GAWMerge expands GWAS sample size and diversity by combining array-based genotyping and whole-genome sequencing.

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Authors: Mathur, Ravi; Fang, Fang; Gaddis, Nathan; Hancock, Dana B; Cho, Michael H; Hokanson, John E; Bierut, Laura J; Lutz, Sharon M; Young, Kendra; Smith, Albert V; NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium; Silverman, Edwin K; Page, Grier P; Johnson, Eric O
Year: 2022
Journal: Communications biology
PMID: 35953715
DOI: 10.1038/s42003-022-03738-6
PMCID: PMC9372058

Genome-wide association studies (GWAS) have made impactful discoveries for complex diseases, often by amassing very large sample sizes. Yet, GWAS of many diseases remain underpowered, especially for non-European ancestries. One cost-effective approach to increase sample size is to combine existing cohorts, which may have limited sample size or be case-only, with public controls, but this approach is limited by the need for a large overlap in variants across genotyping arrays and the scarcity of non-European controls. We developed and validated a protocol, Genotyping Array-WGS Merge (GAWMerge), for combining genotypes from arrays and whole-genome sequencing, ensuring complete variant overlap, and allowing for diverse samples like Trans-Omics for Precision Medicine to be used. Our protocol involves phasing, imputation, and filtering. We illustrated its ability to control technology driven artifacts and type-I error, as well as recover known disease-associated signals across technologies, independent datasets, and ancestries in smoking-related cohorts. GAWMerge enables genetic studies to leverage existing cohorts to validly increase sample size and enhance discovery for understudied traits and ancestries.

Fig. 1

Overview of the protocol to use whole-genome sequencing (WGS) data as public control in GWAS.*The quality control (QC) of the case and public control data is conducted independently according to the steps outlined in the methods.

Fig. 2

Evaluation design for GAWMerge.Evaluation design for a technical comparison, b type-I error assessment, and c known GWAS hits. *The samples with European ancestry in COPDGene were evenly divided into two subsets of samples. EA1 includes all COPD cases and some COPD controls to match the COPD prevalence in ECLIPSE. EA2 has all the rest COPD free samples.

Fig. 3

Meta-analysis results from evaluation for type-I error.The Manhattan plot (a) shows the expected no signal, while the QQ-plot (b) shows no inflation.

Fig. 4

Meta-analysis results for replication of GWAS hits for COPD.The Manhattan plot (a) shows the replicated signals, while the QQ-plot (b) shows inflation due to the true signal.

#	Section	Preview
20	Discussion	reference population, although method can be applied using other reference populations, such as the…
21	Discussion	GAWMerge has some limitations. First, careful consideration of not only ancestry, sex, and age…
22	Discussion	COPDGene EA with WGS data and smoking controls in COGEND EA with array data, due to high R2…
23	Discussion	dominant scenario for use of GAWMerge, the WGS is a substantial strength, accounting for all the…
24	Discussion	Overall, GAWMerge presents a practical application of integrating case-only array-genotyped data…
25	Methods — Dataset descriptions	The Trans-Omics for Precision Medicine (TOPMed) program aims to improve understanding of the…
26	Methods — Dataset descriptions	COPDGene (ClinicalTrials.gov: NCT00608764) is an ongoing study of over 10,000 non-Hispanic White and…
27	Methods — Dataset descriptions	ECLIPSE was an observational study launched in 200623. It recruited 2,164 COPD subjects, 337 smoking…
28	Methods — Dataset descriptions	COGEND was initiated in 2001 as a genetic study of nicotine dependence20,21. Nicotine dependent…
29	Methods — Dataset descriptions	The use of the TOPMed WGS data was approved by the TOPMed Methods working group. Data approval of…
30	Methods — GAWMerge development	Below we provide further details on the protocol steps, and iterations used to devise the…
31	Methods — GAWMerge development — Quality control (QC)	We performed standard QC steps for both array genotyped data and the subset of WGS data extracted in…
32	Methods — GAWMerge development — Combining array and WGS data	GAWMerge, a protocol for integrating array and WGS data is shown in Fig. 1 and described in more…
33	Methods — GAWMerge development — Imputation strategy	The merged array and WGS data were first imputed using Minimac419 using the thousand genomes phase 3…
34	Methods — GAWMerge development — Imputation strategy	smokers and no disease was being tested between the datasets. Without the ER2 filter, we found many…
35	Methods — GAWMerge development — Filtering association test results	Association analysis was conducted using rvTest36 with ten principal components included to account…
36	Methods — GAWMerge implementation	GAWMerge was developed within the DNANexus computing environment (https://www.dnanexus.com/) and the…
37	Methods — GAWMerge implementation	To discover optimal controls to combine with available cases, TOPMed phenotypic data were easily…
38	Methods — GAWMerge implementation	Computation of GAWMerge is comparable to other GWAS efforts. For example, in the analysis comparing…
39	Methods — Reporting summary	Further information on research design is available in the Nature Research Reporting Summary linked…

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