RT Journal
A1 Yoon, Christopher J.
A1 Kim, Su Yeon
A1 Nam, Chang Hyun
A1 Lee, Junehawk
A1 Park, Jung Woo
A1 Mun, Jihyeob
A1 Park, Seongyeol
A1 Lee, Soyoung
A1 Yi, Boram
A1 Min, Kyoung Il
A1 Wiley, Brian
A1 Bolton, Kelly L.
A1 Lee, Jeong Ho
A1 Kim, Eunjoon
A1 Yoo, Hee Jeong
A1 Jun, Jong Kwan
A1 Choi, Ji Seon
A1 Griffith, Malachi
A1 Griffith, Obi L.
A1 Ju, Young Seok
T1 Estimation of intrafamilial DNA contamination in family trio genome sequencing using deviation from Mendelian inheritance
JF Genome Research 
JO Genome Research 
YR 2022 
FD November 01 
VO 32 
IS 11-12 
SP 2134 
OP 2144 
DO 10.1101/gr.276794.122 
UL http://genome.cshlp.org/content/32/11-12/2134.abstract 
AB 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.