Estimation of intrafamilial DNA contamination in family trio genome sequencing using deviation from Mendelian inheritance.
Estimation of intrafamilial DNA contamination in family trio genome sequencing using deviation from Mendelian inheritance.
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DOI：
10.1101/gr.276794.122
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发表时间：
2022-11
期刊：
影响因子：
7
通讯作者：
Ju, Young Seok
中科院分区：
文献类型：
--
作者：
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
随着涉及家庭的测序项目越来越多，需要针对家庭基因组测序优化的质量控制工具。然而，当 DNA 混合物中的污染源是遗传相关的家庭成员时，准确定量污染就特别困难。我们开发了 TrioMix，这是一个基于孟德尔遗传定律的最大似然估计 (MLE) 框架，用于量化亲子三人基因组测序数据中家庭成员之间的 DNA 混合。 TrioMix 可以准确地消除任何家庭内 DNA 污染，包括父母与子女、兄弟姐妹、父母与父母，甚至多个家庭来源。此外，TrioMix 可用于检测偏离孟德尔遗传模式的基因组异常，例如单亲二体性 (UPD) 和嵌合现象。 TrioMix 生成的全基因组深度和变异等位基因频率图有助于追踪孟德尔遗传偏差的起源。我们证明 TrioMix 可以在模拟和真实数据集中准确地对基因组进行解卷积。
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.
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影响因子：
64.8
作者：
Kong A;Frigge ML;Masson G;Besenbacher S;Sulem P;Magnusson G;Gudjonsson SA;Sigurdsson A;Jonasdottir A;Jonasdottir A;Wong WS;Sigurdsson G;Walters GB;Steinberg S;Helgason H;Thorleifsson G;Gudbjartsson DF;Helgason A;Magnusson OT;Thorsteinsdottir U;Stefansson K
通讯作者：
Stefansson K
影响因子：
64.8
作者：
Karczewski, Konrad J;Francioli, Laurent C;MacArthur, Daniel G
通讯作者：
MacArthur, Daniel G
影响因子：
9.8
作者：
Pedersen, Brent S.;Quinlan, Aaron R.
通讯作者：
Quinlan, Aaron R.
影响因子：
7
作者：
King DA;Fitzgerald TW;Miller R;Canham N;Clayton-Smith J;Johnson D;Mansour S;Stewart F;Vasudevan P;Hurles ME;DDD Study
通讯作者：
DDD Study
DOI：
10.1093/bioinformatics/btp352
发表时间：
2009-08-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Li H;Handsaker B;Wysoker A;Fennell T;Ruan J;Homer N;Marth G;Abecasis G;Durbin R;1000 Genome Project Data Processing Subgroup
通讯作者：
1000 Genome Project Data Processing Subgroup