在明确的因果假设下，评估根据真实世界基因分型数据预测抗菌药物耐药性的推定偏倚
Assessing putative bias in prediction of anti-microbial resistance from real-world genotyping data under explicit causal assumptions
摘要：
全基因组测序（WGS）正迅速成为鉴定抗菌素耐药性（AMR）的常规手段，因为它能够获得有关导致耐药性和驱动病原体移动的基因和机制的高分辨率信息。相比之下，传统的表型（基因图）测试不能很容易地阐明这样的信息。然而，从基因型-表型数据开发AMR预测工具可能会有偏差，因为采样是非随机的。样品种源、采集时期和物种代表性可能混淆遗传性状与AMR的关联。因此，预测模型在采样分布偏移的新数据上的表现可能很差。在这项工作中，在一组明确的因果假设下，我们使用病理系统资源整合中心（PATRIC）的基因型-表型AMR数据评估了基于倾向的再平衡和混杂调整对AMR预测的有效性。我们选择细菌基因型（编码为k-mer签名，即长度为k的DNA片段），国家，年份，物种和四环素类药物的AMR表型，准备来自单个国家的最新基因组的测试数据。我们测试了有/没有偏倚处理的增强逻辑回归（BLR）和随机森林（RF）。在10，936例中，我们发现了物种，地点和年份不平衡的证据。基因签名对AMR的影响的粗略变化与偏倚调整变化不同，但仅适度变化（从4000多万k-mer中选择前20，000个）。对于来自自助法和外部测试（n= 1，085）的袋外样本，RF（0.95）的受试者工作特征（AUROC）下的面积与BLR（0.94）相当，其中AUROC未降低。我们观察到，与单独使用遗传签名相比，使用偏倚处理的AUROC获得了1%-5%的增益。...
英文摘要：
Whole genome sequencing (WGS) is quickly becoming the customary means for identification of antimicrobial resistance (AMR) due to its ability to obtain high resolution information about the genes and mechanisms that are causing resistance and driving pathogen mobility. By contrast, traditional phenotypic (antibiogram) testing cannot easily elucidate such information. Yet development of AMR prediction tools from genotype-phenotype data can be biased, since sampling is non-randomized. Sample provenience, period of collection, and species representation can confound the association of genetic traits with AMR. Thus, prediction models can perform poorly on new data with sampling distribution shifts. In this work -- under an explicit set of causal assumptions -- we evaluate the effectiveness of propensity-based rebalancing and confounding adjustment on AMR prediction using genotype-phenotype AMR data from the Pathosystems Resource Integration Center (PATRIC). We select bacterial genotypes (encoded as k-mer signatures, i.e. DNA fragments of length k), country, year, species, and AMR phenotypes for the tetracycline drug class, preparing test data with recent genomes coming from a single country. We test boosted logistic regression (BLR) and random forests (RF) with/without bias-handling. On 10,936 instances, we find evidence of species, location and year imbalance with respect to the AMR phenotype. The crude versus bias-adjusted change in effect of genetic signatures on AMR varies but only moderately (selecting the top 20,000 out of 40+ million k-mers). The area under the receiver operating characteristic (AUROC) of the RF (0.95) is comparable to that of BLR (0.94) on both out-of-bag samples from bootstrap and the external test (n=1,085), where AUROCs do not decrease. We observe a 1%-5% gain in AUROC with bias-handling compared to the sole use of genetic signatures. ...
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