Number Of SNP Markers Research Articles

A novel diagnostic SNP panel for fishery management of green abalone (Haliotis fulgens).

The green abalone (Haliotis fulgens) represents an important artisanal fishery in the Northwest Pacific in Mexico but with populations below the optimum recovery level. Illegal fishing and the potential impact on wild populations because of the stock enhancement programs would benefit from a genomic tool to discriminate among populations and provide traceability. In this study, SNP panels for green abalone were developed from ddRad-seq data and used to evaluate individual assignment accuracy at two hierarchical scales. One included three genetic groups: 1) Guadalupe Island (GI), 2) Northern Coastal Group (NCG) and 3) Southern Coastal Group (SCG); and the second only the two coastal groups. Eight SNP panels (from 50 to 1000 loci), with markers ranked by FST, were evaluated to assess the power to identify individuals from the three genetic groups at both scales. The predictive model found high discriminatory power for almost all the SNP datasets for two of the three groups (GI and SCG), ranging from 0.72 (s.d. = 0.32) to 1. At the coastal scale, assignment accuracies for NCG individuals varied substantially with the number of SNP markers, ranging from 0.01 (s.d. = 0.05) with the 50 most informative SNPs to 0.79 (s.d. = 0.30) using 1000 SNPs. These SNP panels will provide genetic tools for tracing the origin of green abalone in the food-supply chain, which will assist fishing cooperatives in certifying their products and tackling illegal fishing in México.

Estimation of individual animal SNP-BLUP reliability using full Monte Carlo sampling

Calculation of individual animal reliability of estimated genomic breeding value by SNP-BLUP requires inversion of the mixed model equations (MME). When the SNP-BLUP model includes a residual polygenic (RPG) effect, the size of the MME will be at least the number of genotyped animals (n) plus the number of SNP markers (m). Inversion of the MME in SNP-BLUP involves computations proportional to the cube of the MME size; that is, (n + m)3, which can present a considerable computational burden. We introduce a full Monte Carlo (MC) sampling-based method for approximating reliability in the SNP-BLUP model and compare its performance to the genomic BLUP (GBLUP) model. The performance of the full MC approach was evaluated using 2 data sets, including 19,757 and 222,619 genotyped animals selected from populations with 231,186 and 13.35 million pedigree animals, respectively. Genotypes were available in the data sets for 11,729 and 50,240 SNP markers. An advantage of the full MC approximation method was its low computational demand. A drawback was its tendency to overestimate reliability for animals with low reliability, especially when the weight of the RPG effect was high. The overestimation can be lessened by increasing the number of MC samples.

The Guiana cacao genetic group (Theobroma cacao L.): a new core collection in French Guiana

Since 1985, CIRAD has been involved in surveying, collecting, studying and conserving ex situ wild cacao trees of French Guiana. Around 225 accessions of this genetic material (open progenies and clones), derived from 18 demes (sub-populations), are currently conserved in a collection at the Perennial Plants Biological Resource Centre (CRB-PPG) in French Guiana, at CIRAD’s Paracou-Combi station, in Sinnamary. In 2004, an initial “core collection” of clones was founded on a virtually exhaustive representation of the different demes collected, with some phenotypically well-described clones. Following the genotyping of 181 of those clones by GBS (Genotyping by Sequencing), the availability of a large number of SNP markers (Single Nucleotide Polymorphism) enabled us to modernize the “core collection”. Thus, of the 181 wild cacao trees clones, genotyped by 1953 SNPs (without missing data), a new core collection was obtained using COREFINDER software, comprising 41 clones representing 100% of the variability encountered.

Effects of SNPs in the positioning regions of RNA polymerase II on the TBP/promoter affinity in genes of the human circadian clock

Genetic variability in genes of the circadian clock can manifest itself as a phenotypic variability of physiological functions and behavior, as well as functional disorders not only of the clock but also of other systems leading to the development of pathologies. We analyzed the influence of SNPs localized in the [–70,–20] region from the transcription start site on the promoter affinity of the TATA-binding protein (TBP) in two groups of genes that are components of the human circadian clock system. The first group is comprised of the genes of the core of the circadian oscillator (11 genes); the second comprises the genes of nearest regulatory environment of the circadian oscillator components (21 genes).The control comparison group included genes with other functions (31 genes). The SNP_TATA_Comparator web service was used to predict the effects of the SNPs in the regions of the RNA polymerase II positioning on the TBP/promoter dissociation constant. It was shown that the number of SNP markers reducing the TBP/promoter affinity in the first group of genes is significantly lower than the number of SNP markers increasing affinity (α < 10–3). The opposite was true for the comparison group. A significantly greater number of SNP markers reduced the TBP/promoter affinity than increased it (α < 10–6). Thus, this property may represent a characteristic feature of the genes of the circadian oscillator that may ensure its stability during the genetic variability of the analyzed promoter regions. These predictions are important for identifying the candidate SNP markers for various pathologies associated with the dysfunction of the circadian clock genes for further testing in the experimental and clinical studies, as well as for the verification of the mathematical models of the circadian oscillator.

DAIRRy-BLUP: A High-Performance Computing Approach to Genomic Prediction

In genomic prediction, common analysis methods rely on a linear mixed-model framework to estimate SNP marker effects and breeding values of animals or plants. Ridge regression-best linear unbiased prediction (RR-BLUP) is based on the assumptions that SNP marker effects are normally distributed, are uncorrelated, and have equal variances. We propose DAIRRy-BLUP, a parallel, Distributed-memory RR-BLUP implementation, based on single-trait observations ( Y: ), that uses the Average Information algorithm for restricted maximum-likelihood estimation of the variance components. The goal of DAIRRy-BLUP is to enable the analysis of large-scale data sets to provide more accurate estimates of marker effects and breeding values. A distributed-memory framework is required since the dimensionality of the problem, determined by the number of SNP markers, can become too large to be analyzed by a single computing node. Initial results show that DAIRRy-BLUP enables the analysis of very large-scale data sets (up to 1,000,000 individuals and 360,000 SNPs) and indicate that increasing the number of phenotypic and genotypic records has a more significant effect on the prediction accuracy than increasing the density of SNP arrays.

Regression models for DNA-mixtures

This paper deals with the statistical interpretation of DNA mixture evidence. The conventional methods used in forensic casework today use something like 16 STR-markers. Power can be increased by rather using SNP-markers. New statistical methods are then needed, and we present a regression framework. The basic idea is that the traditional forensic hypotheses, commonly denoted HD and HP, are replaced by parametric versions: a person contributes to a mixture if and only if the fraction he contributes is greater than 0. This contributed fraction is a parameter of the regression model. The regression model uses the peak heights directly and there is no need to specify or estimate the number of contributors to the mixture. Also, drop-in and drop-out pose no principal problems. Data from 25 controlled blinded experiments were used to test the model. The number of contributors varied between 2 and 5, and the fractions contributed ranged from 0.01 to 0.99. The fractions were accurately estimated by the regression analyses. There were no false positives (i.e., in no cases were non-contributors declared to contributors). Some false negatives occurred for fractions of 0.1 or lower. Simulations were performed to test the model further. The analyses show that useful estimates can be obtained from a relatively small number of SNP-markers. Reasonable results are achieved using 300 markers which is close to the 313 SNPs in the controlled experiment. Increasing the number of SNPs, the analyses demonstrate that individuals contributing as little as 1% can reliably be detected, which suggests that cases beyond the reach of conventional forensic methods today can be reported.

SNPマーカーを用いたゲノム研究と今後の家畜育種

SNPマーカーを用いたゲノム研究と今後の家畜育種

Pharmacogenomics-Based Drug Response Prediction Model for Acute Myeloid Leukemia with Normal Karyotype

AbstractAbstract 2698 Introduction:AML is a heterogeneous disease characterized by various recurrent cytogenetic abnormalities, which provide the most important prognostic information. Although AML with normal karyotype (AML-NK) constitutes the largest subgroup representing 45–50% of adult AMLs, the prognosis of AML-NK is quite heterogeneous and their clinical outcomes are diverse. Several model capable of identifying non-responders prior to induction treatment has been proposed in AML-NK. While molecular-guided risk assessment (i.e. FLT3/ITD mutation or NPM1 mutation) and stratification are needed to confirm their prognostic significances, it is quite enthusiastic that genetic variations associated with ADMET (adsorption, desorption, metabolism, excretion, and transport) genes predict the responsiveness of anticancer drugs with better predictive power compared to other genomic variations based stratification. It is also expected SNP markers that reside within the loci of these genetic pathways may play an important role to classify responders and non-responders in AML-NK. Methods and materials:A high density SNP genotyping microarray designed by our group (custom PGX chip; Samsung Electronics Co., Suwon, Korea) was evaluated in 139 patients with AML-NK as a discovery set using both custom PGX chip and Affymetrix SNP array 6.0 (Affymetrix Inc., Santa Clara, CA USA). The DNA samples were obtained from the marrow samples at the time of diagnosis. The custom PGx chip included more extensive SNP markers of ADMET genes and cancer-related genes. Genotypes were determined by Birdseed v2 algorithm for Affymetrix SNP 6.0 and by BRLMM-p for custom PGx chip. Genotypes from 906,600 SNPs were obtained in Affymetrix SNP 6.0 chip while custom PGx chip provided genotype information of 90,647 SNPs. Between Affymetrix SNP 6.0 and custom PGx chip, 67,251 SNPs were shared. ADMET and cancer-related SNPs (n=23,396) were only available on custom PGx chip. The predictive model for the achievement of complete remission was developed using Max Test method with leave-one-out LDA cross validation (repeating 10,000 times of permutation test). The analysis compared the data of PGx chip with data from Affymetrix SNP 6.0 microarray. Results:Genotyping results from custom PGx chip was compared to results from Affymetrix SNP 6.0 showing 94.33% of concordance rate. Among the 164,578 SNP markers included in the original design, 90,647 of them were finally chosen to generate reliable genotype calls. Additional SNP markers including missing genotypes were again removed before the construction of prediction model. Finally, the total number of available SNP markers was reduced to 37,676 for the analysis and model contruction.Max Test provided p-values by performing permutation test. Significant SNP markers with p-values less than 0.01 were chosen to construct a prediction model using leave-one-out LDA (linear discriminate analysis) method. The numbers of SNP markers were narrowed down as p-values became more significant (No. of SNPs in p-value): n=329 in p <0.01; n=66 in p <0.001; n=7 in p<0.0001.The selection of SNP markers could be optimized and further narrowed down by extensive search. The accuracy of the model improved as more SNP markers were included in the model: the accuracy reaches 100% when N was 59.Followings are the example of the significant SNP markers included in the genetic predictive model for complete remission after induction therapy for AML-NK: CCDC93, LRP1B, LASS6, ATF2, and PDE11A on chr 2, PDE4D and PPP2R2B on chr 5, PARK2 on chr 6, PTPRD on chr 9, ABCC4 on chr 13, SLC25A21 on chr 14, ABCC1, PRKCB, ITGAX, and CNOT1 on chr 16, and other SNP markers. The biologic significances of these SNP markers are under investigation. Conclusion:The clinical relevance of the predictive model based on pharmacogenomic informations will be further clarified with external replication in the independent cohorts of AML. In patients with AML-NK which has diverse and heterogeneous clinical characteristics, the predictive model could identify non-responders who might benefit from alternative treatments prior to commencement of conventional induction therapy based on idarubicin plus cytarabine. In addition, it will also reduce the risk of adverse events in the non-responders and eventually improve overall survival in AML-NK. Disclosures:No relevant conflicts of interest to declare.