Dataset Information

High-Throughput MICA/B Genotyping of Over Two Million Samples: Workflow and Allele Frequencies.

ABSTRACT: MICA and MICB are ligands of the NKG2D receptor and thereby influence NK and T cell activity. MICA/B gene polymorphisms, expression levels and the amount of soluble MICA/B in the serum have been linked to autoimmune diseases, infections, and cancer. In hematopoietic stem cell transplantation, MICA matching between donor and patient has been correlated with reduced acute and chronic graft-vs.-host disease and improved survival. Hence, we developed an extremely cost-efficient high-throughput workflow for genotyping MICA/B for newly registered potential stem cell donors. Since mid-2017, we have genotyped over two million samples using NGS amplicon sequencing for MICA/B exons 2-5. In donors of German origin, MICA*008 is the most common MICA allele with a frequency of 42.3%. It is followed by MICA*002 (11.7%) and MICA*009 (8.8%). The three most common MICB alleles are MICB*005 (43.9%), MICB*004 (21.7%), and MICB*002 (18.9%). In general, MICB is less diverse than MICA and only 6 alleles, instead of 15, account for a cumulative allele frequency of 99.5%. In 0.5% of the samples we observed at least one allele of MICA or MICB which has so far not been reported to the IPD/IMGT-HLA database. By providing MICA/B typed voluntary donors, clinicians become empowered to include MICA/B into their donor selection process to further improve unrelated hematopoietic stem cell transplantation.

SUBMITTER: Klussmeier A

PROVIDER: S-EPMC7047279 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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High-Throughput MICA/B Genotyping of Over Two Million Samples: Workflow and Allele Frequencies.

Klussmeier Anja A Massalski Carolin C Putke Kathrin K Schäfer Gesine G Sauter Jürgen J Schefzyk Daniel D Pruschke Jens J Hofmann Jan J Fürst Daniel D Carapito Raphael R Bahram Seiamak S Schmidt Alexander H AH Lange Vinzenz V

Frontiers in immunology 20200221

MICA and MICB are ligands of the NKG2D receptor and thereby influence NK and T cell activity. MICA/B gene polymorphisms, expression levels and the amount of soluble MICA/B in the serum have been linked to autoimmune diseases, infections, and cancer. In hematopoietic stem cell transplantation, MICA matching between donor and patient has been correlated with reduced acute and chronic graft-vs.-host disease and improved survival. Hence, we developed an extremely cost-efficient high-th ...[more]

PMID: 32153595

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Project description:Perennial ryegrass is an outbreeding forage species and is one of the most widely used forage grasses in temperate regions. The aim of this study was to investigate the possibility of implementing genomic prediction in tetraploid perennial ryegrass, to study the effects of different sequencing depth when using genotyping-by-sequencing (GBS), and to determine optimal number of single-nucleotide polymorphism (SNP) markers and sequencing depth for GBS data when applied in tetraploids. A total of 1,515 F2 tetraploid ryegrass families were included in the study and phenotypes and genotypes were scored on family-pools. The traits considered were dry matter yield (DM), rust resistance (RUST), and heading date (HD). The genomic information was obtained in the form of allele frequencies of pooled family samples using GBS. Different SNP filtering strategies were designed. The strategies included filtering out SNPs having low average depth (FILTLOW), having high average depth (FILTHIGH), and having both low average and high average depth (FILTBOTH). In addition, SNPs were kept randomly with different data sizes (RAN). The accuracy of genomic prediction was evaluated by using a "leave single F2 family out" cross validation scheme, and the predictive ability and bias were assessed by correlating phenotypes corrected for fixed effects with predicted additive breeding values. Among all the filtering scenarios, the highest estimates for genomic heritability of family means were 0.45, 0.74, and 0.73 for DM, HD and RUST, respectively. The predictive ability generally increased as the number of SNPs included in the analysis increased. The highest predictive ability for DM was 0.34 (137,191 SNPs having average depth higher than 10), for HD was 0.77 (185,297 SNPs having average depth lower than 60), and for RUST was 0.55 (188,832 SNPs having average depth higher than 1). Genomic prediction can help to optimize the breeding of tetraploid ryegrass. GBS data including about 80-100 K SNPs are needed for accurate prediction of additive breeding values in tetraploid ryegrass. Using only SNPs with sequencing depth between 10 and 20 gave highest predictive ability, and showed the potential to obtain accurate prediction from medium-low coverage GBS in tetraploids.

Dataset Information

High-Throughput MICA/B Genotyping of Over Two Million Samples: Workflow and Allele Frequencies.

Publications

High-Throughput <i>MICA/B</i> Genotyping of Over Two Million Samples: Workflow and Allele Frequencies.

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