Unfolding of hidden white blood cell count phenotypes for gene discovery using latent class mixed modeling

Hall, Taryn O.; Stanaway, Ian B.; Carrell, David; Carroll, Robert J.; Denny, Joshua C.; Hákonarson, Hákon; Larson, Eric B.; Mentch, Frank; Peissig, Peggy L.; Pendergrass, Sarah A.; Rosenthal, Elisabeth; Jarvik, Gail P.; Crosslin, David R.

doi:10.1038/s41435-018-0051-y

Cited by 6 publications

(4 citation statements)

References 74 publications

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“…Among the 5 predictors incorporated into the model, WBC counts are often considered as a marker of systemic inflammation and immune system health. 17 The acute variation of WBC counts is related to infections or other environmental exposures. 18 A study found 19 that the WBC count increased in the group of COVID-19 patients who died during hospitalization; comorbid bacterial or fungal infection might have occurred in these patients, causing the WBC count to increase.…”

Section: Discussionmentioning

confidence: 99%

Prediction of the Rehabilitation Duration and Risk Management for Mild-Moderate COVID-19

Zheng

et al. 2020

Disaster med. public health prep.

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Objectives: More than 80% of coronavirus disease 2019 (COVID-19) cases are mild or moderate. In this study, a risk model was developed for predicting rehabilitation duration (the time from hospital admission to discharge) of the mild-moderate COVID-19 cases and was used to conduct refined risk management for different risk populations. Methods: A total of 90 consecutive patients with mild-moderate COVID-19 were enrolled. Large-scale datasets were extracted from clinical practices. Through the multivariable linear regression analysis, the model was based on significant risk factors and was developed for predicting the rehabilitation duration of mild-moderate cases of COVID-19. To assess the local epidemic situation, risk management was conducted by weighing the risk of populations at different risk. Results: Ten risk factors from 44 high-dimensional clinical datasets were significantly correlated to rehabilitation duration (P < 0.05). Among these factors, 5 risk predictors were incorporated into a risk model. Individual rehabilitation durations were effectively calculated. Weighing the local epidemic situation, threshold probability was classified for low risk, intermediate risk, and high risk. Using this classification, risk management was based on a treatment flowchart tailored for clinical decision-making. Conclusions: The proposed novel model is a useful tool for individualized risk management of mild-moderate COVID-19 cases, and it may readily facilitate dynamic clinical decision-making for different risk populations.

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Section: Discussionmentioning

confidence: 99%

Prediction of the Rehabilitation Duration and Risk Management for Mild-Moderate COVID-19

Zheng

et al. 2020

Disaster med. public health prep.

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“…For example, longitudinal data can be clustered into distinct trajectories using a latent class mixed model (Proust-Lima et al, 2017). This technique was used to identify novel genetic variants associated with white blood cell count over time using a 2-group clustering on a cohort of approximately 10,000 individuals (Hall et al, 2019). An additional method used for clustering longitudinal data is a mixture model using latent class growth data (Jones and Nagin, 2007;Nagin, 2014).…”

Section: Introductionmentioning

confidence: 99%

Trajectory analysis of cardiovascular phenotypes from biobank data uncovers novel genetic associations

Pottinger¹,

Pesce²,

Gacita³

et al. 2020

Preprint

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Approximately 6 million adults in the US have heart failure (HF). HF progression is variable due in part to differences in sex, age, and genetic ancestry. Previous population-based genetic studies have largely focused on cross-sectional data related to HF, a disease known to change over time. Utilizing longitudinal data trajectory probabilities as a continuous trait may increase the likelihood of finding significant, biologically relevant associations in a genome-wide association (GWA) analysis. We analyzed data from the electronic health record in a medical biobank from a single, metropolitan US center to gather clinically pertinent data for analyses. We evaluated whole genome sequencing of 896 unrelated biobank participants, including 494 with at least 1 electrocardiogram and 324 who had more than 1 echocardiogram (~3 observations per person). A censored normal distribution multivariable mixture model was used to cluster phenotype measures for genome-wide analyses. GWA analysis on the trajectory probability of the corrected QT measurement (QTc) taken from electrocardiograms identified significant associations with variants in regulatory regions proximal to the WLS gene, which encodes the Wnt ligand secretion mediator, Wntless. WLS was previously associated with QT length using of approximately 16,000 participants supporting the utility of this method to uncover significant genetic associations in small datasets. GWA analysis on the trajectory probability of left ventricular diameter as taken from echocardiograms identified novel significant associations with variants in regulatory regions near MYO10, which encodes the unconventional Myosin-10. We found that trajectory probabilities improved the ability to discover significant and relevant genetic associations. This novel approach increased yield from smaller, well-phenotyped cohorts with longitudinal data from a medical biobank. AUTHOR SUMMARYApproximately 6 million adults in the US have heart failure, a disease known to change over time. In a hospital based electronic health record, electrocardiograms and echocardiograms, used to evaluate heart failure, can be tracked over time. We utilized these data to create a novel trait that can be applied to genetic analyses. We analyzed genome sequence of 896 biobank participants from diverse racial/ethnic backgrounds. Genome-wide association (GWA) analyses were performed on a subset of these individuals for heart failure outcomes. A statistical model that incorporates cardiac data that are tracked over time was used to cluster these data using a probabilistic approach. These probabilities were used for a GWA analysis for corrected QT measurement (QTc) and left ventricular diameter (LVID). The QTc interval analysis identified significant correlations with variants in regulatory regions near the WLS gene which encodes the Wnt ligand secretion mediator, Wntless. Analysis of LVID identified significant associations with variants in regulatory regions near the MYO10 gene which encodes the unconventional Myosin-10. Through t...

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“…80 two recent developments in FMMs applications substantiate a more theoretically founded interpretation of identified trajectories, specifically through criterion validity (genotyping) [170,171] or replicability of findings (meta-analyses) [172]. In these cases, a more lenient posture towards classes' reification may be justified.…”

Section: Model Fit Criteria Curve Behaviour In Class Enumerationmentioning

confidence: 99%

“…Thus, the eccentricities of class enumeration call special attention to the 'reification fallacy' [35] [170,171] or the replicability of findings through metaanalyses [172]. Here, where classes can be theoretically confirmed, a more lenient attitude to class reification may be warranted.…”

Section: Reification Of Classes and Model Validationmentioning

confidence: 99%

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Unfolding of hidden white blood cell count phenotypes for gene discovery using latent class mixed modeling

Cited by 6 publications

References 74 publications

Prediction of the Rehabilitation Duration and Risk Management for Mild-Moderate COVID-19

Prediction of the Rehabilitation Duration and Risk Management for Mild-Moderate COVID-19

Trajectory analysis of cardiovascular phenotypes from biobank data uncovers novel genetic associations

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