Single nucleotide polymorphisms (SNPs) are the most abundant type of genomic variation and the most accessible to genotype in large cohorts. However, they individually explain a small proportion of phenotypic differences between individuals. Ancestry, collective SNP effects, structural variants, somatic mutations or even differences in historic recombination can potentially explain a high percentage of genomic divergence. These genetic differences can be infrequent or laborious to characterize; however, many of them leave distinctive marks on the SNPs across the genome allowing their study in large population samples. Consequently, several methods have been developed over the last decade to detect and analyze different genomic structures using SNP arrays, to complement genome-wide association studies and determine the contribution of these structures to explain the phenotypic differences between individuals. We present an up-to-date collection of available bioinformatics tools that can be used to extract relevant genomic information from SNP array data including population structure and ancestry; polygenic risk scores; identity-by-descent fragments; linkage disequilibrium; heritability and structural variants such as inversions, copy number variants, genetic mosaicisms and recombination histories. From a systematic review of recently published applications of the methods, we describe the main characteristics of R packages, command-line tools and desktop applications, both free and commercial, to help make the most of a large amount of publicly available SNP data.
Polymorphic genomic inversions are chromosomal variants with intrinsic variability that play important roles in evolution, environmental adaptation, and complex traits. We investigated the DNA methylation patterns of three common human inversions, at 8p23.1, 16p11.2, and 17q21.31 in 1,009 blood samples from children from the Human Early Life Exposome (HELIX) project and in 39 prenatal heart tissue samples. We found inversion-state specific methylation patterns within and nearby flanking each inversion region in both datasets. Additionally, numerous inversion-exposure interactions on methylation levels were identified from early-life exposome data comprising 64 exposures. For instance, children homozygous at inv-8p23.1 and higher meat intake were more susceptible to TDH hypermethylation (P = 3.8 × 10−22); being the inversion, exposure, and gene known risk factors for adult obesity. Inv-8p23.1 associated hypermethylation of GATA4 was also detected across numerous exposures. Our data suggests that the pleiotropic influence of inversions during development and lifetime could be substantially mediated by allele-specific methylation patterns which can be modulated by the exposome.
Background: Single-nucleotide polymorphisms (SNPs) in collagen genes are predisposing factors for anterior cruciate ligament (ACL) rupture. Although these events are more frequent in females, the sex-specific risk of reported SNPs has not been evaluated. Purpose: We aimed to assess the sex-specific risk of historic non-contact ACL rupture considering candidate SNPs in genes previously associated with muscle, tendon, ligament and ACL injury in elite footballers. Study Design: This was a cohort genetic association study. Methods: Forty-six (twenty-four females) footballers playing for the first team of FC Barcelona (Spain) during the 2020–21 season were included in the study. We evaluated the association between a history of non-contact ACL rupture before July 2022 and 108 selected SNPs, stratified by sex. SNPs with nominally significant associations in one sex were then tested for their interactions with sex on ACL. Results: Seven female (29%) and one male (4%) participants had experienced non-contact ACL rupture during their professional football career before the last date of observation. We found a significant association between the rs13946 C/C genotype and ACL injury in women footballers (p = 0.017). No significant associations were found in male footballers. The interaction between rs13946 and sex was significant (p = 0.027). We found that the C-allele of rs13946 was exclusive to one haplotype of five SNPs spanning COL5A1. Conclusions: The present study suggests the role of SNPs in genes encoding for collagens as female risk factors for ACL injury in football players. Clinical Relevance: The genetic profiling of athletes at high risk of ACL rupture can contribute to sex-specific strategies for injury prevention in footballers.
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