GRIMM: GRaph IMputation and matching for HLA genotypes

Maiers, Martin; Halagan, Michael; Gragert, Loren; Bashyal, Pradeep; Brelsford, Jason; Schneider, J; Lutsker, Polina; Louzoun, Yoram

doi:10.1093/bioinformatics/btz050

Cited by 15 publications

(14 citation statements)

References 12 publications

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“…The HLA genotypes were imputed to estimate the most probable haplotype pair for each typing for haplotype-based association using GRaph IMputation and Matching for HLA Genotypes. 21 …”

Section: Methodsmentioning

confidence: 99%

New Insights on DR and DQ Human Leukocyte Antigens in Anti-LGI1 Encephalitis

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Nisnboym

Regev

et al. 2023

Neurol Neuroimmunol Neuroinflamm

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Background and ObjectivesTo explore the clinical characteristics and HLA associations of patients with anti–leucine-rich glioma-inactivated 1 encephalitis (LGI1E) from a large single center in Israel. Anti-LGI1E is the most commonly diagnosed antibody-associated encephalitic syndrome in adults. Recent studies of various populations reveal significant associations with specific HLA genes. We examined the clinical characteristics and HLA associations of a cohort of Israeli patients.MethodsSeventeen consecutive patients with anti-LGI1E diagnosed at Tel Aviv Medical Center between the years 2011 and 2018 were included. HLA typing was performed using next-generation sequencing at the tissue typing laboratory of Sheba Medical Center and compared with data from the Ezer Mizion Bone Marrow Donor Registry, containing over 1,000,000 samples.ResultsOur cohort displayed a male predominance and median age at onset in the 7th decade, as previously reported. The most common presenting symptom was seizures. Notably, paroxysmal dizziness spells were significantly more common than previously reported (35%), whereas faciobrachial dystonic seizures were found only in 23%. HLA analysis revealed overrepresentation of DRB1*07:01 (OR: 3.18, CI: 20.9p< 1.e-5) and DRB1*04:02 (OR: 3.8, CI: 20.1p< 1.e-5), as well as of the DQ allele DQB1*02:02 (OR: 2.8, CI: 14.2p< 0.0001) as previously reported. A novel overrepresentation observed among our patients was of the DQB1*03:02 allele (OR: 2.3, CI: 6.9p< 0.008). In addition, we found DR-DQ associations, among patients with anti-LGI1E, that showed complete or near-complete linkage disequilibrium (LD). By applying LD analysis to an unprecedentedly large control cohort, we were able to show that although in the general population, DQB*03:02 is not fully associated with DRB1*04:02, in the patient population, both alleles are always coupled, suggesting the DRB1*04:02 association to be primary to disease predisposition. In silico predictions performed for the overrepresented DQ alleles reveal them to be strong binders of LGI1-derived peptides, similarly to overrepresented DR alleles. These predictions suggest a possible correlation between peptide binding sites of paired DR-DQ alleles.DiscussionOur cohort presents distinct immune characteristics with substantially higher overrepresentation of DRB1*04:02 and slightly lower overrepresentation of DQB1*07:01 compared with previous reports implying differences between different populations. DQ-DR interactions found in our cohort may shed additional light on the complex role of immunogenetics in the pathogenesis of anti-LGI1E, implying a possible relevance of certain DQ alleles and DR-DQ interactions.

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“…The HLA genotypes were imputed to estimate the most probable haplotype pair for each typing for haplotype-based association using GRaph IMputation and Matching for HLA Genotypes. 21 …”

Section: Methodsmentioning

confidence: 99%

New Insights on DR and DQ Human Leukocyte Antigens in Anti-LGI1 Encephalitis

Segal‬‏

Nisnboym

Regev

et al. 2023

Neurol Neuroimmunol Neuroinflamm

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“…We have here presented an extension of our previous HLA imputation algorithm, GRIMM. 6 GRIMM is based on opening all possible phases and then resolving the allele ambiguity within each phase. We extended the individual version of GRIMM to the family version-GRAMM, by first imputing only the (presumed) parents, and subsetting the list of possible phases based on consistency with the children's possible haplotypes assuming inheritance of complete haplotypes (in all children up to at most one recombination event).…”

Section: Discussionmentioning

confidence: 99%

“…As such, haplotype phasing of the HLA genes has to be imputed. 6 Moreover, HLA typing methods often do not unambiguously determine the alleles present, either due to incomplete sequencing or inability to phase within the gene. 7 While the accuracy of imputation algorithms for high-resolution HLA allele determination has been previously tested, 6,8 the HLA haplotype phase determination accuracy has not been tested.…”

Section: Introductionmentioning

confidence: 99%

“…6 Moreover, HLA typing methods often do not unambiguously determine the alleles present, either due to incomplete sequencing or inability to phase within the gene. 7 While the accuracy of imputation algorithms for high-resolution HLA allele determination has been previously tested, 6,8 the HLA haplotype phase determination accuracy has not been tested. We here analyze the haplotype phasing accuracy of donor HLA imputation, and then propose a familybased imputation algorithm to improve this accuracy when family pedigree data are available for related donor transplants.…”

Section: Introductionmentioning

confidence: 99%

“…This can be performed using the recently developed GRIMM imputation. 6 GRIMM is based on the first opening all the possible phasing of the haplotypes and then determining for each phase independently a probability distribution of high-resolution alleles to characterize ambiguity within each respective phase. We here propose to enlarge GRIMM to fix those phases of the parents based on information on their descendants and as such limit the possible phases of the parents.…”

Section: Introductionmentioning

confidence: 99%

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GRAMM: A new method for analysis of HLA in families

Ansbacher-Feldman

Israeli

Maiers

et al. 2023

HLA

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Recently, haplo‐identical transplantation with multiple HLA mismatches has become a viable option for stem cell transplants. Haplotype sharing detection requires the imputation of donor and recipient. We show that even in high‐resolution typing when all alleles are known, there is a 15% error rate in haplotype phasing, and even more in low‐resolution typings. Similarly, in related donors, the parents' haplotypes should be imputed to determine what haplotype each child inherited. We propose graph‐based family imputation (GRAMM) to phase alleles in family pedigree HLA typing data, and in mother‐cord blood unit pairs. We show that GRAMM has practically no phasing errors when pedigree data are available. We apply GRAMM to simulations with different typing resolutions as well as paired cord‐mother typings, and show very high phasing accuracy, and improved allele imputation accuracy. We use GRAMM to detect recombination events and show that the rate of falsely detected recombination events (false‐positive rate) in simulations is very low. We then apply recombination detection to typed families to estimate the recombination rate in Israeli and Australian population datasets. The estimated recombination rate has an upper bound of 10%–20% per family (1%–4% per individual).

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