Dataset Information

Enhancing genetic mapping of complex genomes through the design of highly-multiplexed SNP arrays: application to the large and unsequenced genomes of white spruce and black spruce.

ABSTRACT: BACKGROUND: To explore the potential value of high-throughput genotyping assays in the analysis of large and complex genomes, we designed two highly multiplexed Illumina bead arrays using the GoldenGate SNP assay for gene mapping in white spruce (Picea glauca [Moench] Voss) and black spruce (Picea mariana [Mill.] B.S.P.). RESULTS: Each array included 768 SNPs, identified by resequencing genomic DNA from parents of each mapping population. For white spruce and black spruce, respectively, 69.2% and 77.1% of genotyped SNPs had valid GoldenGate assay scores and segregated in the mapping populations. For each of these successful SNPs, on average, valid genotyping scores were obtained for over 99% of progeny. SNP data were integrated to pre-existing ALFP, ESTP, and SSR markers to construct two individual linkage maps and a composite map for white spruce and black spruce genomes. The white spruce composite map contained 821 markers including 348 gene loci. Also, 835 markers including 328 gene loci were positioned on the black spruce composite map. In total, 215 anchor markers (mostly gene markers) were shared between the two species. Considering lineage divergence at least 10 Myr ago between the two spruces, interspecific comparison of homoeologous linkage groups revealed remarkable synteny and marker colinearity. CONCLUSION: The design of customized highly multiplexed Illumina SNP arrays appears as an efficient procedure to enhance the mapping of expressed genes and make linkage maps more informative and powerful in such species with poorly known genomes. This genotyping approach will open new avenues for co-localizing candidate genes and QTLs, partial genome sequencing, and comparative mapping across conifers.

SUBMITTER: Pavy N

PROVIDER: S-EPMC2246113 | biostudies-literature | 2008

REPOSITORIES: biostudies-literature

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Enhancing genetic mapping of complex genomes through the design of highly-multiplexed SNP arrays: application to the large and unsequenced genomes of white spruce and black spruce.

Pavy Nathalie N Pelgas Betty B Beauseigle Stéphanie S Blais Sylvie S Gagnon France F Gosselin Isabelle I Lamothe Manuel M Isabel Nathalie N Bousquet Jean J

BMC genomics 20080118

<h4>Background</h4>To explore the potential value of high-throughput genotyping assays in the analysis of large and complex genomes, we designed two highly multiplexed Illumina bead arrays using the GoldenGate SNP assay for gene mapping in white spruce (Picea glauca [Moench] Voss) and black spruce (Picea mariana [Mill.] B.S.P.).<h4>Results</h4>Each array included 768 SNPs, identified by resequencing genomic DNA from parents of each mapping population. For white spruce and black spruce, respectiv ...[more]

PMID: 18205909

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Project description:BACKGROUND: The genomic architecture of adaptive traits remains poorly understood in non-model plants. Various approaches can be used to bridge this gap, including the mapping of quantitative trait loci (QTL) in pedigrees, and genetic association studies in non-structured populations. Here we present results on the genomic architecture of adaptive traits in black spruce, which is a widely distributed conifer of the North American boreal forest. As an alternative to the usual candidate gene approach, a candidate SNP approach was developed for association testing. RESULTS: A genetic map containing 231 gene loci was used to identify QTL that were related to budset timing and to tree height assessed over multiple years and sites. Twenty-two unique genomic regions were identified, including 20 that were related to budset timing and 6 that were related to tree height. From results of outlier detection and bulk segregant analysis for adaptive traits using DNA pool sequencing of 434 genes, 52 candidate SNPs were identified and subsequently tested in genetic association studies for budset timing and tree height assessed over multiple years and sites. A total of 34 (65%) SNPs were significantly associated with budset timing, or tree height, or both. Although the percentages of explained variance (PVE) by individual SNPs were small, several significant SNPs were shared between sites and among years. CONCLUSIONS: The sharing of genomic regions and significant SNPs between budset timing and tree height indicates pleiotropic effects. Significant QTLs and SNPs differed quite greatly among years, suggesting that different sets of genes for the same characters are involved at different stages in the tree's life history. The functional diversity of genes carrying significant SNPs and low observed PVE further indicated that a large number of polymorphisms are involved in adaptive genetic variation. Accordingly, for undomesticated species such as black spruce with natural populations of large effective size and low linkage disequilibrium, efficient marker systems that are predictive of adaptation should require the survey of large numbers of SNPs. Candidate SNP approaches like the one developed in the present study could contribute to reducing these numbers.

Project description:BACKGROUND: Compared to classical genotyping, targeted next-generation sequencing (tNGS) can be custom-designed to interrogate entire genomic regions of interest, in order to detect novel as well as known variants. To bring down the per-sample cost, one approach is to pool barcoded NGS libraries before sample enrichment. Still, we lack a complete understanding of how this multiplexed tNGS approach and the varying performance of the ever-evolving analytical tools can affect the quality of variant discovery. Therefore, we evaluated the impact of different software tools and analytical approaches on the discovery of single nucleotide polymorphisms (SNPs) in multiplexed tNGS data. To generate our own test model, we combined a sequence capture method with NGS in three experimental stages of increasing complexity (E. coli genes, multiplexed E. coli, and multiplexed HapMap BRCA1/2 regions). RESULTS: We successfully enriched barcoded NGS libraries instead of genomic DNA, achieving reproducible coverage profiles (Pearson correlation coefficients of up to 0.99) across multiplexed samples, with <10% strand bias. However, the SNP calling quality was substantially affected by the choice of tools and mapping strategy. With the aim of reducing computational requirements, we compared conventional whole-genome mapping and SNP-calling with a new faster approach: target-region mapping with subsequent 'read-backmapping' to the whole genome to reduce the false detection rate. Consequently, we developed a combined mapping pipeline, which includes standard tools (BWA, SAMtools, etc.), and tested it on public HiSeq2000 exome data from the 1000 Genomes Project. Our pipeline saved 12 hours of run time per Hiseq2000 exome sample and detected ~5% more SNPs than the conventional whole genome approach. This suggests that more potential novel SNPs may be discovered using both approaches than with just the conventional approach. CONCLUSIONS: We recommend applying our general 'two-step' mapping approach for more efficient SNP discovery in tNGS. Our study has also shown the benefit of computing inter-sample SNP-concordances and inspecting read alignments in order to attain more confident results.

Project description:The NanoString GeoMx digital spatial profiling is a new multiplexed platform that quantifies the abundance of tumor- and immune-related proteins in a spatially resolved manner. We performed DSP for the simultaneous assessment of 52 analytes within spatially resolved tissue compartments defined by pan-cytokeratin expression. We compared protein targets between 94 African American/Black and 65 European American/White cases, tumor and stromal tissue compartments, estrogen receptor alpha (ER)-positive and ER-negative cases, and explored potential biomarkers of survival. Of 33 analytes with robust signal for analysis, results were highly replicable. For a subset of markers, correlative analyses between DSP analytes and traditional immunohistochemistry scores revealed moderate to very strong associations between the two platforms. Similarly, DSP analytes and gene expression scores were concordant for 21 of 25 markers with overlap between the two datasets. Several analytes varied by ER status, and across the 25 immune markers surveyed, 14 had a significant inverse association with ER expression. B7 homolog 3 (B7-H3; encoded by CD276) was the only analyte to show a significant difference by race, being lower in both the tumor and stromal compartments in Black women. DSP markers that were associated with survival included CD8, CD25, CD56, CD127, EpCAM, ER, Ki-67, and STING. We conclude that DSP is an efficient tool for screening tumor- and immune-related markers in a simultaneous fashion and yields results that are concordant with established immune profiling assays. DSP immune analytes were inversely associated with ER expression, in agreement with a substantial body of previous work that documents higher immune infiltration in ER-negative breast cancers. This technology revealed that scores of the B7-H3 protein were significantly lower in breast cancers from Black women compared with White women, an intriguing finding that requires replication in independent and racially diverse female populations.

Project description:BackgroundAdmixture mapping is a powerful approach for identifying genetic variants involved in human disease that exploits the unique genomic structure in recently admixed populations. To use existing published panels of ancestry-informative markers (AIMs) for admixture mapping, markers have to be genotyped de novo for each admixed study sample and samples representing the ancestral parental populations. The increased availability of dense marker data on commercial chips has made it feasible to develop panels wherein the markers need not be predetermined.ResultsWe developed two panels of AIMs (approximately 2,000 markers each) based on the Affymetrix Genome-Wide Human SNP Array 6.0 for admixture mapping with African American samples. These two AIM panels had good map power that was higher than that of a denser panel of approximately 20,000 random markers as well as other published panels of AIMs. As a test case, we applied the panels in an admixture mapping study of hypertension in African Americans in the Washington, D.C. metropolitan area.ConclusionsDeveloping marker panels for admixture mapping from existing genome-wide genotype data offers two major advantages: (1) no de novo genotyping needs to be done, thereby saving costs, and (2) markers can be filtered for various quality measures and replacement markers (to minimize gaps) can be selected at no additional cost. Panels of carefully selected AIMs have two major advantages over panels of random markers: (1) the map power from sparser panels of AIMs is higher than that of approximately 10-fold denser panels of random markers, and (2) clusters can be labeled based on information from the parental populations. With current technology, chip-based genome-wide genotyping is less expensive than genotyping approximately 20,000 random markers. The major advantage of using random markers is the absence of ascertainment effects resulting from the process of selecting markers. The ability to develop marker panels informative for ancestry from SNP chip genotype data provides a fresh opportunity to conduct admixture mapping for disease genes in admixed populations when genome-wide association data exist or are planned.

Dataset Information

Enhancing genetic mapping of complex genomes through the design of highly-multiplexed SNP arrays: application to the large and unsequenced genomes of white spruce and black spruce.

Publications

Enhancing genetic mapping of complex genomes through the design of highly-multiplexed SNP arrays: application to the large and unsequenced genomes of white spruce and black spruce.

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