Introduction

In the search for common genetic variations associated with medical disorders, the traditional analytic approach examines single disorders in case-control cohorts ascertained for a specific disorder. With the availability of large-scale biobanks with broad ascertainment, multiple approaches to phenome-wide association — ie, looking across a range of clinical phenotypes to detect genetic association — have been proposed (1). However, relying on individual disorders represented in diagnostic codes may not efficiently capture the underlying architecture of genetic risk. First, the ability of claims codes to accurately capture a given diagnosis varies widely, even when diagnosis-specific classifiers are applied to augment single codes (2,3). As such, approaches that focus on individual diagnostic codes are limited by inaccurate, missing or heterogeneous diagnoses; eg, where individuals with cystic fibrosis might be represented by male infertility, diabetes and chronic rhinosinusitis even in the absence of a diagnostic code for cystic fibrosis (4). Second, under conditions of pleiotropy, where a single variant contributes to risk for multiple disorders, as in some autoimmune and neuropsychiatric disorders, standard phenome-wide approaches do not make efficient use of the correlation structure between diagnoses. Finally, single-code approaches do not capture disease subtypes with different genetic architecture, where these subtypes may be reflected in different patterns of comorbidity, as a recent investigation of diabetes mellitus suggests (5–8).

Here, we describe a method for addressing the problem of mapping genetic space to high-dimensional phenotype space that leverages comorbidity and diagnostic uncertainty to allow efficient genome-wide or single-locus association. This approach facilitates association by capturing diagnostic co-occurrence patterns to reduce dimensionality, thereby decreasing the number of hypotheses being tested, while increasing power by including individuals who may have different manifestations of the same underlying pathology. Specifically, we apply latent Dirichilet allocation (LDA), a means of identifying commonly co-occurring features, to derive a set of 50 disease topics. Then we test those topics for association with common genetic variation and compare this approach to standard methods using single International Classification of Diseases, Ninth Revision (ICD-9)/phenome-wide association studies (PheWAS) codes (9).

Materials and Methods

Cohort Derivation and Genotyping

We drew on three cohorts of patients seen in the Brigham and Women’s Hospital network and the Massachusetts General Hospital network, representing the first 15,064 individuals genotyped as part of the Partners HealthCare Biobank initiative (10). These individuals provided informed consent for their electronic health records (EHRs) to be examined in investigations approved by the Partners Institutional Review Board, and provided blood samples for DNA extraction.

DNA was extracted from buffy coat and genotyped using the Illumina Expanded Multi-Ethnic Genotyping Array (MEGA or MEGA-EX) platforms, with common variant arrays incorporating content from the 1000 Genomes Project Phase 3. Single nucleotide polymorphism (SNP) coordinates were remapped based on the TopGenomicSeq provided from Illumina (MEGA_Consortium_v2_15070954_A2.csv); all rsIDs correspond to build 142 of dbSNP. To determine the forward strand of the SNP, we aligned both SNP sequences (alleles A and B) to hg19 using BLAT with default parameters set by the University of California, Santa Cruz Genome Browser (11).

Quality Control and Imputation

Genotyping was done using three versions of the Illumina Multi-Ethnic Global (MEG) array (MEGA n = 4927, MEGA EX n = 5353, MEG n = 4784; mappable variants available for each were 1,411,334, 1,710,339 and 1,747,639, respectively). Each cohort was cleaned, imputed and analyzed separately to avoid batch effects. For each batch, we included subjects with genotyping call rates exceeding 99%; no related individuals based on identity by descent were included (12). From these individuals, any genotyped SNP with a call rate of at least 95%, minor allele frequency of 0.01 or greater and Hardy-Weinberg equilibrium p value < 1 × 10−6 was included. We then imputed using the Michigan Imputation Server implementing Minimac3 (13–15). Imputation used all population subsets from the 1000 Genomes Project Phase 3 v5 as reference panel; haplotype phasing was performed using SHAPEIT (16).

Ancestry

For each cohort, we used principal components analysis of linkage-disequilibrium-pruned genotyped SNPs to characterize population structure, based on EIGENSTRAT, as implemented in PLINK v1.9, and plotted these components with superimposition of HapMap samples to confirm locations of northern European individuals (17–19). Limiting the analysis to these individuals yielded 3,728 + 3,402 + 3,715 = 10,845 analyzable participants.

Topic Identification

For both cohorts, ICD-9 diagnosis codes extracted from each individual’s medical record were grouped into 1,667 PheWAS codes corresponding to clinically meaningful disease categories, with the total number of codes within each PheWAS category preserved (20). Initial model fitting was performed using cohort 1 only, with cohort 2 preserved as an out-of-sample test set. To fit the topic model cohort, the PheWAS code count by subject matrix was frequency-controlled to eliminate PheWAS codes that occurred in < 1% or > 99% of subjects. After frequency control, 508 distinct PheWAS codes were used for the initial unsupervised learning step of model fit.

The PheWAS code count by subject matrix for cohort 1 was used to train an LDA model with 50 topics (9). LDA is a form of unsupervised machine learning typically found in natural language processing (NLP). As topic modeling is drawn from the NLP literature, this preprocessing can be conceptualized as treating each subject’s medical record as a document composed of ICD-9 codes that are lemmatized to PheWAS codes and thereafter analyzed as a term-count document matrix. LDA postulates that the words of a document are a mixture of underlying topics, and documents are composed of each of these topics to varying degrees. The resulting trained LDA model is a distribution of all PheWAS codes over each topic. This distribution can be used to score each collection of PheWAS codes for membership in each of the topics. In the case of illness in a biobank, we use LDA to model biology as a collection of topics or underlying generator processes of observable, but potentially overlapping and incompletely penetrant, pathological states. These states are captured as PheWAS “words.” Having trained the topic model on cohort 1, this model was then used to score each subject in cohort 1 for membership in each of the topics (in-sample) and each subject in cohort 2 for membership in each of the topics (out-of-sample). To perform the PheWAS LDA, we used the Gensim implementation of the LDA algorithm (21,22).

There is no widely accepted method for naming topics, since by definition all PheWAS words arise from all topics at some probability, albeit a vanishingly small probability in many cases. To aid in interpretability, in our discussion of results we name topics subjectively in terms of the preponderance of codes represented toward the top of the list, as interpreted by the two physician authors (THM, RHP); we refer to them below as “topic-name-plus,” as a reminder to the reader that the topic contains more than just a single diagnosis and may contain apparently unrelated terms.

Analysis

Single-locus associations in each cohort were examined individually, and then combined in inverse variance-weighted fixed-effects meta-analyses. In these analyses, only bi-allelic SNPs with minor allele frequencies of at least 1% were retained. Tests for association used linear regression assuming an additive allelic effect, treating each topic as a quantitative trait and adjusting for the first 10 principal components a priori (analyses incorporating 5 or 20 components did not yield meaningfully different results). Association results are presented in terms of independent loci after pruning, using the clump command in PLINK 1.9, with a 250kb window and r2 = 0.2. We present uncorrected p values, but elected to focus on p values less than 1e-15 and loci with at least two associated SNPs (23).

To facilitate comparisons across topics and methods, reported p values are not adjusted for linkage disequilibrium scores. Adjustment for lambda-1000 or linkage disequilibrium score regression intercept did not meaningfully change relative results; lambda values range from 0.990 to 1.017 × across topics (24).

Secondary analyses examined alternate topic-based phenotypes in which either the most strongly loading diagnostic codes (ie, those with loading > 0.05) or least strongly loading diagnostic codes (ie, those with loading < 0.01) for a given topic were omitted, as a means of understanding the relative contributions of these sets of codes. These analyses utilized the same approach as for the primary analysis of topics. For comparison, we also examined association with the presence or absence of the single most strongly loading diagnostic code in each topic, using logistic regression.

All supplementary materials are available online at https://doi.org/www.molmed.org.

Results

After exclusions for genotyping quality control, relatedness and ancestry, cohorts 1, 2 and 3 included 2,141/3,728 (57.4%), 1,690/3,402 (49.7%) and 2,089/3,715 (56.23%) female participants, respectively. Mean ages were 57.9 (standard deviation [SD] 16.2), 62.4 (SD 16.0) and 59.4 (SD 16.5).

After imputation, a total of 7,781,941 SNPs with minor allele frequency (MAF) of 0.01 or greater were analyzed for each of the 50 topics and meta-analyzed. After genome-wide association analysis for each of the 50 topics, a total of 56 loci spanning 24 topics included at least one SNP with p < 1e-11; 39 of these loci across 22 topics included at least one additional associated SNP at p < 0.01. Table 1 reports the physical position, annotation and association for the most strongly associated SNPs for topics with at least one p < 5e-15 and at least two associated SNPs in a given locus, while Supplementary Table S1 reports the 10 most associated independent SNPs for all 50 topics (for effects by cohort, see Supplementary Table S2). The strongest associations (all p < 1e-15) were observed for pulmonary disease/cystic fibrosis-plus, anemia and fracture-plus, rheumatoid arthritis-plus, pregnancy complications-plus, uterine neoplasm-plus, viral-plus, neoplasm-plus, adrenal and electrolyte disorders-plus, and pituitary and adrenal disorder-plus. Figures 1 and 2 show Manhattan and locus plots for pulmonary disease/cystic fibrosis-plus and neoplasm-plus; for plots for the remainder of these, see Supplementary Materials. Diagnostic codes loading most strongly for each of these topics are listed in Table 2; for the codes loading on all 50 topics, see Supplementary Table S3.

Table 1 Loci associated with a topic with p < 1e-15
Full size table
Figure 1
figure 1

Manhattan plots for two example topics: (A) pulmonary disease/cystic fibrosis-plus and (B) neoplasm-plus.

Full size image
Figure 2
figure 2

Locus plots for two example topics: (A) pulmonary disease/cystic fibrosis-plus and (B) neoplasm-plus.

Full size image
Table 2 Diagnostic codes loading on topics associated with p < 1e-
Full size table

We also examined (Table 3) the effect of three alternate phenotypic definitions: examining the topic “tail” only (ie, diagnostic codes with weights < 0.05, the “tail” of the list) or the topic “head” only (ie, diagnostic codes with weights > 0.01, the “head” of the list) and including only the single top-weighted diagnostic code (ie, a standard single diagnosis association). This last comparison allows direct contrast with nominal associations returned by traditional PheWAS, recognizing that here only 50 phenotypes are examined rather than 500 or more.

Table 3 Sensitivity analysis examining alternate phenotypes omitting the most- or least-strongly loading diagnoses
Full size table

Discussion

We applied a topic-modeling approach to identify 50 groups of diagnostic codes in biobank-associated EHR data and then used genome-wide data to examine common-variant associations for each topic. With this novel approach, we identified multiple known loci for autoimmune and pulmonary disease, as well as multiple apparently novel disease loci for pregnancy complications, viral susceptibility, anemia/fracture risk and uterine cancer not previously associated at a genome-wide threshold with disease (based on searching the National Human Genome Research Institute-European Bioinformatics Institute Catalog of published genome-wide association studies) (25). We compared our results to those arising from a standard single-diagnostic-code PheWAS; this approach would not have yielded association at this threshold. Moreover, omitting either the head or the tail of each topic (ie, the most- or least-weighted diagnosis) eliminates the association, suggesting that the observed effect does not arise from a small number of codes.

The identification of robust associations with loci implicated in prior genome-wide association studies demonstrates convergent validity (27,28). We demonstrate that this approach more efficiently detects these known associations (based on magnitude of p value) than single-code association. That is, simply incorporating a single ICD9/PheWAS code yielded weaker evidence of association. These loci and the sensitivity analysis associated with topic pruning (head and tail distributions; Table 1, Figure 1) function as positive controls and illustrations of assay sensitivity.

In nearly all cases, we note that the strongest associations are identified by incorporating all codes loading on a topic, rather than limiting the analysis to only the most strongly loading. Indeed, we observe that omitting such strongly loaded codes does not necessarily reduce the magnitude of association. Interestingly, there is only one example where a single code yielded an association nearly as robust as that observed with the topic, rheumatoid arthritis-plus, which may reflect the distinct genetic architecture of this disorder compared with some others.

In lieu of looking across phenotypes, a recent report describes a method to identify disease subtypes based upon network analysis (29). Our approach is not directly comparable, but may also be valuable in identifying subtypes in the case where a given diagnosis is genetically heterogeneous but the presence of comorbidities helps to define more homogeneous groups. A major advantage of the present approach compared with other unsupervised methods (eg, deep learning) is inspectability: it yields a weighted list of diagnostic codes. This inspectability enhances biological utility, as it allows post hoc clarification of the results, as illustrated by the sensitivity analysis. While we describe its application for genomics, it may be useful for other approaches drawing on coded EHR data where diagnostic codes do not definitively identify a diagnosis or subtype. Notably, it should also be possible to further extend the utility of our approach by incorporating additional coded or uncoded data — concepts extracted by NLP, for example — where such data are available.

The gain in statistical power afforded by this approach is apparent. For a genotypic risk ratio of 1.5 with a minor allele frequency of 25% and a disease prevalence of 5%, nearly 80,000 cases are required to achieve 80% power after Bonferroni correction for 500 PheWAS phenotypes, versus nearly 800 cases with 50 topics, if each is analyzed as a dichotomous outcome. In reality, the increased case reliability that arises from integrating across related codes likely renders these estimates conservative, in some cases markedly so. Empirically, our results show that in no case would a single-code association have yielded stronger nominal association, independent of Bonferroni correction, than the topic-based association, and in most cases the association was markedly less.

Still, we note several important limitations. From a modern genomics perspective, the present cohorts are likely insufficient to robustly detect all but the largest associations. They are, however, large enough to demonstrate the feasibility and efficiency of using aggregated groups of diagnoses as an efficient complement to phenome-wide association. Additionally, the biobank cohorts studied here are insufficient to examine these associations in non-northern European populations; replication in other populations would be informative. We note that the topics in many cases include codes seemingly unrelated to the predominant diagnosis; while these may represent type I error or noise, they may also illustrate the power of topic modeling to detect co-occurring diagnoses where the physiologic relationship is not otherwise recognized. Our analysis of topic “heads” and “tails” suggests that, in most cases, topics are not well captured by a single code or small number of codes. As such, the names we apply represent a best guess at interpretation, and investigation of the mechanism of overlap (in vivo or in silico) represents an important next step. In particular, consideration of orthogonal biological data, such as investigating pathways or expression quantitative trait loci, could further clarify the way in which groups of associated diagnoses relate to one another mechanistically.

Conclusion

In sum, our results indicate the utility of an approach to large-scale biobank data that aggregates over groups of diagnostic codes by treating groups of codes as relating to underlying topics. This approach is superior to single-code association for diagnoses with shared liability or groups of diagnostic codes that more reliably identify an underlying phenotype. It identifies multiple apparently novel disease loci while replicating existing associations, and suggests multiple other regions as well as phenotypes that merit further investigation in biobank cohorts or registries.

Disclosure

RHP has served on advisory boards for, or provided consulting to, Genomind, Healthrageous, Perfect Health, Pfizer, Psy Therapeutics, and RIDVentures.