput genotyping of two mapping populations derived from crosses between ‘Micro-Tom’ and either ‘Ailsa Craig’ or ‘M82’. A total of 1137 markers, including 793 out of the 1338 successfully genotyped SNPs, along with 344 simple sequence repeat and intronic polymorphism markers, were mapped onto two linkage maps, which covered 1467.8 and 1422.7 cM, respectively. The SNP markers developed were then screened against cultivated tomato lines in order to estimate the transferability of these SNPs to other breeding materials. The molecular markers and linkage maps represent a milestone in the genomics and genetics, and are the first step toward molecular breeding of cultivated tomato. Information on the DNA markers, linkage maps, and SNP genotypes for these tomato lines is available at http://www.kazusa.or.jp/tomato/.     

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.     

Heterozygous Mapping Strategy (HetMappS) for High Resolution Genotyping-By-Sequencing Markers: A Case Study in Grapevine

Genotyping by sequencing (GBS) provides opportunities to generate high-resolution genetic maps at a low genotyping cost, but for highly heterozygous species, missing data and heterozygote undercalling complicate the creation of GBS genetic maps. To overcome these issues, we developed a publicly available, modular approach called HetMappS, which functions independently of parental genotypes and corrects for genotyping errors associated with heterozygosity. For linkage group formation, HetMappS includes both a reference-guided synteny pipeline and a reference-independent de novo pipeline. The de novo pipeline can be utilized for under-characterized or high diversity families that lack an appropriate reference. We applied both HetMappS pipelines in five half-sib F₁ families involving genetically diverse Vitis spp. Starting with at least 116,466 putative SNPs per family, the HetMappS pipelines identified 10,440 to 17,267 phased pseudo-testcross (Pt) markers and generated high-confidence maps. Pt marker density exceeded crossover resolution in all cases; up to 5,560 non-redundant markers were used to generate parental maps ranging from 1,047 cM to 1,696 cM. The number of markers used was strongly correlated with family size in both de novo and synteny maps (r = 0.92 and 0.91, respectively). Comparisons between allele and tag frequencies suggested that many markers were in tandem repeats and mapped as single loci, while markers in regions of more than two repeats were removed during map curation. Both pipelines generated similar genetic maps, and genetic order was strongly correlated with the reference genome physical order in all cases. Independently created genetic maps from shared parents exhibited nearly identical results. Flower sex was mapped in three families and correctly localized to the known sex locus in all cases. The HetMappS pipeline could have wide application for genetic mapping in highly heterozygous species, and its modularity provides opportunities to adapt portions of the pipeline to other family types, genotyping technologies or applications.     

<Emphasis Type="Italic">In silico</Emphasis> genotyping of the maize nested association mapping population

Nested Association Mapping (NAM) has been proposed as a means to combine the power of linkage mapping with the resolution of association mapping. It is enabled through sequencing or array genotyping of parental inbred lines while using low-cost, low-density genotyping technologies for their segregating progenies. For purposes of data analyses of NAM populations, parental genotypes at a large number of Single Nucleotide Polymorphic (SNP) loci need to be projected to their segregating progeny. Herein we demonstrate how approximately 0.5 million SNPs that have been genotyped in 26 parental lines of the publicly available maize NAM population can be projected onto their segregating progeny using only 1,106 SNP loci that have been genotyped in both the parents and their 5,000 progeny. The challenge is to estimate both the genotype and genetic location of the parental SNP genotypes in segregating progeny. Both challenges were met by estimating their expected genotypic values conditional on observed flanking markers through the use of both physical and linkage maps. About 90%, of 500,000 genotyped SNPs from the maize HapMap project, were assigned linkage map positions using linear interpolation between the maize Accessioned Gold Path (AGP) and NAM linkage maps. Of these, almost 70% provided high probability estimates of genotypes in almost 5,000 recombinant inbred lines.     

RADtyping: An Integrated Package for Accurate De Novo Codominant and Dominant RAD Genotyping in Mapping Populations

Genetic linkage maps are indispensable tools in genetic, genomic and breeding studies. As one of genotyping-by-sequencing methods, RAD-Seq (restriction-site associated DNA sequencing) has gained particular popularity for construction of high-density linkage maps. Current RAD analytical tools are being predominantly used for typing codominant markers. However, no genotyping algorithm has been developed for dominant markers (resulting from recognition site disruption). Given their abundance in eukaryotic genomes, utilization of dominant markers would greatly diminish the extensive sequencing effort required for large-scale marker development. In this study, we established, for the first time, a novel statistical framework for de novo dominant genotyping in mapping populations. An integrated package called RADtyping was developed by incorporating both de novo codominant and dominant genotyping algorithms. We demonstrated the superb performance of RADtyping in achieving remarkably high genotyping accuracy based on simulated and real mapping datasets. The RADtyping package is freely available at http://www2.ouc.edu.cn/mollusk/ detailen.asp?id=727.     

A detailed multipoint gene map of chromosome 1q

Utilizing genotyping data for 23 markers, we have constructed a 21-locus multipoint genetic map of the long arm of chromosome 1. Five new RFLPs are reported. The map integrates anonymous loci from previous primary linkage maps and incorporates markers for 10 coding sequences. These markers form a continuous linkage group of 85 cM in males and 141 cM in females. The map was constructed employing the LINKAGE and CRIMAP computational methodologies via a stepwise algorithm.     

A pipeline for high throughput detection and mapping of SNPs from EST databases

Single nucleotide polymorphisms (SNPs) represent the most abundant type of genetic variation that can be used as molecular markers. The SNPs that are hidden in sequence databases can be unlocked using bioinformatic tools. For efficient application of these SNPs, the sequence set should be error-free as much as possible, targeting single loci and suitable for the SNP scoring platform of choice. We have developed a pipeline to effectively mine SNPs from public EST databases with or without quality information using QualitySNP software, select reliable SNP and prepare the loci for analysis on the Illumina GoldenGate genotyping platform. The applicability of the pipeline was demonstrated using publicly available potato EST data, genotyping individuals from two diploid mapping populations and subsequently mapping the SNP markers (putative genes) in both populations. Over 7000 reliable SNPs were identified that met the criteria for genotyping on the GoldenGate platform. Of the 384 SNPs on the SNP array approximately 12% dropped out. For the two potato mapping populations 165 and 185 SNPs segregating SNP loci could be mapped on the respective genetic maps, illustrating the effectiveness of our pipeline for SNP selection and validation.     

A nearest-neighboring-end algorithm for genetic mapping

MOTIVATION: High-throughput methods are beginning to make possible the genotyping of thousands of loci in thousands of individuals, which could be useful for tightly associating phenotypes to candidate loci. Current mapping algorithms cannot handle so many data without building hierarchies of framework maps. RESULTS: A version of Kruskal's minimum spanning tree algorithm can solve any genetic mapping problem that can be stated as marker deletion from a set of linkage groups. These include backcross, recombinant inbred, haploid and double-cross recombinational populations, in addition to conventional deletion and radiation hybrid populations. The algorithm progressively joins linkage groups at increasing recombination fractions between terminal markers, and attempts to recognize and correct erroneous joins at peaks in recombination fraction. The algorithm is O (mn3) for m individuals and n markers, but the mean run time scales close to mn2. It is amenable to parallel processing and has recovered true map order in simulations of large backcross, recombinant inbred and deletion populations with up to 37,005 markers. Simulations were used to investigate map accuracy in response to population size, allelic dominance, segregation distortion, missing data and random typing errors. It produced accurate maps when marker distribution was sufficiently uniform, although segregation distortion could induce translocated marker orders. The algorithm was also used to map 1003 loci in the F7 ITMI population of bread wheat, Triticum aestivum L. emend Thell., where it shortened an existing standard map by 16%, but it failed to associate blocks of markers properly across gaps within linkage groups. This was because it depends upon the rankings of recombination fractions at individual markers, and is susceptible to sampling error, typing error and joint selection involving the terminal markers of nearly finished linkage groups. Therefore, the current form of the algorithm is useful mainly to improve local marker ordering in linkage groups obtained in other ways. AVAILABILITY: The source code and supplemental data are http://www.iubio.bio.indiana.edu/soft/molbio/qtl/flipper/ CONTACT: ccrane@purdue.edu.     

Genetic mapping in the presence of genotyping errors

Genetic maps are built using the genotypes of many related individuals. Genotyping errors in these data sets can distort genetic maps, especially by inflating the distances. We have extended the traditional likelihood model used for genetic mapping to include the possibility of genotyping errors. Each individual marker is assigned an error rate, which is inferred from the data, just as the genetic distances are. We have developed a software package, called TMAP, which uses this model to find maximum-likelihood maps for phase-known pedigrees. We have tested our methods using a data set in Vitis and on simulated data and confirmed that our method dramatically reduces the inflationary effect caused by increasing the number of markers and leads to more accurate orders.     

Radiation hybrid maps of Medaka chromosomes LG 12, 17, and 22.

The Medaka is an excellent genetic system for studies of vertebrate development and disease and environmental and evolutionary biology studies. To facilitate the mapping of markers or the cloning of affected genes in Medaka mutants identified by forward-genetic screens, we have established a panel of whole-genome radiation hybrids (RHs) and RH maps for three Medaka chromosomes. RH mapping is useful, since markers to be mapped need not be polymorphic and one can establish the order of markers that are difficult to resolve by genetic mapping owing to low genetic recombination rates. RHs were generated by fusing the irradiated donor, OLF-136 Medaka cell line, with the host B78 mouse melanoma cells. Of 290 initial RH clones, we selected 93 on the basis of high retention of fragments of the Medaka genome to establish a panel that allows genotyping in the 96-well format. RH maps for linkage groups 12, 17, and 22 were generated using 159 markers. The average retention for the three chromosomes was 19% and the average break point frequency was approximately 33 kb/cR. We estimate the potential resolution of the RH panel to be approximately 186 kb, which is high enough for integrating RH data with bacterial artificial chromosome clones. Thus, this first RH panel will be a useful tool for mapping mutated genes in Medaka.     

High-throughput identification of genetic markers using representational oligonucleotide microarray analysis

We describe a novel approach for high-throughput development of genetic markers using representational oligonucleotide microarray analysis. We test the performance of the method in sugar beet (Beta vulgaris L.) as a model for crop plants with little sequence information available. Genomic representations of both parents of a mapping population were hybridized on microarrays containing in total 146,554 custom made oligonucleotides based on sugar beet bacterial artificial chromosome (BAC) end sequences and expressed sequence tags (ESTs). Oligonucleotides showing a signal with one parental line only, were selected as potential marker candidates and placed onto an array, designed for genotyping of 184 F₂ individuals from the mapping population. Utilizing known co-dominant anchor markers we obtained 511 new dominant markers (392 derived from BAC end sequences, and 119 from ESTs) distributed over all nine sugar beet linkage groups and calculated genetic maps. Further improvements for large-scale application of the approach are discussed and its feasibility for the cost-effective and flexible generation of genetic markers is presented.     

Alignment of molecular and classical linkage maps of rice,Oryza sativa

Application of genetic linkage maps in plant genetics and breeding can be greatly facilitated by integrating the available classical and molecular genetic linkage maps. In rice, Oryza sativa L., the classical linkage map includes about 300 genes which correspond to various important morphological, physiological, biochemical and agronomic characteristics. The molecular maps consist of more than 500 DNA markers which cover most of the genome within relatively short intervals. Little effort has been made to integrate these two genetic maps. In this paper we report preliminary results of an ongoing research project aimed at the complete integration and alignment of the two linkage maps of rice. Six different F₂ populations segregating for various phenotypic and RFLP markers were used and a total of 12 morphological and physiological markers (Table 1) were mapped onto our recently constructed molecular map. Six linkage groups (i.e., chr. 1, 3, 7, 9, 11 and 12) on our RFLP map were aligned with the corresponding linkage groups on the classical map, and the previous alignment for chromosome 6 was further confirmed by RFLP mapping of an additional physiological marker on this chromosome. Results from this study, combined with our previous results, indicate that, for most chromosomes in rice, the RFLP map encompasses the classical map. The usefulness of an integrated genetic linkage map for rice genetics and breeding is discussed.Abbreviations RFLP restriction fragment length polymorphism - chr chromosome - cM centiMorgan     

High-density genotyping: an overkill for QTL mapping? Lessons learned from a case study in maize and simulations

High-density genotyping is extensively exploited in genome-wide association mapping studies and genomic selection in maize. By contrast, linkage mapping studies were until now mostly based on low-density genetic maps and theoretical results suggested this to be sufficient. This raises the question, if an increase in marker density would be an overkill for linkage mapping in biparental populations, or if important QTL mapping parameters would benefit from it. In this study, we addressed this question using experimental data and a simulation based on linkage maps with marker densities of 1, 2, and 5 cM. QTL mapping was performed for six diverse traits in a biparental population with 204 doubled haploid maize lines and in a simulation study with varying QTL effects and closely linked QTL for different population sizes. Our results showed that high-density maps neither improved the QTL detection power nor the predictive power for the proportion of explained genotypic variance. By contrast, the precision of QTL localization, the precision of effect estimates of detected QTL, especially for small and medium sized QTL, as well as the power to resolve closely linked QTL profited from an increase in marker density from 5 to 1 cM. In conclusion, the higher costs for high-density genotyping are compensated for by more precise estimates of parameters relevant for knowledge-based breeding, thus making an increase in marker density for linkage mapping attractive.     

Physical location of homoeologous groups 5 and 6 molecular markers mapped in Triticum aestivum L

In situ hybridization was used to map 21 restriction fragment length polymorphism (RFLP) probes to linkage groups 5 and 6 of hexaploid wheat (Triticum aestivum L. em Thell.) in order to compare physical distances and genetic distances between adjacent markers. All 21 probes hybridized to the corresponding homoeologous chromosome arms. The linear order and linkage relationships among the DNA probes on the in situ-based physical maps were generally the same as those on the RFLP-based genetic maps. However, significant differences were observed between the centiMorgan distances on a linkage map and the physical distances of the probes using in situ-based techniques. The results indicated a clustering of polymorphic RFLP markers in the middle of all of the homoeologous group 5 and 6 chromosome arms. This suggests that the available linkage maps do not completely cover the physical length of the chromosomes. As with the genetic maps, the physical map clearly showed the presence of nonhomoeologous rearrangements in the terminal regions of chromosome arms 5AL and 6BS. However, the physical mapping gave an indication of the physical size of the rearrangements as well as their arm location.     

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.