The Experts below are selected from a list of 312 Experts worldwide ranked by ideXlab platform
Nicolas Gengler - One of the best experts on this subject based on the ideXlab platform.
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Genetic diversity and joint-Pedigree Analysis of two importing Holstein populations.
Journal of dairy science, 2007Co-Authors: Hedi Hammami, Coraline Croquet, Jean Stoll, B. Rekik, Nicolas GenglerAbstract:Genetic diversity and relatedness between 2 geographically distant Holstein populations (in Luxembourg and Tunisia) were studied by Pedigree Analysis. These 2 populations have similar sizes and structures and are essentially importing populations. Edited Pedigrees included 140,392 and 151,381 animals for Tunisia and Luxembourg, respectively. To partially account for Pedigree completeness levels, a modified algorithm was used to compute inbreeding. The effective numbers of ancestors were derived from probabilities of gene origin for the 2 populations of cows born between 1990 and 2000. The 10 ancestors with the highest contributions to genetic diversity in the cow populations accounted for more than 32% of the genes. Eight of these 10 ancestors were the same in both populations. The rates of inbreeding were different in the 2 populations but were generally comparable to those found in the literature for the Holstein breed. Average inbreeding coefficients per year, estimated from the data, ranged from 0.91 and 0.50 in 1990 to 3.10 and 2.12 in 2000 for the Tunisian and Luxembourg populations, respectively. Genetic links have also strengthened with time. Average additive relationships between the 2 populations were as high as 2.2% in 2000. Results suggest that it would be possible to investigate genotype by environment interactions for milk traits using the Tunisian and Luxembourg dairy populations.
Andre Faaij - One of the best experts on this subject based on the ideXlab platform.
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improving uncertainty evaluation of process models by using Pedigree Analysis a case study on co2 capture with monoethanolamine
Computers & Chemical Engineering, 2016Co-Authors: Mijndert Van Der Spek, Andrea Ramirez, Andre FaaijAbstract:Abstract This article aims to improve uncertainty evaluation of process models by combining a quantitative uncertainty evaluation method (data validation) with a qualitative uncertainty evaluation method (Pedigree Analysis). The approach is tested on a case study of monoethanolamine based postcombustion CO 2 capture from a coal power plant. Data validation was used to quantitatively assess the uncertainty of the inputs and outputs of the MEA model. Pedigree Analysis was used to qualitatively assess the uncertainty in the current knowledge base on MEA carbon capture systems, the uncertainty in the MEA process model, and the uncertainty of the MEA model results. The Pedigree review was done by 13 international experts in the field of postcombustion carbon capture with chemical solvents. The data validation showed that our MEA model is accurate in predicting specific reboiler duty, and CO 2 stream purity (4% and 1% difference respectively between model and pilot plant results), but in first instance it was less accurate in predicting liquid over gas ratio, and cooling water requirement (54% and 23% difference respectively between model and pilot plant results). The Pedigree Analysis complemented these results by showing that there was fairly high uncertainty in the thermodynamic, and chemistry submodels, as reflected in the low Pedigree scores on most indicators. Therefore, the model was improved to better resemble pilot plant results. The results indicate that using a Pedigree approach improved uncertainty evaluation in three ways. First, by highlighting sources of uncertainty that quantitative uncertainty Analysis does not take into account, such as uncertainty in the knowledge base regarding a specific phenomenon. Second, by providing a systematic approach to uncertainty evaluation, thereby increasing the awareness of modeller and model user. And finally, by presenting the outcomes in easy to understand numerical scores and colours, improving the communication of model uncertainty. In combination with quantitative validation efforts, the Pedigree approach can provide a strong method to gain deep insight into the strengths and weaknesses of a process model, and to communicate this to policy and decision-makers.
Raymond J. Peterson - One of the best experts on this subject based on the ideXlab platform.
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Statistical estimation and Pedigree Analysis of CCR2-CCR5 haplotypes
Human genetics, 2001Co-Authors: Vanessa J. Clark, Noah Metheny, Michael Dean, Raymond J. PetersonAbstract:As more SNP marker data becomes available, researchers have used haplotypes of markers, rather than individual polymorphisms, for association Analysis of candidate genes. In order to perform haplotype Analysis in a population-based case-control study, haplotypes must be determined by estimation in the absence of family information or laboratory methods for establishing phase. Here, we test the accuracy of the Expectation-Maximization (EM) algorithm for estimating haplotype state and frequency in theCCR2-CCR5 gene region by comparison with haplotype state and frequency determined by Pedigree Analysis. To do this, we have characterized haplotypes comprising alleles at seven biallelic loci in theCCR2-CCR5 chemokine receptor gene region, a span of 20 kb on chromosome 3p21. Three-generation CEPH families (n=40), totaling 489 individuals, were genotyped by the 5'nuclease assay (TaqMan). Haplotype states and frequencies were compared in 103 grandparents who were assumed to have mated at random. Both Pedigree Analysis and the EM algorithm yielded the same small number of haplotypes for which linkage disequilibrium was nearly maximal. The haplotype frequencies generated by the two methods were nearly identical. These results suggest that the EM algorithm estimation of haplotype states, frequency, and linkage disequilibrium Analysis will be an effective strategy in theCCR2-CCR5 gene region. For genetic epidemiology studies,CCR2-CCR5 allele and haplotype frequencies were determined in African-American (n=30), Hispanic (n=24) and European-American (n=34) populations.
Siqi Liu - One of the best experts on this subject based on the ideXlab platform.
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Pedigree Analysis of an elite rice hybrid using proteomic approach.
Proteomics, 2006Co-Authors: Zhensheng Xie, Jingqiang Wang, Mengliang Cao, Caifeng Zhao, Kang Zhao, Jianmin Shao, Tingting Lei, Siqi LiuAbstract:The definition of dominance or epistasis is generally on the basis of a descriptive characterization for these crops in the field, such as yield per hectare and the weight of grain. Since these trait examinations lack molecular information, how to precisely predict the phenotypic changes in filial generation is still a problem in heterosis studies. For rice, the genetic information caused by hybridization can be archived through analyzing of proteomes of rice seeds. Differential Analysis of proteomes was introduced for the rice seeds of three cultivars, 9311, PA64S and LYP9, an elite rice hybrid from cross between 9311 and PA64S. In the three rice endosperms, the expression profiles of proteins were similar with the stained spots of 47 +/- 1, 46 +/- 0.6 and 44 +/- 0.6, for 9311, PA64S and LYP9, respectively; however, the number of proteins expressed in the rice embryos was significantly increased with the stained spots of 395.3 +/- 12.9, 350 +/- 9.2, and 389.3 +/- 16.4, for 9311, PA64S and LYP9, respectively. Importantly, the image comparisons and protein identifications have revealed in significantly different embryo protein spots among the three rice cultivars. By carefully analyzing these different 2-DE spots, many of them from the three embryos were shown to display a mirrored relationships between parents and the first filial generation. Furthermore, all of stained spots in LYP9 embryo were found on the 2-DEs from its parents, indicating that there was a genetic linkage. These results suggest that proteomic approach is able to serve Pedigree Analysis and functional prediction for new rice breeds.
Jeffrey R Walters - One of the best experts on this subject based on the ideXlab platform.
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Inbreeding rate and effective population size: A comparison of estimates from Pedigree Analysis and a demographic model
Biological Conservation, 2000Co-Authors: Bradley F. Blackwell, Phillip D. Doerr, J. Michael Reed, Jeffrey R WaltersAbstract:Abstract Demographic models have been used to calculate effective population size, (Ne) which is a measure of the expected rate of loss of genetic variability. However, accurately calculating effective size for most populations of wild vertebrates is difficult because the required demographic or Pedigree data are unavailable. We used data from a long-term study of the endangered red-cockaded woodpecker Picoides borealis in south-central North Carolina to construct a Pedigree, which we then used to calculate the realized rate of inbreeding (F). We compared our values, estimated via Pedigree Analysis, with published, expected values of F calculated from a demographic model. The change in inbreeding coefficient per generation (ΔF) based on a demographic model fell below the 95% confidence limit around the Pedigree value. Thus, ΔF, as calculated from a demographic model, significantly underestimated the ΔF estimated via Pedigree Analysis. We suggest that a multi-method approach can be useful to managers in increasing the accuracy of estimates of rate of loss of genetic variability.
