AIMC Topic: Polymorphism, Single Nucleotide

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Effects of using deep learning to predict the geographic origin of barley genebank accessions on genome-environment association studies.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
Genome-environment association (GEA) is an approach for identifying adaptive loci by combining genetic variation with environmental parameters, offering potential for improving crop resilience. However, its application to genebank accessions is limit...

Integrated phenotypic analysis, predictive modeling, and identification of novel trait-associated loci in a diverse Theobroma cacao collection.

BMC plant biology
BACKGROUND: Cacao (Theobroma cacao L.) breeding and improvement rely on understanding germplasm diversity and trait architecture. This study characterized a cacao collection (173 accessions) evaluated in Puerto Rico, examining phenotypic diversity, t...

Whole genome resequencing reveals genetic diversity, population structure, and selection signatures in local duck breeds.

BMC genomics
BACKGROUND: Shandong's local duck breeds are renowned for their outstanding egg-laying performance and are regarded as valuable assets within China's waterfowl germplasm. Understanding the genetic characteristics of these populations, along with moni...

AdaptiveGS: an explainable genomic selection framework based on adaptive stacking ensemble machine learning.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
We developed an adaptive and unified stacking genomic selection framework and designed a model interpretation strategy to identify the candidate significant SNPs of target traits. Genomic selection (GS) is an important technique in modern molecular b...

Integrating machine learning for rapid and accurate multiplex identification of the allelic variants in single nucleotide polymorphisms by lateral flow genotyping assays.

Biosensors & bioelectronics
Single nucleotide polymorphisms (SNPs) are widely used in precision medicine, disease predisposition assessment, nutrigenetics and authenticity testing of agricultural and food products. SNP genotyping is much more challenging than detecting longer D...

Genetic analyses of eight complex diseases using predicted continuous representations of disease.

Cell reports methods
We evaluated whether predicted continuous disease representations could enhance genetic discovery beyond case-control genome-wide association study (GWAS) phenotypes across eight complex diseases in up to 485,448 UK Biobank participants. Predicted ph...

Machine learning in Alzheimer's disease genetics.

Nature communications
Traditional statistical approaches have advanced our understanding of the genetics of complex diseases, yet are limited to linear additive models. Here we applied machine learning (ML) to genome-wide data from 41,686 individuals in the largest Europe...

Developing a machine-learning model to enable treatment selection for neoadjuvant chemotherapy for esophageal cancer.

Scientific reports
Although neoadjuvant chemotherapy with docetaxel + cisplatin + 5-fluorouracil (CF) has been the standard treatment for stage II and III esophageal cancers, it is associated with severe adverse events caused by docetaxel. Consequently, this study aime...

Machine learning-based prediction of antimicrobial resistance and identification of AMR-related SNPs in Mycobacterium tuberculosis.

BMC genomic data
BACKGROUND: Mycobacterium tuberculosis (MTB) is a human-specific pathogen that primarily infects humans, causing tuberculosis (TB). Antimicrobial resistance (AMR) in MTB presents a formidable challenge to global health. The employment of machine lear...