scholarly journals Personal Genomes: For One and for All

Science ◽  
2011 ◽  
Vol 331 (6018) ◽  
pp. 690-690 ◽  
Author(s):  
J. Wang
Keyword(s):  
Personal Genomes

scholarly journals Korean Genome Project: 1094 Korean personal genomes with clinical information

2020 ◽  
Vol 6 (22) ◽  
pp. eaaz7835 ◽  
Author(s):  
Sungwon Jeon ◽  
Youngjune Bhak ◽  
Yeonsong Choi ◽  
Yeonsu Jeon ◽  
Seunghoon Kim ◽  
...  
Keyword(s):  
Genome Wide Association Study ◽  
Imputation Accuracy ◽  
Clinical Information ◽  
Genome Project ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Genome Wide ◽  
A Genome ◽  
Whole Genomes ◽  
Personal Genomes

We present the initial phase of the Korean Genome Project (Korea1K), including 1094 whole genomes (sequenced at an average depth of 31×), along with data of 79 quantitative clinical traits. We identified 39 million single-nucleotide variants and indels of which half were singleton or doubleton and detected Korean-specific patterns based on several types of genomic variations. A genome-wide association study illustrated the power of whole-genome sequences for analyzing clinical traits, identifying nine more significant candidate alleles than previously reported from the same linkage disequilibrium blocks. Also, Korea1K, as a reference, showed better imputation accuracy for Koreans than the 1KGP panel. As proof of utility, germline variants in cancer samples could be filtered out more effectively when the Korea1K variome was used as a panel of normals compared to non-Korean variome sets. Overall, this study shows that Korea1K can be a useful genotypic and phenotypic resource for clinical and ethnogenetic studies.

scholarly journals Genomic GPS: using genetic distance from individuals to public data for genomic analysis without disclosing personal genomes

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Kunhee Kim ◽  
Hyungryul Baik ◽  
Chloe Soohyun Jang ◽  
Jin Kyung Roh ◽  
Eleazer Eskin ◽  
...  
Keyword(s):  
Genetic Distance ◽  
Genomic Analysis ◽  
Public Data ◽  
Personal Genomes

scholarly journals Forward Individualized Medicine from Personal Genomes to Interactomes

2015 ◽  
Vol 6 ◽  
Author(s):  
Xiang Zhang ◽  
Jan A. Kuivenhoven ◽  
Albert K. Groen
Keyword(s):  
Individualized Medicine ◽  
Personal Genomes

scholarly journals Pharmacogenetics and personal genomes

2009 ◽  
Vol 6 (6) ◽  
pp. 643-652 ◽  
Author(s):  
Michael J Wagner
Keyword(s):  
Personal Genomes

scholarly journals An analytical framework for optimizing variant discovery from personal genomes

2015 ◽  
Vol 6 (1) ◽  
Author(s):  
Gareth Highnam ◽  
Jason J. Wang ◽  
Dean Kusler ◽  
Justin Zook ◽  
Vinaya Vijayan ◽  
...  
Keyword(s):  
Analytical Framework ◽  
Variant Discovery ◽  
Personal Genomes

scholarly journals NextSV: a meta-caller for structural variants from low-coverage long-read sequencing data

2016 ◽  
Author(s):  
Li Fang ◽  
Jiang Hu ◽  
Depeng Wang ◽  
Kai Wang
Keyword(s):  
Whole Genome ◽  
Ashkenazi Jewish ◽  
Structural Variants ◽  
Sequencing Data ◽  
Short Read ◽  
Short Read Sequencing ◽  
Human Genomes ◽  
Long Read ◽  
Personal Genomes ◽  
Low Coverage

AbstractBackgroundStructural variants (SVs) in human genomes are implicated in a variety of human diseases. Long-read sequencing delivers much longer read lengths than short-read sequencing and may greatly improve SV detection. However, due to the relatively high cost of long-read sequencing, it is unclear what coverage is needed and how to optimally use the aligners and SV callers.ResultsIn this study, we developed NextSV, a meta-caller to perform SV calling from low coverage long-read sequencing data. NextSV integrates three aligners and three SV callers and generates two integrated call sets (sensitive/stringent) for different analysis purposes. We evaluated SV calling performance of NextSV under different PacBio coverages on two personal genomes, NA12878 and HX1. Our results showed that, compared with running any single SV caller, NextSV stringent call set had higher precision and balanced accuracy (F1 score) while NextSV sensitive call set had a higher recall. At 10X coverage, the recall of NextSV sensitive call set was 93.5% to 94.1% for deletions and 87.9% to 93.2% for insertions, indicating that ~10X coverage might be an optimal coverage to use in practice, considering the balance between the sequencing costs and the recall rates. We further evaluated the Mendelian errors on an Ashkenazi Jewish trio dataset.ConclusionsOur results provide useful guidelines for SV detection from low coverage whole-genome PacBio data and we expect that NextSV will facilitate the analysis of SVs on long-read sequencing data.

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