Genetic diversity of HLA system in two populations from Oaxaca, Mexico: Oaxaca city and rural Oaxaca

Hernández-Hernández, Ofelia; Hernández-Zaragoza, Diana Iraíz; Barquera, Rodrigo; Warinner, Christina; López-Gil, Concepción; Arrieta-Bolaños, Esteban; Clayton, Stephen; Bravo-Acevedo, Alicia; Cruz, Flor del Rocío Ramos-de la; Méndez-Mani, Patricia; Pavón-Vargas, María los Ángeles de; Zúñiga, Joaquı́n; Yunis, Edmond J.; Bekker-Méndez, Carolina; Granados, Julio

doi:10.1016/j.humimm.2019.07.278

Cited by 4 publications

(2 citation statements)

References 15 publications

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“…Mexican Mestizo populations from the Center of Mexico (14 populations): Aguascalientes ( Bravo-Acevedo et al, 2020 ), Guanajuato ( Pantoja-Torres et al, 2020 ), Guerrero ( Juárez-Nicolás et al, 2020 ), Jalisco ( Bravo-Acevedo et al, 2020 ), Mexico City Center, Mexico City Western, Mexico City Eastern, Mexico City Southern, Mexico City Northern ( Barquera et al, 2020e ), Michoacán ( Ballesteros-Romero et al, 2020 ), Morelos ( Ortega-Yáñez et al, 2020 ), Nayarit ( Goné-Vázquez et al, 2020 ), Querétaro ( Martínez-Álvarez et al, 2020 ), and San Luis Potosí ( Hernández-Zaragoza et al, 2020 ). Mexican Mestizo populations from Southern Mexico (4 populations): Chiapas ( Barquera et al, 2020d ), Oaxaca ( Hernández-Hernández et al, 2020 ), Quintana Roo ( Medina-Escobedo et al, 2020 ) and Tabasco ( Solís-Martínez et al, 2020 ). Native American Mexican populations (8 populations): Amerindian pooled population 1 (Tarahumaras, Mixtecs, and Zapotecan) ( Hollenbach et al, 2001 ; García-Ortiz et al, 2006 ) and Amerindian pooled population 2 (Mixtecs and Tarahumaras), Mixe ( Hollenbach et al, 2001 ), Mixtecs ( Arnaiz-Villena et al, 2014 ), Nahuas ( Vargas-Alarcón et al, 2003 ), Lacandon ( Barquera et al, 2020f ), Seri ( Gorodezky, 2006 ), and Zapotecan ( Hollenbach et al, 2001 ).…”

Section: Subjects Materials and Methodsmentioning

confidence: 99%

Heterogeneity of Genetic Admixture Determines SLE Susceptibility in Mexican

et al. 2021

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Systemic Lupus Erythematosus (SLE) is an autoimmune inflammatory disorder for which Major Histocompatibility Complex (MHC) genes are well identified as risk factors. SLE patients present different clinical phenotypes, which are partly explained by admixture patterns variation among Mexicans. Population genetic has insight into the high genetic variability of Mexicans, mainly described through HLA gene studies with anthropological and biomedical importance. A prospective, case-control study was performed. In this study, we recruited 146 SLE patients, and 234 healthy individuals were included as a control group; both groups were admixed Mexicans from Mexico City. The HLA typing methods were based on Next Generation Sequencing and Sequence-Based Typing (SBT). The data analysis was performed with population genetic programs and statistical packages. The admixture estimations based on HLA-B and -DRB1 revealed that SLE patients have a higher Southwestern European ancestry proportion (48 ± 8%) than healthy individuals (30 ± 7%). In contrast, Mexican Native American components are diminished in SLE patients (44 ± 1%) and augmented in Healthy individuals (63 ± 4%). HLA alleles and haplotypes’ frequency analysis found variants previously described in SLE patients from Mexico City. Moreover, a conserved extended haplotype that confers risk to develop SLE was found, the HLA-A∗29:02∼C∗16:01∼B∗44:03∼DRB1∗07:01∼DQB1∗02:02, pC = 0.02, OR = 1.41. Consistent with the admixture estimations, the origin of all risk alleles and haplotypes found in this study are European, while the protection alleles are Mexican Native American. The analysis of genetic distances supported that the SLE patient group is closer to the Southwestern European parental populace and farthest from Mexican Native Americans than healthy individuals. Heterogeneity of genetic admixture determines SLE susceptibility and protection in Mexicans. HLA sequencing is helpful to determine susceptibility alleles and haplotypes restricted to some populations.

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Section: Subjects Materials and Methodsmentioning

confidence: 99%

Heterogeneity of Genetic Admixture Determines SLE Susceptibility in Mexican

et al. 2021

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“…The specific typical matching algorithm can be divided into direct matching, grid matching, mixed matching, classification matching, mobile intersection matching, etc. according to the matching principle or method [ 25 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

Empirical Study of Large-Scale HLA Simulation of Parallel Region-Matching Knowledge Recognition Algorithm Based on Region Matching

Guohua

Wang

2022

Computational Intelligence and Neuroscience

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Most of the existing region-matching algorithms need to match all regions, resulting in a waste of computing resources, increasing the cost of simulation technology and data redundancy, and resulting in the reduction of network data stream transmission efficiency. This paper presents a parallel region-matching knowledge recognition algorithm. Combined with the shortcomings of existing matching algorithms, a simulation technology is constructed to realize the parallel matching of multiple regions in HLA distributed simulation. The algorithm can realize the parallel matching calculation of multiple changed regions in one simulation. At the same time, the basic idea based on mobile intersection is adopted in the matching calculation, and the historical information before and after the region range is moved is used. The matching is limited to the moving interval, and the moving crossover theory is applied to the matching calculation to realize the relevant historical information before and after the region. Simulation results show that the parallel region-matching knowledge recognition algorithm can support HLA distributed simulation evaluation. In the matching calculation, the basic idea based on moving intersection is adopted, and the matching is limited to the moving interval by using the historical information before and after the region is moved, which reduces a large number of irrelevant calculations. Theoretical analysis and experimental results show that the algorithm is particularly suitable for the application needs of building large-scale distributed simulation based on multi-core computing platform.

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