Biology subjects

Ritari, J.

Publications and source records attributed to Ritari, J..

3 recordsLinked to original sources

bioRxiv2023

Accurate multi-population imputation of MICA, MICB, HLA-E, HLA-F and HLA-G alleles from genome SNP data

In addition to the classical HLA genes, the major histocompatibility complex (MHC) harbors a high number of other polymorphic genes with less established roles in disease associations and transplantation matching. To facilitate studies of the non-classical and non-HLA genes in large patient and biobank cohorts, we trained imputation models for MICA, MICB, HLA-E, HLA-F and HLA-G alleles on genome SNP array data. We show, using both population-specific and multi-population 1000 Genomes references, that the alleles of these genes can be accurately imputed for screening and research purposes. The best imputation model for MICA, MICB, HLA-E, -F and -G achieved a mean accuracy of 99.3% (min, max: 98.6, 99.9). Furthermore, validation of the 1000 Genomes exome short-read sequencing-based allele calling against a clinical-grade reference data showed an average accuracy of 99.8%, testifying for the quality of the 1000 Genomes data as an imputation reference. The imputation models, trained using the HIBAG algorithm, are available at GitHub (https://github.com/FRCBS/HLA_EFG_MICAB_imputation) and can be run locally, thus avoiding the need of sending sensitive genome data to remote portals.

bioinformatics↗

bioRxiv2021

KIR gene content imputation from single-nucleotide polymorphisms in the Finnish population

The killer cell immunoglobulin-like receptor (KIR) gene cluster on chromosome 19 encodes cell surface glycoproteins that bind class I human leukocyte antigen (HLA) molecules as well as some other ligands. Through regulation of natural killer (NK) cell activity KIRs participate in tumour surveillance and clearing viral infections. KIR gene gene copy number variation associates with the outcome of transplantations and susceptibility to immune-mediated diseases. Inferring KIR gene content from genetic variant data is therefore desirable for immunogenetic analysis, particularly in the context of growing biobank genome data collections that rely on genotyping by microarray. Here we describe a stand-alone and freely available gene content imputation for 12 KIR genes. The models were trained using 818 Finnish biobank samples genotyped for 5774 KIR-region SNPs and analysed for KIR gene content with targeted sequencing. Cross-validation results demonstrate a high mean overall accuracy of 99.2% (95% CI: 97.8-99.7%) which compares favourably with previous methods including short-read sequencing based approaches.

genomics↗

bioRxiv2019

A comparative and functional genomics analysis of the genus Romboutsia provides insight into adaptation to an intestinal lifestyle

Cultivation-independent surveys have shown that the recently described genus Romboutsia within the family Peptostreptococcaceae is more diverse than previously acknowledged. The majority of Romboutsia-associated 16S rRNA gene sequences have an intestinal origin, but the specific roles that Romboutsia species play in the digestive tract are largely unknown. The complete genomes of the human intestinal isolate Romboutsia hominis FRIFIT (DSM 28814) and the soil isolate Romboutsia lituseburensis A25KT (DSM 797) were sequenced. An evaluation of the common traits of this recently defined genus was done based on comparative genome analysis of the two strains together with the previously elucidated genome of the type species Romboutsia ilealis CRIBT. These analyses showed that the genus Romboutsia covers a broad range of metabolic capabilities with respect to carbohydrate utilization, fermentation of single amino acids, anaerobic respiration and metabolic end products. Main differences between strains were found in their abilities to utilize specific carbohydrates, to synthesize vitamins and other cofactors, and their nitrogen assimilation capabilities. In addition, differences were found with respect to bile metabolism and motility-related gene clusters.

microbiology↗