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PubMed2026

Genomic resources to advance seed coat color and patterning genetics and breeding in common bean (Phaseolus vulgaris L.).

Seed coat color and patterning are key quality traits in common bean (Phaseolus vulgaris L.) that define market classes and strongly influence consumer preference and market value. These traits are controlled by a complex network of major genes (sometimes with epistatic interactions), which complicates the recovery of desired market class phenotypes following inter-market class hybridization. Although many of the underlying loci have been genetically mapped, diagnostic, high-throughput molecular markers for efficient allele tracking across the Middle American and Andean gene pools remain limited. In this study, we developed and validated 24 gene-specific PCR Allele Competitive Extension (PACE) markers targeting seven major seed coat color genes (G, B, V, J, Rk, T, and Z) and two patterning genes (CPi and CSt), together with a previously reported marker associated with the postharvest seed coat darkening locus (Psd). An additional PACE marker targeting the Phaseolin (Phs) locus was developed to distinguish Middle American (S-type) and Andean (T-type) gene pools, providing a complementary tool for assessing genetic background alongside seed coat-specific loci. Marker performance was evaluated across three diverse panels, revealing high diagnostic accuracy for most loci (90%-100%). However, for loci such as J, V, Rk, T, and Z, allele-specific markers or marker combinations were required to capture full allelic diversity. Haplotype analysis further revealed substantial allelic diversity across market classes and identified background-specific interactions. Collectively, these results provide a comprehensive set of high-resolution, gene-anchored PACE markers for seed coat color, patterning, and gene pool classification in common bean. These markers enable rapid and precise allele tracking in breeding populations and germplasm collections, facilitating marker-assisted selection for market class-specific seed coat traits and accelerating genetic improvement.

Phaseolus↗

PubMed2025

GE-IA-NAM: gene-environment interaction analysis via imaging-assisted neural additive model.

MOTIVATION: Gene-environment (G-E) interaction analysis is crucial in cancer research, offering insights into how genetic and environmental factors jointly influence cancer outcomes. Most existing G-E interaction methods are regression-based, which may lack flexibility to capture complex data patterns. Recent advances have investigated deep neural network-based G-E models. However, these methods may be more vulnerable to information deficiency due to challenges such as limited sample size and high dimensionality. Apart from genetic and environmental data, pathological images have emerged as a widely accessible and informative resource for cancer modeling, presenting its potential to enhance G-E modeling. RESULTS: We propose the pathological imaging-assisted neural additive model for G-E analysis (GE-IA-NAM). The flexible and interpretable additive network architecture is adopted to account for individualized effects associated with genetic factors, environmental factors, and their interactions. To improve G-E modeling, an assisted-learning strategy is investigated, which adopts a joint analysis to integrate information from pathological images. Simulations and the analysis of lung and skin cancer datasets from The Cancer Genome Atlas demonstrate the competitive performance of the proposed method. AVAILABILITY AND IMPLEMENTATION: Python code implementing the proposed method is available at https://github.com/Mr-maoge/NAM-IA-GE. The data that support the findings in this article are openly available in TCGA (The Cancer Genome Atlas) at https://portal.gdc.cancer.gov/.

Gene-Environment Interaction↗

PubMed2026

ALPAR: automated learning pipeline for antimicrobial resistance.

SUMMARY: The field of machine learning in antimicrobial resistance (AMR) research has experienced rapid growth, fueled by advancements in high-throughput genome sequencing and the growing capacity of computational resources. However, the complexity and lack of standardized data preparation and bioinformatic analyses present significant challenges, especially for newcomers to the domain. In response to these challenges, we introduce ALPAR (Automated Learning Pipeline for Antimicrobial Resistance), a comprehensive AMR data analysis tool covering the entire process from processing of raw genomic data to training machine learning models to interpretation of results. Our method relies on a reproducible pipeline that integrates widely used bioinformatics tools, presenting a simplified, automatic workflow specifically tailored for single-reference AMR analysis. Accepting genomic data in the form of FASTA files as input, ALPAR facilitates the generation of machine learning-ready data tables and both the training of machine learning and the execution of genome-wide association studies (GWAS) experiments. Additionally, our tool offers supplementary functionalities such as phylogeny-based analysis of the distribution of mutations, enhancing its utility for researchers. The tool has also proven its performance in competitive benchmarks, winning the 2024 CAMDA Anti-Microbial Resistance Prediction Challenge and placing third in the 2025 edition. AVAILABILITY AND IMPLEMENTATION: ALPAR is open-source and freely accessible via GitHub (https://github.com/kalininalab/ALPAR). The pipeline is fully reproducible and can be easily installed as a Conda package (https://anaconda.org/kalininalab/ALPAR).

Machine Learning↗