Identification of Novel Intronic Variants Implicating L3MBTL4 and AOAH in Indonesian Ovarian Endometriosis

BACKGROUND: Genetic factors are contributing substantially to endometriosis risk, however, there is limited data on genetic variant associations specific to ovarian endometriosis, especially in Indonesian populations. Understanding the genetic variants associated with this condition is essential for improving diagnosis and identifying potential therapeutic targets. Therefore, this study was conducted to analyze the association of genetic variants with ovarian endometriosis in Indonesian women using the single nucleotide polymorphisms (SNP)-array method, followed by an in-silico functional enrichment and gene expression analysis to explore their biological context.METHODS: This case-control study utilized the Infinium Asian Screening Array microarray platform to examine 46 samples, consisting of 22 ovarian endometriosis and 24 controls. The analysis included quality control, genetic association testing, and enrichment analysis. Genotypes were analyzed under dominant and recessive inheritance models, and functional insights were explored using gene expression databases.RESULTS: Ten intronic SNPs were significantly associated with endometrioma (p<0.05). Eight variants conferred increased risk (OR>1): rs77360595 (IFNLR1), rs2325558 (KLF12), rs1654499 (NLRP2), rs4809494 (LOC105376996), rs168482 (TMPRSS11A), rs17026725 (STPG2-AS1), rs59330070 (GFOD1), and rs58909364 (AOAH). Two variants were protective (OR<1): rs180732 (L3MBTL4) and rs7356507 (CRMP1). Notably, AOAH variant showed the highest odds ratio among risk variants, while L3MBTL4 variant showed the strongest statistical significance among protective variants in the dominant model. In-silico expression analysis confirmed that key implicated genes (KLF12, L3MBTL4, AOAH, and GFOD1) are expressed in relevant female reproductive tissues, generally at low-to-moderate levels.CONCLUSION: Ten novel genetic variants associated with ovarian endometriosis in Indonesian were identified. In particular, variants in L3MBTL4 and AOAH represent promising candidates that may play roles in disease pathophysiology of endometriosis, suggesting the importance of population-specific genetic studies and these specific loci as potential biomarkers or therapeutic targets.KEYWORDS: ovarian endometriosis, SNP, microarray, genetic association, plink, bioinformatics

Authors

Institutions

Publication Details

Journal
The Indonesian Biomedical Journal
Published
2025-12-27
DOI
https://doi.org/10.18585/inabj.v17i6.3732
Primary Topic
Endometriosis Research and Treatment
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Identification of Novel Intronic Variants Implicating L3MBTL4 and AOAH in Indonesian Ovarian Endometriosis

Rafika Indah Paramita, Surya Dwira, Trijayani Trijayani, Raden Muharam
The Indonesian Biomedical Journal
Endometriosis Research and Treatment
article

Identification of Novel Intronic Variants Implicating L3MBTL4 and AOAH in Indonesian Ovarian Endometriosis

Rafika Indah Paramita, Surya Dwira, Trijayani Trijayani, Raden Muharam
article en

Abstract

BACKGROUND: Genetic factors are contributing substantially to endometriosis risk, however, there is limited data on genetic variant associations specific to ovarian endometriosis, especially in Indonesian populations. Understanding the genetic variants associated with this condition is essential for improving diagnosis and identifying potential therapeutic targets. Therefore, this study was conducted to analyze the association of genetic variants with ovarian endometriosis in Indonesian women using the single nucleotide polymorphisms (SNP)-array method, followed by an in-silico functional enrichment and gene expression analysis to explore their biological context.METHODS: This case-control study utilized the Infinium Asian Screening Array microarray platform to examine 46 samples, consisting of 22 ovarian endometriosis and 24 controls. The analysis included quality control, genetic association testing, and enrichment analysis. Genotypes were analyzed under dominant and recessive inheritance models, and functional insights were explored using gene expression databases.RESULTS: Ten intronic SNPs were significantly associated with endometrioma (p<0.05). Eight variants conferred increased risk (OR>1): rs77360595 (IFNLR1), rs2325558 (KLF12), rs1654499 (NLRP2), rs4809494 (LOC105376996), rs168482 (TMPRSS11A), rs17026725 (STPG2-AS1), rs59330070 (GFOD1), and rs58909364 (AOAH). Two variants were protective (OR<1): rs180732 (L3MBTL4) and rs7356507 (CRMP1). Notably, AOAH variant showed the highest odds ratio among risk variants, while L3MBTL4 variant showed the strongest statistical significance among protective variants in the dominant model. In-silico expression analysis confirmed that key implicated genes (KLF12, L3MBTL4, AOAH, and GFOD1) are expressed in relevant female reproductive tissues, generally at low-to-moderate levels.CONCLUSION: Ten novel genetic variants associated with ovarian endometriosis in Indonesian were identified. In particular, variants in L3MBTL4 and AOAH represent promising candidates that may play roles in disease pathophysiology of endometriosis, suggesting the importance of population-specific genetic studies and these specific loci as potential biomarkers or therapeutic targets.KEYWORDS: ovarian endometriosis, SNP, microarray, genetic association, plink, bioinformatics

The Indonesian Biomedical JournalVol. 17(6)
University of Indonesia (ID)
Universitas Indonesia
Gender equality
Openalex Percentile: Top 27%
Endometriosis Research and Treatment
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.