[go: up one dir, main page]

 
 
Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Article Types

Countries / Regions

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Search Results (1,936)

Search Parameters:
Keywords = GWAS

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
23 pages, 3000 KiB  
Article
Extreme-Phenotype Genome-Wide Association Analysis for Growth Traits in Spotted Sea Bass (Lateolabrax maculatus) Using Whole-Genome Resequencing
by Zhaolong Zhou, Guangming Shao, Yibo Shen, Fengjiao He, Xiaomei Tu, Jiawen Ji, Jingqun Ao and Xinhua Chen
Animals 2024, 14(20), 2995; https://doi.org/10.3390/ani14202995 - 17 Oct 2024
Viewed by 120
Abstract
Spotted sea bass (Lateolabrax maculatus) is an important marine economic fish in China, ranking third in annual production among marine fish. However, a declined growth rate caused by germplasm degradation has severely increased production costs and reduced economic benefits. There is [...] Read more.
Spotted sea bass (Lateolabrax maculatus) is an important marine economic fish in China, ranking third in annual production among marine fish. However, a declined growth rate caused by germplasm degradation has severely increased production costs and reduced economic benefits. There is an urgent need to develop the fast-growing varieties of L. maculatus and elucidate the genetic mechanisms underlying growth traits. Here, whole-genome resequencing technology combined with extreme phenotype genome-wide association analysis (XP-GWAS) was used to identify candidate markers and genes associated with growth traits in L. maculatus. Two groups of L. maculatus, consisting of 100 fast-growing and 100 slow-growing individuals with significant differences in body weight, body length, and carcass weight, underwent whole-genome resequencing. A total of 4,528,936 high-quality single nucleotide polymorphisms (SNPs) were used for XP-GWAS. These SNPs were evenly distributed across all chromosomes without large gaps, and the average distance between SNPs was only 175.8 bp. XP-GWAS based on the Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (Blink) and Fixed and random model Circulating Probability Unification (FarmCPU) identified 50 growth-related markers, of which 17 were related to body length, 19 to body weight, and 23 to carcass weight. The highest phenotypic variance explained (PVE) reached 15.82%. Furthermore, significant differences were observed in body weight, body length, and carcass weight among individuals with different genotypes. For example, there were highly significant differences in body weight among individuals with different genotypes for four SNPs located on chromosome 16: chr16:13133726, chr16:13209537, chr16:14468078, and chr16:18537358. Additionally, 47 growth-associated genes were annotated. These genes are mainly related to the metabolism of energy, glucose, and lipids and the development of musculoskeletal and nervous systems, which may regulate the growth of L. maculatus. Our study identified growth-related markers and candidate genes, which will help to develop the fast-growing varieties of L. maculatus through marker-assisted breeding and elucidate the genetic mechanisms underlying the growth traits. Full article
(This article belongs to the Section Aquatic Animals)
Show Figures

Figure 1

Figure 1
<p>The comparison of growth traits between the fast-growing and slow-growing <span class="html-italic">L. maculatus</span> and the distribution of the SNPs used for GWAS on the chromosomes. (<b>A</b>–<b>C</b>) The comparison of body weight (<b>A</b>), body length (<b>B</b>) and carcass weight (<b>C</b>) between the 100 fast-growing <span class="html-italic">L. maculatus</span> and the 100 slow-growing ones. (<b>D</b>) The distribution of SNPs used for GWAS on the chromosomes. The redder the color, the more SNPs within a 1 Mb region; the bluer the color, the fewer SNPs. **** refers to <span class="html-italic">p</span> value &lt; 0.0001.</p>
Full article ">Figure 2
<p>Q–Q plot for body weight, body length, and carcass weight based on the Blink and FarmCPU model. (<b>A</b>) Quantile–quantile plot for body weight based on the Blink model. (<b>B</b>) Quantile–quantile plot for body length based on the Blink model. (<b>C</b>) Quantile–quantile plot for carcass weight based on the Blink model. (<b>D</b>) Quantile–quantile plot for body weight based on the FarmCPU model. (<b>E</b>) Quantile–quantile plot for body length based on the FarmCPU model. (<b>F</b>) Quantile–quantile plot for carcass weight based on the FarmCPU model.</p>
Full article ">Figure 3
<p>Manhattan plot for the GWAS of body weight, body length, and carcass weight in <span class="html-italic">L. maculatus</span>. (<b>A</b>,<b>B</b>) Manhattan plot of GWAS for body weight based on the Blink (<b>A</b>) and FarmCPU (<b>B</b>) models. (<b>C</b>,<b>D</b>) Manhattan plot of GWAS for body length based on the Blink (<b>C</b>) and FarmCPU (<b>D</b>) models. (<b>E</b>,<b>F</b>) Manhattan plot of GWAS for carcass weight based on the Blink (<b>E</b>) and FarmCPU (<b>F</b>) models.</p>
Full article ">Figure 4
<p>Boxplot of body weight for different genotypes of <span class="html-italic">L. maculatus</span>. The <span class="html-italic">y</span>-axis represents the body weight of the individuals with different genotypes, and different colors indicate different genotypes, with the “n” representing the number of individuals for each genotype. (<b>A</b>) Body weight of individuals with different genotypes for the SNP on chr16:13133726; (<b>B</b>) Body weight of individuals with different genotypes for the SNP on chr16:13209537; (<b>C</b>) Body weight of individuals with different genotypes for the SNP on chr16:14468078; (<b>D</b>) Body weight of individuals with different genotypes for the SNP on chr16:18537358.</p>
Full article ">Figure 5
<p>Protein–protein interaction network and tissue expression profile analysis of the growth-related candidate genes. (<b>A</b>) The protein–protein interaction network of the growth-related candidate genes. (<b>B</b>–<b>F</b>) Tissue expression profile analysis of <span class="html-italic">PTPRA</span> (<b>B</b>), <span class="html-italic">SLC7A8</span> (<b>C</b>), <span class="html-italic">PARK2</span> (<b>D</b>), <span class="html-italic">ZNF436</span> (<b>E</b>), and <span class="html-italic">SORCS2</span> (<b>F</b>).</p>
Full article ">
13 pages, 648 KiB  
Article
Polymorphism rs259983 of the Zinc Finger Protein 831 Gene Increases Risk of Superimposed Preeclampsia in Women with Gestational Diabetes Mellitus
by Nataliia Karpova, Olga Dmitrenko and Malik Nurbekov
Int. J. Mol. Sci. 2024, 25(20), 11108; https://doi.org/10.3390/ijms252011108 - 16 Oct 2024
Viewed by 211
Abstract
Hypertensive disorders of pregnancy (HDP) are a great danger. A previous GWAS found a relationship between rs259983 of the ZNF831 gene and HDP, such as for chronic hypertension (CHTN) and preeclampsia (PE). We conducted the case-control study to determine the association between rs259983 [...] Read more.
Hypertensive disorders of pregnancy (HDP) are a great danger. A previous GWAS found a relationship between rs259983 of the ZNF831 gene and HDP, such as for chronic hypertension (CHTN) and preeclampsia (PE). We conducted the case-control study to determine the association between rs259983 of the ZNF831 gene and HDP in women with Gestational Diabetes Mellitus (GDM). For target genotyping, we developed primers and TaqMan probes. In analyzing the population, we did not manage to find a relationship between PE and rs259983 of the ZNF831 gene. Additional study of women with PE and PE superimposed on CHTN (SIPE) establishes an association between rs259983 of the ZNF831 gene only with SIPE. Carriers of CC genotypes have been discovered to have a 5.05 times higher risk of SIPE development in women with GDM. Full article
(This article belongs to the Special Issue Molecular Pathogenesis and Treatment of Pregnancy Complications)
Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>ROC curves for multiple logistic regression models: (<b>a</b>) Model 1 adjusted for obesity; (<b>b</b>) Model 2 adjusted for CHTN; (<b>c</b>) Model 3 adjusted for obesity and CHTN; (<b>d</b>) Model 4 adjusted for IDA; AUC (Area Under the Curve): AUC is a single scalar value that summarizes the performance of a binary classifier across all classification thresholds. It represents the probability that a randomly chosen positive instance is ranked higher than a randomly chosen negative instance. An AUC of 0.5 indicates no discrimination (similar to random guessing), while an AUC of 1.0 indicates perfect discrimination; The dots on the ROC curve represent different threshold values used to classify the predictions into positive and negative classes. Each violet dot corresponds to a specific sensitivity (true-positive rate) and specificity (false-positive rate) for that threshold. The straight diagonal pink line on the ROC curve represents the performance of a random classifier. It serves as a baseline for comparison; any classifier that performs better than this diagonal line has some discriminative power, while a classifier that falls below it performs worse than random guessing.</p>
Full article ">
19 pages, 1558 KiB  
Article
Genome of Russian Snow-White Chicken Reveals Genetic Features Associated with Adaptations to Cold and Diseases
by Ivan S. Yevshin, Elena I. Shagimardanova, Anna S. Ryabova, Sergey S. Pintus, Fedor A. Kolpakov and Oleg A. Gusev
Int. J. Mol. Sci. 2024, 25(20), 11066; https://doi.org/10.3390/ijms252011066 (registering DOI) - 15 Oct 2024
Viewed by 284
Abstract
Russian Snow White (RSW) chickens are characterized by high egg production, extreme resistance to low temperatures, disease resistance, and by the snow-white color of the day-old chicks. Studying the genome of this unique chicken breed will reveal its evolutionary history and help to [...] Read more.
Russian Snow White (RSW) chickens are characterized by high egg production, extreme resistance to low temperatures, disease resistance, and by the snow-white color of the day-old chicks. Studying the genome of this unique chicken breed will reveal its evolutionary history and help to understand the molecular genetic mechanisms underlying the unique characteristics of this breed, which will open new breeding opportunities and support future studies. We have sequenced and made a de novo assembly of the whole RSW genome using deep sequencing (250×) by the short reads. The genome consists of 40 chromosomes with a total length of 1.1 billion nucleotide pairs. Phylogenetic analysis placed the RSW near the White Leghorn, Fayoumi, and Houdan breeds. Comparison with other chicken breeds revealed a wide pool of mutations unique to the RSW. The functional annotation of these mutations showed the adaptation of genes associated with the development of the nervous system, thermoreceptors, purine receptors, and the TGF-beta pathway, probably caused by selection for low temperatures. We also found adaptation of the immune system genes, likely driven by selection for resistance to viral diseases. Integration with previous genome-wide association studies (GWAS) suggested several causal single nucleotide polymorphisms (SNPs). Specifically, we identified an RSW-specific missense mutation in the RALYL gene, presumably causing the snow-white color of the day-old chicks, and an RSW-specific missense mutation in the TLL1 gene, presumably affecting the egg weight. Full article
(This article belongs to the Special Issue Molecular Research in Avian Genetics)
Show Figures

Figure 1

Figure 1
<p>The BUSCO assessment results for the 4 genome assemblies, showing the percentages and categories of the single-copy orthologs from the aves_odb10 data set (total genes = 8338) in each genome assembly: GGRsw1—the genome assembly of the RSW in this study; GGswu—the genome assembly of the Huxu breed [<a href="#B9-ijms-25-11066" class="html-bibr">9</a>]; GRCg6a—the genome assembly of the Red Junglefowl (official reference genome from the Genome Reference Consortium); and GRCg7b—the genome assembly of the broiler (official reference genome from the Genome Reference Consortium). GGRSw1 contains a greater number of single-copy orthologs, and lower numbers of missing and fragmented genes.</p>
Full article ">Figure 2
<p>A phylogenetic tree based on the comparison of the whole genome sequences of chickens of different breeds. Red—The Red Junglefowl is a chicken from Southeast Asia, from which domestic chickens probably originate; blue—“European” breeds; purple—American breeds; green—broiler breeds; yellow—Chinese breeds from Yunnan province; Brown—Chinese breeds not of Yunnan origin; and gray—other breeds.</p>
Full article ">Figure 3
<p>(<b>A</b>). The genomic regions unique to the Russian Snow White longer than 1000 bp. A map of all GGRsw1 chromosomes is shown, with green bars marking the genomic regions unique to the Russian White breed. (<b>B</b>). The figure shows, for each RSW-specific sequence, what proportion of that sequence is composed of G4s and tandem repeats: X axis—the fraction of G4s; Y axis—the fraction of tandem repeats.</p>
Full article ">Figure 3 Cont.
<p>(<b>A</b>). The genomic regions unique to the Russian Snow White longer than 1000 bp. A map of all GGRsw1 chromosomes is shown, with green bars marking the genomic regions unique to the Russian White breed. (<b>B</b>). The figure shows, for each RSW-specific sequence, what proportion of that sequence is composed of G4s and tandem repeats: X axis—the fraction of G4s; Y axis—the fraction of tandem repeats.</p>
Full article ">
10 pages, 214 KiB  
Article
Validating Disease Associations of Drug-Metabolizing Enzymes through Genome-Wide Association Study Data Analysis
by Evan Leskiw, Adeline Whaley, Peter Hopwood, Tailyn Houston, Nehal Murib, Donna Al-Falih and Ryoichi Fujiwara
Genes 2024, 15(10), 1326; https://doi.org/10.3390/genes15101326 (registering DOI) - 15 Oct 2024
Viewed by 357
Abstract
Background and Objectives: Phase I and phase II drug-metabolizing enzymes are crucial for the metabolism and elimination of various endogenous and exogenous compounds, such as small-molecule hormones, drugs, and xenobiotic carcinogens. While in vitro and animal studies have suggested a link between genetic [...] Read more.
Background and Objectives: Phase I and phase II drug-metabolizing enzymes are crucial for the metabolism and elimination of various endogenous and exogenous compounds, such as small-molecule hormones, drugs, and xenobiotic carcinogens. While in vitro and animal studies have suggested a link between genetic mutations in these enzymes and an increased risk of cancer, human in vivo studies have provided limited supportive evidence. Methods: Genome-wide association studies (GWASs) are a powerful tool for identifying genes associated with specific diseases by comparing two large groups of individuals. In the present study, we analyzed a GWAS database to identify key diseases genetically associated with drug-metabolizing enzymes, focusing on UDP-glucuronosyltransferases (UGTs). Results: Our analysis confirmed a strong association between the UGT1 gene and hyperbilirubinemia. Additionally, over ten studies reported a link between the UGT1 gene and increased low-density lipoprotein (LDL) cholesterol levels. UGT2B7 was found to be associated with testosterone levels, total cholesterol levels, and vitamin D levels. Conclusions: Despite the in vitro capability of UGT1 and UGT2 family enzymes to metabolize small-molecule carcinogens, the GWAS data did not indicate their genetic association with cancer, except for one study that linked UGT2B4 to ovarian cancer. Further investigations are necessary to fill the gap between in vitro, animal, and human in vivo data. Full article
10 pages, 1100 KiB  
Article
Multi-Ancestry Causal Association between Rheumatoid Arthritis and Interstitial Lung Disease: A Bidirectional Two-Sample Mendelian Randomization Study
by Bo-Guen Kim, Sanghyuk Yoon, Sun Yeop Lee, Eun Gyo Kim, Jung Oh Kim, Jong Seung Kim and Hyun Lee
J. Clin. Med. 2024, 13(20), 6080; https://doi.org/10.3390/jcm13206080 - 12 Oct 2024
Viewed by 377
Abstract
Abstract: Background: Rheumatoid arthritis (RA) is associated with diverse extra-articular manifestations, including interstitial lung disease (ILD). No previous studies have examined the bidirectional relationship between RA and ILD using the Mendelian randomization (MR) analyses. Therefore, we aimed to investigate this subject using [...] Read more.
Abstract: Background: Rheumatoid arthritis (RA) is associated with diverse extra-articular manifestations, including interstitial lung disease (ILD). No previous studies have examined the bidirectional relationship between RA and ILD using the Mendelian randomization (MR) analyses. Therefore, we aimed to investigate this subject using a two-sample bidirectional MR method. Methods: We performed bidirectional two-sample MR using summary statistics from genome-wide association studies (GWASs). The data are publicly available, de-identified, and from European (EUR) and East Asian (EAS) ancestries. Results: A total of 474,450 EUR participants and 351,653 EAS participants were included for either forward or reverse MR analysis. In our primary analysis, we found significant evidence of an increased risk of ILD associated with RA among individuals of EUR ancestry (ORMR-cML = 1.08; 95% confidence interval [CI] = 1.03–1.14; p = 0.003) and EAS ancestry (ORMR-cML = 1.37; 95% CI = 1.23–1.54; p < 0.001). Additionally, the reverse MR showed significant evidence of an increased risk of RA associated with ILD among those of EUR ancestry (ORMR-cML = 1.12; 95% CI = 1.05–1.19; p < 0.001). However, only one instrumental variable was selected in the EAS ILD GWAS, and there was no increased risk of RA associated with ILD in those of EAS ancestry (ORMR-cML = 1.02; 95% CI = 0.91–1.14; p = 0.740). Conclusions: Our findings indicate that RA and ILD have a bidirectional causal inference when using the MR analysis of GWAS datasets. The findings are only relevant for genetic predisposition; thus, further research is needed to determine the impact of non-genetic predispositions. Full article
(This article belongs to the Section Pulmonology)
Show Figures

Figure 1

Figure 1
<p>The workflow of the Mendelian randomization study to estimate the causal effect of rheumatoid arthritis on the development of interstitial lung disease in European and East Asian populations [<a href="#B23-jcm-13-06080" class="html-bibr">23</a>,<a href="#B24-jcm-13-06080" class="html-bibr">24</a>].</p>
Full article ">Figure 2
<p>Mendelian randomization results regarding the causal effect of rheumatoid arthritis (RA) on the development of interstitial lung disease in European and East Asian populations. MR-cML, MR-constrained maximum likelihood; IVW, inverse-variance weighted; OR, odds ratio; 95% CI, 95% confidence interval; MR-PRESSO, MR pleiotropy residual sum and outlier.</p>
Full article ">Figure 3
<p>Mendelian randomization results regarding the causal effect of interstitial lung disease on the development of rheumatoid arthritis in European and East Asian populations. MR-cML, MR-constrained maximum likelihood; IVW, inverse-variance weighted; OR, odds ratio; 95% CI, 95% confidence interval; MR-PRESSO, MR pleiotropy residual sum and outlier.</p>
Full article ">
15 pages, 1713 KiB  
Article
Genome-Wide and Exome-Wide Association Study Identifies Genetic Underpinning of Comorbidity between Myocardial Infarction and Severe Mental Disorders
by Bixuan Jiang, Xiangyi Li, Mo Li, Wei Zhou, Mingzhe Zhao, Hao Wu, Na Zhang, Lu Shen, Chunling Wan, Lin He, Cong Huai and Shengying Qin
Biomedicines 2024, 12(10), 2298; https://doi.org/10.3390/biomedicines12102298 - 10 Oct 2024
Viewed by 482
Abstract
Background: Myocardial Infarction (MI) and severe mental disorders (SMDs) are two types of highly prevalent and complex disorders and seem to have a relatively high possibility of mortality. However, the contributions of common and rare genetic variants to their comorbidity arestill unclear. Methods: [...] Read more.
Background: Myocardial Infarction (MI) and severe mental disorders (SMDs) are two types of highly prevalent and complex disorders and seem to have a relatively high possibility of mortality. However, the contributions of common and rare genetic variants to their comorbidity arestill unclear. Methods: We conducted a combined genome-wide association study (GWAS) and exome-wide association study (EWAS) approach. Results: Using gene-based and gene-set association analyses based on the results of GWAS, we found the common genetic underpinnings of nine genes (GIGYF2, KCNJ13, PCCB, STAG1, HLA-C, HLA-B, FURIN, FES, and SMG6) and nine pathways significantly shared between MI and SMDs. Through Mendelian randomization analysis, we found that twenty-seven genes were potential causal genes for SMDs and MI. Based on the exome sequencing data of MI and SMDs patients from the UK Biobank, we found that MUC2 was exome-wide significant in the two diseases. The gene-set analyses of the exome-wide association study indicated that pathways related to insulin processing androgen catabolic process and angiotensin receptor binding may be involved in the comorbidity between SMDs and MI. We also found that six candidate genes were reported to interact with known therapeutic drugs based on the drug–gene interaction information in DGIdb. Conclusions: Altogether, this study revealed the overlap of common and rare genetic underpinning between SMDs and MI and may provide useful insights for their mechanism study and therapeutic investigations. Full article
(This article belongs to the Section Molecular Genetics and Genetic Diseases)
Show Figures

Figure 1

Figure 1
<p>Shared gene sets among Myocardial Infarction and severe mental disorders in GWAS analyses. Gene sets enriched in MAGMA analyses of the BD (<b>A</b>), SCZ (<b>B</b>), and MI (<b>C</b>) significant common architecture are shown in the plots. The dots colored red represent significant common gene sets based on a Benjamini–Hochberg correction for multiple testing at FDR &lt; 0.25 and nominal <span class="html-italic">p</span> value &lt; 0.05. The data are available in <a href="#app1-biomedicines-12-02298" class="html-app">Supplementary Tables S1–S3</a>. The specific term names of gene sets from GO or KEGG are listed below. GO:0048878, chemical homeostasis; GO:0001505, regulation of neurotransmitter levels; GO:0006836, neurotransmitter transport; GO:0034364, high-density lipoprotein particle; GO:0042627, chylomicron; GO:0034385, triglyceride-rich plasma lipoprotein particle; GO:0140059, dendrite arborization; GO:0051940, regulation of catecholamine uptake involved in synaptic transmission; PATHWAY: hsa04720, long-term potentiation. BD: Bipolar Disorder; SCZ: Schizophrenia; MI: Myocardial Infarction.</p>
Full article ">Figure 2
<p>QQ plots for gene-level association tests in WES analyses. Genes enriched in RVtests analyses of the BD (<b>A</b>), SCZ (<b>B</b>), and MI (<b>C</b>) significant rare architecture are shown in the plots. The dots colored red represent statistically significant genes based on a Benjamini–Hochberg correction for multiple testing at FDR &lt; 0.25 and <span class="html-italic">p</span> value &lt; 0.05. BD: Bipolar Disorder; SCZ: Schizophrenia; MI: Myocardial Infarction. The data are available in <a href="#app1-biomedicines-12-02298" class="html-app">Supplementary Tables S10–S12</a>.</p>
Full article ">Figure 3
<p>Gene–drug interactions for BD, SCZ, and MI. The diagram reveals the correlation between the disorders, genes, and drugs. BD: Bipolar Disorder; SCZ: Schizophrenia; MI: Myocardial Infarction. The data are available in <a href="#app1-biomedicines-12-02298" class="html-app">Supplementary Table S16</a>.</p>
Full article ">
20 pages, 7174 KiB  
Article
Genome-Wide Association Studies (GWAS) and Transcriptome Analysis Reveal Male Heterogametic Sex-Determining Regions and Candidate Genes in Northern Snakeheads (Channa argus)
by Haiyang Liu, Jin Zhang, Tongxin Cui, Weiwei Xia, Qing Luo, Shuzhan Fei, Xinping Zhu, Kunci Chen, Jian Zhao and Mi Ou
Int. J. Mol. Sci. 2024, 25(20), 10889; https://doi.org/10.3390/ijms252010889 - 10 Oct 2024
Viewed by 477
Abstract
The Northern snakehead (Channa argus) is a significant economic aquaculture species in China. Exhibiting sexual dimorphism in the growth rate between females and males, mono-sex breeding holds substantial value for aquaculture. This study employed GWAS and transcriptome analysis were applied to [...] Read more.
The Northern snakehead (Channa argus) is a significant economic aquaculture species in China. Exhibiting sexual dimorphism in the growth rate between females and males, mono-sex breeding holds substantial value for aquaculture. This study employed GWAS and transcriptome analysis were applied to identify sex determination genomic regions and develop sex-specific markers. A total of 270 single-nucleotide polymorphisms (SNPs) and 31 insertion-deletions (InDels) were identified as being sexually dimorphic through GWAS and fixation index (Fst) scanning. Based on GWAS results, two sex-specific InDel markers were developed, effectively distinguishing genetic sex for XX females, XY males, and YY super-males via (polymerase chain reaction) PCR amplification. A major genomic segment of approximately 115 kb on chromosome 3 (Chr 03) was identified as the sex-determination region. A comparative transcriptome analysis of gonads for three sexes identified 158 overlapping differentially expressed genes (DEGs). Additionally, three sex-related candidate genes were identified near the sex determination region, including id2, sox11, and rnf144a. Further studies are required to elucidate the functions of these genes. Overall, two sex-specific InDel markers support a male heterogametic XX/XY sex-determination system in Northern snakeheads and three candidate genes offer new insights into sex determination and the evolution of sex chromosomes in teleost fish. Full article
Show Figures

Figure 1

Figure 1
<p>High-quality SNPs were mapped to 24 <span class="html-italic">C. argus</span> chromosomes, with the gradient color ranging from green to red to indicate increasing SNP density at 1 Mb intervals.</p>
Full article ">Figure 2
<p>Results of GWAS analyzing sex characteristics. (<b>A</b>,<b>C</b>) show Manhattan plots of the optimal patterns of SNPs and InDels, along with Q-Q plots for SNPs, InDels, and corresponding λ values. The <span class="html-italic">x</span>-axis represents the chromosome number (Chr) while the <span class="html-italic">y</span>-axis represents the −log10(P) value for each SNP. Red dots indicate SNP signals exceeding the significance threshold, whereas blue and yellow dots fall below the threshold. The red points indicated by the black arrows represent significant loci. (<b>B</b>,<b>D</b>) present Q-Q plots of the optimal patterns of SNPs and InDels.</p>
Full article ">Figure 3
<p>Fine mapping on Chr 03 revealed the association between SNPs and sex as well as the combined linkage disequilibrium in the sex-related region. Red dots indicate SNP signals exceeding the significance threshold, whereas blue dots fall below the threshold. The axis below the chart represents the region where candidate genes are annotated.</p>
Full article ">Figure 4
<p>Result of Fst scan for females and males on chromosomes. (<b>A</b>,<b>B</b>), fine mapping on Chr 3 shows the combination of associations of SNPs and InDels with sex and Fst, respectively. Candidate sign inferred by Fst (&gt;0.3490) are marked by red dots, whereas blue and yellow dots fall below the Fst (&gt;0.3490). The <span class="html-italic">x</span>-axis indicates the Chr numbers. The <span class="html-italic">y</span>-axis is the Fst value of the female and male of the SNPs or InDels.</p>
Full article ">Figure 5
<p>Result of PCA for females and males with traits. (<b>A</b>,<b>B</b>), the PCA of 59 individuals to distinguish two sexes using SNPs or InDels on Chr03. The red and blue dots represent females and males, respectively.</p>
Full article ">Figure 6
<p>Results of PCR products for females, males, and super-males with two primers. The band above the green frame denotes female samples (XX), the red frame represents male samples (XY), and the blue frame signifies super-male samples (YY) within the Northern snakehead population.</p>
Full article ">Figure 7
<p>Transcriptome analyses based on gonads in Northern snakeheads. (<b>A</b>–<b>C</b>) Volcano plot of DEGs shows all the individuals’ gonads within RNA-seq. Red and blue dots indicate significantly upregulated and downregulated genes, respectively (|log2FC| ≥ 1 and <span class="html-italic">p</span> &lt; 0.01). Gray dots indicate genes not significantly differentially expressed between XX vs. XY, XY vs. YY, and XX vs. YY. (<b>D</b>) Venn maps of DEGs obtained from RNA-seq based on gonads, whereas A is XX vs. XY, B is. XY vs. YY, and C is XX vs. YY. The overlapping intersection is compared with the RNA-seq result, and the resulting region of 158 represents the final conservative DEGs.</p>
Full article ">Figure 8
<p>Functional enrichment analysis of overlapping DEGs. (<b>A</b>) presents the top 21 significantly enriched Gene Ontology (GO) terms for the 158 conserved DEGs. (<b>B</b>) shows the 20 significantly enriched KEGG pathways for the 158 conserved DEGs.</p>
Full article ">Figure 9
<p>qPCR validation for (<b>A</b>) five genes within the spermathecal differentiation and development DEGs of gonads RNA-seq and (<b>B</b>) five genes within the ovarian differentiation and development DEGs of gonads RNA-seq. The gray bars represent the relative expression levels of the gene, corresponding to the <span class="html-italic">y</span>-axis on the left side of each figure, and are presented as mean ± standard deviation. The red line indicates the gene expression levels from RNA-seq data, represented in FPKM, and corresponds to the <span class="html-italic">y</span>-axis on the right side of each figure, displayed as the mean. The significance of the qPCR results between the XX-F, XY-M, and YY-M individuals is indicated by letters. The <span class="html-italic">x</span> axis that XX-F (genetic sex is XX and phenotypic sex is female), XY-M (genetic sex is XY and phenotypic sex is male) and YY-M (genetic sex is YY and phenotypic sex is male).</p>
Full article ">Figure 10
<p>Final candidate genes and their functional enrichment analysis. (<b>A</b>,<b>B</b>) Enriched GO terms and KEGG pathways for candidate genes. (<b>C</b>) Heatmap analysis of candidate genes differentially expressed between XX-F, XY-M and YY-M Northern snakeheads. XX-F, female gonad individuals. XY-M, male gonad individuals. YY-M, super-male gonad individuals. Matrix blocks in red are upregulated while those in blue are downregulated in gens. The red and bold name is interested in candidate gene.</p>
Full article ">Figure 11
<p>Individuals in <span class="html-italic">C. argus</span>.</p>
Full article ">Figure 12
<p>Map of sampling sites for <span class="html-italic">C. argus</span>. The red dot shows the specific sampling locations.</p>
Full article ">
21 pages, 3802 KiB  
Article
Mining of Oil Content Genes in Recombinant Maize Inbred Lines with Introgression from Temperate and Tropical Germplasm
by Mengfei Shi, Jiachen Sun, Fuyan Jiang, Ranjan K. Shaw, Babar Ijaz and Xingming Fan
Int. J. Mol. Sci. 2024, 25(19), 10813; https://doi.org/10.3390/ijms251910813 - 8 Oct 2024
Viewed by 383
Abstract
The oil content of maize kernels is essential to determine its nutritional and economic value. A multiparent population (MPP) consisting of five recombinant inbred line (RIL) subpopulations was developed to elucidate the genetic basis of the total oil content (TOC) in maize. The [...] Read more.
The oil content of maize kernels is essential to determine its nutritional and economic value. A multiparent population (MPP) consisting of five recombinant inbred line (RIL) subpopulations was developed to elucidate the genetic basis of the total oil content (TOC) in maize. The MPP used the subtropical maize inbred lines CML312 and CML384, along with the tropical maize inbred lines CML395, YML46, and YML32 as the female parents, and Ye107 as the male parent. A genome-wide association study (GWAS) was performed using 429 RILs of the multiparent population across three environments, employing 584,847 high-quality single nucleotide polymorphisms (SNPs). Furthermore, linkage analysis was performed in the five subpopulations to identify quantitative trait loci (QTL) linked to TOC in maize. Through QTL mapping and GWAS, 18 QTLs and 60 SNPs that were significantly associated with TOC were identified. Two novel candidate genes, Zm00001d029550 and Zm00001d029551, related to TOC in maize and located on chromosome 1 were reported, which have not been previously reported. These genes are involved in biosynthesis, lipid signal transduction, plant development and metabolism, and stress responses, potentially influencing maize TOC. Haplotype analysis of Zm00001d029550 and Zm00001d029551 revealed that Hap3 could be considered a superior haplotype for increasing TOC in maize. A co-located SNP (SNP-75791466) on chromosome 1, located 5648 bp and 11,951 bp downstream of the candidate genes Zm00001d029550 and Zm00001d029551, respectively, was found to be expressed in various maize tissues. The highest expression was observed in embryos after pollination, indicating that embryos are the main tissue for oil accumulation in maize. This study provides a theoretical basis for understanding the genetic mechanisms underlying maize TOC and developing high-quality, high-oil maize varieties. Full article
(This article belongs to the Special Issue Plant Physiology and Molecular Nutrition)
Show Figures

Figure 1

Figure 1
<p>Correlations between pop1, pop2, pop3, pop4, and pop5 for TOC in three environments. (<b>a</b>) Correlation of pop1 for TOC between the 22YS, 23JH, and 21YS environments; (<b>b</b>) correlation of pop2 for TOC between the 22YS, 23JH, and 21YS environments; (<b>c</b>) correlation of pop3 for TOC between the 22YS, 23JH, and 21YS environments; (<b>d</b>) correlation of pop4 for TOC response between the 22YS, 23JH, and 21YS environments; (<b>e</b>) correlation of pop5 for TOC between the 22YS, 23JH, and 21YS environments. ** indicates <span class="html-italic">p</span> &lt; 0.01.</p>
Full article ">Figure 2
<p>Genetic diversity analysis of the 429 RILs of the MPP. (<b>a</b>) Phylogenetic tree. (<b>b</b>) Principal component analysis. (<b>c</b>) Bayesian clustering plots of 429 maize RILs at K = 5.</p>
Full article ">Figure 3
<p>The LD decay plot of the MPP.</p>
Full article ">Figure 4
<p>Manhattan map (<b>left</b>) and Q–Q plots (<b>right</b>) for (<b>a</b>) 22YS environment, (<b>b</b>) 23JH environ–ment, and (<b>c</b>) 21YS environment; (<b>d</b>) BLUP values indicate the SNPs associated with TOC. In the Manhattan plot, each dot represents an SNP, and the black line denotes the threshold value. Differ–ent colors in the Manhattan plot represent different chromosomes. In the Q–Q plot, the red line denotes the expected significance value, while the blue dots represent the observed significance val–ues.</p>
Full article ">Figure 5
<p>QTL mapping for TOC in Pop3. Blue bands represent the bin markers and orange boxes indicate the significant QTLs linked to TOC.</p>
Full article ">Figure 6
<p>The identification of candidate genes related to maize TOC (<b>a</b>) QTLs identified on different chromosomes of maize in the 23JH environment in pop3. (<b>b</b>) The location of the most significant SNPs on chromosome 1, as identified through GWAS. (<b>c</b>) Haplotype analysis of candidate gene <span class="html-italic">Zm00001d029550</span> for TOC in the MPP. (<b>d</b>) The haplotype distribution of <span class="html-italic">Zm00001d029550</span> in five subpopulations, ** represents <span class="html-italic">p</span> ≤ 0.01. (<b>e</b>) The expression levels (FPKM) of the candidate gene <span class="html-italic">Zm00001d029550</span> in different tissues, with the highest level observed in embryos after pollination. (<b>f</b>) The position of <span class="html-italic">Zm0001d029550</span> and the associated SNP.</p>
Full article ">Figure 7
<p>(<b>a</b>) The location of the significant SNPs on chromosome 1 identified through GWAS. (<b>b</b>) Haplotype analysis of candidate gene <span class="html-italic">Zm00001d029551</span> for TOC in the MPP. (<b>c</b>) The haplotype dis–tribution of <span class="html-italic">Zm00001d029551</span> in five subpopulations. ** represents <span class="html-italic">p</span> ≤ 0.01. (<b>d</b>) The position of <span class="html-italic">Zm0001d029551</span> and the associated SNP.</p>
Full article ">
17 pages, 12032 KiB  
Article
Investigating the Causal Effects of Exercise-Induced Genes on Sarcopenia
by Li Wang and Song Zhang
Int. J. Mol. Sci. 2024, 25(19), 10773; https://doi.org/10.3390/ijms251910773 - 7 Oct 2024
Viewed by 445
Abstract
Exercise is increasingly recognized as an effective strategy to counteract skeletal muscle aging and conditions such as sarcopenia. However, the specific exercise-induced genes responsible for these protective effects remain unclear. To address this, we conducted an eight-week aerobic exercise regimen on late-middle-aged mice [...] Read more.
Exercise is increasingly recognized as an effective strategy to counteract skeletal muscle aging and conditions such as sarcopenia. However, the specific exercise-induced genes responsible for these protective effects remain unclear. To address this, we conducted an eight-week aerobic exercise regimen on late-middle-aged mice and developed an integrated approach that combines mouse exercise-induced genes with human GWAS datasets to identify causal genes for sarcopenia. This approach led to significant improvements in the skeletal muscle phenotype of the mice and the identification of exercise-induced genes and miRNAs. By constructing a miRNA regulatory network enriched with transcription factors and GWAS signals related to muscle function and traits, we focused on 896 exercise-induced genes. Using human skeletal muscle cis-eQTLs as instrumental variables, 250 of these exercise-induced genes underwent two-sample Mendelian randomization analysis, identifying 40, 68, and 62 causal genes associated with sarcopenia and its clinical indicators—appendicular lean mass (ALM) and hand grip strength (HGS), respectively. Sensitivity analyses and cross-phenotype validation confirmed the robustness of our findings. Consistently across the three outcomes, RXRA, MDM1, RBL2, KCNJ2, and ADHFE1 were identified as risk factors, while NMB, TECPR2, MGAT3, ECHDC2, and GINM1 were identified as protective factors, all with potential as biomarkers for sarcopenia progression. Biological activity and disease association analyses suggested that exercise exerts its anti-sarcopenia effects primarily through the regulation of fatty acid oxidation. Based on available drug–gene interaction data, 21 of the causal genes are druggable, offering potential therapeutic targets. Our findings highlight key genes and molecular pathways potentially responsible for the anti-sarcopenia benefits of exercise, offering insights into future therapeutic strategies that could mimic the safe and mild protective effects of exercise on age-related skeletal muscle degeneration. Full article
Show Figures

Figure 1

Figure 1
<p>Histological assessment and RNA-Seq analysis of aging skeletal muscle under exercise intervention. (<b>a</b>) Representative images of hematoxylin and eosin (H&amp;E)-stained cross-sections of quadriceps muscle from old and oldEx groups. Scale bars, 100 μm. (<b>b</b>) Sample similarity clustering. Pairwise correlation (Pearson) among all samples was calculated from the gene expression matrix. (<b>c</b>) Heatmap displaying expression profiles of the DEGs (oldEx vs. old, adjusted <span class="html-italic">p</span>-value &lt; 0.05) through hierarchical clustering. Each column represents a sample, and each row represents a gene. The color scale indicates the raw Z-score, ranging from red (high expression) to blue (low expression). (<b>d</b>,<b>e</b>) The top 20 GO biological processes (<b>d</b>) and top 20 KEGGs signaling pathways (<b>e</b>) enriched by the DEGs.</p>
Full article ">Figure 2
<p>Construction of an exercise-induced miRNA regulatory network and analysis of its upstream TFs. (<b>a</b>) Heatmap displaying expression profiles of the DEmiRs (oldEx vs. old, adjusted <span class="html-italic">p</span>-value &lt; 0.05) through hierarchical clustering. Each column represents a sample, and each row represents a gene. The color scale indicates the raw Z-score, ranging from red (high expression) to blue (low expression). (<b>b</b>) Potential miRNA–target relationships between DEGs and DEmiRs. DEGs and DEmiRs exhibiting inverse expression changes were further filtered by the starBase database based on predictions of targeted degradation. (<b>c</b>) The enrichment analysis of upstream TFs for these miRNAs in the network is conducted through a hypergeometric test. Red dots represent significantly enriched TFs (<span class="html-italic">p</span>-value &lt; 0.05). The upstream TFs of miRNAs are sourced from the TransmiR database. (<b>d</b>) Enrichment analysis of GWAS trait-related SNPs within the TRRs of the network. These SNPs were sourced from the GWAS catalog database and showed significant associations with the target traits, meeting a stringent significance threshold of <span class="html-italic">p</span> &lt; 5 × 10<sup>−8</sup>. Red dots indicate FDR &lt; 0.05.</p>
Full article ">Figure 3
<p>Causal estimates of genes associated with sarcopenia-related traits. (<b>a</b>) An overview of the MR framework. The genes under investigation are derived from the miRNA–target network. IV, instrumental variables; <span class="html-italic">cis</span>-eQTLs, cis expression quantitative trait loci; IVW, inverse variance weighted. (<b>b</b>–<b>d</b>) Identification of causal genes for sarcopenia (<b>b</b>), ALM (<b>c</b>), and HGS (<b>d</b>). Genes meeting the significance criteria for MR assessment but exhibiting horizontal pleiotropy or heterogeneity in IVs were excluded. OR (Odds Ratio) is used for binary outcomes, while beta values are used for continuous outcome variables in the MR analysis to quantify the magnitude of causal effects. pve, proportion of variance explained. (<b>e</b>) Intersection of causal genes for sarcopenia, ALM, and HGS.</p>
Full article ">Figure 4
<p>Causal effects of 10 shared genes associated with sarcopenia-related traits. (<b>a</b>–<b>c</b>) Forest plots showing the two-sample MR estimation of the association between 10 shared causal genes and three sarcopenia-related traits: sarcopenia (<b>a</b>), ALM (<b>b</b>), and HGS (<b>c</b>). nsnp, number of single nucleotide polymorphisms; CI, confidence interval.</p>
Full article ">Figure 5
<p>Functions and druggable potential of causal genes in sarcopenia. (<b>a</b>–<b>d</b>) The Enrichr platform was used to explore the significant biological processes (<b>a</b>), cellular signaling pathways (<b>b</b>), human diseases (<b>c</b>), and drugs (<b>d</b>) associated with these genes using the GO Biological Process 2023, WikiPathway 2023 Human, ClinVar 2019, and DSigDB options, respectively. Red indicates a significance level of padj &lt; 0.05, while blue indicates a significance level of padj ≥ 0.05. (<b>e</b>) Screening of druggable genes among the 113 causal genes for sarcopenia. A total of 21 druggable genes were identified and are displayed within the rectangle.</p>
Full article ">
23 pages, 8471 KiB  
Article
Construction of a Growth Model and Screening of Growth-Related Genes for a Hybrid Puffer (Takifugu obscurus ♀ × Takifugu rubripes ♂)
by Chaoyu Wang, Yan Shi, Yuanye Gao, Shuo Shi, Mengmeng Wang, Yunlong Yao, Zhenlong Sun, Yaohui Wang and Zhe Zhao
Fishes 2024, 9(10), 404; https://doi.org/10.3390/fishes9100404 - 6 Oct 2024
Viewed by 713
Abstract
The obscure puffer (Takifugu obscurus) is a popular cultured species and accounts for around 50% of the total pufferfish production in China. A hybrid puffer was generated by crossing a female obscure puffer with a male tiger puffer (T. rubripes [...] Read more.
The obscure puffer (Takifugu obscurus) is a popular cultured species and accounts for around 50% of the total pufferfish production in China. A hybrid puffer was generated by crossing a female obscure puffer with a male tiger puffer (T. rubripes). Its growth model has not been developed and the genetic basis underlying its growth superiority has not yet been fully investigated. In this study, the growth model and morphological traits of the hybrid puffer were explored. The results indicated that the hybrid puffer exhibited a significant growth advantage compared to the obscure puffer. There were also significant differences in their morphological traits. We conducted genotyping-by-sequencing (GBS) on hybrid and obscure puffer groups, identifying 215,288 high-quality single nucleotide polymorphisms (SNPs) on 22 chromosomes. Subsequently, a total of 13 growth-related selection regions were identified via a combination of selection signatures and a genome-wide association study (GWAS); these regions were mainly located on chromosomes 10 and 22. Ultimately, the screened regions contained 13 growth-related genes, including itgav, ighv3-43, ighm, atp6v1b2, pld1, xmrk, inhba, dsp, dsg2, and dsc2, which regulate growth through a variety of pathways. Taken together, the growth models and candidate genes used in this study will aid our understanding of production characteristics and the genetic basis of growth rates. The hybrid will also be of great significance for the genome-assisted breeding of pufferfish in the future. Full article
(This article belongs to the Special Issue Genetics and Breeding in Aquaculture)
Show Figures

Figure 1

Figure 1
<p>Phenotypes of the obscure puffer (<span class="html-italic">T</span>. <span class="html-italic">obscurus</span>), tiger puffer (<span class="html-italic">T. rubripes</span>), and hybrid puffer. (<b>A</b>) Parental obscure and tiger puffers. (<b>B</b>) Experimental offspring of the hybrid and obscure puffer groups.</p>
Full article ">Figure 2
<p>Diagram depicting growth index measurement. (<b>A</b>) Lateral view. (<b>B</b>) Dorsal view. BW, body weight; TL, total length; BL, body length; CH, caudal peduncle height; HL, head length; SL, snout length; HBL, head-behind length; EL, eye length; ES, eye spacing; NS, nostril spacing; OS, outlet hole spacing; SC, snout cleft; CL, chest length; AL, abdominal length; CG, caudal girth.</p>
Full article ">Figure 3
<p>Principal component analysis of the morphological traits of the pufferfish. (<b>A</b>) Morphological trait principal component analysis scree plot. (<b>B</b>) Principal component analysis of 15 morphological traits. (<b>C</b>) Principal component analysis of 150 experimental individuals based on morphological traits.</p>
Full article ">Figure 4
<p>The relationship between body weight and body length. (<b>A</b>) The relationship in the obscure puffer group. (<b>B</b>) The relationship in the hybrid group. Wi represents the formula for the body length weight relationship; Ttp represents the time of taken to reach the inflection point for growth.</p>
Full article ">Figure 5
<p>The von Bertalanffy growth models of the obscure puffer and hybrid. (<b>A</b>) The von Bertalanffy growth model of body weight for the obscure puffer. (<b>B</b>) The von Bertalanffy growth model of body weight for the hybrid puffer. (<b>C</b>) The von Bertalanffy growth model of body length for the obscure puffer. (<b>D</b>) The body length von Bertalanffy growth model of body length for the hybrid. <span class="html-italic">Wt</span> represents the von Bertalanffy growth model for body weight; <span class="html-italic">Lt</span> represents the von Bertalanffy growth model for body length.</p>
Full article ">Figure 6
<p>The distribution of SNPs and the experimental groups’ population structures. (<b>A</b>) The distribution of SNPs on 22 chromosomes. (<b>B</b>) Principal component analysis of 80 experimental individuals based on SNPs. (<b>C</b>) The experimental phylogenetic tree. A1–A100 represent the individual IDs of the obscure puffer; Z1–Z100 represent the individual IDs of the hybrid puffer.</p>
Full article ">Figure 7
<p>Candidate selection regions of the hybrid and obscure puffer groups. (<b>A</b>,<b>B</b>) Candidate selection regions detected using F<sub>st</sub> (<b>A</b>) and π ratio (<b>B</b>) statistics are plotted across the genome. The <span class="html-italic">y</span>-axis of the Manhattan plots displays the F<sub>st</sub> values and π ratio scores calculated in 100 kb with steps of 50 kb. The red horizontal dashed line represents the top 1% threshold in the F<sub>st</sub> value (0.60) and π ratio scores (1.05). (<b>C</b>) The candidate selection region intersection of the F<sub>st</sub> and π ratio.</p>
Full article ">Figure 8
<p>Top 20 enriched GO terms of genes identified under selection regions. (<b>A</b>) Biological process. (<b>B</b>) Cellular component. (<b>C</b>) Molecular function.</p>
Full article ">Figure 9
<p>Top 20 enriched KEGG terms of genes identified under selection regions. (<b>A</b>) KEGG enrichment analysis results of the F<sub>st</sub> screening region genes. (<b>B</b>) KEGG enrichment analysis results of π ratio screening region genes. (<b>C</b>) KEGG enrichment analysis results of genes in the intersection region screened using the F<sub>st</sub> and π ratio.</p>
Full article ">
14 pages, 3726 KiB  
Article
Genome-Wide Association Study of Reproductive Traits in Large White Pigs
by Yifeng Hong, Cheng Tan, Xiaoyan He, Dan Wu, Yuxing Zhang, Changxu Song and Zhenfang Wu
Animals 2024, 14(19), 2874; https://doi.org/10.3390/ani14192874 - 6 Oct 2024
Viewed by 525
Abstract
(1) Background: Reproductive performance is crucial for the pork industry’s success. The Large White pig is central to this, yet the genetic factors influencing its reproductive traits are not well understood, highlighting the need for further research. (2) Methods: This study utilized Genome-Wide [...] Read more.
(1) Background: Reproductive performance is crucial for the pork industry’s success. The Large White pig is central to this, yet the genetic factors influencing its reproductive traits are not well understood, highlighting the need for further research. (2) Methods: This study utilized Genome-Wide Association Studies to explore the genetic basis of reproductive traits in the Large White pig. We collected data from 2237 Large White sows across four breeding herds in southern China, focusing on eight reproductive traits. Statistical analyses included principal component analysis, linkage disequilibrium analysis, and univariate linear mixed models to identify significant single-nucleotide polymorphisms and candidate genes. (3) Results: Forty-five significantly related SNPs and 17 potential candidate genes associated with litter traits were identified. Individuals with the TT genotype at SNP rs341909772 showed an increase of 1.24 in the number of piglets born alive (NBA) and 1.25 in the number of healthy births (NHBs) compared with those with the CC genotype. (4) Conclusions: The SNPs and genes identified in this study offer insights into the genetics of reproductive traits in the Large White pig, potentially guiding the development of breeding strategies to improve litter size. Full article
(This article belongs to the Section Animal Genetics and Genomics)
Show Figures

Figure 1

Figure 1
<p>Manhattan plots of GWAS with SNPs for litter traits at the first parity in the Large White populations. In the Manhattan plots, the yellow and red lines represent the 5% genome-wide and chromosome-wide (suggestive) Bonferroni-corrected thresholds, respectively.</p>
Full article ">Figure 2
<p>Manhattan plots of GWAS with SNPs for litter traits at the second parity in the Large White populations. In the Manhattan plots, the yellow and red lines represent the 5% genome-wide and chromosome-wide (suggestive) Bonferroni-corrected thresholds, respectively.</p>
Full article ">Figure 3
<p>The genotype effect plot of the pleiotropic SNP rs341909772, which is associated with both NBA and NHB in the Large White population at the second parity, is displayed. On the left, the plot shows the effect of SNP rs341909772 on NBA, and on the right, the effect on NHB (** <span class="html-italic">p</span> &lt; 0.001).</p>
Full article ">Figure 4
<p>The regional association plot illustrates the primary signal (rs332278643) associated with NWB on SSC15 in the Large White population at the second parity. The plot displays negative log10 <span class="html-italic">p</span>-values of SNPs (<span class="html-italic">y</span>-axis) according to their chromosomal positions (<span class="html-italic">x</span>-axis). The red line indicates the genome-wide significance level. The primary SNPs are marked by red triangles. The left and right panels of the figure show the association results for NWB before and after conditional analysis on rs332278643, respectively. The color of the dots in the plot represents the level of linkage disequilibrium (LD) with the primary signal (rs332278643), where redder dots indicate higher LD levels, and yellower dots indicate lower LD levels.</p>
Full article ">
14 pages, 3089 KiB  
Article
The Causal Relationship between Inflammatory Cytokines and Liver Cirrhosis in European Descent: A Bidirectional Two-Sample Mendelian Randomization Study and the First Conclusions
by Shiya Shi, Yanjie Zhou, He Zhang, Yalan Zhu, Pengjun Jiang, Chengxia Xie, Tianyu Feng, Yuping Zeng, He He, Yao Luo and Jie Chen
Biomedicines 2024, 12(10), 2264; https://doi.org/10.3390/biomedicines12102264 - 4 Oct 2024
Viewed by 652
Abstract
Background: Observational studies have highlighted the pivotal role of inflammatory cytokines in cirrhosis progression. However, the existence of a causal link between inflammatory cytokines and cirrhosis remains uncertain. In this study, we conducted a bidirectional Mendelian randomization (MR) analysis at a summarized level [...] Read more.
Background: Observational studies have highlighted the pivotal role of inflammatory cytokines in cirrhosis progression. However, the existence of a causal link between inflammatory cytokines and cirrhosis remains uncertain. In this study, we conducted a bidirectional Mendelian randomization (MR) analysis at a summarized level to illuminate the potential causal relationship between the two variables. Methods: This study utilized genetic variance in cirrhosis and inflammatory cytokines from a genome-wide association study (GWAS) of European descent. The MR-PRESSO outlier test, Cochran’s Q test, and MR-Egger regression were applied to assess outliers, heterogeneity, and pleiotropy. The inverse variance weighted method and multiple sensitivity analyses were used to evaluate causalities. Furthermore, the validation set was used for simultaneous data validation. Results: The inflammatory cytokine monocyte chemoattractant protein 3 (MCP-3) was supposedly associated with a greater risk of cirrhosis. And cirrhosis was significantly correlated with increased levels of hepatocyte growth factor (HGF). Conclusions: This study suggests that MCP-3 might be associated with the etiology of cirrhosis, while several inflammatory cytokines could potentially play a role in its downstream development. Additionally, the progression of cirrhosis was associated with elevated levels of HGF, suggesting a possible role for liver repair functions. Full article
(This article belongs to the Section Immunology and Immunotherapy)
Show Figures

Figure 1

Figure 1
<p>The workflow diagram of this study.</p>
Full article ">Figure 2
<p>Forest plots of MR analysis of the relationship between 41 circulating cytokine levels and the risk of cirrhosis for (<b>A</b>) Dataset 1 and (<b>B</b>) Dataset 2. Bold in figure indicates <span class="html-italic">p</span> ≤ 0.05, which is statistically significant. CI, confidence interval; OR, odds ratio; MR, mendelian randomization.</p>
Full article ">Figure 3
<p>Heatmaps of the five MR analysis methods for the association between 41 circulating cytokine levels and the risk of cirrhosis for (<b>A</b>) Dataset 1 and (<b>B</b>) Dataset 2. MR, Mendelian randomization. <span class="html-italic">p</span> values for variance inverse variance weighting, IVW-lasso, weighted median, MR-Egger, weighted mode and simple mode are indicated from the outside in. * <span class="html-italic">p</span> &lt; 0.05.</p>
Full article ">Figure 4
<p>Forest plots of MR analysis of the relationship between cirrhosis and the risk of 41 circulating cytokine levels for (<b>A</b>) Dataset 1 and (<b>B</b>) Dataset 2. Bold in figure indicates <span class="html-italic">p</span> ≤ 0.05, which is statistically significant. CI, confidence interval; OR, odds ratio; MR, mendelian randomization.</p>
Full article ">Figure 5
<p>Heatmaps of the five MR analysis methods for the association between cirrhosis and the risk of 41 circulating cytokine levels for (<b>A</b>) Dataset 1 and (<b>B</b>) Dataset 2. MR, Mendelian randomization. <span class="html-italic">p</span> values for variance inverse variance weighting, IVW-lasso, weighted median, MR-Egger, weighted mode, and simple mode are indicated from the outside in. * <span class="html-italic">p</span> &lt; 0.05.</p>
Full article ">
14 pages, 2555 KiB  
Article
Associations between Disc Hemorrhage and Primary Open-Angle Glaucoma Based on Genome-Wide Association and Mendelian Randomization Analyses
by Je Hyun Seo, Young Lee and Hyuk Jin Choi
Biomedicines 2024, 12(10), 2253; https://doi.org/10.3390/biomedicines12102253 - 3 Oct 2024
Viewed by 493
Abstract
Background/Objectives: We aimed to investigate the genetic loci related to disc hemorrhage (DH) and the relationship of causation between DH and primary open-angle glaucoma (POAG) using a genome-wide association study (GWAS) in East Asian individuals. Methods: The GWAS included 8488 Koreans who underwent [...] Read more.
Background/Objectives: We aimed to investigate the genetic loci related to disc hemorrhage (DH) and the relationship of causation between DH and primary open-angle glaucoma (POAG) using a genome-wide association study (GWAS) in East Asian individuals. Methods: The GWAS included 8488 Koreans who underwent ocular examination including fundus photography to determine the presence of DH and POAG. We performed a GWAS to identify significant single-nucleotide polymorphisms (SNPs) associated with DH and analyzed the heritability of DH and genetic correlation between DH and POAG. The identified SNPs were utilized as instrumental variables (IVs) for two-sample Mendelian randomization (MR) analysis. The POAG outcome dataset was adopted from Biobank Japan data (n = 179,351). Results: We found that the rs62463744 (TMEM270;ELN), rs11658281 (CCDC42), and rs77127203 (PDE10A;LINC00473) SNPs were associated with DH. The SNP heritability of DH was estimated to be 6.7%, with an absence of a genetic correlation with POAG. MR analysis did not reveal a causal association between DH and POAG for East Asian individuals. Conclusions: The novel loci underlying DH in the Korean cohort revealed SNPs in the ELN, CCDC41, and LINC00473 genes. The absence of a causal association between DH and POAG implies that DH is a shared risk factor, rather than an independent culprit factor, and warrants further investigation. Full article
(This article belongs to the Section Molecular and Translational Medicine)
Show Figures

Figure 1

Figure 1
<p>Diagram presentation of the study design. POAG, primary open-angle glaucoma; DH, disc hemorrhage; GENIE cohort, Gene-Environmental Interaction and Phenotype; SNP, single-nucleotide polymorphism.</p>
Full article ">Figure 2
<p>Quantile-quantile and Manhattan plots for disc hemorrhage in the genome-wide association study. (<b>A</b>). Quantile-quantile (Q-Q) plot. The expected line is shown in red and confidence bands are shown in gray. (<b>B</b>). Manhattan plot. The red line indicates the preset threshold of <span class="html-italic">p</span> = 1.0 × 10<sup>−6</sup>.</p>
Full article ">Figure 3
<p>Regional association plots for top 4 SNPs. SNP, single-nucleotide polymorphism. (<b>A</b>): rs62463744, (<b>B</b>): rs11658281, (<b>C</b>): rs77127203, (<b>D</b>): rs7589033.</p>
Full article ">Figure 4
<p>Schematic design of Mendelian randomization analysis. SNP, single-nucleotide polymorphism. Solid lines indicate the presence of an association, dashed lines indicate the absence of an association.</p>
Full article ">Figure 5
<p>MR visualizations of the effect of DH on POAG. CI, confidence interval; DH, disc hemorrhage; POAG, primary open-angle glaucoma; OR, odds ratio; SIMEX, simulation extrapolation; SNP, single-nucleotide polymorphism.</p>
Full article ">Figure 6
<p>Scatter plot of MR analyses using different MR methods evaluating the impact of the existence of DH on POAG. Light blue, dark blue, light green, and dark green regression lines represent IVW, MR-Egger, MR-Egger (SIMEX), and weighted median estimate, respectively. SNP, single-nucleotide polymorphism; DH, disc hemorrhage; POAG, primary open-angle glaucoma; MR, Mendelian randomization; SIMEX, simulation extrapolation.</p>
Full article ">
31 pages, 4454 KiB  
Article
Exploring Novel Genomic Loci and Candidate Genes Associated with Plant Height in Bulgarian Bread Wheat via Multi-Model GWAS
by Tania Kartseva, Vladimir Aleksandrov, Ahmad M. Alqudah, Matías Schierenbeck, Krasimira Tasheva, Andreas Börner and Svetlana Misheva
Plants 2024, 13(19), 2775; https://doi.org/10.3390/plants13192775 - 3 Oct 2024
Viewed by 480
Abstract
In the context of crop breeding, plant height (PH) plays a pivotal role in determining straw and grain yield. Although extensive research has explored the genetic control of PH in wheat, there remains an opportunity for further advancements by integrating genomics with growth-related [...] Read more.
In the context of crop breeding, plant height (PH) plays a pivotal role in determining straw and grain yield. Although extensive research has explored the genetic control of PH in wheat, there remains an opportunity for further advancements by integrating genomics with growth-related phenomics. Our study utilizes the latest genome-wide association scan (GWAS) techniques to unravel the genetic basis of temporal variation in PH across 179 Bulgarian bread wheat accessions, including landraces, tall historical, and semi-dwarf modern varieties. A GWAS was performed with phenotypic data from three growing seasons, the calculated best linear unbiased estimators, and the leveraging genotypic information from the 25K Infinium iSelect array, using three statistical methods (MLM, FarmCPU, and BLINK). Twenty-five quantitative trait loci (QTL) associated with PH were identified across fourteen chromosomes, encompassing 21 environmentally stable quantitative trait nucleotides (QTNs), and four haplotype blocks. Certain loci (17) on chromosomes 1A, 1B, 1D, 2A, 2D, 3A, 3B, 4A, 5B, 5D, and 6A remain unlinked to any known Rht (Reduced height) genes, QTL, or GWAS loci associated with PH, and represent novel regions of potential breeding significance. Notably, these loci exhibit varying effects on PH, contribute significantly to natural variance, and are expressed during seedling to reproductive stages. The haplotype block on chromosome 6A contains five QTN loci associated with reduced height and two loci promoting height. This configuration suggests a substantial impact on natural variation and holds promise for accurate marker-assisted selection. The potentially novel genomic regions harbor putative candidate gene coding for glutamine synthetase, gibberellin 2-oxidase, auxin response factor, ethylene-responsive transcription factor, and nitric oxide synthase; cell cycle-related genes, encoding cyclin, regulator of chromosome condensation (RCC1) protein, katanin p60 ATPase-containing subunit, and expansins; genes implicated in stem mechanical strength and defense mechanisms, as well as gene regulators such as transcription factors and protein kinases. These findings enrich the pool of semi-dwarfing gene resources, providing the potential to further optimize PH, improve lodging resistance, and achieve higher grain yields in bread wheat. Full article
Show Figures

Figure 1

Figure 1
<p>Frequency distribution (<b>a</b>) and boxplots (<b>b</b>) for plant height (in cm) in 179 wheat accessions from Bulgaria in three crop seasons and with the best linear unbiased estimator (BLUE) values. The boxplots show the median as a dot and the 1st and 3rd quartile as a box; whiskers depict the non-outlier range.</p>
Full article ">Figure 2
<p>Manhattan plots for genome-wide associated scan of plant height (PH) in 179 Bulgarian wheat accessions, detected by (<b>a</b>) a single-locus model, and (<b>b</b>) two multivariate models. The colored horizontal lines indicate the significance threshold at −log<sub>10</sub> (<span class="html-italic">p</span>-value) = FDR (False Discovery Rate), at a significance level of adjusted <span class="html-italic">p</span>-value &lt; 0.001 (in (<b>a</b>)) and <span class="html-italic">p</span>-value &lt; 0.01 (in (<b>b</b>)), computed for each environment. Arrows point to the novel loci associated with PH. In haplotype blocks on chromosomes 1B, 3A, and 6A, only peak SNPs are marked and underlined (see also <a href="#plants-13-02775-t002" class="html-table">Table 2</a>).</p>
Full article ">Figure 2 Cont.
<p>Manhattan plots for genome-wide associated scan of plant height (PH) in 179 Bulgarian wheat accessions, detected by (<b>a</b>) a single-locus model, and (<b>b</b>) two multivariate models. The colored horizontal lines indicate the significance threshold at −log<sub>10</sub> (<span class="html-italic">p</span>-value) = FDR (False Discovery Rate), at a significance level of adjusted <span class="html-italic">p</span>-value &lt; 0.001 (in (<b>a</b>)) and <span class="html-italic">p</span>-value &lt; 0.01 (in (<b>b</b>)), computed for each environment. Arrows point to the novel loci associated with PH. In haplotype blocks on chromosomes 1B, 3A, and 6A, only peak SNPs are marked and underlined (see also <a href="#plants-13-02775-t002" class="html-table">Table 2</a>).</p>
Full article ">Figure 3
<p>Scatter plots showing linear regressions (solid red lines) and 95% confidence intervals of the regressions (dashed red lines) of nine selected SNPs with (<b>a</b>) high reducing and (<b>b</b>) high increasing effects on plant height (PH) with PH BLUE values in 179 wheat accessions. The blue dots represent the PH BLUE values of accessions with corresponding number of SNPs.</p>
Full article ">Figure 4
<p>Allelic effects on plant height (PH) phenotype showing differences in PH BLUE values between accessions with PH-affecting alleles (left) and accessions with alternative alleles (right) at nine selected stable SNP loci. (<b>a</b>) PH-reducing SNPs; (<b>b</b>) PH-increasing SNPs. In parentheses, the full name of a QTL is noted for the SNPs belonging to a haplotype block; for the remaining SNPs, only the chromosome is annotated. The boxplots show the mean as a dot and the mean ± SE (standard error) as a box; whiskers depict the mean ± 1.96SE range. *, **, *** indicate significant difference at <span class="html-italic">p</span> ≤ 0.05, 0.01, 0.001, respectively, as calculated by Student’s <span class="html-italic">t</span> test.</p>
Full article ">Figure 4 Cont.
<p>Allelic effects on plant height (PH) phenotype showing differences in PH BLUE values between accessions with PH-affecting alleles (left) and accessions with alternative alleles (right) at nine selected stable SNP loci. (<b>a</b>) PH-reducing SNPs; (<b>b</b>) PH-increasing SNPs. In parentheses, the full name of a QTL is noted for the SNPs belonging to a haplotype block; for the remaining SNPs, only the chromosome is annotated. The boxplots show the mean as a dot and the mean ± SE (standard error) as a box; whiskers depict the mean ± 1.96SE range. *, **, *** indicate significant difference at <span class="html-italic">p</span> ≤ 0.05, 0.01, 0.001, respectively, as calculated by Student’s <span class="html-italic">t</span> test.</p>
Full article ">Figure 5
<p>Heatmap showing gene expression levels (log<sub>2</sub> tpm, transcripts per million) of selected putative genes controlling plant height in wheat: (<b>a</b>) genes containing significant SNPs or very close to them; (<b>b</b>) genes in the support interval around significant SNPs and haplotype blocks.</p>
Full article ">Figure 5 Cont.
<p>Heatmap showing gene expression levels (log<sub>2</sub> tpm, transcripts per million) of selected putative genes controlling plant height in wheat: (<b>a</b>) genes containing significant SNPs or very close to them; (<b>b</b>) genes in the support interval around significant SNPs and haplotype blocks.</p>
Full article ">
20 pages, 2121 KiB  
Article
Genome-Wide Association Studies of Agronomic and Quality Traits in Durum Wheat
by Stefan Tsonev, Rangel Dragov, Krasimira Taneva, Nikolai Kirilov Christov, Violeta Bozhanova and Elena Georgieva Todorovska
Agriculture 2024, 14(10), 1743; https://doi.org/10.3390/agriculture14101743 - 3 Oct 2024
Viewed by 473
Abstract
Durum wheat is mainly used for products for human consumption, the quality of which depends on the content of protein and yellow pigments in the semolina. The challenges faced by modern breeding, related to population growth and climate change, imply improvement of both [...] Read more.
Durum wheat is mainly used for products for human consumption, the quality of which depends on the content of protein and yellow pigments in the semolina. The challenges faced by modern breeding, related to population growth and climate change, imply improvement of both grain yields and quality in durum wheat germplasm well adapted to specific agro-climatic conditions. To address those challenges, a better understanding of the genetic architecture of agronomic and quality traits is needed. In the current study we used the Genome-Wide Association Study (GWAS) approach in a panel of Bulgarian and foreign genotypes to define loci controlling agronomic and quality traits in durum wheat. We mapped 26 marker traits associations (MTAs) for four of the six studied traits—grain yield, grain protein content, seed yellow colour (CIELAB b*), and plant height. The greatest number of MTAs was detected for grain yield. Seven MTAs were detected for each grain protein content and seed colour, and one MTA for plant height. Most of the reported associations had confidence intervals overlapping with already reported quantitative trait loci (QTLs). Two loci controlling grain yield were not reported previously. The MTAs reported here may be a valuable tool in future breeding for improvement of both grain yield and quality in durum wheat. Full article
(This article belongs to the Special Issue Breeding and Genetic Research of Cereal Grain Quality)
Show Figures

Figure 1

Figure 1
<p>Violin plots of the studied traits with pairwise comparison of seasons’ means. GY—grain yield (t/ha); PH—plant height (cm); GPC—grain protein content (%); SL—spike length (cm); TKW—thousand kernel weight (g); SC—seed colour.</p>
Full article ">Figure 2
<p>Phenotypic correlations between studied traits in the three seasons: 2019, 2020, and 2021, and the joint analysis across seasons (3Yrs). The correlation indices on a white background are non-significant.</p>
Full article ">Figure 3
<p>Genetic structure of the studied association panel. Most probable number of clusters, defined with Evanno method (<b>a</b>). Clusters according to STRUCTURE analysis (<b>b</b>). Most probable number of groups for DAPC was defined using BIC plotted against the number of clusters (<b>c</b>). Grouping of the genotypes in DAPC (<b>d</b>).</p>
Full article ">Figure 4
<p>Graphical representation of whole-genome LD decay in the experimental panel. The red line represents the LD decay with physical distance, calculated by the method of Remington et al. [<a href="#B36-agriculture-14-01743" class="html-bibr">36</a>]. The black horizontal line designates a critical value of <span class="html-italic">r</span><sup>2</sup> = 0.3, and the green horizontal line shows <span class="html-italic">r</span><sup>2</sup> = 0.21 derived as 95th percentile of the distribution of LD values between unlinked loci.</p>
Full article ">
Back to TopTop