scholarly journals A multi-task convolutional deep learning method for HLA allelic imputation and its application to trans-ethnic MHC fine-mapping of type 1 diabetes

2020 ◽  
Author(s):  
Tatsuhiko Naito ◽  
Ken Suzuki ◽  
Jun Hirata ◽  
Yoichiro Kamatani ◽  
Koichi Matsuda ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Deep Learning ◽  
Fine Mapping ◽  
Low Frequency ◽  
Amino Acid Position ◽  
Genotype Imputation ◽  
Learning Method ◽  
Frequency Spectra ◽  
Shared Risk

Conventional HLA imputation methods drop their performance for infrequent alleles, which reduces reliability of trans-ethnic MHC fine-mapping due to inter-ethnic heterogeneity in allele frequency spectra. We developed DEEP*HLA, a deep learning method for imputing HLA genotypes. Through validation using the Japanese and European HLA reference panels (n = 1,118 and 5,112), DEEP*HLA achieved the highest accuracies in both datasets (0.987 and 0.976) especially for low-frequency and rare alleles. DEEP*HLA was less dependent of distance-dependent linkage disequilibrium decay of the target alleles and might capture the complicated region-wide information. We applied DEEP*HLA to type 1 diabetes GWAS data of BioBank Japan (n = 62,387) and UK Biobank (n = 356,855), and successfully disentangled independently associated class I and II HLA variants with shared risk between diverse populations (the top signal at HLA-DRβ1 amino acid position 71; P = 6.2 × 10-119). Our study illustrates a value of deep learning in genotype imputation and trans-ethnic MHC fine-mapping.

scholarly journals A deep learning method for HLA imputation and trans-ethnic MHC fine-mapping of type 1 diabetes

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Tatsuhiko Naito ◽  
Ken Suzuki ◽  
Jun Hirata ◽  
Yoichiro Kamatani ◽  
Koichi Matsuda ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Deep Learning ◽  
Fine Mapping ◽  
Human Leukocyte ◽  
Amino Acid Position ◽  
Genotype Imputation ◽  
Learning Method ◽  
Frequency Spectra ◽  
Shared Risk

AbstractConventional human leukocyte antigen (HLA) imputation methods drop their performance for infrequent alleles, which is one of the factors that reduce the reliability of trans-ethnic major histocompatibility complex (MHC) fine-mapping due to inter-ethnic heterogeneity in allele frequency spectra. We develop DEEP*HLA, a deep learning method for imputing HLA genotypes. Through validation using the Japanese and European HLA reference panels (n = 1,118 and 5,122), DEEP*HLA achieves the highest accuracies with significant superiority for low-frequency and rare alleles. DEEP*HLA is less dependent on distance-dependent linkage disequilibrium decay of the target alleles and might capture the complicated region-wide information. We apply DEEP*HLA to type 1 diabetes GWAS data from BioBank Japan (n = 62,387) and UK Biobank (n = 354,459), and successfully disentangle independently associated class I and II HLA variants with shared risk among diverse populations (the top signal at amino acid position 71 of HLA-DRβ1;P = 7.5 × 10−120). Our study illustrates the value of deep learning in genotype imputation and trans-ethnic MHC fine-mapping.

Low Frequency of Regulatory T Cells in the Peripheral Blood of Children with Type 1 Diabetes Diagnosed under the Age of Five

2012 ◽  
Vol 60 (4) ◽  
pp. 307-313 ◽  
Author(s):  
Agnieszka Szypowska ◽  
Anna Stelmaszczyk-Emmel ◽  
Urszula Demkow ◽  
Włodzimierz Łuczyński
Keyword(s):  
T Cells ◽  
Type 1 Diabetes ◽  
Regulatory T Cells ◽  
Peripheral Blood ◽  
Low Frequency

scholarly journals Vitamin D and Type 1 Diabetes Risk: A Systematic Review and Meta-Analysis of Genetic Evidence

Nutrients ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 4260
Author(s):  
Liana Najjar ◽  
Joshua Sutherland ◽  
Ang Zhou ◽  
Elina Hyppönen
Keyword(s):  
Systematic Review ◽  
Vitamin D ◽  
Type 1 Diabetes ◽  
Meta Analysis ◽  
Low Frequency ◽  
Nucleotide Polymorphisms ◽  
Hydroxyvitamin D ◽  
Quantitative Synthesis ◽  
25 Hydroxyvitamin D

Several observational studies have examined vitamin D pathway polymorphisms and their association with type 1 diabetes (T1D) susceptibility, with inconclusive results. We aimed to perform a systematic review and meta-analysis assessing associations between selected variants affecting 25-hydroxyvitamin D [25(OH)D] and T1D risk. We conducted a systematic search of Medline, Embase, Web of Science and OpenGWAS updated in April 2021. The following keywords “vitamin D” and/or “single nucleotide polymorphisms (SNPs)” and “T1D” were selected to identify relevant articles. Seven SNPs (or their proxies) in six genes were analysed: CYP2R1 rs10741657, CYP2R1 (low frequency) rs117913124, DHCR7/NADSYN1 rs12785878, GC rs3755967, CYP24A1 rs17216707, AMDHD1 rs10745742 and SEC23A rs8018720. Seven case-control and three cohort studies were eligible for quantitative synthesis (n = 10). Meta-analysis results suggested no association with T1D (range of pooled ORs for all SNPs: 0.97–1.02; p > 0.01). Heterogeneity was found in DHCR7/NADSYN1 rs12785878 (I2: 64.8%, p = 0.02). Sensitivity analysis showed exclusion of any single study did not alter the overall pooled effect. No association with T1D was observed among a Caucasian subgroup. In conclusion, the evidence from the meta-analysis indicates a null association between selected variants affecting serum 25(OH)D concentrations and T1D.

scholarly journals Fine-mapping and functional studies highlight potential causal variants for rheumatoid arthritis and type 1 diabetes

2018 ◽  
Vol 50 (10) ◽  
pp. 1366-1374 ◽  
Author(s):  
Harm-Jan Westra ◽  
Marta Martínez-Bonet ◽  
Suna Onengut-Gumuscu ◽  
Annette Lee ◽  
Yang Luo ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Type 1 Diabetes ◽  
Fine Mapping ◽  
Functional Studies ◽  
Causal Variants

Risk genes and autoantibodies in Egyptian children with type 1 diabetes – low frequency of autoantibodies in carriers of the HLA‐DRB1*04:05‐DQA1*03‐DQB1*02 risk haplotype

2014 ◽  
Vol 31 (3) ◽  
pp. 287-294 ◽  
Author(s):  
Mostafa I. El‐Amir ◽  
Mohamed Ali El‐Feky ◽  
Antti‐Pekka Laine ◽  
Taina Härkönen ◽  
Omnia El‐Badawy ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Low Frequency ◽  
Risk Haplotype ◽  
Risk Genes ◽  
Egyptian Children

546-P: Deep Learning Predictions of Diabetic Retinopathy Associated with Progression of Renal Disease in Type 1 Diabetes

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 546-P
Author(s):  
JOSEPH C. MELLOR ◽  
AMOS J. STORKEY ◽  
HELEN COLHOUN ◽  
PAUL M. MCKEIGUE ◽  
Keyword(s):  
Type 1 Diabetes ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Renal Disease ◽  
Progression Of Renal Disease

Genetic prediction of type 1 diabetes in a population with low frequency of HLA risk genotypes and low incidence of the disease(the DIABFIN study)

2004 ◽  
Vol 20 (2) ◽  
pp. 137-143 ◽  
Author(s):  
R. Buzzetti ◽  
A. Galgani ◽  
A. Petrone ◽  
M. L. Del Buono ◽  
H. A. Erlich ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Low Frequency ◽  
Genetic Prediction ◽  
Low Incidence
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