Genetic determinants and absence of breast cancer in Xavante Indians in Sangradouro Reserve, Brazil

Abstract

Genetic compositions of distinct human populations are different. How genomic variants influence many common and rare genetic diseases is always of great medical and anthropological interest, and understanding of genetic architectures of population groups in relation to diseases can advance our knowledge of medicine. Here, we have studied the genomic architecture of a group of Xavante Indians, an indigenous population in Brazil, and compared them with normal populations from the 1000 Genomes Projects. Principal component analysis (PCA) indicates that the Xavante Indians are genetically distinctive when compared to other ethnic groups. No incidence of breast cancer cases has ever been reported in the population, and polygenic risk analysis indicates extremely low breast cancer risk in this population when compared with germline TCGA (The Cancer Genome Atlas) breast cancer normal control samples. Low germinal mutation burden among this population is also observed. Our findings will help to deepen the understanding of breast cancer and might also provide new approaches to study the disease.

An integrative machine learning framework for classifying SEER breast cancer

“…Feature selection techniques such as Recursive Feature Elimination, Forward Feature Selection, f-test and correlation are used with Wisconsin breast cancer data for extraction of important features 9 . Principal Component Analysis technique was used to indicate the genomic variants in rare genetic diseases 10 . Chi-Square, Singular Vector Decomposition and PCA are used to select the features from the breast cancer dataset 11 .…”