基因组学表型预测中种群偏差的对抗性去除

Honggang Zhao, Wenlu Wang
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引用次数: 1

摘要

许多因素影响着基因型数据的性状预测。其中一个主要的混杂因素来自于样本个体中存在的种群结构,即种群分层。当存在时,会导致定量表型预测的偏差,从而影响预测的明确结论,限制下游的使用,如疾病评估或流行病学调查。人口分层是一种不容易通过数据预处理消除的隐性偏差。为了训练表型预测模型,我们提出了一种对抗性训练框架,以确保基因组编码器对样本群体不可知。为了更好地泛化,我们的对抗性训练框架与基因组编码器和表型预测模型正交。我们通过大豆基因组学在实际产量(表型)预测数据集上进行测试,实验确定了我们的去偏框架。开发的框架是为一般基因组数据(例如,人类,牲畜和作物)设计的,而表型可以是连续变量或分类变量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adversarial Removal of Population Bias in Genomics Phenotype Prediction
Many factors impact trait prediction from genotype data. One of the major confounding factors comes from the presence of population structure among sampled individuals, namely population stratification. When exists, it will lead to biased quantitative phenotype prediction, therefore hampering the unambiguous conclusions about prediction and limiting the downstream usage like disease evaluation or epidemiology survey. Population stratification is an implicit bias that can not be easily removed by data preprocessing. With the purpose of training a phenotype prediction model, we propose an adversarial training framework that ensures the genomics encoder is agnostic to sample populations. For better generalization, our adversarial training framework is orthogonal to the genomics encoder and phenotype prediction model. We experimentally ascertain our debiasing framework by testing on a real-world yield (phenotype) prediction dataset with soybean genomics. The developed frame-work is designed for general genomic data (e.g., human, livestock, and crops) while the phenotype can be either continuous or categorical variables.
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