Métodos eficientes para colapsagem e seleção de SNPs raros em dados familiares

Abstract

The present work presents methods for identifying rare variants, which consist of genetic variations that occur with low frequency in the population, for understanding human health and how next-generation sequencing (NGS) has enabled a more comprehensive assessment of individuals' genetic variation. The detection of genomic variation is performed using SNP markers (Single Nucleotide Polymorphism). The linear mixed model (LMM) approach stands out in these studies for encompassing both fixed and random effects, and the LASSO (Least Absolute Shrinkage and Selection Operator) method is used for variable selection. This work also describes the most recent approaches for GWAS (genome-wide association studies) and NGS studies that include family data and how rare variants play an important role in the genetic architecture of complex disorders. Finally, the main methodologies used in genomic studies based on collapsing are discussed, as well as the need to bring new techniques or combine them to better understand the influence of rare variants on complex diseases.