2020
DOI: 10.1038/s41467-019-13762-6
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Abstract: The extent to which the impact of regulatory genetic variants may depend on other factors, such as the expression levels of upstream transcription factors, remains poorly understood. Here we report a framework in which regulatory variants are first aggregated into sets, and using these as estimates of the total cis-genetic effects on a gene we model their nonadditive interactions with the expression of other genes in the genome. Using 1220 lymphoblastoid cell lines across platforms and independent datasets we … Show more

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Cited by 7 publications
(6 citation statements)
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“…Sample sequencing, variant calling and quality control methods for each dataset are broadly comparable. These are described elsewhere for Project MinE (Project MinE ALS Sequencing Consortium, 2018) and the LBC (Wragg et al, 2020). Information for SARM1 variants in the Answer ALS project was obtained using ANNOVAR (Wang et al, 2010) annotation on GRCh38 positions, after read mapping with Burrows-Wheeler Alignment tool (BWA) (Li & Durbin, 2010), variant calling with GATK (McKenna et al, 2010), and joint-genotype using Sentieon (Freed et al, 2017).…”
Section: Methodsmentioning
confidence: 99%
“…Sample sequencing, variant calling and quality control methods for each dataset are broadly comparable. These are described elsewhere for Project MinE (Project MinE ALS Sequencing Consortium, 2018) and the LBC (Wragg et al, 2020). Information for SARM1 variants in the Answer ALS project was obtained using ANNOVAR (Wang et al, 2010) annotation on GRCh38 positions, after read mapping with Burrows-Wheeler Alignment tool (BWA) (Li & Durbin, 2010), variant calling with GATK (McKenna et al, 2010), and joint-genotype using Sentieon (Freed et al, 2017).…”
Section: Methodsmentioning
confidence: 99%
“…Sample sequencing, variant calling and quality control methods for each dataset are broadly comparable. These are described elsewhere for Project MinE (Project MinE ALS Sequencing Consortium, 2018) and the LBC (Wragg et al, 2020). Information for SARM1 variants in the Answer ALS project was obtained using ANNOVAR (Wang et al, 2010) annotation on GRCh38 positions, after read mapping with Burrows-Wheeler Alignment tool (BWA) (Li & Durbin, 2010), variant calling with GATK (McKenna et al, 2010), and jointgenotype using Sentieon (Freed et al, 2017).…”
Section: Database Resourcesmentioning
confidence: 99%
“…We found that 16 methQTLs were shared across all of the pairwise comparisons and lay below the methQTL P-value cutoff of 8.65×10 −11 in all tissues and are thus common methQTLs ( Figure 3B, Supplementary Table 4 ). The common methQTLs included well established methQTLs and eQTLs, such as the ones present in the PON1 [39], LGR6 [40], and RIBC2 [41] loci ( Figure 3C ). We found substantially more methQTLs shared across different tissues than tissue-specific methQTLs.…”
Section: Resultsmentioning
confidence: 99%
“…Tissue-specific methQTLs are only present in one of the tissues/cell types and not shared in any pairwise comparison according to the colocalization analysis We found that 16 methQTLs were shared across all of the pairwise comparisons and lay below the methQTL P-value cutoff of 8.65x10 -11 in all tissues and are thus common methQTLs (Figure 3B, Supplementary Table 4). The common methQTLs included well established methQTLs and eQTLs, such as the ones present in the PON1 [39], LGR6 [40], and RIBC2 [41] loci (Figure 3C).…”
Section: Colocalization Analysis Identifies Common Methqtlsmentioning
confidence: 91%