Efficient Genome-wide Association in Biobanks Using Topic Modeling Identifies Multiple Novel Disease Loci

McCoy, Thomas H.; Castro, Víctor M.; Snapper, Leslie A.; Hart, Kamber L.; Perlis, Roy H.

doi:10.2119/molmed.2017.00100

Cited by 18 publications

(19 citation statements)

References 25 publications

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“…After extraction of DNA from buffy coat, samples were genotyped via one of the Illumina Multi-Ethnic Genotyping Arrays, which include content from phase 3 of the 1000 Genomes Project. For details of genotyping, see our prior publication 7 . To address potential batch effects across the four genotyping waves, we cleaned, imputed, and analyzed each one separately.…”

Section: Methodsmentioning

confidence: 99%

“…The remaining PheWAS code count by subject matrix was used to fit a latent Dirichlet allocation (LDA) model with 50 topics; the 50 topic count was selected for consistency with our own prior work and in the absence of well-established methods for optimal topic count selection 23 . As we have described 7 , this unsupervised machine learning method treats each subject’s medical record as if it were a document composed of PheWAS codes reflecting a mixture of underlying topics, or disease categories. The LDA model that results reflects a distribution of all PheWAS codes over each topic, although most codes contribute only a trivial amount.…”

Section: Methodsmentioning

confidence: 99%

“…Moreover, many biobanks and registries rely on individual billing or claims codes for which reliability varies substantially 3–6 . To address both of these limitations, we have previously demonstrated that the use of probabilistic topic models, an approach drawn from natural language processing that draws on groups of related diagnostic codes rather than individual codes, provides interpretable dimensionality reduction as well as making efficient use of sparse count data (i.e., the fact that most individuals will not have any given diagnosis) 7,8 .…”

Section: Introductionmentioning

confidence: 99%

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Using phenome-wide association to investigate the function of a schizophrenia risk locus at SLC39A8

2019

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While nearly all common genomic variants associated with schizophrenia have no known function, one corresponds to a missense variant associated with change in efficiency of a metal ion transporter, ZIP8, coded by SLC39A8. This variant has been linked to a range of phenotypes and is believed to be under recent selection pressure, but its impact on health is poorly understood. We sought to understand phenotypic implications of this variant in a large genomic biobank using an unbiased phenome-wide approach. Specifically, we generated 50 topics based on diagnostic codes using latent Dirichlet allocation, and examined them for association with the risk variant. Then, any significant topics were further characterized by examining association with individual diagnostic codes contributing to the topic. Among 50 topics, 1 was associated at an experiment-wide significance threshold (beta = 0.003, uncorrected p = 0.00049), comprising predominantly brain-related codes, including intracranial hemorrhage, cerebrovascular disease, and delirium/dementia. These results suggest that a functional variant previously associated with schizophrenia risk also increases liability to cerebrovascular disease. They further illustrate the utility of a topic-based approach to phenome-wide association.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Using phenome-wide association to investigate the function of a schizophrenia risk locus at SLC39A8

2019

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“…To address the contribution of the microbiota in our model, we used a co-housing experiment to homogenise the microbiota between experimental groups. Co-housing of mice has been shown to resolve microbiota drifts due to experimental treatments, resulting in significant clustering of co-housed animals (24,25). Co-housing was simulated by transfer of bedding and faecal pellets between cages.…”

Section: Vnmaa Treatment Severely Disrupts the Gut Microbiota Landscapementioning

confidence: 99%

Antibiotic-induced disturbances of the gut microbiota result in accelerated breast tumour growth via a mast cell-dependent pathway

Madgwick

et al. 2020

Preprint

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One Sentence Summary:We show that breast cancer progression is accelerated through a unique/novel immune response involving mast cells as a result of an antibiotic induced perturbation of the gut microbiota in a mouse model. AbstractThe diverse community of commensal microbes that comprise the gut microbiota is known to play an integral role in human health, not least through its ability to regulate host immune responses and metabolic pathways. Alterations to the homeostasis of this community, including through the use of broad-spectrum antibiotics, have already been associated with the progression of several cancers, namely melanoma and liver. The aggressive nature of breast cancer (BrCa), largely due to its ability to metastasize early, has ranked the disease with the second highest mortality rate of all cancers globally. Yet the body of research into the complex relationship between the microbiota and BrCa is still limited. This study found that a depletion of the microbiota, through the administration of antibiotics, significantly increased the rate of primary tumour progression in mouse BrCa models. We show that antibiotic-induced microbiota disturbances lead to changes in behaviour of a relatively obscure tumour-immune cell population: mast cells. We observed increases in tumour stroma-associated mast cells in antibiotic treated animals. Moreover, inhibition of mast cell degranulation, via cromolyn, slowed tumour progression in antibiotic treated animals but not in control animals. Thus, it appears that a perturbed microbiota drives stroma-associated mast cell recruitment and activation, which in turn promotes primary tumour growth through an as yet unknown mechanism.

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“…In addition to its wide adoption in the text mining field, topic modeling has achieved many successes in computer vision and biomedical science. Recently, a few groups have used this approach to analyze electronic health records (EHRs) [9,10] and genetic data to capture the characteristic of data [11,12].…”

Section: Introductionmentioning

confidence: 99%

Using Topic Modeling via Non-negative Matrix Factorization to Identify Relationships between Genetic Variants and Disease Phenotypes: A Case Study of Lipoprotein(a) (LPA)

Zhao

Feng

et al. 2018

Preprint

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23Genome-wide and phenome-wide association studies are commonly used to identify 24 important relationships between genetic variants and phenotypes. Most of these studies have 25 treated diseases as independent variables and suffered from heavy multiple adjustment burdens 26 due to the large number of genetic variants and disease phenotypes. In this study, we propose 27 using topic modeling via non-negative matrix factorization (NMF) for identifying associations 28 between disease phenotypes and genetic variants. Topic modeling is an unsupervised machine 29 learning approach that can be used to learn the semantic patterns from electronic health record 30 data. We chose rs10455872 in LPA as the predictor since it has been shown to be associated with 31 increased risk of hyperlipidemia and cardiovascular diseases (CVD). Using data of 12,759 32 individuals from the biobank at Vanderbilt University Medical Center, we trained a topic model 33 using NMF from 1,853 distinct phecodes extracted from the cohort's electronic health records 34 and generated six topics. We quantified their associations with rs10455872 in LPA. Topics 35 indicating CVD had positive correlations with rs10455872 (P < 0.001), replicating a previous 36 finding. We also identified a negative correlation between LPA and a topic representing lung 37 cancer (P < 0.001). Our results demonstrate the applicability of topic modeling in exploring the 38 relationship between the genome and clinical diseases. 39 40 Author summary 41 Identifying the clinical associations of genetic variants remains crucial in understanding 42 how the human genome modulates disease risk. Traditional phenome-wide association studies 43 consider each disease phenotype as an independent variable, however, diseases often present as 44 complex clusters of comorbid conditions. In this study, we propose using topic modeling to 45 model electronic health record data as a mixture of topics (e.g., disease clusters or relevant 46 comorbidities) and testing associations between topics and genetic variants. Our results 47 demonstrated the feasibility of using topic modeling to replicate and discover novel associations 48 between the human genome and clinical diseases. 49 50 51 52 Introduction 53 Elucidating associations between genetic variants and human diseases creates new 54 avenues for disease prevention and enables more precise treatment of diseases [1,2]. During the 55 past two decades, genetic studies have uncovered thousands of genetic variants that influence 56 risk for disease phenotypes [3], e.g., the discovery of a variant in proprotein convertase 57 subtilisin/kexin type 9 (PCSK9[4]) associated with low plasma low-density lipoprotein, which 58 led to a new therapeutic drug class that was approved by the US Food and Drug Administration 59 in 2015. Many of these discoveries come from large-scale association analyses. The two most 60 notable approaches are genome-wide (GWAS) and phenome-wide association studies (PheWAS) 61 [2, 5]. For a given phenotype, GWAS scans hundreds of thou...

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Efficient Genome-wide Association in Biobanks Using Topic Modeling Identifies Multiple Novel Disease Loci

Cited by 18 publications

References 25 publications

Using phenome-wide association to investigate the function of a schizophrenia risk locus at SLC39A8

Using phenome-wide association to investigate the function of a schizophrenia risk locus at SLC39A8

Antibiotic-induced disturbances of the gut microbiota result in accelerated breast tumour growth via a mast cell-dependent pathway

Using Topic Modeling via Non-negative Matrix Factorization to Identify Relationships between Genetic Variants and Disease Phenotypes: A Case Study of Lipoprotein(a) (LPA)

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