Genome-wide Association Studies Methods Research Articles

Genome-wide association study of Verticillium longisporum resistance in Brassica genotypes.

Verticillium stripe, caused by Verticillium longisporum, presents an emerging threat to Canadian canola (Brassica napus). Initially detected in Manitoba in 2014, the presence of this pathogen has since been confirmed across western Canada. Infections by V. longisporum can result in yield losses of up to 50%, which is a cause for concern given the susceptibility of most commercial Canadian canola cultivars. The objective of this study was to screen a collection of 211 Brassica genotypes for their reactions to V. longisporum, and to use genome-wide association study (GWAS) to identify single nucleotide polymorphism (SNP) markers for resistance. The plant material consisted of 110 rutabaga (B. napus ssp. napobrassica), 35 canola, 40 Brassica rapa, and 15 Brassica oleracea accessions or cultivars, alongside 11 hosts of the European Clubroot Differential (ECD) set. These materials were screened for resistance under greenhouse conditions and were genotyped using a 19K Brassica SNP array. Three general linear models (GLM), four mixed linear models (MLM), and three GWAS methods were employed to evaluate the markers. Eleven non-commercial Brassica accessions and 9 out of 35 commercial canola cultivars displayed a low normalized area under the disease progress curve (AUDPCnorm.). The non-commercial accessions could prove valuable as potential sources of resistance against V. longisporum. Forty-five SNP markers were identified to be significantly associated with V. longisporum resistance using single-SNP based GWAS analysis. In comparison, haplotype-based GWAS analyses identified 10 to 25 haplotype blocks to be significantly associated with V. longisporum resistance. Between 20% and 56% of QTLs identified by the more conventional single-SNP based GWAS analysis were also detected by the haplotype-based GWAS analysis. The overlapping genomic regions identified by the two GWAS methods present promising hotspots for marker-assisted selection in the future development of Verticillium stripe-resistant canola.

Accurate identification of genes associated with brain disorders by integrating heterogeneous genomic data into a Bayesian framework

Genome-wide association studies (GWAS) have revealed many brain disorder-associated SNPs residing in the noncoding genome, rendering it a challenge to decipher the underlying pathogenic mechanisms. Here, we present an unsupervised Bayesian framework to identify disease-associated genes by integrating risk SNPs with long-range chromatin interactions (iGOAT), including SNP-SNP interactions extracted from ∼500,000 patients and controls from the UK Biobank, and enhancer-promoter interactions derived from multiple brain cell types at different developmental stages. The application of iGOAT to three psychiatric disorders and three neurodegenerative/neurological diseases predicted sets of high-risk (HRGs) and low-risk (LRGs) genes for each disorder. The HRGs were enriched in drug targets, and exhibited higher expression during prenatal brain developmental stages than postnatal stages, indicating their potential to affect brain development at an early stage. The HRGs associated with Alzheimer's disease were found to share genetic architecture with schizophrenia, bipolar disorder and major depressive disorder according to gene co-expression module analysis and rare variants analysis. Comparisons of this method to the eQTL-based method, the TWAS-based method, and the gene-level GWAS method indicated that the genes identified by our method are more enriched in known brain disorder-related genes, and exhibited higher precision. Finally, the method predicted 205 risk genes not previously reported to be associated with any brain disorder, of which one top-risk gene, MLH1, was experimentally validated as being schizophrenia-associated. iGOAT can successfully leverage epigenomic data, phenotype-genotype associations, and protein-protein interactions to advance our understanding of brain disorders, thereby facilitating the development of new therapeutic approaches. The work was funded by the National Key Research and Development Program of China (2024YFF1204902), the Natural Science Foundation of China (82371482), Guangzhou Science and Technology Research Plan (2023A03J0659) and Natural Science Foundation of Guangdong (2024A1515011363).

MTML: An Efficient Multitrait Multilocus GWAS Method Based on the Cauchy Combination Test.

Genome-wide association study (GWAS) by measuring the joint effect of multiple loci on multiple traits, has recently attracted interest, due to the decreased costs of high-throughput genotyping and phenotyping technologies. Previous studies mainly focused on either multilocus models that identify associations with a single trait or multitrait models that scan a single marker at a time. Since these types of models cannot fully utilize the association information, the powers of the tests are usually low. To potentially address this problem, we present here a multitrait multilocus (MTML) modeling framework that implements in three steps: (1) simplify the complex calculation; (2) reduce the model dimension; (3) integrate the joint contribution of single markers to multiple traits by Cauchy combination. The performances of MTML are evaluated and compared with other three published methods by Monte Carlo simulations. Simulation results show that MTML is more powerful for quantitative trait nucleotide detection and robust for various numbers of traits. In the meanwhile, MTML can effectively control type I error rate at a reasonable level. Real data analysis of Arabidopsis thaliana shows that MTML identifies more pleiotropic genetic associations. Therefore, we conclude that MTML is an efficient GWAS method for joint analysis of multiple quantitative traits. The R package MTML, which facilitates the implementation of the proposed method, is publicly available on GitHub https://github.com/Guohongping/MTML.

Mendelian randomization analysis of psoriasis and psoriatic arthritis associated with risks of ulcerative colitis.

This study is designed to explore the potential causal relationship between psoriasis and psoriatic arthritis (PsA) while investigating the genetic basis shared by these inflammatory diseases. Significant single nucleotide polymorphisms (SNPs) associated with UC, psoriasis, and PsA were selected as genetic instrumental variables using Genome-Wide Association Study (GWAS) datasets. Additionally, Mendelian randomization (MR) methods, including inverse-variance weighting (IVW), MR-Egger regression, and Weighted Median (WME), were utilized to evaluate the causal relationships between these diseases. Moreover, sensitivity analysis and heterogeneity testing were conducted to validate the stability of the results. A total of 123 significant SNPs associated with psoriasis, PsA, and UC were identified as genetic instrumental variables based on GWAS datasets. The analysis revealed a 36% increased risk of UC with psoriasis (odds ratio [OR]=1.350, 95% confidence interval [CI]=1.065-1.729, P=0.012) and a 32.9% increased risk of UC with PsA (OR=1.329, 95% CI=1.176-1.592, P<0.001). Further analysis showed a 43.5% increased risk of psoriasis with UC (OR=1.435, 95% CI=1.274-1.831, P<0.001) and a 45.8% increased risk of PsA with UC (OR=1.458, 95% CI=1.166-1.822, P=0.0013). In addition, sensitivity analysis and heterogeneity testing demonstrated the high stability of these results. Particularly, neither MR-Egger regression analysis nor leave-one-out analysis revealed significant heterogeneity or pleiotropy bias, indicating the reliability of these causal estimates. Moreover, the use of the MR-PRESSO further confirmed the positive correlation between psoriasis and UC, and the corrected estimates remained consistent with IVW analysis results after excluding potential outlier SNPs, enhancing the credibility of the analysis. This study strengthens the understanding of the genetic and causal relationships among UC, psoriasis, and PsA through GWAS and MR methods, revealing the genetic basis they may share. These findings not only provide a novel perspective on the comorbidity mechanisms of these diseases but also offer a valuable reference for the development of future treatment strategies and intervention measures.

Multi-locus genome-wide association study for phosphorus use efficiency in a tropical maize germplasm.

Phosphorus (P) is an essential macronutrient for maize (Zea mays L.) growth and development. Therefore, generating cultivars with upgraded P use efficiency (PUE) represents one of the main strategies to reduce the global agriculture dependence on phosphate fertilizers. In this work, genome-wide association studies (GWAS) were performed to detect quantitative trait nucleotide (QTN) and potential PUE-related candidate genes and associated traits in greenhouse and field trials under contrasting P conditions. The PUE and other agronomy traits of 132 maize inbred lines were assessed in low and normal P supply through the greenhouse and field experiments and Multi-locus GWAS was used to map the associated QTNs. Wide genetic variability was observed among the maize inbred lines under low and normal P supply. In addition, we confirm the complex and quantitative nature of PUE. A total of 306 QTNs were associated with the 24 traits evaluated using different multi-locus GWAS methods. A total of 186 potential candidate genes were identified, mainly involved with transcription regulator, transporter, and transference activity. Further studies are still needed to elucidate the functions and relevance of these genes regarding PUE. Nevertheless, pyramiding the favorable alleles pinpointed in the present study can be considered an efficient strategy for molecular improvement to increase maize PUE.

KGWASflow: a modular, flexible, and reproducible Snakemake workflow for k-mers-based GWAS.

Genome-wide association studies (GWAS) have been widely used to identify genetic variation associated with complex traits. Despite its success and popularity, the traditional GWAS approach comes with a variety of limitations. For this reason, newer methods for GWAS have been developed, including the use of pan-genomes instead of a reference genome and the utilization of markers beyond single-nucleotide polymorphisms, such as structural variations and k-mers. The k-mers-based GWAS approach has especially gained attention from researchers in recent years. However, these new methodologies can be complicated and challenging to implement. Here, we present kGWASflow, a modular, user-friendly, and scalable workflow to perform GWAS using k-mers. We adopted an existing kmersGWAS method into an easier and more accessible workflow using management tools like Snakemake and Conda and eliminated the challenges caused by missing dependencies and version conflicts. kGWASflow increases the reproducibility of the kmersGWAS method by automating each step with Snakemake and using containerization tools like Docker. The workflow encompasses supplemental components such as quality control, read-trimming procedures, and generating summary statistics. kGWASflow also offers post-GWAS analysis options to identify the genomic location and context of trait-associated k-mers. kGWASflow can be applied to any organism and requires minimal programming skills. kGWASflow is freely available on GitHub (https://github.com/akcorut/kGWASflow) and Bioconda (https://anaconda.org/bioconda/kgwasflow).

Reduced ambiguity and improved interpretability of bacterial genome-wide associations using gene-cluster-centric k-mers.

The wide adoption of bacterial genome sequencing and encoding both core and accessory genome variation using k-mers has allowed bacterial genome-wide association studies (GWAS) to identify genetic variants associated with relevant phenotypes such as those linked to infection. Significant limitations still remain because of k-mers being duplicated across gene clusters and as far as the interpretation of association results is concerned, which affects the wider adoption of GWAS methods on microbial data sets. We have developed a simple computational method (panfeed) that explicitly links each k-mer to their gene cluster at base-resolution level, which allows us to avoid biases introduced by a global de Bruijn graph as well as more easily map and annotate associated variants. We tested panfeed on two independent data sets, correctly identifying previously characterized causal variants, which demonstrates the precision of the method, as well as its scalable performance. panfeed is a command line tool written in the python programming language and is available at https://github.com/microbial-pangenomes-lab/panfeed.

Improving power of genome-wide association studies via transforming ordinal phenotypes into continuous phenotypes.

Ordinal traits are important complex traits in crops, while genome-wide association study (GWAS) is a widely-used method in their gene mining. Presently, GWAS of continuous quantitative traits (C-GWAS) and single-locus association analysis method of ordinal traits are the main methods used for ordinal traits. However, the detection power of these two methods is low. To address this issue, we proposed a new method, named MTOTC, in which hierarchical data of ordinal traits are transformed into continuous phenotypic data (CPData). Then, FASTmrMLM, one C-GWAS method, was used to conduct GWAS for CPData. The results from the simulation studies showed that, MTOTC+FASTmrMLM for ordinal traits was better than the classical methods when there were four and fewer hierarchical levels. In addition, when MTOTC was combined with FASTmrEMMA, mrMLM, ISIS EM-BLASSO, pLARmEB, and pKWmEB, relatively high power and low false positive rate in QTN detection were observed as well. Subsequently, MTOTC was applied to analyze the hierarchical data of soybean salt-alkali tolerance. It was revealed that more significant QTNs were detected when MTOTC was combined with any of the above six C-GWAs. Accordingly, the new method increases the choices of the GWAS methods for ordinal traits and helps to mine the genes for ordinal traits in resource populations.

GENOME-WIDE ASSOCIATION STUDY IN BREAST CANCER: A LITERATURE REVIEW

Relevance: Breast cancer (BC) is one of the major health problems in the Republic of Kazakhstan. Genome-wide association studies (GWAS) are used to identify genetic factors contributing to breast cancer development in Kazakhstan and worldwide. The GWAS method consistently helps to find associations between certain single nucleotide polymorphisms (SNPs) in the genome and the development of pathological conditions, including breast cancer, by identifying more than 170 genomic sites. In addition to searching for loci associated with BC, these studies have expanded our understanding of BC heritability using SNPs located in regulatory areas and identifying DNA variants associated with drug metabolism for treatment personalization purposes.&#x0D; The study aimed to highlight the key concepts of modern methods in genetics based on GWAS.&#x0D; Methods: A literature search in PubMed, Scopus, GWAS Catalog, Cochrane Database of Systematic Reviews, etc., was performed to select and analyze relevant information.&#x0D; Results: The article describes significant GWAS studies in the field of breast cancer, describes certain genes associated with the development of the disease, identifies shortcomings, and identifies further strategies for the development of GWAS in the Republic of Kazakhstan.&#x0D; Conclusion: The results of this study have improved our understanding of the biological mechanisms that contribute to breast cancer risk and may ultimately lead to the development of new targeted therapies for the disease.

GENOME-WIDE ASSOCIATION STUDY IN BREAST CANCER:&#x0D; A LITERATURE REVIEW

Relevance: Breast cancer (BC) is one of the major health problems in the Republic of Kazakhstan. Genome-wide association studies (GWAS)&#x0D; are used to identify genetic factors contributing to breast cancer development in Kazakhstan and worldwide. The GWAS method consistently helps to find associations between certain single nucleotide polymorphisms (SNPs) in the genome and the development of pathological conditions, including breast cancer, by identifying more than 170 genomic sites. In addition to searching for loci associated with BC, these studies have expanded&#x0D; our understanding of BC heritability using SNPs located in regulatory areas and identifying DNA variants associated with drug metabolism for&#x0D; treatment personalization purposes.&#x0D; The study aimed to highlight the key concepts of modern methods in genetics based on GWAS.&#x0D; Methods: A literature search in PubMed, Scopus, GWAS Catalog, Cochrane Database of Systematic Reviews, etc., was performed to select&#x0D; and analyze relevant information&#x0D; Results: The article describes significant GWAS studies in the field of breast cancer, describes certain genes associated with the development&#x0D; of the disease, identifies shortcomings, and identifies further strategies for the development of GWAS in the Republic of Kazakhstan&#x0D; Conclusion: The results of this study have improved our understanding of the biological mechanisms that contribute to breast cancer risk and&#x0D; may ultimately lead to the development of new targeted therapies for the disease

Testing for association between ordinal traits and genetic variants in pedigree-structured samples by collapsing and kernel methods.

In genome-wide association studies (GWAS), logistic regression is one of the most popular analytics methods for binary traits. Multinomial regression is an extension of binary logistic regression that allows for multiple categories. However, many GWAS methods have been limited application to binary traits. These methods have improperly often been used to account for ordinal traits, which causes inappropriate type I error rates and poor statistical power. Owing to the lack of analysis methods, GWAS of ordinal traits has been known to be problematic and gaining attention. In this paper, we develop a general framework for identifying ordinal traits associated with genetic variants in pedigree-structured samples by collapsing and kernel methods. We use the local odds ratios GEE technology to account for complicated correlation structures between family members and ordered categorical traits. We use the retrospective idea to treat the genetic markers as random variables for calculating genetic correlations among markers. The proposed genetic association method can accommodate ordinal traits and allow for the covariate adjustment. We conduct simulation studies to compare the proposed tests with the existing models for analyzing the ordered categorical data under various configurations. We illustrate application of the proposed tests by simultaneously analyzing a family study and a cross-sectional study from the Genetic Analysis Workshop 19 (GAW19) data.

Guidelines for Evaluating the Comparability of Down-Sampled GWAS Summary Statistics

Proprietary genetic datasets are valuable for boosting the statistical power of genome-wide association studies (GWASs), but their use can restrict investigators from publicly sharing the resulting summary statistics. Although researchers can resort to sharing down-sampled versions that exclude restricted data, down-sampling reduces power and might change the genetic etiology of the phenotype being studied. These problems are further complicated when using multivariate GWAS methods, such as genomic structural equation modeling (Genomic SEM), that model genetic correlations across multiple traits. Here, we propose a systematic approach to assess the comparability of GWAS summary statistics that include versus exclude restricted data. Illustrating this approach with a multivariate GWAS of an externalizing factor, we assessed the impact of down-sampling on (1) the strength of the genetic signal in univariate GWASs, (2) the factor loadings and model fit in multivariate Genomic SEM, (3) the strength of the genetic signal at the factor level, (4) insights from gene-property analyses, (5) the pattern of genetic correlations with other traits, and (6) polygenic score analyses in independent samples. For the externalizing GWAS, although down-sampling resulted in a loss of genetic signal and fewer genome-wide significant loci; the factor loadings and model fit, gene-property analyses, genetic correlations, and polygenic score analyses were found robust. Given the importance of data sharing for the advancement of open science, we recommend that investigators who generate and share down-sampled summary statistics report these analyses as accompanying documentation to support other researchers’ use of the summary statistics.

ADuLT: An efficient and robust time-to-event GWAS

Proportional hazards models have been proposed to analyse time-to-event phenotypes in genome-wide association studies (GWAS). However, little is known about the ability of proportional hazards models to identify genetic associations under different generative models and when ascertainment is present. Here we propose the age-dependent liability threshold (ADuLT) model as an alternative to a Cox regression based GWAS, here represented by SPACox. We compare ADuLT, SPACox, and standard case-control GWAS in simulations under two generative models and with varying degrees of ascertainment as well as in the iPSYCH cohort. We find Cox regression GWAS to be underpowered when cases are strongly ascertained (cases are oversampled by a factor 5), regardless of the generative model used. ADuLT is robust to ascertainment in all simulated scenarios. Then, we analyse four psychiatric disorders in iPSYCH, ADHD, Autism, Depression, and Schizophrenia, with a strong case-ascertainment. Across these psychiatric disorders, ADuLT identifies 20 independent genome-wide significant associations, case-control GWAS finds 17, and SPACox finds 8, which is consistent with simulation results. As more genetic data are being linked to electronic health records, robust GWAS methods that can make use of age-of-onset information will help increase power in analyses for common health outcomes.

Mining the candidate genes of rice panicle traits via a genome-wide association study.

Panicle traits are important for improving the panicle architecture and grain yield of rice. Therefore, we performed a genome-wide association study (GWAS) to analyze and determine the genetic determinants of five panicle traits. A total of 1.29 million single nucleotide polymorphism (SNP) loci were detected in 162 rice materials. We carried out a GWAS of panicle length (PL), total grain number per panicle (TGP), filled grain number per panicle (FGP), seed setting rate (SSR) and grain weight per panicle (GWP) in 2019, 2020 and 2021. Four quantitative trait loci (QTLs) for PL were detected on chromosomes 1, 6, and 9; one QTL for TGP, FGP, and GWP was detected on chromosome 4; two QTLs for FGP were detected on chromosomes 4 and 7; and one QTL for SSR was detected on chromosome 1. These QTLs were detected via a general linear model (GLM) and mixed linear model (MLM) in both years of the study period. In this study, the genomic best linear unbiased prediction (BLUP) method was used to verify the accuracy of the GWAS results. There are nine QTLs were both detected by the multi-environment GWAS method and the BLUP method. Moreover, further analysis revealed that three candidate genes, LOC_Os01g43700, LOC_Os09g25784, and LOC_Os04g47890, may be significantly related to panicle traits of rice. Haplotype analysis indicated that LOC_Os01g43700 and LOC_Os09g25784 are highly associated with PL and that LOC_Os04g47890 is highly associated with TGP, FGP, and GWP. Our results offer essential genetic information for the molecular improvement of panicle traits. The identified candidate genes and elite haplotypes could be used in marker-assisted selection to improve rice yield through pyramid breeding.

Automated machine learning for genome wide association studies.

Genome-wide association studies (GWAS) present several computational and statistical challenges for their data analysis, including knowledge discovery, interpretability, and translation to clinical practice. We develop, apply, and comparatively evaluate an automated machine learning (AutoML) approach, customized for genomic data that delivers reliable predictive and diagnostic models, the set of genetic variants that are important for predictions (called a biosignature), and an estimate of the out-of-sample predictive power. This AutoML approach discovers variants with higher predictive performance compared to standard GWAS methods, computes an individual risk prediction score, generalizes to new, unseen data, is shown to better differentiate causal variants from other highly correlated variants, and enhances knowledge discovery and interpretability by reporting multiple equivalent biosignatures. Code for this study is available at: https://github.com/mensxmachina/autoML-GWAS. JADBio offers a free version at: https://jadbio.com/sign-up/. SNP data can be downloaded from the EGA repository (https://ega-archive.org/). PRS data are found at: https://www.aicrowd.com/challenges/opensnp-height-prediction. Simulation data to study population structure can be found at: https://easygwas.ethz.ch/data/public/dataset/view/1/.

Multivariate genome-wide associations for immune traits in two maternal pig lines

BackgroundImmune traits are considered to serve as potential biomarkers for pig’s health. Medium to high heritabilities have been observed for some of the immune traits suggesting genetic variability of these phenotypes. Consideration of previously established genetic correlations between immune traits can be used to identify pleiotropic genetic markers. Therefore, genome-wide association study (GWAS) approaches are required to explore the joint genetic foundation for health biomarkers. Usually, GWAS explores phenotypes in a univariate (uv), trait-by-trait manner. Besides two uv GWAS methods, four multivariate (mv) GWAS approaches were applied on combinations out of 22 immune traits for Landrace (LR) and Large White (LW) pig lines.ResultsIn total 433 (LR: 351, LW: 82) associations were identified with the uv approach implemented in PLINK and a Bayesian linear regression uv approach (BIMBAM) software. Single Nucleotide Polymorphisms (SNPs) that were identified with both uv approaches (n = 32) were mostly associated with immune traits such as haptoglobin, red blood cell characteristics and cytokines, and were located in protein-coding genes. Mv GWAS approaches detected 647 associations for different mv immune trait combinations which were summarized to 133 Quantitative Trait Loci (QTL). SNPs for different trait combinations (n = 66) were detected with more than one mv method. Most of these SNPs are associated with red blood cell related immune trait combinations. Functional annotation of these QTL revealed 453 immune-relevant protein-coding genes. With uv methods shared markers were not observed between the breeds, whereas mv approaches were able to detect two conjoint SNPs for LR and LW. Due to unmapped positions for these markers, their functional annotation was not clarified.ConclusionsThis study evaluated the joint genetic background of immune traits in LR and LW piglets through the application of various uv and mv GWAS approaches. In comparison to uv methods, mv methodologies identified more significant associations, which might reflect the pleiotropic background of the immune system more accurately. In genetic research of complex traits, the SNP effects are generally small. Furthermore, one genetic variant can affect several correlated immune traits at the same time, termed pleiotropy. As mv GWAS methods consider strong dependencies among traits, the power to detect SNPs can be boosted. Both methods revealed immune-relevant potential candidate genes. Our results indicate that one single test is not able to detect all the different types of genetic effects in the most powerful manner and therefore, the methods should be applied complementary.

Research progress and applications of genome‐wide association study in farm animals

AbstractExploring the genetic loci underlying economic traits is foundational for innovation in modern animal breeding technology. Genome‐wide association studies (GWAS) have been valid and commonly used tools to dissect the genomic variants associated with phenotypes for the past ∼20 years and have contributed to our understanding of genetic and molecular bases of various traits. Here, we comprehensively review the recent research progress on GWAS methods. We highlight the methodological advancements enabled by the combination of new proteome, transcriptome, epigenome, and metagenome information and multi‐omics analysis algorithms. The advances in GWAS investigations of chickens, pigs, cattle, and other animals during the last 5 years are also described. Finally, we discuss the current applications of GWAS in cutting‐edge breeding technologies and overall future perspectives of the post‐GWAS era.

Germline mechanisms of immunotherapy toxicities in the era of genome-wide association studies.

Cancer immunotherapy has revolutionized the treatment of advanced cancers and is quickly becoming an option for early-stage disease. By reactivating the host immune system, immunotherapy harnesses patients' innate defenses to eradicate the tumor. By putatively similar mechanisms, immunotherapy can also substantially increase the risk of toxicities or immune-related adverse events (irAEs). Severe irAEs can lead to hospitalization, treatment discontinuation, lifelong immune complications, or even death. Many irAEs present with similar symptoms to heritable autoimmune diseases, suggesting that germline genetics may contribute to their onset. Recently, genome-wide association studies (GWAS) of irAEs have identified common germline associations and putative mechanisms, lending support to this hypothesis. A wide range of well-established GWAS methods can potentially be harnessed to understand the etiology of irAEs specifically and immunotherapy outcomes broadly. This review summarizes current findings regarding germline effects on immunotherapy outcomes and discusses opportunities and challenges for leveraging germline genetics to understand, predict, and treat irAEs.

Comparison of the statistical methods for genome-wide association studies on simulated quantitative traits of domesticated goats (Capra hircus L.)

Genome-wide association studies (GWAS) are one of the essential methods used to find associations between phenotypes and genes. The main problem in GWAS methods is false positives that occur from family relationships and population structure. In this study, the performances and powers of traditional and state-of-the-art statistical methods used in genome-wide association studies were compared on nine simulated quantitative traits using genotypic data of the domesticated goats (Capra hircus L.). The traits were simulated with varying degrees of heritability from 0.05 to 0.8. The findings revealed that Blink and FarmCPU are the outperforming methods to control false positives more efficiently for the medium and high heritability traits. At the same time, Blink promises to identify true positive signals more efficiently for the low heritability traits. The other multi-loci statistical methods SUPER and MLMM perform worse than Blink and FarmCPU but better than all of the single-locus methods GLM, MLM, CMLM and ECLMM. Additionally, Bonferroni or Holm's adjustment procedures were better in multiple testing of associations at the 1 % significance level to get more accurate results for the high heritability traits.

Application of SVR-Mediated GWAS for Identification of Durable Genetic Regions Associated with Soybean Seed Quality Traits.

Soybean (Glycine max L.) is an important food-grade strategic crop worldwide because of its high seed protein and oil contents. Due to the negative correlation between seed protein and oil percentage, there is a dire need to detect reliable quantitative trait loci (QTL) underlying these traits in order to be used in marker-assisted selection (MAS) programs. Genome-wide association study (GWAS) is one of the most common genetic approaches that is regularly used for detecting QTL associated with quantitative traits. However, the current approaches are mainly focused on estimating the main effects of QTL, and, therefore, a substantial statistical improvement in GWAS is required to detect associated QTL considering their interactions with other QTL as well. This study aimed to compare the support vector regression (SVR) algorithm as a common machine learning method to fixed and random model circulating probability unification (FarmCPU), a common conventional GWAS method in detecting relevant QTL associated with soybean seed quality traits such as protein, oil, and 100-seed weight using 227 soybean genotypes. The results showed a significant negative correlation between soybean seed protein and oil concentrations, with heritability values of 0.69 and 0.67, respectively. In addition, SVR-mediated GWAS was able to identify more relevant QTL underlying the target traits than the FarmCPU method. Our findings demonstrate the potential use of machine learning algorithms in GWAS to detect durable QTL associated with soybean seed quality traits suitable for genomic-based breeding approaches. This study provides new insights into improving the accuracy and efficiency of GWAS and highlights the significance of using advanced computational methods in crop breeding research.