Exact model comparisons in the plausibility framework

Böhringer, Stefan; Lohmann, Dietmar

doi:10.1016/j.jspi.2021.07.013

Cited by 2 publications

(3 citation statements)

References 19 publications

(22 reference statements)

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“…This interpretation is supported by the fact that ill‐conditioned design matrices most frequently appear for simulation scenarios with low LD and/or high ambiguity, both being cases where haplotype diversity is high. A major conclusion concerning model building is that models should be kept sparse or that a penalized model should be used, although for the latter statistical testing becomes challenging (Böhringer & Lohmann, 2022).…”

Section: Discussionmentioning

confidence: 99%

Haplotype reconstruction for genetically complex regions with ambiguous genotype calls: Illustration by the KIR gene region

van der Burg,

de Wreede,

Baldauf

et al. 2023

Genetic Epidemiology

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Advances in DNA sequencing technologies have enabled genotyping of complex genetic regions exhibiting copy number variation and high allelic diversity, yet it is impossible to derive exact genotypes in all cases, often resulting in ambiguous genotype calls, that is, partially missing data. An example of such a gene region is the killer‐cell immunoglobulin‐like receptor (KIR) genes. These genes are of special interest in the context of allogeneic hematopoietic stem cell transplantation. For such complex gene regions, current haplotype reconstruction methods are not feasible as they cannot cope with the complexity of the data. We present an expectation–maximization (EM)‐algorithm to estimate haplotype frequencies (HTFs) which deals with the missing data components, and takes into account linkage disequilibrium (LD) between genes. To cope with the exponential increase in the number of haplotypes as genes are added, we add three components to a standard EM‐algorithm implementation. First, reconstruction is performed iteratively, adding one gene at a time. Second, after each step, haplotypes with frequencies below a threshold are collapsed in a rare haplotype group. Third, the HTF of the rare haplotype group is profiled in subsequent iterations to improve estimates. A simulation study evaluates the effect of combining information of multiple genes on the estimates of these frequencies. We show that estimated HTFs are approximately unbiased. Our simulation study shows that the EM‐algorithm is able to combine information from multiple genes when LD is high, whereas increased ambiguity levels increase bias. Linear regression models based on this EM, show that a large number of haplotypes can be problematic for unbiased effect size estimation and that models need to be sparse. In a real data analysis of KIR genotypes, we compare HTFs to those obtained in an independent study. Our new EM‐algorithm‐based method is the first to account for the full genetic architecture of complex gene regions, such as the KIR gene region. This algorithm can handle the numerous observed ambiguities, and allows for the collapsing of haplotypes to perform implicit dimension reduction. Combining information from multiple genes improves haplotype reconstruction.

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Section: Discussionmentioning

confidence: 99%

Haplotype reconstruction for genetically complex regions with ambiguous genotype calls: Illustration by the KIR gene region

van der Burg,

de Wreede,

Baldauf

et al. 2023

Genetic Epidemiology

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“…The plausibility model of Böhringer & Lohmann was used to derive a global p-value for the glmnet interaction model. [30]…”

Section: Methodsmentioning

confidence: 99%

“…The plausibility model of Böhringer & Lohmann was used to derive a global p-value for the glmnet interaction model. [30] Lastly, we compared the odds ratios (OR) of SNPs for disease risk of all RA cases versus healthy controls R-package glmnet) with the ORs of cluster specific RA cases with controls. We obtained the OR from a logistic regression assuming additive effect of the minor allele.…”

Section: Genetic Analysismentioning

confidence: 99%

Location of joint involvement differentiates Rheumatoid arthritis into different clinical subsets

Maarseveen,

Maurits,

Böhringer

et al. 2023

Preprint

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To aid etiology and treatment research of the very heterogeneous rheumatoid arthritis (RA) population, we aimed to identify phenotypically distinct RA subsets using baseline clinical data. We collected baseline numerical- (hematology work-up & age) and categorical variables (serology, joint location & sex) from the Electronic Health records (EHR) repository of the Leiden University Medical Center, comprising 1,387 unique first visits to the outpatient clinic. We used deep learning and graph clustering to identify phenotypically distinct RA subsets. To ensure the robustness of our findings, we tested a) cluster stability (1000 fold) b) physician confounding, c) association with remission and methotrexate failure, d) generalizability to a second different data set (the Leiden Early Arthritis clinic; n=769). In total we identified four subsets (C1-C4) of patients with rheumatoid arthritis that were delineated on the following characteristics: C1) arthritis in feet, C2) seropositive oligo-articular disease, C3) seronegative hand arthritis, C4) polyarthritis. Our validity analyses showed high stability (mean 78%-91%), no physician confounding, and a significant difference in methotrexate failure(P-value=6.1e-4) and occurrence of remission(P-value=7.4e-3), and generalizability to a second dataset. The hand-cluster (III) had the most favorable outcomes (HR remission=1.65 (95%CI:1.20-2.29), HR methotrexate=0.48 (95%CI:0.35-0.77)), particularly the ACPA-positive patients in this cluster, while in the other clusters the ACPA-negative patients did best. The clusters outperformed standard clinical variables, which were attributed to the hand and feet differentiation. In conclusion, we discovered four phenotypically distinct subgroups of rheumatoid arthritis at baseline that associate with clinical outcomes. Furthermore, our study provides evidence for the presence of separate hand and foot subgroups in RA.

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Exact model comparisons in the plausibility framework

Cited by 2 publications

References 19 publications

Haplotype reconstruction for genetically complex regions with ambiguous genotype calls: Illustration by the KIR gene region

Haplotype reconstruction for genetically complex regions with ambiguous genotype calls: Illustration by the KIR gene region

Location of joint involvement differentiates Rheumatoid arthritis into different clinical subsets

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