Methods for statistical fine-mapping and their applications to auto-immune diseases

Wang, Qingbo; Huang, Hailiang

doi:10.1007/s00281-021-00902-8

Cited by 17 publications

(14 citation statements)

References 119 publications

(155 reference statements)

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“…To address these concerns, we attempted to address this issue by applying an MDS plot and conducting a fine-mapping analysis. Nevertheless, the variants identified as significant genome-wide in our study were not all fine-mapped because fine-mapping analysis requires high-quality genetic data and a much larger sample size than that required for a GWAS [ 76 ].…”

Section: Discussionmentioning

confidence: 99%

Identification of genetic variants associated with diabetic kidney disease in multiple Korean cohorts via a genome-wide association study mega-analysis

Jin

Kim

Lee

et al. 2023

BMC Med

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Background The pathogenesis of diabetic kidney disease (DKD) is complex, involving metabolic and hemodynamic factors. Although DKD has been established as a heritable disorder and several genetic studies have been conducted, the identification of unique genetic variants for DKD is limited by its multiplex classification based on the phenotypes of diabetes mellitus (DM) and chronic kidney disease (CKD). Thus, we aimed to identify the genetic variants related to DKD that differentiate it from type 2 DM and CKD. Methods We conducted a large-scale genome-wide association study mega-analysis, combining Korean multi-cohorts using multinomial logistic regression. A total of 33,879 patients were classified into four groups—normal, DM without CKD, CKD without DM, and DKD—and were further analyzed to identify novel single-nucleotide polymorphisms (SNPs) associated with DKD. Additionally, fine-mapping analysis was conducted to investigate whether the variants of interest contribute to a trait. Conditional analyses adjusting for the effect of type 1 DM (T1D)-associated HLA variants were also performed to remove confounding factors of genetic association with T1D. Moreover, analysis of expression quantitative trait loci (eQTL) was performed using the Genotype-Tissue Expression project. Differentially expressed genes (DEGs) were analyzed using the Gene Expression Omnibus database (GSE30529). The significant eQTL DEGs were used to explore the predicted interaction networks using search tools for the retrieval of interacting genes and proteins. Results We identified three novel SNPs [rs3128852 (P = 8.21×10−25), rs117744700 (P = 8.28×10−10), and rs28366355 (P = 2.04×10−8)] associated with DKD. Moreover, the fine-mapping study validated the causal relationship between rs3128852 and DKD. rs3128852 is an eQTL for TRIM27 in whole blood tissues and HLA-A in adipose-subcutaneous tissues. rs28366355 is an eQTL for HLA-group genes present in most tissues. Conclusions We successfully identified SNPs (rs3128852, rs117744700, and rs28366355) associated with DKD and verified the causal association between rs3128852 and DKD. According to the in silico analysis, TRIM27 and HLA-A can define DKD pathophysiology and are associated with immune response and autophagy. However, further research is necessary to understand the mechanism of immunity and autophagy in the pathophysiology of DKD and to prevent and treat DKD.

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Section: Discussionmentioning

confidence: 99%

Identification of genetic variants associated with diabetic kidney disease in multiple Korean cohorts via a genome-wide association study mega-analysis

Jin

Kim

Lee

et al. 2023

BMC Med

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“…Despite the challenges in calling cis-interactions from typically under-sampled Hi-C/CHi-C data (Aljogol et al, 2022), deep learning methods have been developed that provide improved DNA interaction coverage and resolution for sequencing and sample costs (Zhang, 2022). Multiple reviews cover recent statistical advances that estimate causal genetic risk loci, accounting for LD and functional annotation (Spain and Barrett, 2015;Pasaniuc and Price, 2017;Hutchinson et al, 2020;Wang and Huang, 2022). Briefly, methods have evolved that use GWAS summary statistics rather than individual genotype level data, and relax the single causal variant per locus assumption of earlier landmark fine mapping work (Wakefield, 2009;Maller et al, 2012).…”

Section: Chromatin Conformation Capture Approachesmentioning

confidence: 99%

“…Multiple reviews cover recent statistical advances that estimate causal genetic risk loci, accounting for LD and functional annotation ( Spain and Barrett, 2015 ; Pasaniuc and Price, 2017 ; Hutchinson et al, 2020 ; Wang and Huang, 2022 ). Briefly, methods have evolved that use GWAS summary statistics rather than individual genotype level data, and relax the single causal variant per locus assumption of earlier landmark fine mapping work ( Wakefield, 2009 ; Maller et al, 2012 ).…”

Section: Bioinformatic Tools To Process and Integrate Genome Wide Ass...mentioning

confidence: 99%

3D genome organization links non-coding disease-associated variants to genes

Orozco

Schoenfelder

Walker

et al. 2022

Front. Cell Dev. Biol.

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Genome sequencing has revealed over 300 million genetic variations in human populations. Over 90% of variants are single nucleotide polymorphisms (SNPs), the remainder include short deletions or insertions, and small numbers of structural variants. Hundreds of thousands of these variants have been associated with specific phenotypic traits and diseases through genome wide association studies which link significant differences in variant frequencies with specific phenotypes among large groups of individuals. Only 5% of disease-associated SNPs are located in gene coding sequences, with the potential to disrupt gene expression or alter of the function of encoded proteins. The remaining 95% of disease-associated SNPs are located in non-coding DNA sequences which make up 98% of the genome. The role of non-coding, disease-associated SNPs, many of which are located at considerable distances from any gene, was at first a mystery until the discovery that gene promoters regularly interact with distal regulatory elements to control gene expression. Disease-associated SNPs are enriched at the millions of gene regulatory elements that are dispersed throughout the non-coding sequences of the genome, suggesting they function as gene regulation variants. Assigning specific regulatory elements to the genes they control is not straightforward since they can be millions of base pairs apart. In this review we describe how understanding 3D genome organization can identify specific interactions between gene promoters and distal regulatory elements and how 3D genomics can link disease-associated SNPs to their target genes. Understanding which gene or genes contribute to a specific disease is the first step in designing rational therapeutic interventions.

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“…Utilization of functional genomic resources the latest technologies is essential to interpret functional impacts of the disease risk variants. Wang et al [12] reviews statistical methods to fine-map causal variants from GWAS findings, as well as empirical application examples in autoimmune disease genetics. Orozco et al [13] introduces fine-mapping efforts based on a set of epigenetic information including high dimensional chromatin structure in cells.…”

Section: Genetics and Functional Genetics Of Autoimmune Diseasesmentioning

confidence: 99%