Abstract
With the advent of Next Generation Sequencing (NGS) technologies, whole genome and whole exome DNA sequencing has become affordable for routine genetic studies. Coupled with improved genotyping arrays and genotype imputation methodologies, it is increasingly feasible to obtain rare genetic variant information in large datasets. Such datasets allow researchers to gain a more complete understanding of the genetic architecture of complex traits caused by rare variants. State-of-the-art statistical methods for the statistical genetics analysis of sequence-based association, including efficient algorithms for association analysis in biobank-scale datasets, gene-association tests, meta-analysis, fine mapping methods that integrate functional genomic dataset, and phenome-wide association studies (PheWAS), are reviewed here. These methods are expected to be highly useful for next generation statistical genetics analysis in the era of precision medicine.
| Original language | English (US) |
|---|---|
| Article number | e83 |
| Journal | Current protocols in human genetics |
| Volume | 101 |
| Issue number | 1 |
| DOIs | |
| State | Published - Apr 1 2019 |
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All Science Journal Classification (ASJC) codes
- Genetics
- Genetics(clinical)
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Methods for the Analysis and Interpretation for Rare Variants Associated with Complex Traits. / Weissenkampen, J. Dylan; Jiang, Yu; Eckert, Scott; Jiang, Bibo; Li, Bingshan; Liu, Dajiang.
In: Current protocols in human genetics, Vol. 101, No. 1, e83, 01.04.2019.Research output: Contribution to journal › Article
TY - JOUR
T1 - Methods for the Analysis and Interpretation for Rare Variants Associated with Complex Traits
AU - Weissenkampen, J. Dylan
AU - Jiang, Yu
AU - Eckert, Scott
AU - Jiang, Bibo
AU - Li, Bingshan
AU - Liu, Dajiang
PY - 2019/4/1
Y1 - 2019/4/1
N2 - With the advent of Next Generation Sequencing (NGS) technologies, whole genome and whole exome DNA sequencing has become affordable for routine genetic studies. Coupled with improved genotyping arrays and genotype imputation methodologies, it is increasingly feasible to obtain rare genetic variant information in large datasets. Such datasets allow researchers to gain a more complete understanding of the genetic architecture of complex traits caused by rare variants. State-of-the-art statistical methods for the statistical genetics analysis of sequence-based association, including efficient algorithms for association analysis in biobank-scale datasets, gene-association tests, meta-analysis, fine mapping methods that integrate functional genomic dataset, and phenome-wide association studies (PheWAS), are reviewed here. These methods are expected to be highly useful for next generation statistical genetics analysis in the era of precision medicine.
AB - With the advent of Next Generation Sequencing (NGS) technologies, whole genome and whole exome DNA sequencing has become affordable for routine genetic studies. Coupled with improved genotyping arrays and genotype imputation methodologies, it is increasingly feasible to obtain rare genetic variant information in large datasets. Such datasets allow researchers to gain a more complete understanding of the genetic architecture of complex traits caused by rare variants. State-of-the-art statistical methods for the statistical genetics analysis of sequence-based association, including efficient algorithms for association analysis in biobank-scale datasets, gene-association tests, meta-analysis, fine mapping methods that integrate functional genomic dataset, and phenome-wide association studies (PheWAS), are reviewed here. These methods are expected to be highly useful for next generation statistical genetics analysis in the era of precision medicine.
UR - http://www.scopus.com/inward/record.url?scp=85062784974&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85062784974&partnerID=8YFLogxK
U2 - 10.1002/cphg.83
DO - 10.1002/cphg.83
M3 - Article
C2 - 30849219
AN - SCOPUS:85062784974
VL - 101
JO - Current Protocols in Human Genetics
JF - Current Protocols in Human Genetics
SN - 1934-8266
IS - 1
M1 - e83
ER -