Estimation of intrafamilial DNA contamination in family trio genome sequencing using deviation from Mendelian inheritance

With the increasing number of sequencing projects involving families, quality control tools optimized for family genome sequencing are needed. However, accurately quantifying contamination in a DNA mixture is particularly difficult when genetically related family members are the sources. We develope...

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Detalles Bibliográficos
Autores principales: Yoon, Christopher J., Kim, Su Yeon, Nam, Chang Hyun, Lee, Junehawk, Park, Jung Woo, Mun, Jihyeob, Park, Seongyeol, Lee, Soyoung, Yi, Boram, Min, Kyoung Il, Wiley, Brian, Bolton, Kelly L., Lee, Jeong Ho, Kim, Eunjoon, Yoo, Hee Jeong, Jun, Jong Kwan, Choi, Ji Seon, Griffith, Malachi, Griffith, Obi L., Ju, Young Seok
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9808622/
https://www.ncbi.nlm.nih.gov/pubmed/36617634
http://dx.doi.org/10.1101/gr.276794.122
Descripción
Sumario:With the increasing number of sequencing projects involving families, quality control tools optimized for family genome sequencing are needed. However, accurately quantifying contamination in a DNA mixture is particularly difficult when genetically related family members are the sources. We developed TrioMix, a maximum likelihood estimation (MLE) framework based on Mendel's law of inheritance, to quantify DNA mixture between family members in genome sequencing data of parent–offspring trios. TrioMix can accurately deconvolute any intrafamilial DNA contamination, including parent–offspring, sibling–sibling, parent–parent, and even multiple familial sources. In addition, TrioMix can be applied to detect genomic abnormalities that deviate from Mendelian inheritance patterns, such as uniparental disomy (UPD) and chimerism. A genome-wide depth and variant allele frequency plot generated by TrioMix facilitates tracing the origin of Mendelian inheritance deviations. We showed that TrioMix could accurately deconvolute genomes in both simulated and real data sets.