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
0	Introduction	Genome-wide association studies (GWAS) offer a powerful tool for identifying genetic variants for…
1	Introduction	this way is feasible even with samples genotyped on different array-based technologies10–13. A…
2	Introduction	January 2021, there are at least 217 case-only studies containing >136,000 samples across many…
3	Introduction	The NHLBI-supported TOPMed program15 with its collection of >155,000 human subjects with WGS data…
4	Introduction	While incorporating public controls to maximize the utility of genetic discovery is desirable, there…
5	Results — Protocol to integrate array and WGS data	GAWMerge is a protocol that we developed to integrate array and WGS genotyping technologies that…
6	Results — Protocol to integrate array and WGS data	For selection of controls in step 1, it is crucial to choose samples with an ancestral composition…
7	Results — Protocol to integrate array and WGS data	Our previous work13 suggested potential bias in association testing when using genotypes imputed…
8	Results — Protocol to integrate array and WGS data	WGS-derived SNPs, a second round of imputation is performed after removing genotyped SNPs with low…
9	Results — Protocol evaluation design	To evaluate the performance of GAWMerge, we used three smoking-related datasets: Collaborative…
10	Results — Protocol evaluation design	HumanOmni1-Quad_v1-0_BIllumina HumanHap550v3.0Array-genotyped dataN,…
11	Results — Reproducibility across genotyping technologies	COPDGene has both array and WGS genotype data on the same samples available through TOPMed.…
12	Results — Reproducibility across genotyping technologies	We suspected that the false positives we observed derived from the phasing step since phasing of the…
13	Results — Controlling type-I error in case-only vs. public control GWAS	We assessed type-I error in a comprehensive analysis involving three smoking-related datasets and…
14	Results — Controlling type-I error in case-only vs. public control GWAS	= 712) vs. WGS from COPDGene AA (N = 1710). All association models include ten principal components…
15	Results — Recovery of known COPD loci in case-only vs. public control GWAS	The last evaluation step was to recover known GWAS hits for COPD24,27. As shown in Fig. 2c, we…
16	Results — Recovery of known COPD loci in case-only vs. public control GWAS	are presented in Supplementary Fig. 6. Meta-analysis of the 3 analyses successfully recovered 5 out…
17	Discussion	In summary, we present GAWMerge, a protocol for integrating array and WGS genotype data to conduct…
18	Discussion	evidenced by the expected lack of genome-wide significant findings in a GWAS meta-analysis comparing…
19	Discussion	The development of GAWMerge was done with TOPMed WGS and array genotyped-data, although it can be…

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