Assessing putative bias in prediction of anti-microbial resistance from real-world genotyping data under explicit causal assumptions. (arXiv:2107.03383v1 [q-bio.GN])

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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