PMID- 29124304
OWN - NLM
STAT- MEDLINE
DCOM- 20190104
LR  - 20190104
IS  - 1432-1211 (Electronic)
IS  - 0093-7711 (Linking)
VI  - 70
IP  - 5
DP  - 2018 May
TI  - Modeling coverage gaps in haplotype frequencies via Bayesian inference to improve 
      stem cell donor selection.
PG  - 279-292
LID - 10.1007/s00251-017-1040-4 [doi]
AB  - Regardless of sampling depth, accurate genotype imputation is limited in regions 
      of high polymorphism which often have a heavy-tailed haplotype frequency 
      distribution. Many rare haplotypes are thus unobserved. Statistical methods to 
      improve imputation by extending reference haplotype distributions using linkage 
      disequilibrium patterns that relate allele and haplotype frequencies have not yet 
      been explored. In the field of unrelated stem cell transplantation, imputation of 
      highly polymorphic human leukocyte antigen (HLA) genes has an important 
      application in identifying the best-matched stem cell donor when searching large 
      registries totaling over 28,000,000 donors worldwide. Despite these large 
      registry sizes, a significant proportion of searched patients present novel HLA 
      haplotypes. Supporting this observation, HLA population genetic models have 
      indicated that many extant HLA haplotypes remain unobserved. The absent 
      haplotypes are a significant cause of error in haplotype matching. We have 
      applied a Bayesian inference methodology for extending haplotype frequency 
      distributions, using a model where new haplotypes are created by recombination of 
      observed alleles. Applications of this joint probability model offer significant 
      improvement in frequency distribution estimates over the best existing 
      alternative methods, as we illustrate using five-locus HLA frequency data from 
      the National Marrow Donor Program registry. Transplant matching algorithms and 
      disease association studies involving phasing and imputation of rare variants may 
      benefit from this statistical inference framework.
FAU - Louzoun, Yoram
AU  - Louzoun Y
AUID- ORCID: 0000-0003-1714-6148
AD  - Department of Mathematics, Bar-Ilan University, Ramat Gan, Israel. 
      louzouy@math.biu.ac.il.
FAU - Alter, Idan
AU  - Alter I
AD  - Department of Mathematics, Bar-Ilan University, Ramat Gan, Israel.
FAU - Gragert, Loren
AU  - Gragert L
AD  - Bioinformatics Research, National Marrow Donor Program, Minneapolis, MN, USA.
AD  - Department of Pathology and Laboratory Medicine, Tulane University School of 
      Medicine, Tulane Cancer Center, New Orleans, LA, USA.
FAU - Albrecht, Mark
AU  - Albrecht M
AD  - Bioinformatics Research, National Marrow Donor Program, Minneapolis, MN, USA.
FAU - Maiers, Martin
AU  - Maiers M
AD  - Bioinformatics Research, National Marrow Donor Program, Minneapolis, MN, USA.
LA  - eng
PT  - Journal Article
DEP - 20171109
PL  - United States
TA  - Immunogenetics
JT  - Immunogenetics
JID - 0420404
RN  - 0 (HLA Antigens)
SB  - IM
MH  - *Algorithms
MH  - *Bayes Theorem
MH  - *Donor Selection
MH  - Genotype
MH  - HLA Antigens/*genetics
MH  - *Haplotypes
MH  - Histocompatibility Testing
MH  - Humans
MH  - *Models, Statistical
MH  - Polymorphism, Genetic
MH  - Registries
MH  - Stem Cells/*cytology
MH  - Tissue Donors
OTO - NOTNLM
OT  - Bayesian inference
OT  - DNA typing
OT  - HLA
OT  - Imputation
OT  - Rare variants
EDAT- 2017/11/11 06:00
MHDA- 2019/01/05 06:00
CRDT- 2017/11/11 06:00
PHST- 2017/08/11 00:00 [received]
PHST- 2017/10/23 00:00 [accepted]
PHST- 2017/11/11 06:00 [pubmed]
PHST- 2019/01/05 06:00 [medline]
PHST- 2017/11/11 06:00 [entrez]
AID - 10.1007/s00251-017-1040-4 [pii]
AID - 10.1007/s00251-017-1040-4 [doi]
PST - ppublish
SO  - Immunogenetics. 2018 May;70(5):279-292. doi: 10.1007/s00251-017-1040-4. Epub 2017 
      Nov 9.