Parentage analysis using genome-wide high-density SNP microarray.
Gene
; 785: 145605, 2021 Jun 15.
Article
en En
| MEDLINE
| ID: mdl-33771603
ABSTRACT
OBJECTIVE:
Parentage analysis is a technology that uses genetic methods to verify or exclude relationships between individuals. STR technology is often used in parentage analysis. We received three sets of samples from three families. Each set of samples consisted of a male individual and a female individual. Their test requirements were meant to determine whether they were a paternity relationship, a sibling relationship, or grandparent-grandchild relationship. However, only one STR locus mismatch was detected in each group. Other family members to assist in testing could not be identified; therefore, other methods were needed to assist in judgment. Using high-density SNP microarrays, we analyzed the feasibility of its application in paternity analysis.RESULTS:
A total of 180 samples were tested, including 100 unrelated samples, and 74 samples from 30 families, and six samples from three families. The data were analyzed, grouped according to the chromosome of SNP, and the mismatching rate was counted. The total mismatching rate of SNP in unrelated individuals was 8-10 times higher than that of parent-child individuals. Individuals with a total mismatch rate of more than 5.3% were defined as individuals with no kinship, and the individuals with a total mismatch rate of less than 0.6% were defined as the individuals with a parent-child relationship.CONCLUSIONS:
Through the use of high-density gene chips for analysis, we also completed an auxiliary analysis of the kinship of the three families. The gene chip is a better method for auxiliary analysis of the kinship between individuals.Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Linaje
/
Análisis de Secuencia por Matrices de Oligonucleótidos
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Polimorfismo de Nucleótido Simple
Tipo de estudio:
Clinical_trials
Límite:
Adult
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Child
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Female
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Humans
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Male
Idioma:
En
Revista:
Gene
Año:
2021
Tipo del documento:
Article