RASEL: An Ensemble Model for Selection of Core SNPs and Its Application for Identification and Classification of Cattle Breeds.

Abstract

Identifying and classifying different cattle populations as per their breed and utility holds immense practical importance in effective breeding management. For accurate identification and classification of cattle breeds, a reference panel of 10 breeds, 657 identified ancestry informative markers and different machine learning classifiers were employed. To boost the accuracy of breed identification, three distinct machine learning classification models: logistic regression, XGBoost, and random forest, each one having an accuracy of > 95%, were ensembled achieving an accuracy of > 98% with just 207 markers [breed informative markers (BIMs)]. Further, for classification of dairy and draft purpose cattle, the breed informative markers along with those in selection signatures specific to dairy and draft utility were explored, and 17 utility informative markers (UIMs) including 12 BIMs and 5 markers in selection signatures were identified based on an ensemble approach. The accuracy of classification of cattle based on the utility (dairy or draft) was > 96%. To demonstrate the application of UIMs, these markers were used to identify the utility of non-descript cattle of Maharashtra, India and found that many of these cattle were draft purpose and were aligning with their production performance. This information can further be used for taking breeding decisions for their grading up to dairy or draft cattle. Here, a novel pipeline which utilized [R-] reference panel, [A-] ancestry informative markers, [S-] selection signatures and the power of [EL-] ensemble machine learning for identifying and classifying the cattle, breed- and utility-wise, was developed, and we called it as RASEL (available at: https://github.com/kkokay07/RASEL ).