Results Of Genome-wide Association Studies Research Articles

Polygenic risk score of metabolic dysfunction-associated steatotic liver disease amplifies the health impact on severe liver disease and metabolism-related outcomes

BackgroundAlthough the inherited risk factors associated with fatty liver disease are well understood, little is known about the genetic background of metabolic dysfunction-associated steatotic liver disease (MASLD) and its related health impacts. Compared to non-alcoholic fatty liver disease (NAFLD), MASLD presents significantly distinct diagnostic criteria, and epidemiological and clinical features, but the related genetic variants are yet to be investigated. Therefore, we conducted this study to assess the genetic background of MASLD and interactions between MASLD-related genetic variants and metabolism-related outcomes.MethodsParticipants from the UK Biobank were grouped into discovery and replication cohorts for an MASLD genome-wide association study (GWAS), and base and target cohorts for polygenic risk score (PRS) analysis. Autosomal genetic variants associated with NAFLD were compared with the MASLD GWAS results. Kaplan–Meier and Cox regression analyses were used to assess associations between MASLD and metabolism-related outcomes.ResultsSixteen single-nucleotide polymorphisms (SNPs) were identified at genome-wide significance levels for MASLD and duplicated in the replication cohort. Differences were found after comparing these SNPs with the results of NAFLD-related genetic variants. MASLD cases with high PRS had a multivariate-adjusted hazard ratio of 3.15 (95% confidence interval, 2.54–3.90) for severe liver disease (SLD), and 2.81 (2.60–3.03) for type 2 diabetes mellitus. The high PRS amplified the impact of MASLD on SLD and extrahepatic outcomes.ConclusionsHigh PRS of MASLD GWAS amplified the impact of MASLD on SLD and metabolism-related outcomes, thereby refining the process of identification of individuals at high risk of MASLD. Supplementation of this process with relevant genetic backgrounds may lead to more effective MASLD prevention and management.

Genome-wide association study identifies novel susceptible loci and evaluation of polygenic risk score for chronic obstructive pulmonary disease in a Taiwanese population

BackgroundChronic Obstructive Pulmonary Disease (COPD) describes a group of progressive lung diseases causing breathing difficulties. While COPD development typically involves a complex interplay between genetic and environmental factors, genetics play a role in disease susceptibility. This study used genome-wide association studies (GWAS) and polygenic risk score (PRS) to elucidate the genetic basis for COPD in Taiwanese patients.ResultsGWAS was performed on a Taiwanese COPD case–control cohort with a sample size of 5,442 cases and 17,681 controls. Additionally, the PRS was calculated and assessed in our target groups. GWAS results indicate that although there were no single nucleotide polymorphisms (SNPs) of genome-wide significance, prominent COPD susceptibility loci on or nearby genes such as WWTR1, EXT1, INTU, MAP3K7CL, MAMDC2, BZW1/CLK1, LINC01197, LINC01894, and CFAP95 (C9orf135) were identified, which had not been reported in previous studies. Thirteen susceptibility loci, such as CHRNA4, AFAP1, and DTWD1, previously reported in other populations were replicated and confirmed to be associated with COPD in Taiwanese populations. The PRS was determined in the target groups using the summary statistics from our base group, yielding an effective association with COPD (odds ratio [OR] 1.09, 95% confidence interval [CI] 1.02–1.17, p = 0.011). Furthermore, replication a previous lung function trait PRS model in our target group, showed a significant association of COPD susceptibility with PRS of Forced Expiratory Volume in one second (FEV1)/Forced Vital Capacity (FCV) (OR 0.89, 95% CI 0.83–0.95, p = 0.001).ConclusionsNovel COPD-related genes were identified in the studied Taiwanese population. The PRS model, based on COPD or lung function traits, enables disease risk estimation and enhances prediction before suffering. These results offer new perspectives on the genetics of COPD and serve as a basis for future research.

#162 Single-nucleus RNA-seq of biopsy-confirmed IgA nephropathy reveals cell-specific transcriptomic alteration

Abstract Background and Aims Single-nucleus RNA-seq (SnRNA-seq) is a valuable method for identifying cell-specific transcriptomic alterations in kidney cells. Our objective was to investigate SnRNA-seq data from biopsy-confirmed IgA nephropathy (IgAN) cases and identify cell-specific alterations in the transcriptome. Method We collected snap-frozen kidney biopsy tissues from 6 cases of IgA nephropathy (IgAN) and 7 nephrectomy control cases. The disease control group included 3 cases of diabetic kidney disease, 6 cases of minimal change disease, and 6 PLA2R-Ab positive membranous nephropathy cases. SnRNA-seq was performed on the kidney tissues, and kidney cells were identified in the dataset through UMAP clustering. We investigated differences in the proportion of kidney cell types and identified differentially expressed genes (DEGs). Additionally, we searched for representative gene loci previously reported in a multiethnic genome-wide association study (GWAS). Results We successfully collected transcriptomic profiles of >50 000 cells from IgAN, with a considerable enrichment of intraglomerular cells. The DEG analysis revealed a high number of DEGs in mesangial cells, fibroblasts, and vascular smooth muscle cells in the IgAN transcriptome compared to the nephrectomy and disease control cases. In the gene set enrichment analysis, the epithelial-mesenchymal transition pathway was enriched in the glomerular cells of IgAN. Furthermore, among various GWAS loci, ETS1 was the gene significantly highly expressed in the mesangial cells of IgAN, consistent across the compared control groups. Conclusion This investigation represents the largest SnRNA-Seq profiling of IgAN kidney cells to date. We identified notable enriched cell-specific pathways in IgAN. ETS1 may be a kidney-indwelling causal factor for IgAN pathophysiology, considering its notable significance in both previous GWAS results and the current SnRNA-Seq data.

SharePro: an accurate and efficient genetic colocalization method accounting for multiple causal signals.

Colocalization analysis is commonly used to assess whether two or more traits share the same genetic signals identified in genome-wide association studies (GWAS), and is important for prioritizing targets for functional follow-up of GWAS results. Existing colocalization methods can have suboptimal performance when there are multiple causal variants in one genomic locus. We propose SharePro to extend the COLOC framework for colocalization analysis. SharePro integrates linkage disequilibrium (LD) modeling and colocalization assessment by grouping correlated variants into effect groups. With an efficient variational inference algorithm, posterior colocalization probabilities can be accurately estimated. In simulation studies, SharePro demonstrated increased power with a well-controlled false positive rate at a low computational cost. Compared to existing methods, SharePro provided stronger and more consistent colocalization evidence for known lipid-lowering drug target proteins and their corresponding lipid traits. Through an additional challenging case of the colocalization analysis of the circulating abundance of R-spondin 3 GWAS and estimated bone mineral density GWAS, we demonstrated the utility of SharePro in identifying biologically plausible colocalized signals. SharePro for colocalization analysis is written in Python and openly available at https://github.com/zhwm/SharePro_coloc.

Association between Cytotoxic T-Lymphocyte-Associated Antigen 4 (CTLA-4) Locus and Early-Onset Anti-acetylcholine Receptor-Positive Myasthenia Gravis in Serbian Patients.

Genome-wide association studies (GWAS) have provided strong evidence that early- and late-onset MG have different genetic backgrounds. Recent in silico analysis based on GWAS results revealed rs231735 and rs231770 variants within CTLA-4 locus as possible MG causative genetic factors. We aimed to explore the association of rs231735 and rs231770 with MG in a representative cohort of Serbian patients. We conducted an age-, sex-, and ethnicity-matched case-control study. Using TaqMan allele discrimination assays, the frequency of rs231735 and rs231770 genetic variants was examined in 447 AChR-MG patients and 447 matched controls. There was no significant association of rs231735 and rs231770 with the entire MG cohort (P > 0.05). Nevertheless, when stratifying patients into early-onset (n = 183) and late-onset MG (n = 264), we found early-onset patients had a significantly lower frequency of the rs231735 allele T compared to controls (OR = 0.734, 95% CI = 0.575-0.938, p10e6 permutation < 0.05), and rs231735 genotype TT and rs231770 genotype TT had a protective effect on early-onset MG (OR = 0.548, 95% CI = 0.339-0.888, and OR = 0.563, 95% CI = 0.314-1.011, p10e6 permutation < 0.05). Consequently, we found that individuals with the rs231735-rs231770 haplotype GC had a higher risk for developing early-onset MG (OR = 1.360, P = 0.027, p10e6 permutation < 0.05). Our results suggest that CTLA-4 rs231735 and rs231770 may be risk factors only for patients with early-onset MG in Serbian population.

Impact of the inaccessible genome on genotype imputation and genome-wide association studies.

Genotype imputation is widely used in genome-wide association studies (GWAS). However, both the genotyping chips and imputation reference panels are dependent on next-generation sequencing (NGS). Due to the nature of NGS, some regions of the genome are inaccessible to sequencing. To date, there has been no complete evaluation of these regions and their impact on the identification of associations in GWAS remains unclear. In this study, we systematically assess the extent to which variants in inaccessible regions are underrepresented on genotyping chips and imputation reference panels, in GWAS results and in variant databases. We also determine the proportion of genes located in inaccessible regions and compare the results across variant masks defined by the 1000 Genomes Project and the TOPMed program. Overall, fewer variants were observed in inaccessible regions in all categories analyzed. Depending on the mask used and normalized for region size, only 4%-17% of the genotyped variants are located in inaccessible regions and 52 to 581 genes were almost completely inaccessible. From the Cooperative Health Research in South Tyrol (CHRIS) study, we present a case study of an association located in an inaccessible region that is driven by genotyped variants and cannot be reproduced by imputation in GRCh37. We conclude that genotyping, NGS, genotype imputation and downstream analyses such as GWAS and fine mapping are systematically biased in inaccessible regions, due to missed variants and spurious associations. To help researchers assess gene and variant accessibility, we provide an online application (https://gab.gm.eurac.edu).

Genetic dissection of ten photosynthesis-related traits based on InDel- and SNP-GWAS in soybean.

A total of 416 InDels and 112 SNPs were significantly associated with soybean photosynthesis-related traits. GmIWS1 and GmCDC48 might be related to chlorophyll fluorescence and gas-exchange parameters, respectively. Photosynthesis is one of the main factors determining crop yield. A better understanding of the genetic architecture for photosynthesis is of great significance for soybean yield improvement. Our previous studies identified 5,410,112 single nucleotide polymorphisms (SNPs) from the resequencing data of 219 natural soybean accessions. Here, we identified 634,106 insertions and deletions (InDels) from these 219 accessions and used these InDel variations to perform principal component and linkage disequilibrium analysis of this population. The genome-wide association study (GWAS) were conducted on six chlorophyll fluorescence parameters (chlorophyll content, light energy absorbed per reaction center, quantum yield for electron transport, probability that a trapped exciton moves an electron into the electron transport chain beyond primary quinone acceptor, maximum quantum yield of photosystem II primary photochemistry in the dark-adapted state, performance index on absorption basis) and four gas-exchange parameters (intercellular carbon dioxide concentration, stomatal conductance, net photosynthesis rate, transpiration rate) and revealed 416 significant InDels and 112 significant SNPs. Based on GWAS results, GmIWS1 (encoding a transcription elongation factor) and GmCDC48 (encoding a cell division cycle protein) with the highest expression in the mapping region were determined as the candidate genes responsible for chlorophyll fluorescence and gas-exchange parameters, respectively. Further identification of favorable haplotypes with higher photosynthesis, seed weight and seed yield were carried out for GmIWS1 and GmCDC48. Overall, this study revealed the natural variations and candidate genes underlying the photosynthesis-related traits based on abundant phenotypic and genetic data, providing valuable insights into the genetic mechanisms controlling photosynthesis and yield in soybean.

Genome-wide association study identifies DRAM1 associated with Tourette syndrome in Taiwan

BackgroundTourette syndrome (TS) is a neurodevelopmental disorder characterized by motor and vocal tics. Several susceptibility loci associated with TS have been identified previously in populations of European descent using genome-wide association studies (GWAS). However, the exact pathogenic mechanism underlying TS is unknown; additionally, the results of previous GWAS for TS were based on Western populations, which may not translate to other populations. Therefore, we conducted a GWAS in Taiwanese patients with TS and chronic tic disorders (CTDs), with an aim to elucidate the genetic basis and potential risk factors for TS in this population. MethodsGWAS was performed on a Taiwanese TS/CTDs cohort with a sample size of 1,007 patients with TS and 25,522 ancestry-matched controls. Additionally, polygenic risk score was calculated and assessed. ResultsGenome-wide significant locus, rs12313062 (p=1.43 × 10-8) and other 9 single nucleotide polymorphisms, were identified in chromosomes 12q23.2, associated with DRAM1 and was a novel susceptibility locus identified in TS/CTDs group. DRAM1, a lysosomal transmembrane protein regulated by p53, modulates autophagy and apoptosis, with potential implications for neuropsychiatric conditions associated with autophagy disruption. ConclusionsThis study conducted the first GWAS for TS in a Taiwanese population, identifying a significant locus on chromosome 12q23.2 associated with DRAM1. These findings provide novel insights into the neurobiology of TS and potential directions for future research in this area.

Genome-wide association study and haplotype analysis reveal novel candidate genes for resistance to powdery mildew in soybean.

Powdery mildew disease (PMD) is caused by the obligate biotrophic fungus Microsphaera diffusa Cooke & Peck (M. diffusa) and results in significant yield losses in soybean (Glycine max (L.) Merr.) crops. By identifying disease-resistant genes and breeding soybean accessions with enhanced resistance, we can effectively mitigate the detrimental impact of PMD on soybeans. We analyzed PMD resistance in a diversity panel of 315 soybean accessions in two locations over 3 years, and candidate genes associated with PMD resistance were identified through genome-wide association studies (GWAS), haplotype analysis, qRT-PCR, and EMS mutant analysis. Based on the GWAS approach, we identified a region on chromosome 16 (Chr16) in which 21 genes form a gene cluster that is highly correlated with PMD resistance. In order to validate and refine these findings, we conducted haplotype analysis of 21 candidate genes and indicated there are single nucleotide polymorphisms (SNPs) and insertion-deletions (InDels) variations of Glyma.16G214000, Glyma.16G214200, Glyma.16G215100 and Glyma.16G215300 within the coding and promoter regions that exhibit a strong association with resistance against PMD. Subsequent structural analysis of candidate genes within this cluster revealed that in 315 accessions, the majority of accessions exhibited resistance to PMD when Glyma.16G214300, Glyma.16G214800 and Glyma.16G215000 were complete; however, they demonstrated susceptibility to PMD when these genes were incomplete. Quantitative real-time PCR assays (qRT-PCR) of possible candidate genes showed that 14 candidate genes (Glyma.16G213700, Glyma.16G213800, Glyma.16G213900, Glyma.16G214000, Glyma.16G214200, Glyma.16G214300, Glyma.16G214500, Glyma.16G214585, Glyma.16G214669, Glyma.16G214700, Glyma.16G214800, Glyma.16G215000, Glyma.16G215100 and Glyma.16G215300) were involved in PMD resistance. Finally, we evaluated the PMD resistance of mutant lines from the Williams 82 EMS mutations library, which revealed that mutants of Glyma.16G214000, Glyma.16G214200, Glyma.16G214300, Glyma.16G214800, Glyma.16G215000, Glyma.16G215100 and Glyma.16G215300, exhibited sensitivity to PMD. Combined with the analysis results of GWAS, haplotypes, qRT-PCR and mutants, the genes Glyma.16G214000, Glyma.16G214200, Glyma.16G214300, Glyma.16G214800, Glyma.16G215000, Glyma.16G215100 and Glyma.16G215300 were identified as highly correlated with PMD resistance. The candidate genes identified above are all NLR family genes, and these discoveries deepen our understanding of the molecular basis of PMD resistance in soybeans and will be useful for guiding breeding strategies.

Altered Fhod3 expression involved in progressive high-frequency hearing loss via dysregulation of actin polymerization stoichiometry in the cuticular plate.

Age-related hearing loss (ARHL) is a common sensory impairment with complex underlying mechanisms. In our previous study, we performed a meta-analysis of genome-wide association studies (GWAS) in mice and identified a novel locus on chromosome 18 associated with ARHL specifically linked to a 32 kHz tone burst stimulus. Consequently, we investigated the role of Formin Homology 2 Domain Containing 3 (Fhod3), a newly discovered candidate gene for ARHL based on the GWAS results. We observed Fhod3 expression in auditory hair cells (HCs) primarily localized at the cuticular plate (CP). To understand the functional implications of Fhod3 in the cochlea, we generated Fhod3 overexpression mice (Pax2-Cre+/-; Fhod3Tg/+) (TG) and HC-specific conditional knockout mice (Atoh1-Cre+/-; Fhod3fl/fl) (KO). Audiological assessments in TG mice demonstrated progressive high-frequency hearing loss, characterized by predominant loss of outer hair cells, and a decreased phalloidin intensities of CP. Ultrastructural analysis revealed loss of the shortest row of stereocilia in the basal turn of the cochlea, and alterations in the cuticular plate surrounding stereocilia rootlets. Importantly, the hearing and HC phenotype in TG mice phenocopied that of the KO mice. These findings suggest that balanced expression of Fhod3 is critical for proper CP and stereocilia structure and function. Further investigation of Fhod3 related hearing impairment mechanisms may lend new insight towards the myriad mechanisms underlying ARHL, which in turn could facilitate the development of therapeutic strategies for ARHL.

Genome-wide scanning to identify and validate single nucleotide polymorphism markers associated with drought tolerance in spring wheat seedlings.

Unlike other growth stages of wheat, very few studies on drought tolerance have been done at the seedling stage, and this is due to the complexity and sensitivity of this stage to drought stress resulting from climate change. As a result, the drought tolerance of wheat seedlings is poorly understood and very few genes associated with drought tolerance at this stage were identified. To address this challenge, a set of 172 spring wheat genotypes representing 20 different countries was evaluated under drought stress at the seedling stage. Drought stress was applied on all tested genotypes by water withholding for 13 days. Two types of traits, namely morphological and physiological traits were scored on the leaves of all tested genotypes. Genome-wide association study (GWAS) is one of the effective genetic analysis methods that was used to identify target single nucleotide polymorphism (SNP) markers and candidate genes for later use in marker-assisted selection. The tested plant materials were genotyped using 25k Infinium iSelect array (25K) (herein after it will be identified as 25K) (for 172 genotypes) and genotyping-by-sequencing (GBS) (for 103 genotypes), respectively. The results of genotyping revealed 21,093 25K and 11,362 GBS-SNPs, which were used to perform GWAS analysis for all scored traits. The results of GWAS revealed that 131 and 55 significant SNPs were controlling morphological and physiological traits, respectively. Moreover, a total of eight and seven SNP markers were found to be associated with more than one morphological and physiological trait under drought stress, respectively. Remarkably, 10 significant SNPs found in this study were previously reported for their association with drought tolerance in wheat. Out of the 10 validated SNP markers, four SNPs were associated with drought at the seedling stage, while the remaining six SNPs were associated with drought stress at the reproductive stage. Moreover, the results of gene enrichment revealed 18 and six pathways as highly significant biological and molecular pathways, respectively. The selection based on drought-tolerant alleles revealed 15 genotypes with the highest number of different drought-tolerant alleles. These genotypes can be used as candidate parents in future breeding programs to produce highly drought-tolerant genotypes with high genetic diversity. Our findings in this study provide novel markers and useful information on the genetic basis of drought tolerance at early growth stages.

Genome-Wide Association to Identify the Genetic loci Associated with Various Agro-Economical Traits in Mungbean (Vigna radiata L. Wilczek)

Mungbean (Vigna radiata L. Wilczek) is a significant food legume globally, particularly in Asia, contributing to nutritional security and environmental sustainability. However, understanding its genetic basis for agro-economic traits remains incomplete. To address this, 126 mungbean genotypes were studied for eleven agronomic traits in two environments, revealing significant phenotypic diversity. Using genotyping-by-sequencing (GBS), 55,634 genomic variations were identified, with 15,926 SNPs retained for genetic diversity and linkage disequilibrium analysis. Subgroups were identified, and LD decayed at 68 kilo base (kb). Genome-wide association studies (GWAS) using BLINK identified 50 significantly associated signals for agronomic traits. In-silico analysis identified candidate genes within 30 kb of each SNP, with 11 genes likely regulating traits such as flowering time, plant height, pod characteristics, nitrogen status, seed traits, and yield. For traits like days to maturity and primary branch, candidate genes were not identified. Understanding genetic control of these traits is crucial for mungbean breeding, especially for developing varieties adaptable to climate change. GWAS results can aid in integrating favorable alleles into elite germplasm through marker-assisted selection (MAS), enhancing mungbean breeding efforts.

GWABLUP: genome-wide association assisted best linear unbiased prediction of genetic values

BackgroundSince the very beginning of genomic selection, researchers investigated methods that improved upon SNP-BLUP (single nucleotide polymorphism best linear unbiased prediction). SNP-BLUP gives equal weight to all SNPs, whereas it is expected that many SNPs are not near causal variants and thus do not have substantial effects. A recent approach to remedy this is to use genome-wide association study (GWAS) findings and increase the weights of GWAS-top-SNPs in genomic predictions. Here, we employ a genome-wide approach to integrate GWAS results into genomic prediction, called GWABLUP.ResultsGWABLUP consists of the following steps: (1) performing a GWAS in the training data which results in likelihood ratios; (2) smoothing the likelihood ratios over the SNPs; (3) combining the smoothed likelihood ratio with the prior probability of SNPs having non-zero effects, which yields the posterior probability of the SNPs; (4) calculating a weighted genomic relationship matrix using the posterior probabilities as weights; and (5) performing genomic prediction using the weighted genomic relationship matrix. Using high-density genotypes and milk, fat, protein and somatic cell count phenotypes on dairy cows, GWABLUP was compared to GBLUP, GBLUP (topSNPs) with extra weights for GWAS top-SNPs, and BayesGC, i.e. a Bayesian variable selection model. The GWAS resulted in six, five, four, and three genome-wide significant peaks for milk, fat and protein yield and somatic cell count, respectively. GWABLUP genomic predictions were 10, 6, 7 and 1% more reliable than those of GBLUP for milk, fat and protein yield and somatic cell count, respectively. It was also more reliable than GBLUP (topSNPs) for all four traits, and more reliable than BayesGC for three of the traits. Although GWABLUP showed a tendency towards inflation bias for three of the traits, this was not statistically significant. In a multitrait analysis, GWABLUP yielded the highest accuracy for two of the traits. However, for SCC, which was relatively unrelated to the yield traits, including yield trait GWAS-results reduced the reliability compared to a single trait analysis.ConclusionsGWABLUP uses GWAS results to differentially weigh all the SNPs in a weighted GBLUP genomic prediction analysis. GWABLUP yielded up to 10% and 13% more reliable genomic predictions than GBLUP for single and multitrait analyses, respectively. Extension of GWABLUP to single-step analyses is straightforward.

The cells of the sensory epithelium, and not the stria vascularis, are the main cochlear cells related to the genetic pathogenesis of age-related hearing loss

Age-related hearing loss (ARHL) is a major health concern among the elderly population. It is hoped that increasing our understanding of its underlying pathophysiological processes will lead to the development of novel therapies. Recent genome-wide association studies (GWASs) discovered a few dozen genetic variants in association with elevated risk for ARHL. Integrated analysis of GWAS results and transcriptomics data is a powerful approach for elucidating specific cell types that are involved in disease pathogenesis. Intriguingly, recent studies that applied such bioinformatics approaches to ARHL resulted in disagreeing findings as for the key cell types that are most strongly linked to the genetic pathogenesis of ARHL. These conflicting studies pointed either to cochlear sensory epithelial or to stria vascularis cells as the cell types most prominently involved in the genetic basis of ARHL. Seeking to resolve this discrepancy, we integrated the analysis of four ARHL GWAS datasets with four independent inner-ear single-cell RNA-sequencing datasets. Our analysis clearly points to the cochlear sensory epithelial cells as the key cells for the genetic predisposition to ARHL. We also explain the limitation of the bioinformatics analysis performed by previous studies that led to missing the enrichment for ARHL GWAS signal in sensory epithelial cells. Collectively, we show that cochlear epithelial cells, not stria vascularis cells, are the main inner-ear cells related to the genetic pathogenesis of ARHL.

Genomic prediction based on preselected single-nucleotide polymorphisms from genome-wide association study and imputed whole-genome sequence data annotation for growth traits in Duroc pigs.

The use of whole-genome sequence (WGS) data is expected to improve genomic prediction (GP) power of complex traits because it may contain mutations that in strong linkage disequilibrium pattern with causal mutations. However, a few previous studies have shown no or small improvement in prediction accuracy using WGS data. Incorporating prior biological information into GP seems to be an attractive strategy that might improve prediction accuracy. In this study, a total of 6334 pigs were genotyped using 50K chips and subsequently imputed to the WGS level. This cohort includes two prior discovery populations that comprise 294 Landrace pigs and 186 Duroc pigs, as well as two validation populations that consist of 3770 American Duroc pigs and 2084 Canadian Duroc pigs. Then we used annotation information and genome-wide association study (GWAS) from the WGS data to make GP for six growth traits in two Duroc pig populations. Based on variant annotation, we partitioned different genomic classes, such as intron, intergenic, and untranslated regions, for imputed WGS data. Based on GWAS results of WGS data, we obtained trait-associated single-nucleotide polymorphisms (SNPs). We then applied the genomic feature best linear unbiased prediction (GFBLUP) and genomic best linear unbiased prediction (GBLUP) models to estimate the genomic estimated breeding values for growth traits with these different variant panels, including six genomic classes and trait-associated SNPs. Compared with 50K chip data, GBLUP with imputed WGS data had no increase in prediction accuracy. Using only annotations resulted in no increase in prediction accuracy compared to GBLUP with 50K, but adding annotation information into the GFBLUP model with imputed WGS data could improve the prediction accuracy with increases of 0.00%-2.82%. In conclusion, a GFBLUP model that incorporated prior biological information might increase the advantage of using imputed WGS data for GP.

Integration of expression QTLs with fine mapping via SuSiE.

Genome-wide association studies (GWASs) have achieved remarkable success in associating thousands of genetic variants with complex traits. However, the presence of linkage disequilibrium (LD) makes it challenging to identify the causal variants. To address this critical gap from association to causation, many fine-mapping methods have been proposed to assign well-calibrated probabilities of causality to candidate variants, taking into account the underlying LD pattern. In this manuscript, we introduce a statistical framework that incorporates expression quantitative trait locus (eQTL) information to fine-mapping, built on the sum of single-effects (SuSiE) regression model. Our new method, SuSiE2, connects two SuSiE models, one for eQTL analysis and one for genetic fine-mapping. This is achieved by first computing the posterior inclusion probabilities (PIPs) from an eQTL-based SuSiE model with the expression level of the candidate gene as the phenotype. These calculated PIPs are then utilized as prior inclusion probabilities for risk variants in another SuSiE model for the trait of interest. By prioritizing functional variants within the candidate region using eQTL information, SuSiE2 improves SuSiE by increasing the detection rate of causal SNPs and reducing the average size of credible sets. We compared the performance of SuSiE2 with other multi-trait fine-mapping methods with respect to power, coverage, and precision through simulations and applications to the GWAS results of Alzheimer's disease (AD) and body mass index (BMI). Our results demonstrate the better performance of SuSiE2, both when the in-sample linkage disequilibrium (LD) matrix and an external reference panel is used in inference.

P1202 Genome wide association study result for inflammatory bowel disease: A single institute experience in Taiwan

Abstract Background Inflammatory bowel disease (IBD) have two major subtype disease, including Crohn’s disease(CD) and Ulcerative colitis(UC). These two diseases of IBD in which abnormal reactions of the immune system induced inflammation and ulcers in any part of gastrointestinal tract from oral cavity to rectum, especially in colon and small intestine. Now, IBD still not have an identifiable cause and many articles that discussion about risk loci of IBD was published in recent years. Hence, we try to identify the risky locus related with IBD. Methods From January 1992 to December 2023, we retrospectively reviewed the medical records of patients diagnosed as inflammatory bowel disease at our hospital. We also use Phecode Map with ICD9 codes for finding out more pateints. Then, we used the our hospital’s genetic data warehouse to assay these patient’s genome for identifying the specific loci associated with inflammatory bowel disease. Results A total of 286 patients with inflammatory bowel disease from January 1992 to December 2023 was in medical record. The number of Crohn's disease and ulcerative colitis were 102 and 184 respectively. However, only 85 patient with CD and 145 patient with UC were in our hospital’s genetic data warehouse. In addition, we seek cooperation with our hospital’s artificial Intelligence Center for using Phecode Map with ICD9 codes for finding out more patients. Finally, 660 case was in genetic data warehouse.The our result of Genome-wide association study (GWAS) for inflammatory bowel disease that identified 15 potential SNPs, such as RTN4, EML6, CLHC1, MYO10, EFEMP1, ADCY2, TPD52 etc.. Two of 15 potential SNPs, NPAS3, DSCAM has been found to be a risky SNPs of IBD in past study and our result of GWAS for IBD. Another 13 SNPs was potential association with IBD in our result, but not well-known risky gene in past research. Only one genetic locus: CRY2 had statistically significant difference and vaule of -log10(p) is more than 6. But, no similar thesis was reported after we review other published papers about loci associated with inflammatory bowel disease. The relationship between CRY2 and inflammatory bowel disease is still pending investigation. Conclusion Although, around 200 genomic loci was considered to be associated with inflammatory bowel disease. But, the precise mechanism by which these loci contribute to IBD were still being investigated. However, more genetic loci associated with IBD was identified can lead us to approach the understanding of the underlying biology of the disease.

RicePilaf: a post-GWAS/QTL dashboard to integrate pangenomic, coexpression, regulatory, epigenomic, ontology, pathway, and text-mining information to provide functional insights into rice QTLs and GWAS loci.

As the number of genome-wide association study (GWAS) and quantitative trait locus (QTL) mappings in rice continues to grow, so does the already long list of genomic loci associated with important agronomic traits. Typically, loci implicated by GWAS/QTL analysis contain tens to hundreds to thousands of single-nucleotide polmorphisms (SNPs)/genes, not all of which are causal and many of which are in noncoding regions. Unraveling the biological mechanisms that tie the GWAS regions and QTLs to the trait of interest is challenging, especially since it requires collating functional genomics information about the loci from multiple, disparate data sources. We present RicePilaf, a web app for post-GWAS/QTL analysis, that performs a slew of novel bioinformatics analyses to cross-reference GWAS results and QTL mappings with a host of publicly available rice databases. In particular, it integrates (i)pangenomic information from high-quality genome builds of multiple rice varieties, (ii)coexpression information from genome-scale coexpression networks, (iii)ontology and pathway information, (iv)regulatory information from rice transcription factor databases, (v)epigenomic information from multiple high-throughput epigenetic experiments, and (vi)text-mining information extracted from scientific abstracts linking genes and traits. We demonstrate the utility of RicePilaf by applying it to analyze GWAS peaks of preharvest sprouting and genes underlying yield-under-drought QTLs. RicePilaf enables rice scientists and breeders to shed functional light on their GWAS regions and QTLs, and it provides them with a means to prioritize SNPs/genes for further experiments. The source code, a Docker image, and a demo version of RicePilaf are publicly available at https://github.com/bioinfodlsu/rice-pilaf.

Alzheimer's disease as a causal risk factor for diabetic retinopathy: a Mendelian randomization study.

This study aims to investigate the causal relationship between Alzheimer's Disease (AD) and Diabetic Retinopathy (DR). Employing Mendelian Randomization (MR), Generalized Summary-data-based Mendelian Randomization (GSMR), and the MR-Steiger test, this study scrutinizes the genetic underpinnings of the hypothesized causal association between AD and DR, as well as its Proliferative DR (PDR) and Non-Proliferative DR (NPDR) subtypes. Comprehensive data from Genome-Wide Association Studies (GWAS) were analyzed, specifically AD data from the Psychiatric Genomics Consortium (71,880 cases/383,378 controls), and DR, PDR, and NPDR data from both the FinnGen consortium (FinnGen release R8, DR: 5,988 cases/314,042 controls; PDR: 8,383 cases/329,756 controls; NPDR: 3,446 cases/314,042 controls) and the IEU OpenGWAS (DR: 14,584 cases/176,010 controls; PDR: 8,681 cases/204,208 controls; NPDR: 2,026 cases/204,208 controls). The study also incorporated Functional Mapping and Annotation (FUMA) for an in-depth analysis of the GWAS results. The MR analyses revealed that genetic susceptibility to AD significantly increases the risk of DR, as evidenced by GWAS data from the FinnGen consortium (OR: 2.5090; 95% confidence interval (CI):1.2102-5.2018, false discovery rate P-value (PFDR)=0.0201; GSMR: bxy=0.8936, bxy_se=0.3759, P=0.0174), NPDR (OR: 2.7455; 95% CI: 1.3178-5.7197, PFDR=0.0166; GSMR: bxy=0.9682, bxy_se=0.3802, P=0.0126), and PDR (OR: 2.3098; 95% CI: 1.2411-4.2986, PFDR=0.0164; GSMR: bxy=0.7962, bxy_se=0.3205, P=0.0129) using DR GWAS from FinnGen consortium. These results were corroborated by DR GWAS datasets from IEU OpenGWAS. The MR-Steiger test confirmed a significant association of all identified instrumental variables (IVs) with AD. While a potential causal effect of DR and its subtypes on AD was identified, the robustness of these results was constrained by a low power value. FUMA analysis identified OARD1, NFYA, TREM1 as shared risk genes between DR and AD, suggesting a potential genetic overlap between these complex diseases. This study underscores the contribution of AD to an increased risk of DR, as well as NPDR and PDR subtypes, underscoring the necessity of a holistic approach in the management of patients affected by these conditions.

SparsePro: An efficient fine-mapping method integrating summary statistics and functional annotations.

Identifying causal variants from genome-wide association studies (GWAS) is challenging due to widespread linkage disequilibrium (LD) and the possible existence of multiple causal variants in the same genomic locus. Functional annotations of the genome may help to prioritize variants that are biologically relevant and thus improve fine-mapping of GWAS results. Classical fine-mapping methods conducting an exhaustive search of variant-level causal configurations have a high computational cost, especially when the underlying genetic architecture and LD patterns are complex. SuSiE provided an iterative Bayesian stepwise selection algorithm for efficient fine-mapping. In this work, we build connections between SuSiE and a paired mean field variational inference algorithm through the implementation of a sparse projection, and propose effective strategies for estimating hyperparameters and summarizing posterior probabilities. Moreover, we incorporate functional annotations into fine-mapping by jointly estimating enrichment weights to derive functionally-informed priors. We evaluate the performance of SparsePro through extensive simulations using resources from the UK Biobank. Compared to state-of-the-art methods, SparsePro achieved improved power for fine-mapping with reduced computation time. We demonstrate the utility of SparsePro through fine-mapping of five functional biomarkers of clinically relevant phenotypes. In summary, we have developed an efficient fine-mapping method for integrating summary statistics and functional annotations. Our method can have wide utility in understanding the genetics of complex traits and increasing the yield of functional follow-up studies of GWAS. SparsePro software is available on GitHub at https://github.com/zhwm/SparsePro.