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Table 5 Genes with high prevalence across more than one large gene set cluster in factor 4

From: Multi-omic latent variable data integration reveals multicellular structure pathways associated with resistance to tuberculin skin test (TST)/interferon gamma release assay (IGRA) conversion in Uganda

Gene

Clusters

Dataset

Feature ID

MOFA Weight

Groupwise StatisticsA

P-value

TBI

RSTR

SRC

F4-4, F4-26, F4-3, F4-30, F4-14

SNP

rs12329503

0.529

00:12

00:04

0.003

01:03

01:11

02:00

02:03

rs6018088

0.493

00:10

00:04

0.011

01:05

01:08

02:00

02:06

rs6018148

0.465

00:10

00:04

0.018

01:05

01:10

02:00

02:04

rs6018257

0.662

00:12

00:04

0.002

01:01

01:10

02:01

02:04

HCK

F4-4, F4-3

SNP

rs4561724

0.459

00:09

00:03

0.025

01:06

01:13

02:00

02:02

BLK

F4-4, F4-14

SNP

rs2248932

0.558

00:07

00:04

0.037

01:08

01:08

02:00

02:06

DSCAM

F4-7, F4-5

SNP

rs1012854

0.964

00:08

00:01

< 0.001

01:07

01:07

02:00

02:10

rs11700509

0.677

00:08

00:02

0.031

01:04

01:10

02:03

02:06

PRKCZ

F4-19, F4-6

SNP

rs2803310

0.597

00:10

00:02

0.002

01:05

01:10

02:00

02:06

VAV2

F4-26, F4-20, F4-14

Methylation

cg21223341

-0.293

4.015 ± 0.122

3.554 ± 0.154

0.029

EPHB2

F4-7, F4-19, F4-14

Methylation

cg13102231

-0.237

2.759 ± 0.101

2.767 ± 0.170

0.97

GNA12

F4-30, F4-20, F4-14

ATAC-seq

ID_chr7_2742848_2743374

-0.112

2.152 ± 0.236

0.834 ± 0.233

< 0.001

  1. A Groupwise summary statistics are calculated as count by genotype for SNP data and means ± SE for all other data types. P-values represent chi-squared tests for SNP features and ANOVAs for other data types