NingXin-II large yellow croaker SNP array
Ontology highlight
ABSTRACT:
ORGANISM(S): Larimichthys crocea (large yellow croacker)
SUBMITTER: Baohua Chen
PROVIDER: S-BSST675 | biostudies-other |
REPOSITORIES: biostudies-other
Similar Datasets
Project description:BackgroundGenome-wide single-nucleotide polymorphism (SNP) arrays containing hundreds of thousands of SNPs from the human genome have proven useful for studying important human genome questions. Data quality of SNP arrays plays a key role in the accuracy and precision of downstream data analyses. However, good indices for assessing data quality of SNP arrays have not yet been developed.ResultsWe developed new quality indices to measure the quality of SNP arrays and/or DNA samples and investigated their statistical properties. The indices quantify a departure of estimated individual-level allele frequencies (AFs) from expected frequencies via standardized distances. The proposed quality indices followed lognormal distributions in several large genomic studies that we empirically evaluated. AF reference data and quality index reference data for different SNP array platforms were established based on samples from various reference populations. Furthermore, a confidence interval method based on the underlying empirical distributions of quality indices was developed to identify poor-quality SNP arrays and/or DNA samples. Analyses of authentic biological data and simulated data show that this new method is sensitive and specific for the detection of poor-quality SNP arrays and/or DNA samples.ConclusionsThis study introduces new quality indices, establishes references for AFs and quality indices, and develops a detection method for poor-quality SNP arrays and/or DNA samples. We have developed a new computer program that utilizes these methods called SNP Array Quality Control (SAQC). SAQC software is written in R and R-GUI and was developed as a user-friendly tool for the visualization and evaluation of data quality of genome-wide SNP arrays. The program is available online (http://www.stat.sinica.edu.tw/hsinchou/genetics/quality/SAQC.htm).
| S-EPMC3101186 | biostudies-literature
Project description:BackgroundFixed arrays of single nucleotide polymorphism (SNP) markers have advantages over reduced representation sequencing in their ease of data analysis, consistently higher call rates, and rapid turnaround times. A 6 K SNP array represents a cost-benefit "sweet spot" for routine genetics and breeding applications in rice. Selection of informative SNPs across species and subpopulations during chip design is essential to obtain useful polymorphism rates for target germplasm groups. This paper summarizes results from large-scale deployment of an Illumina 6 K SNP array for rice.ResultsDesign of the Illumina Infinium 6 K SNP chip for rice, referred to as the Cornell_6K_Array_Infinium_Rice (C6AIR), includes 4429 SNPs from re-sequencing data and 1571 SNP markers from previous BeadXpress 384-SNP sets, selected based on polymorphism rate and allele frequency within and between target germplasm groups. Of the 6000 attempted bead types, 5274 passed Illumina's production quality control. The C6AIR was widely deployed at the International Rice Research Institute (IRRI) for genetic diversity analysis, QTL mapping, and tracking introgressions and was intensively used at Cornell University for QTL analysis and developing libraries of interspecific chromosome segment substitution lines (CSSLs) between O. sativa and diverse accessions of O. rufipogon or O. meridionalis. Collectively, the array was used to genotype over 40,000 rice samples. A set of 4606 SNP markers was used to provide high quality data for O. sativa germplasm, while a slightly expanded set of 4940 SNPs was used for O. sativa X O. rufipogon populations. Biparental polymorphism rates were generally between 1900 and 2500 well-distributed SNP markers for indica x japonica or interspecific populations and between 1300 and 1500 markers for crosses within indica, while polymorphism rates were lower for pairwise crosses within U.S. tropical japonica germplasm. Recently, a second-generation array containing ~7000 SNP markers, referred to as the C7AIR, was designed by removing poor-performing SNPs from the C6AIR and adding markers selected to increase the utility of the array for elite tropical japonica material.ConclusionsThe C6AIR has been successfully used to generate rapid and high-quality genotype data for diverse genetics and breeding applications in rice, and provides the basis for an optimized design in the C7AIR.
| S-EPMC5577349 | biostudies-literature
Project description:Development and characterisation of a high density SNP array for Pacific and European oysters (Crassostrea gigas & Ostrea edulis)
| PRJEB20253 | ENA
Project description:The concurrent development of high-throughput genotyping platforms and next generation sequencing (NGS) has increased the number and density of genetic markers, the efficiency of constructing detailed linkage maps, and our ability to overlay recombination and physical maps of the genome. We developed an array for tomato with 8,784 Single Nucleotide Polymorphisms (SNPs) mainly discovered based on NGS-derived transcriptome sequences. Of the SNPs, 7,720 (88%) passed manufacturing quality control and could be scored in tomato germplasm. The array was used to generate high-density linkage maps for three interspecific F(2) populations: EXPEN 2000 (Solanum lycopersicum LA0925 x S. pennellii LA0716, 79 individuals), EXPEN 2012 (S. lycopersicum Moneymaker x S. pennellii LA0716, 160 individuals), and EXPIM 2012 (S. lycopersicum Moneymaker x S. pimpinellifolium LA0121, 183 individuals). The EXPEN 2000-SNP and EXPEN 2012 maps consisted of 3,503 and 3,687 markers representing 1,076 and 1,229 unique map positions (genetic bins), respectively. The EXPEN 2000-SNP map had an average marker bin interval of 1.6 cM, while the EXPEN 2012 map had an average bin interval of 0.9 cM. The EXPIM 2012 map was constructed with 4,491 markers (1,358 bins) and an average bin interval of 0.8 cM. All three linkage maps revealed an uneven distribution of markers across the genome. The dense EXPEN 2012 and EXPIM 2012 maps showed high levels of colinearity across all 12 chromosomes, and also revealed evidence of small inversions between LA0716 and LA0121. Physical positions of 7,666 SNPs were identified relative to the tomato genome sequence. The genetic and physical positions were mostly consistent. Exceptions were observed for chromosomes 3, 10 and 12. Comparing genetic positions relative to physical positions revealed that genomic regions with high recombination rates were consistent with the known distribution of euchromatin across the 12 chromosomes, while very low recombination rates were observed in the heterochromatic regions.
| S-EPMC3393668 | biostudies-literature
Project description:The pathogenesis of diffuse large B cell lymphomas (DLBCL) is only partly understood. We analyzed 148 DLBCL by high resolution single nucleotide polymorphism (SNP)-chips to characterize genomic imbalances. Seventy-nine cases were of the germinal center B-cell like (GCB) type of DLBCL, 49 of the activated B-cell like (ABC) subtype and 20 were type 3 DLBCL. Twenty-four regions of recurrent genomic gains and 38 regions of recurrent genomic losses were identified over the whole cohort, with a median of 25 imbalances per case for ABC-DLBCL and 19 per case for GCB-DLBCL. Several recurrent copy number changes showed differential frequencies in the GCB- and ABC-DLBCL subgroups, including gains of HDAC7A predominantly in GCB-DLBCL (38% of cases) and losses of BACH2 and CASP8AP2 predominantly in ABC-DLBCL (35%), hinting at disparate pathogenetic mechanisms in these entities. Correlating gene expression and copy number revealed a strong gene dosage effect in all tumors, with 34% of probesets showing a concordant expression change in affected regions. Two new potential tumor suppressor genes emerging from the analysis, CASP3 and IL5RA, were sequenced in 10 and 16 candidate cases, respectively. However, no mutations were found, pointing to a potential haploinsufficiency effect of these genes, considering their reduced expression in cases with deletions. This work thus describes differences and similarities in the landscape of genomic aberrations in the DLBCL subgroups in a large collection of cases, confirming already known targets, but also discovering novel copy number changes with possible pathogenetic relevance.
2014-07-22 | GSE57277 | GEO
Project description:Genetic alterations lead to resistance to immunotherapy
2017-05-24 | GSE96884 | GEO
Project description:BACKGROUND:High levels of pairwise linkage disequilibrium (LD) in single nucleotide polymorphism (SNP) array or whole-genome sequence data may affect both performance and efficiency of genomic prediction models. Thus, this warrants pruning of genotyping data for high LD. We developed an algorithm, named SNPrune, which enables the rapid detection of any pair of SNPs in complete or high LD throughout the genome. METHODS:LD, measured as the squared correlation between phased alleles (r2), can only reach a value of 1 when both loci have the same count of the minor allele. Sorting loci based on the minor allele count, followed by comparison of their alleles, enables rapid detection of loci in complete LD. Detection of loci in high LD can be optimized by computing the range of the minor allele count at another locus for each possible value of the minor allele count that can yield LD values higher than a predefined threshold. This efficiently reduces the number of pairs of loci for which LD needs to be computed, instead of considering all pairwise combinations of loci. The implemented algorithm SNPrune considered bi-allelic loci either using phased alleles or allele counts as input. SNPrune was validated against PLINK on two datasets, using an r2 threshold of 0.99. The first dataset contained 52k SNP genotypes on 3534 pigs and the second dataset contained simulated whole-genome sequence data with 10.8 million SNPs and 2500 animals. RESULTS:SNPrune removed a similar number of SNPs as PLINK from the pig data but SNPrune was almost 12 times faster than PLINK. From the simulated sequence data with 10.8 million SNPs, SNPrune removed 6.4 and 1.4 million SNPs due to complete and high LD. Results were very similar regardless of whether phased alleles or allele counts were used. Using allele counts and multi-threading with 10 threads, SNPrune completed the analysis in 21 min. Using a sliding window of up to 500,000 SNPs, PLINK removed ~ 43,000 less SNPs (0.6%) in the sequence data and SNPrune was 24 to 170 times faster, using one or ten threads, respectively. CONCLUSIONS:The SNPrune algorithm developed here is able to remove SNPs in high LD throughout the genome very efficiently in large datasets.
| S-EPMC6019535 | biostudies-literature
Project description:Cherry breeding and genetic studies can benefit from genome-wide genetic marker assays. Currently, a 6K SNP array enables genome scans in cherry; however, only a third of these SNPs are informative, with low coverage in many genomic regions. Adding previously detected SNPs to this array could provide a cost-efficient upgrade with increased genomic coverage across the 670?cM/352.9?Mb cherry whole genome sequence. For sweet cherry, new SNPs were chosen following a focal point strategy, grouping six to eight SNPs within 10-kb windows with an average of 0.6?cM (627?kb) between focal points. Additional SNPs were chosen to represent important regions. Sweet cherry, the fruticosa subgenome of sour cherry, and cherry organellar genomes were targeted with 6942, 2020, and 38 new SNPs, respectively. The +9K add-on provided 2128, 1091, and 70 new reliable, polymorphic SNPs for sweet cherry and the avium and the fruticosa subgenomes of sour cherry, respectively. For sweet cherry, 1241 reliable polymorphic SNPs formed 237 informative focal points, with another 2504 SNPs in-between. The +9K SNPs increased genetic resolution and genome coverage of the original cherry SNP array and will help increase understanding of the genetic control of key traits and relationships among individuals in cherry.
| S-EPMC7203174 | biostudies-literature
Project description:BACKGROUND:China has the richest local chicken breeding resources in the world and is the world's second largest producer of meat-type chickens. Development of a moderate-density SNP array for genetic analysis of chickens and breeding of meat-type chickens taking utility of those resources is urgently needed for conventional farms, breeding industry, and research areas. RESULTS:Eight representative local breeds or commercial broiler lines with 3 pools of 48 individuals within each breed/line were sequenced and supplied the major SNPs resource. There were 7.09 million - 9.41 million SNPs detected in each breed/line. After filtering using multiple criteria such as preferred incorporation of trait-related SNPs and uniformity of distribution across the genome, 52.18?K SNPs were selected in the final array. It consists of: (i) 19.22?K SNPs from the genomes of yellow-feathered, cyan-shank partridge and white-feathered chickens; (ii) 5.98?K SNPs related to economic traits from the Illumina 60?K SNP Bead Chip, which were found as significant associated SNPs with 15 traits in a Beijing-You crossed Cobb F2 resource population by genome-wide association study analysis; (iii) 7.63?K SNPs from 861 candidate genes of economic traits; (iv) the 0.94?K SNPs related to residual feed intake; and (v) 18.41?K from chicken SNPdb. The polymorphisms of 9 extra local breeds and 3 commercial lines were examined with this array, and 40?K - 47?K SNPs were polymorphic (with minor allele frequency?>?0.05) in those breeds. The MDS result showed that those breeds can be clearly distinguished by this newly developed genotyping array. CONCLUSIONS:We successfully developed a 55K genotyping array by using SNPs segregated from typical local breeds and commercial lines. Compared to the existing Affy 600?K and Illumina 60?K arrays, there were 21,41?K new SNPs included on our Affy 55K array. The results of the 55K genotyping data can therefore be imputed to high-density SNPs genotyping data. The array offers a wide range of potential applications such as genomic selection breeding, GWAS of interested traits, and investigation of diversity of different chicken breeds.
| S-EPMC6532155 | biostudies-literature
Project description:Single nucleotide polymorphisms (SNPs), genotyped with arrays, have become a widely used marker type in population genetic analyses over the last 10 years. However, compared to whole genome re-sequencing data, arrays are known to lack a substantial proportion of globally rare variants and tend to be biased towards variants present in populations involved in the development process of the respective array. This affects population genetic estimators and is known as SNP ascertainment bias. We investigated factors contributing to ascertainment bias in array development by redesigning the Axiom™ Genome-Wide Chicken Array in silico and evaluating changes in allele frequency spectra and heterozygosity estimates in a stepwise manner. A sequential reduction of rare alleles during the development process was shown. This was mainly caused by the identification of SNPs in a limited set of populations and a within-population selection of common SNPs when aiming for equidistant spacing. These effects were shown to be less severe with a larger discovery panel. Additionally, a generally massive overestimation of expected heterozygosity for the ascertained SNP sets was shown. This overestimation was 24% higher for populations involved in the discovery process than not involved populations in case of the original array. The same was observed after the SNP discovery step in the redesign. However, an unequal contribution of populations during the SNP selection can mask this effect but also adds uncertainty. Finally, we make suggestions for the design of specialized arrays for large scale projects where whole genome re-sequencing techniques are still too expensive.
| S-EPMC8009414 | biostudies-literature