scholarly journals Exclusion and genomic relatedness methods for assignment of parentage using genotyping-by-sequencing data

2019 ◽  
Author(s):  
K. G. Dodds ◽  
J. C. McEwan ◽  
R. Brauning ◽  
T. C. van Stijn ◽  
S. J. Rowe ◽  
...  
Keyword(s):  
Genotyping By Sequencing ◽  
Parentage Analysis ◽  
Random Selection ◽  
Parentage Assignment ◽  
Sequencing Data ◽  
Putative Parent ◽  
The Difference ◽  
Genomic Relatedness ◽  
Selection Of ◽  
Mismatch Rate

SummaryGenotypes are often used to assign parentage in agricultural and ecological settings. Sequencing can be used to obtain genotypes but does not provide unambiguous genotype calls, especially when sequencing depth is low in order to reduce costs. In that case, standard parentage analysis methods no longer apply. A strategy for using low-depth sequencing data for parentage assignment is developed here. It entails the use of relatedness estimates along with a metric termed excess mismatch rate which, for parent-offspring pairs or trios, is the difference between the observed mismatch rate and the rate expected under a model of inheritance and allele reads without error. When more than one putative parent has similar statistics, bootstrapping can provide a measure of the relatedness similarity. Putative parent-offspring trios can be further checked for consistency by comparing the offspring’s estimated inbreeding to half the parent relatedness. Suitable thresholds are required for each metric. These methods were applied to a deer breeding operation consisting of two herds of different breeds. Relatedness estimates were more in line with expectation when the herds were analysed separately than when combined, although this did not alter which parents were the best matches with each offspring. Parentage results were largely consistent with those based on a microsatellite parentage panel with three discordant parent assignments out of 1561. Two models are investigated to allow the parentage metrics to be calculated with non-random selection of alleles. The tools and strategies given here allow parentage to be assigned from low-depth sequencing data.

scholarly journals Exclusion and Genomic Relatedness Methods for Assignment of Parentage Using Genotyping-by-Sequencing Data

2019 ◽  
Vol 9 (10) ◽  
pp. 3239-3247 ◽  
Author(s):  
Ken G. Dodds ◽  
John C. McEwan ◽  
Rudiger Brauning ◽  
Tracey C. van Stijn ◽  
Suzanne J. Rowe ◽  
...  
Keyword(s):  
Genotyping By Sequencing ◽  
Parentage Analysis ◽  
Random Selection ◽  
Parentage Assignment ◽  
Sequencing Data ◽  
Putative Parent ◽  
The Difference ◽  
Genomic Relatedness ◽  
Selection Of ◽  
Mismatch Rate

Genotypes are often used to assign parentage in agricultural and ecological settings. Sequencing can be used to obtain genotypes but does not provide unambiguous genotype calls, especially when sequencing depth is low in order to reduce costs. In that case, standard parentage analysis methods no longer apply. A strategy for using low-depth sequencing data for parentage assignment is developed here. It entails the use of relatedness estimates along with a metric termed excess mismatch rate which, for parent-offspring pairs or trios, is the difference between the observed mismatch rate and the rate expected under a model of inheritance and allele reads without error. When more than one putative parent has similar statistics, bootstrapping can provide a measure of the relatedness similarity. Putative parent-offspring trios can be further checked for consistency by comparing the offspring’s estimated inbreeding to half the parent relatedness. Suitable thresholds are required for each metric. These methods were applied to a deer breeding operation consisting of two herds of different breeds. Relatedness estimates were more in line with expectation when the herds were analyzed separately than when combined, although this did not alter which parents were the best matches with each offspring. Parentage results were largely consistent with those based on a microsatellite parentage panel with three discordant parent assignments out of 1561. Two models are investigated to allow the parentage metrics to be calculated with non-random selection of alleles. The tools and strategies given here allow parentage to be assigned from low-depth sequencing data.

scholarly journals Parentage Analysis in Giant Grouper (Epinephelus lanceolatus) Using Microsatellite and SNP Markers from Genotyping-by-Sequencing Data

Genes ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 1042
Author(s):  
Zhuoying Weng ◽  
Yang Yang ◽  
Xi Wang ◽  
Lina Wu ◽  
Sijie Hua ◽  
...  
Keyword(s):  
Fishery Management ◽  
Genotyping By Sequencing ◽  
Parentage Analysis ◽  
Snp Markers ◽  
Individual Identification ◽  
Pedigree Information ◽  
Nucleotide Polymorphisms ◽  
Sequencing Data ◽  
Polymorphic Snps ◽  
Mixed Family

Pedigree information is necessary for the maintenance of diversity for wild and captive populations. Accurate pedigree is determined by molecular marker-based parentage analysis, which may be influenced by the polymorphism and number of markers, integrity of samples, relatedness of parents, or different analysis programs. Here, we described the first development of 208 single nucleotide polymorphisms (SNPs) and 11 microsatellites for giant grouper (Epinephelus lanceolatus) taking advantage of Genotyping-by-sequencing (GBS), and compared the power of SNPs and microsatellites for parentage and relatedness analysis, based on a mixed family composed of 4 candidate females, 4 candidate males and 289 offspring. CERVUS, PAPA and COLONY were used for mutually verification. We found that SNPs had a better potential for relatedness estimation, exclusion of non-parentage and individual identification than microsatellites, and > 98% accuracy of parentage assignment could be achieved by 100 polymorphic SNPs (MAF cut-off < 0.4) or 10 polymorphic microsatellites (mean Ho = 0.821, mean PIC = 0.651). This study provides a reference for the development of molecular markers for parentage analysis taking advantage of next-generation sequencing, and contributes to the molecular breeding, fishery management and population conservation.

scholarly journals Parentage assignment with genotyping-by-sequencing data

2018 ◽  
Author(s):  
Andrew Whalen ◽  
Gregor Gorjanc ◽  
John M Hickey
Keyword(s):  
False Positive Rate ◽  
Simulated Data ◽  
Genotyping By Sequencing ◽  
Unrelated Individual ◽  
Parentage Assignment ◽  
Sequencing Data ◽  
High Coverage ◽  
Array Data ◽  
Assignment Method ◽  
Low Coverage

AbstractIn this paper we evaluate using genotype-by-sequencing (GBS) data to perform parentage assignment in lieu of traditional array data. The use of GBS data raises two issues: First, for low-coverage GBS data, it may not be possible to call the genotype at many loci, a critical first step for detecting opposing homozygous markers. Second, the amount of sequencing coverage may vary across individuals, making it challenging to directly compare the likelihood scores between putative parents. To address these issues we extend the probabilistic framework of Huisman (2017) and evaluate putative parents by comparing their (potentially noisy) genotypes to a series of proposal distributions. These distributions describe the expected genotype probabilities for the relatives of an individual. We assign putative parents as a parent if they are classified as a parent (as opposed to e.g., an unrelated individual), and if the assignment score passes a threshold. We evaluated this method on simulated data and found that (1) high-coverage GBS data performs similarly to array data and requires only a small number of markers to correctly assign parents and (2) low-coverage GBS data (as low as 0.1x) can also be used, provided that it is obtained across a large number of markers. When analysing the low-coverage GBS data, we also found a high number of false positives if the true parent is not contained within the list of candidate parents, but that this false positive rate can be greatly reduced by hand tuning the assignment threshold. We provide this parentage assignment method as a standalone program called AlphaAssign.

scholarly journals Parentage assignment with genotyping‐by‐sequencing data

2018 ◽  
Author(s):  
Andrew Whalen ◽  
Gregor Gorjanc ◽  
John M. Hickey
Keyword(s):  
Genotyping By Sequencing ◽  
Parentage Assignment ◽  
Sequencing Data

COMBINATORIAL POLYNOMIALLY COMPUTABLE CHARACTERISTICS OF SUBSTITUTIONS AND THEIR PROPERTIES

2020 ◽  
Vol 7 (2) ◽  
pp. 34-41
Author(s):  
VLADIMIR NIKONOV ◽  
◽  
ANTON ZOBOV ◽  
Keyword(s):  
Mathematical Expectation ◽  
Point Of View ◽  
Small Range ◽  
Computational Point ◽  
Wide Range ◽  
Bijective Function ◽  
The Difference ◽  
Element Base ◽  
Selection Of

The construction and selection of a suitable bijective function, that is, substitution, is now becoming an important applied task, particularly for building block encryption systems. Many articles have suggested using different approaches to determining the quality of substitution, but most of them are highly computationally complex. The solution of this problem will significantly expand the range of methods for constructing and analyzing scheme in information protection systems. The purpose of research is to find easily measurable characteristics of substitutions, allowing to evaluate their quality, and also measures of the proximity of a particular substitutions to a random one, or its distance from it. For this purpose, several characteristics were proposed in this work: difference and polynomial, and their mathematical expectation was found, as well as variance for the difference characteristic. This allows us to make a conclusion about its quality by comparing the result of calculating the characteristic for a particular substitution with the calculated mathematical expectation. From a computational point of view, the thesises of the article are of exceptional interest due to the simplicity of the algorithm for quantifying the quality of bijective function substitutions. By its nature, the operation of calculating the difference characteristic carries out a simple summation of integer terms in a fixed and small range. Such an operation, both in the modern and in the prospective element base, is embedded in the logic of a wide range of functional elements, especially when implementing computational actions in the optical range, or on other carriers related to the field of nanotechnology.

Equality and Random Selection of Representatives

2011 ◽  
Author(s):  
Joel Parker
Keyword(s):  
Random Selection ◽  
Selection Of

Tax Practitioner Credentials and the Incidence of IRS Audit Adjustments

2003 ◽  
Vol 17 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Peggy A. Hite ◽  
John Hasseldine
Keyword(s):  
Random Selection ◽  
Internal Revenue Service ◽  
Internal Revenue ◽  
Tax Returns ◽  
Selection Of

This study analyzes a random selection of Internal Revenue Service (IRS) office audits from October 1997 to July 1998, the type of audit that concerns most taxpayers. Taxpayers engage paid preparers in order to avoid this type of audit and to avoid any resulting tax adjustments. The study examines whether there are more audit adjustments and penalty assessments on tax returns with paid-preparer assistance than on tax returns without paid-preparer assistance. By comparing the frequency of adjustments on IRS office audits, the study finds that there are significantly fewer tax adjustments on paid-preparer returns than on self-prepared returns. Moreover, CPA-prepared returns resulted in fewer audit adjustments than non CPA-prepared returns.

scholarly journals Age-dependency in mortality of Finnish family caregivers: a nationwide register-based study

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
T M Mikkola ◽  
H Kautiainen ◽  
M Mänty ◽  
M B von Bonsdorff ◽  
T Kröger ◽  
...  
Keyword(s):  
General Population ◽  
Family Caregivers ◽  
Family Caregiver ◽  
Causes Of Death ◽  
Lower Mortality ◽  
All Cause Mortality ◽  
The Difference ◽  
Caregiver Role ◽  
Selection Of

Abstract Purpose Mortality appears to be lower in family caregivers than in the general population. However, there is lack of knowledge whether the difference in mortality between family caregivers and the general population is dependent on age. The purpose of this study was to analyze all-cause mortality in relation to age in family caregivers and to study their cause-specific mortality using data from multiple Finnish national registers. Methods The data included all individuals, who received family caregiver's allowance in Finland in 2012 (n = 42 256, mean age 67 years, 71% women) and a control population matched for age, sex, and municipality of residence (n = 83 618). Information on dates and causes of death between 2012 and 2017 were obtained from the Finnish Causes of Death Register. Flexible parametric survival modeling and competing risk regression adjusted for socioeconomic status were used. Results The total follow-up time was 717 877 person-years. Family caregivers had lower all-cause mortality than the controls over the follow-up (8.1% vs. 11.6%) both among women (hazard ratio [HR]: 0.64, 95% CI: 0.61-0.68) and men (HR: 0.73, 95% CI: 0.70-0.77). Younger adult caregivers had equal or only slightly lower mortality than their controls, but after age 60, the difference increased markedly resulting in over 10% lower mortality in favor of the caregivers in the oldest age groups. Caregivers had lower mortality for all the causes of death studied, namely cardiovascular, cancer, neurological, external, respiratory, gastrointestinal and dementia than the controls. Of these, the lowest was the risk for dementia (subhazard ratio=0.29, 95%CI: 0.25-0.34). Conclusions Older family caregivers have lower mortality than the age-matched controls from the general population while younger caregivers have similar mortality to their peers. This age-dependent advantage in mortality is likely to reflect selection of healthier individuals into the family caregiver role. Key messages The difference in mortality between family caregivers and the age-matched general population varies considerably with age. Advantage in mortality observed in family caregiver studies is likely to reflect the selection of healthier individuals into the caregiver role, which underestimates the adverse effects of caregiving.

scholarly journals A microfluidic device enabling drug resistance analysis of leukemia cells via coupled dielectrophoretic detection and impedimetric counting

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yağmur Demircan Yalçın ◽  
Taylan Berkin Töral ◽  
Sertan Sukas ◽  
Ender Yıldırım ◽  
Özge Zorlu ◽  
...  
Keyword(s):  
Drug Resistance ◽  
K562 Cells ◽  
Leukemia Cells ◽  
Drug Resistant ◽  
Wild Type ◽  
Gene Expressions ◽  
Before And After ◽  
The Difference ◽  
Selection Of ◽  
Resistant Cells

AbstractWe report the development of a lab-on-a-chip system, that facilitates coupled dielectrophoretic detection (DEP-D) and impedimetric counting (IM-C), for investigating drug resistance in K562 and CCRF-CEM leukemia cells without (immuno) labeling. Two IM-C units were placed upstream and downstream of the DEP-D unit for enumeration, respectively, before and after the cells were treated in DEP-D unit, where the difference in cell count gave the total number of trapped cells based on their DEP characteristics. Conductivity of the running buffer was matched the conductivity of cytoplasm of wild type K562 and CCRF-CEM cells. Results showed that DEP responses of drug resistant and wild type K562 cells were statistically discriminative (at p = 0.05 level) at 200 mS/m buffer conductivity and at 8.6 MHz working frequency of DEP-D unit. For CCRF-CEM cells, conductivity and frequency values were 160 mS/m and 6.2 MHz, respectively. Our approach enabled discrimination of resistant cells in a group by setting up a threshold provided by the conductivity of running buffer. Subsequent selection of drug resistant cells can be applied to investigate variations in gene expressions and occurrence of mutations related to drug resistance.

scholarly journals Selection of Tree Nut Allergen Peptide Markers: A Need for Improved Protein Sequence Databases

2019 ◽  
Vol 102 (5) ◽  
pp. 1263-1270 ◽  
Author(s):  
Weili Xiong ◽  
Melinda A McFarland ◽  
Cary Pirone ◽  
Christine H Parker
Keyword(s):  
Food Allergen ◽  
Protein Sequence ◽  
Sequence Information ◽  
Sequencing Data ◽  
Reference Tree ◽  
Candidate Peptide ◽  
Tree Nut ◽  
Allergen Detection ◽  
Sequence Databases ◽  
Selection Of

Abstract Background: To effectively safeguard the food-allergic population and support compliance with food-labeling regulations, the food industry and regulatory agencies require reliable methods for food allergen detection and quantification. MS-based detection of food allergens relies on the systematic identification of robust and selective target peptide markers. The selection of proteotypic peptide markers, however, relies on the availability of high-quality protein sequence information, a bottleneck for the analysis of many plant-based proteomes. Method: In this work, data were compiled for reference tree nut ingredients and evaluated using a parsimony-driven global proteomics workflow. Results: The utility of supplementing existing incomplete protein sequence databases with translated genomic sequencing data was evaluated for English walnut and provided enhanced selection of candidate peptide markers and differentiation between closely related species. Highlights: Future improvements of protein databases and release of genomics-derived sequences are expected to facilitate the development of robust and harmonized LC–tandem MS-based methods for food allergen detection.

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