TABLE 3.

Summary of methods using external controls for improvement of statistical power.

Method	External control data	Require internal control?	Require sequencing depth for cases and controls?	Method correcting for batch differences between case controls	Can the method adjust for covariates?	Test
RVS (Derkach et al., 2014)	Individual genotype likelihood	N	N	Modeling the effect of sequencing depth	N	Single variant based test, burden test and variance component based test
TASER (Hu et al., 2016)	Individual Bam files	N	N	Modeling the effect of sequencing depth	N	Burden test
Chen and Lin (Chen and Lin, 2020)	Individual genotype likelihood	N	N	Modeling the effect of sequencing depth	Y	Single common variant based test
iECAT-Score (Li and Lee, 2021)	Individual genotypes	Y	N	Only use the external control if no batch effect exists	Y	Single variant based test for common and rare
iECAT-O (Lee et al., 2017)	Summary counts	Y	N	Only use the external control if no batch effect exists	N	A combination of burden test and variance component based test
ProxECAT (Hendricks et al., 2018)	Summary counts	N	N	Use non-functional variants as a baseline in the test	N	Burden test based on rare allele counts
TRAPD (Guo et al., 2018)	Summary counts	N	≥ 10 in 90% of samples	Adjusting filtering criteria	N	Burden test based on sample counts
RV- EXCALIBER (Lali et al., 2021)	Summary counts	Preferred	≥ 20 in 90% of samples	Adjust the expected counts sample-wise and gene-wise	N	Burden test based on rare allele counts
CoCoRV (Chen et al., 2022)	Summary counts	N	≥10 in 90% of samples	Consistent filtering to keep high quality variants	N	Burden test based on sample counts