Novel Design of Imputation-Enabled SNP Arrays for Breeding and Research Applications Supporting Multi-Species Hybridization.
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ABSTRACT:
SUBMITTER: Keeble-Gagnere G
PROVIDER: S-EPMC8728019 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Low-density (LD) single nucleotide polymorphism (SNP) arrays provide a cost-effective solution for genomic prediction and selection, but algorithms and computational tools are needed for the optimal design of LD SNP chips. A multiple-objective, local optimization (MOLO) algorithm was developed for design of optimal LD SNP chips that can be imputed accurately to medium-density (MD) or high-density (HD) SNP genotypes for genomic prediction. The objective function facilitates maximization of non-gap map length and system information for the SNP chip, and the latter is computed either as locus-averaged (LASE) or haplotype-averaged Shannon entropy (HASE) and adjusted for uniformity of the SNP distribution. HASE performed better than LASE with ?1,000 SNPs, but required considerably more computing time. Nevertheless, the differences diminished when >5,000 SNPs were selected. Optimization was accomplished conditionally on the presence of SNPs that were obligated to each chromosome. The frame location of SNPs on a chip can be either uniform (evenly spaced) or non-uniform. For the latter design, a tunable empirical Beta distribution was used to guide location distribution of frame SNPs such that both ends of each chromosome were enriched with SNPs. The SNP distribution on each chromosome was finalized through the objective function that was locally and empirically maximized. This MOLO algorithm was capable of selecting a set of approximately evenly-spaced and highly-informative SNPs, which in turn led to increased imputation accuracy compared with selection solely of evenly-spaced SNPs. Imputation accuracy increased with LD chip size, and imputation error rate was extremely low for chips with ?3,000 SNPs. Assuming that genotyping or imputation error occurs at random, imputation error rate can be viewed as the upper limit for genomic prediction error. Our results show that about 25% of imputation error rate was propagated to genomic prediction in an Angus population. The utility of this MOLO algorithm was also demonstrated in a real application, in which a 6K SNP panel was optimized conditional on 5,260 obligatory SNP selected based on SNP-trait association in U.S. Holstein animals. With this MOLO algorithm, both imputation error rate and genomic prediction error rate were minimal.
| S-EPMC5008792 | biostudies-literature
Project description:Molecular markers are valuable tools for breeders to help accelerate crop improvement. High throughput sequencing technologies facilitate the discovery of large-scale variations such as single nucleotide polymorphisms (SNPs) and simple sequence repeats (SSRs). Sequencing of chickpea genome along with re-sequencing of several chickpea lines has enabled the discovery of 4.4 million variations including SNPs and InDels. Here we report a repository of 1.9 million variations (SNPs and InDels) anchored on eight pseudomolecules in a custom database, referred as CicArVarDB that can be accessed at http://cicarvardb.icrisat.org/. It includes an easy interface for users to select variations around specific regions associated with quantitative trait loci, with embedded webBLAST search and JBrowse visualisation. We hope that this database will be immensely useful for the chickpea research community for both advancing genetics research as well as breeding applications for crop improvement. Database URL: http://cicarvardb.icrisat.org.
| S-EPMC4541373 | biostudies-literature
Project description:BACKGROUND: Copy number variation (CNV) is essential to understand the pathology of many complex diseases at the DNA level. Affymetrix SNP arrays, which are widely used for CNV studies, significantly depend on accurate copy number (CN) estimation. Nevertheless, CN estimation may be biased by several factors, including cross-hybridization and training sample batch, as well as genomic waves of intensities induced by sequence-dependent hybridization rate and amplification efficiency. Since many available algorithms only address one or two of the three factors, a high false discovery rate (FDR) often results when identifying CNV. Therefore, we have developed a new CNV detection pipeline which is based on hybridization and amplification rate correction (CNVhac). METHODS: CNVhac first estimates the allelic concentrations (ACs) of target sequences by using the sample independent parameters trained through physicochemical hybridization law. Then the raw CN is estimated by taking the ratio of AC to the corresponding average AC from a reference sample set for one specific site. Finally, a hidden Markov model (HMM) segmentation process is implemented to detect CNV regions. RESULTS: Based on public HapMap data, the results show that CNVhac effectively smoothes the genomic waves and facilitates more accurate raw CN estimates compared to other methods. Moreover, CNVhac alleviates, to a certain extent, the sample dependence of inference and makes CNV calling with appreciable low FDRs. CONCLUSION: CNVhac is an effective approach to address the common difficulties in SNP array analysis, and the working principles of CNVhac can be easily extended to other platforms.
| S-EPMC3428662 | biostudies-literature
Project description:Affymetrix SNP arrays have been widely used for single-nucleotide polymorphism (SNP) genotype calling and DNA copy number variation inference. Although numerous methods have achieved high accuracy in these fields, most studies have paid little attention to the modeling of hybridization of probes to off-target allele sequences, which can affect the accuracy greatly. In this study, we address this issue and demonstrate that hybridization with mismatch nucleotides (HWMMN) occurs in all SNP probe-sets and has a critical effect on the estimation of allelic concentrations (ACs). We study sequence binding through binding free energy and then binding affinity, and develop a probe intensity composite representation (PICR) model. The PICR model allows the estimation of ACs at a given SNP through statistical regression. Furthermore, we demonstrate with cell-line data of known true copy numbers that the PICR model can achieve reasonable accuracy in copy number estimation at a single SNP locus, by using the ratio of the estimated AC of each sample to that of the reference sample, and can reveal subtle genotype structure of SNPs at abnormal loci. We also demonstrate with HapMap data that the PICR model yields accurate SNP genotype calls consistently across samples, laboratories and even across array platforms.
| S-EPMC2761258 | biostudies-literature
Project description:The Illumina BovineLD BeadChip was designed to support imputation to higher density genotypes in dairy and beef breeds by including single-nucleotide polymorphisms (SNPs) that had a high minor allele frequency as well as uniform spacing across the genome except at the ends of the chromosome where densities were increased. The chip also includes SNPs on the Y chromosome and mitochondrial DNA loci that are useful for determining subspecies classification and certain paternal and maternal breed lineages. The total number of SNPs was 6,909. Accuracy of imputation to Illumina BovineSNP50 genotypes using the BovineLD chip was over 97% for most dairy and beef populations. The BovineLD imputations were about 3 percentage points more accurate than those from the Illumina GoldenGate Bovine3K BeadChip across multiple populations. The improvement was greatest when neither parent was genotyped. The minor allele frequencies were similar across taurine beef and dairy breeds as was the proportion of SNPs that were polymorphic. The new BovineLD chip should facilitate low-cost genomic selection in taurine beef and dairy cattle.
| S-EPMC3314603 | biostudies-literature
Project description:Whole genome analysis on normal karyotype lung tumors from heavy smokers. Our study regarded a population of 5203 heavy smokers volunteers, enrolled in a screening program set up at the European Institute of Oncology. Each volunteer yearly undergoes low dose CT-scan. At the end of the second year, among 89 screening-identified patients with lung cancers, 57 cases with clinical localized lung malignancy were evaluated for the present analysis. These, together with 21 other asymptomatic patients (pilot population), carrying non-screening detected lung cancers, but with same characteristics of the screened population (asymptomatic, smokers, older than 50, with an occasionally detected localized cancer), constituted our study population (78 cases). At the time of surgical resection, a portion of the tumoral tissue was collected, snap-frozen and stored at -80 C, for the subsequent tumor cellularity evaluation and DNA extraction. Tumor cellularity was determined by analyzing ematossilin-eosin stained sections of the tumor samples. The evaluation was carried out by a pathologist of the Molecular Pathology Unit, at the IFOM-IEO campus. Only NK samples with an evaluated tumor cellularity of at least 70% were used for the high-resolution genomic analysis: this allowed us to restrict normal cells contamination of the tumor genome pattern in our analysis. A genomic macroscopic analysis was carried out in order to identify tumors harboring a normal karyotype, which were then subjected to a high-resolution genomic analysis, through the Affymetrix SNPs technology.
| S-ECPF-MEXP-2450 | biostudies-other
Project description:Reliable and high-throughput genotyping platforms are of immense importance for identifying and dissecting genomic regions controlling important phenotypes, supporting selection processes in breeding programs, and managing wild populations and germplasm collections. Amongst available genotyping tools, single nucleotide polymorphism arrays have been shown to be comparatively easy to use and generate highly accurate genotypic data. Single-species arrays are the most commonly used type so far; however, some multi-species arrays have been developed for closely related species that share single nucleotide polymorphism markers, exploiting inter-species cross-amplification. In this study, the suitability of a multiplexed plant-animal single nucleotide polymorphism array, including both closely and distantly related species, was explored. The performance of the single nucleotide polymorphism array across species for diverse applications, ranging from intra-species diversity assessments to parentage analysis, was assessed. Moreover, the value of genotyping pooled DNA of distantly related species on the single nucleotide polymorphism array as a technique to further reduce costs was evaluated. Single nucleotide polymorphism performance was generally high, and species-specific single nucleotide polymorphisms proved suitable for diverse applications. The multi-species single nucleotide polymorphism array approach reported here could be transferred to other species to achieve cost savings resulting from the increased throughput when several projects use the same array, and the pooling technique adds another highly promising advancement to additionally decrease genotyping costs by half.
| S-EPMC10542201 | biostudies-literature
Project description:BackgroundThe development and utilization of genetic markers play a pivotal role in marker-assisted breeding of rice cultivars during pyramiding of valuable genes. Among molecular markers, SNPs have become the most promising due to their wide distribution within genomes and suitability for high -throughput automated genotyping. Although metadata of SNPs have been identified via next generation sequencing in rice, a large gap between the development of SNP markers and the application in breeding still exists. To promote the application of SNP markers based on the KASP (Kompetitive Allele-Specific PCR) method in rice breeding, a set of core SNP arrays was built via the screening of SNP databases and literature resources based on the KASP method.ResultsFive hundred and ninety six SNPs classified into eight subsets including quality control, indica-indica variation, highly polymorphic, functional genes, key genes targeting sites, gene cloned region, important trait associated and gap filling sites were chosen to design KASP primers and 565 out of them were successfully designed, and the assay design success rate was 94.8%. Finally, 467 out of the 565 successfully-designed SNPs can display diversity at the loci were used to develop a set of core SNP arrays. To evaluate the application value of the core SNP markers in rice breeding, 481 rice germplasms were genotyped with three functional KASP markers designed from the sequences of GBSSI, SSIIa, and Badh2 from the core SNP arrays for estimation of their grain quality performance. Eighteen rice lines, including Xiangwanxian 13, Basmati 370, Ruanhua A, and PR 33319-9-1-1-5-3-5-4-1, harbor all three favorable alleles. The core KASP arrays were also used for rice germplasm assessment, genetic diversity and population evaluation. Four hundred and eighty-one rice germplasms were divided into 3 groups: POP1, POP2 and POP3. POP1 and POP2 were indica rice subgroups consisting of 263 and 186 rice germplasms, respectively. POP3 was a japonica rice subgroup consisting of 32 rice germplasms. The average FST value for the three subgroups was 0.3501; the FST value of POP1 and POP3 was the largest (0.5482), while that of POP1 and POP2 was the smallest (0.0721). The results showed that the genetic distance between the japonica and indica rice subspecies was large, indicating that the core SNP markers were effective at discriminating the population structure of the germplasms. Finally, the core KASP arrays were used for association analysis with milled grain traits. A total of 31 KASP markers were significantly associated (P < 0.01) with ML and the LWR. Among the 31 markers, 13 were developed based on cloned genes or on identified loci related to yield traits. Notably, several KASP markers associated with grain quality were also found to be associated with brown planthopper resistance or green leafhopper resistance simultaneously.ConclusionsThe core KASP arrays developed in our study were efficient and versatile for rice germplasm assessment, genetic diversity and population evaluation and are valuable for promoting SNP molecular breeding in rice. Our study demonstrated that useful assays combined with molecular breeding can be exploited for important economic trait improvement in rice breeding.
| S-EPMC6453994 | biostudies-literature
Project description:BackgroundImputation of partially missing or unobserved genotypes is an indispensable tool for SNP data analyses. However, research and understanding of the impact of initial SNP-data quality control on imputation results is still limited. In this paper, we aim to evaluate the effect of different strategies of pre-imputation quality filtering on the performance of the widely used imputation algorithms MaCH and IMPUTE.ResultsWe considered three scenarios: imputation of partially missing genotypes with usage of an external reference panel, without usage of an external reference panel, as well as imputation of completely un-typed SNPs using an external reference panel. We first created various datasets applying different SNP quality filters and masking certain percentages of randomly selected high-quality SNPs. We imputed these SNPs and compared the results between the different filtering scenarios by using established and newly proposed measures of imputation quality. While the established measures assess certainty of imputation results, our newly proposed measures focus on the agreement with true genotypes. These measures showed that pre-imputation SNP-filtering might be detrimental regarding imputation quality. Moreover, the strongest drivers of imputation quality were in general the burden of missingness and the number of SNPs used for imputation. We also found that using a reference panel always improves imputation quality of partially missing genotypes. MaCH performed slightly better than IMPUTE2 in most of our scenarios. Again, these results were more pronounced when using our newly defined measures of imputation quality.ConclusionEven a moderate filtering has a detrimental effect on the imputation quality. Therefore little or no SNP filtering prior to imputation appears to be the best strategy for imputing small to moderately sized datasets. Our results also showed that for these datasets, MaCH performs slightly better than IMPUTE2 in most scenarios at the cost of increased computing time.
| S-EPMC4236550 | biostudies-literature
Project description:BackgroundWestern white pine (WWP, Pinus monticola Douglas ex D. Don) is of high interest in forest breeding and conservation because of its high susceptibility to the invasive disease white pine blister rust (WPBR, caused by the fungus Cronartium ribicola J. C. Fisch). However, WWP lacks genomic resource development and is evolutionarily far away from plants with available draft genome sequences. Here we report a single nucleotide polymorphism (SNP) study by bulked segregation-based RNA-Seq analysis.ResultsA collection of resistance germplasm was used for construction of cDNA libraries and SNP genotyping. Approximately 36-89 million 2 × 100-bp reads were obtained per library and de-novo assembly generated the first shoot-tip reference transcriptome containing a total of 54,661 unique transcripts. Bioinformatic SNP detection identified >100,000 high quality SNPs in three expressed candidate gene groups: Pinus highly conserved genes (HCGs), differential expressed genes (DEGs) in plant defense response, and resistance gene analogs (RGAs). To estimate efficiency of in-silico SNP discovery, genotyping assay was developed by using Sequenom iPlex and it unveiled SNP success rates from 40.1% to 61.1%. SNP clustering analyses consistently revealed distinct populations, each composed of multiple full-sib seed families by parentage assignment in the WWP germplasm collection. Linkage disequilibrium (LD) analysis identified six genes in significant association with major gene (Cr2) resistance, including three RGAs (two NBS-LRR genes and one receptor-like protein kinase -RLK gene), two HCGs, and one DEG. At least one SNP locus provided an excellent marker for Cr2 selection across P. monticola populations.ConclusionsThe WWP shoot tip transcriptome and those validated SNP markers provide novel genomic resources for genetic, evolutionary and ecological studies. SNP loci of those candidate genes associated with resistant phenotypes can be used as positional and functional variation sites for further characterization of WWP major gene resistance against C. ribicola. Our results demonstrate that integration of RNA-seq-based transcriptome analysis and high-throughput genotyping is an effective approach for discovery of a large number of nucleotide variations and for identification of functional gene variants associated with adaptive traits in a non-model species.
| S-EPMC4302426 | biostudies-literature