Polymarker, HLA-DQA1, and D1S80 Allele Frequency Data in Chamorro and Filipino Populations from Guam

1998 ◽  
Vol 43 (6) ◽  
pp. 14384J ◽  
Author(s):  
Bruce Budowle ◽  
F. Samuel Baechtel ◽  
Rose Fejeran
Keyword(s):  
Allele Frequency ◽  
Frequency Data ◽  
Allele Frequency Data ◽  
Hla Dqa1

scholarly journals Multiple Haplotype Reconstruction from Allele Frequency Data

2020 ◽  
Author(s):  
Marta Pelizzola ◽  
Merle Behr ◽  
Housen Li ◽  
Axel Munk ◽  
Andreas Futschik
Keyword(s):  
Allele Frequency ◽  
Real Data ◽  
Coefficient Matrix ◽  
Point Of View ◽  
Design Matrix ◽  
Frequency Data ◽  
Computationally Efficient ◽  
Regression Problem ◽  
Allele Frequency Data ◽  
Multiple Samples

AbstractSince haplotype information is of widespread interest in biomedical applications, effort has been put into their reconstruction. Here, we propose a new, computationally efficient method, called haploSep, that is able to accurately infer major haplotypes and their frequencies just from multiple samples of allele frequency data. Our approach seems to be the first that is able to estimate more than one haplotype given such data. Even the accuracy of experimentally obtained allele frequencies can be improved by re-estimating them from our reconstructed haplotypes. From a methodological point of view, we model our problem as a multivariate regression problem where both the design matrix and the coefficient matrix are unknown. The design matrix, with 0/1 entries, models haplotypes and the columns of the coefficient matrix represent the frequencies of haplotypes, which are non-negative and sum up to one. We illustrate our method on simulated and real data focusing on experimental evolution and microbial data.

Allele frequency data for VNTR locus D17S79: Identification of an internalHaeIII polymorphism in the black population

1994 ◽  
Vol 3 (3) ◽  
pp. 248-253 ◽  
Author(s):  
John S. Waye ◽  
Melanie Richard ◽  
George Carmody ◽  
Pamela J. Newall
Keyword(s):  
Allele Frequency ◽  
Black Population ◽  
Frequency Data ◽  
Vntr Locus ◽  
Allele Frequency Data

Optimization of cytochrome P4502D6 (CYP2D6) phenotype assignment using a genotyping algorithm based on allele frequency data

1999 ◽  
Vol 9 (6) ◽  
pp. 669???682 ◽  
Author(s):  
Andrea Gaedigk ◽  
R. Russell Gotschall ◽  
Nata??a S. Forbes ◽  
Stephen D. Simon ◽  
Gregory L. Kearns ◽  
...  
Keyword(s):  
Allele Frequency ◽  
Frequency Data ◽  
Cyp2d6 Phenotype ◽  
Allele Frequency Data

Allele Frequency Data for 19 Short Tandem Repeats (PowerPlexR 16 and FFFl) in a Belgian Population Sample

2006 ◽  
Vol 51 (2) ◽  
pp. 436-437 ◽  
Author(s):  
Ronny Decorte ◽  
Elke Verhoeven ◽  
Elisabeth Vanhoutte ◽  
Katleen Knaepen ◽  
Jean-Jacques Cassiman
Keyword(s):  
Allele Frequency ◽  
Short Tandem Repeats ◽  
Tandem Repeats ◽  
Population Sample ◽  
Frequency Data ◽  
Allele Frequency Data ◽  
Belgian Population ◽  
Short Tandem

scholarly journals Multivariate analysis of five chicken breed in Indonesia based on microsatellite allele frequency

2021 ◽  
Vol 19 (1) ◽  
pp. 48
Author(s):  
Ferdy Saputra ◽  
Tike Sartika ◽  
Anneke Anggraeni ◽  
Andi Baso Lompengeng Ishak ◽  
Komarudin Komarudin ◽  
...  
Keyword(s):  
Allele Frequency ◽  
Hierarchical Clustering ◽  
Correspondence Analysis ◽  
Principal Components ◽  
Principal Component ◽  
Frequency Data ◽  
White Leghorn ◽  
Microsatellite Allele ◽  
Allele Frequency Data ◽  
Combination Of Methods

<p class="MDPI17abstract"><strong>Objective: </strong>This study tries to examine several multivariate methods in classifying genetic diversity using microsatellite allele frequency data.</p><p class="MDPI17abstract"><strong>Methods: </strong>This study used microsatellite allele frequency data from White Leghorn (n = 48), Kampung (n = 48), Pelung (n = 24), Sentul (n = 24), and Black Kedu (n = 25) from Indonesian Research Institute for Animal Production. Allele frequency data were analyzed by the Neighbor-Joining (NJ) method using the POPTREE2 program. The data was also analyzed by the Principal Component Analysis (PCA), Correspondence Analysis (CA), and Hierarchical Clustering on Principal Components (HCPC) methods using the factoextra and FactoMineR packages in the R 4.0.0 program.<strong></strong></p><p class="MDPI17abstract"><strong>Results: </strong>Correspondence Analysis (CA) found Sentul is more closer to Black Kedu. However, based on NJ, PCA, and HCPC showed Sentul is closer to Kampung. Based on the value of Dimension 1, Correspondence Analysis (80.7%) can explain greater variation than PCA (58.9%). However, CA method generated different results compared to NJ, PCA, and HCPC. NJ, PCA, and HCPC found four chicken clusters, namely cluster 1 (White Leghorn), cluster 2 (Pelung), cluster 3 (Black Kedu), and cluster 4 (Kampung and Sentul).<strong></strong></p><p class="MDPI17abstract"><strong>Conclusions: </strong>In conclusion, HCPC is a better multivariate method for analyzing allele frequency data than PCA and CA. HCPC can be used to analyze allele frequency data better than PCA, because HCPC is a combination of methods from hierarchical clustering and principal components.</p>

scholarly journals Allele frequency data for 15 autosomal strs and ancestral proportions using aims-indels in the shuar ethnic group from Ecuador

2019 ◽  
Vol 7 (1) ◽  
pp. 65-67
Author(s):  
C. Paz-y-Miño ◽  
O. Astudillo-González ◽  
D. Maldonado-Oyervide ◽  
A. López-Cortés ◽  
A. Pérez-Villa ◽  
...  
Keyword(s):  
Ethnic Group ◽  
Allele Frequency ◽  
Frequency Data ◽  
Autosomal Strs ◽  
Allele Frequency Data
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