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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2021.10
- 수록면
- 1,143 - 1,155 (13page)
- DOI
- 10.1007/s13258-021-01057-4
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초록· 키워드
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Background Population stratifcation modeling is essential in Genome-Wide Association Studies.
Objective In this paper, we aim to build a fne-scale population stratifcation model to efciently infer individual genetic ancestry.
Methods Kernel Principal Component Analysis (PCA) and random forest are adopted to build the population stratifcation model, together with parameter optimization. We explore diferent PCA methods, including standard PCA and kernel PCA to extract relevant features from the genotype data that is transformed by vcf2geno, a pipeline from LASER software. These extracted features are fed into a random forest for ensemble learning. Parameter tuning is performed to jointly fnd the optimal number of principal components, kernel function for PCA and parameters of the random forest.
Results Experiments based on HGDP dataset show that kernel PCA with Sigmoid function and Gaussian function can achieve higher prediction accuracy than the standard PCA. Compared to standard PCA with the two principal components, the accuracy by using KPCA-Sigmoid with the optimal number of principal components can achieve around 100% and 200% improvement for East Asian and European populations, respectively.
Conclusion With the optimal parameter confguration on both PCA and random forest, our proposed method can infer the individual genetic ancestry more accurately, given their variants.
목차
등록된 정보가 없습니다.
참고문헌 (29)
참고문헌 신청
Alexander DH / 2009 / Fast model-based estimation of ancestry in unrelated individuals / Genome Res 19(9):1655~1664
Bergstra J / 2012 / Random search for hyper-parameter optimization / J Mach Learn Res 13:281~305
Bishop CM / 2006 / Pattern recognition and machine learning / Springer
Breiman L / 2001 / Random forests / Mach Learn 45(1):5~32
Brieuc MS / 2018 / A practical introduction to random forest for genetic association studies in ecology and evolution / Mol Ecol Resour 18(4):755~766
Bergstra J / 2012 / Random search for hyper-parameter optimization / J Mach Learn Res 13:281~305
Bishop CM / 2006 / Pattern recognition and machine learning / Springer
Breiman L / 2001 / Random forests / Mach Learn 45(1):5~32
Brieuc MS / 2018 / A practical introduction to random forest for genetic association studies in ecology and evolution / Mol Ecol Resour 18(4):755~766
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