BMC Genomics最新文献

Background: Peanut (Arachis hypogaea L.) is a vital global crop, frequently threatened by both abiotic and biotic stresses. Among the most damaging biotic stresses is Tomato spotted wilt virus (TSWV), which causes peanut spotted wilt disease resulting in significant yield loss. Developing TSWV-resistant cultivars is crucial to new cultivar release. Previous studies have used a subset of the "S" recombinant inbred line (RIL) population derived from SunOleic 97R and NC94022 and identified quantitative trait loci (QTLs) for resistance to TSWV. These studies utilized different genotyping techniques and found large consistent genomic regions on chromosome A01. The objective of this study was to fine map the QTL and identify candidate genes using the entire population of 352 RILs and high-density, high-quality peanut SNP arrays.

Results: We used both versions of the peanut SNP arrays with five years of disease ratings, and successfully mapped the long-sought peanut spotted wilt disease resistance locus, PSWDR-1. QTL analyses identified two major QTLs, explaining 41.43% and 43.69% of the phenotypic variance within 3.6 cM and 0.28 cM intervals using the peanut Axiom_Arachis-v1 and Axiom_Arachis-v2 SNP arrays, respectively, on chromosome A01. These QTLs corresponded to 295 kb and 235 kb physical intervals. The unique overlap region of these two QTLs was 488 kb. A comparison of the genetic linkage map with the reference genome revealed a 1.3 Mb recombination "cold spot" (11.325-12.646 Mb) with only two recombination events of RIL-S1 and RIL-S17, which displayed contrasting phenotypes. Sequencing of these two recombinants confirmed the cold spot with only five SNPs detected within this region.

Conclusions: This study successfully identified a peanut spotted wilt disease resistance locus, PSWDR-1, on chromosome A01 within a recombination "cold spot". The PSWDR-1 locus contains three candidate genes, a TIR-NBS-LRR gene (Arahy.1PK53M), a glutamate receptor-like gene (Arahy.RI1BYW), and an MLO-like protein (Arahy.FX71XI). These findings provide a foundation for future functional studies to validate the roles of these candidate genes in resistance and application in breeding TSWV-resistant peanut cultivars.

Background: The case-only design is a powerful approach to identify gene $\times$ gene and gene $\times$ environment interactions for complex traits. It has been demonstrated that for the case-only design to be valid the genetic and environmental factors must be independent in the population. Additionally, there is a rare disease assumption for the case-only design, but the impact of disease prevalence and other factors, e.g., size of main effects, on type I and II error rates has not been investigated.

Methods: Through theoretical and extensive simulation studies, we investigated type I error, power, and bias of interaction term for a wide variety of disease prevalences, main and interaction effect sizes, sample sizes, and variant and environmental exposure frequencies.

Results: For diseases with prevalence $<$ 4%, the case-only design usually has well controlled type I error rates and is substantially more powerful to detect interactions than the case-control design, but for higher disease prevalences both type I and II error rates can be inflated and the estimate of interaction term biased. However, when one or both main effects are large there can be inflated type I error rate even for low disease prevalences, e.g., $<$ 1%, but if there is no or only one main effect, type I error rate is controlled regardless of the disease prevalence. Additionally, type I error rate can increase with sample size.

Conclusions: We determined the upper bound of the disease prevalence in order not to violate the rare disease assumption for the case-only design. To verify that a case-only design study does not have increased type I error rate, the bias of the interaction term should be estimated. Although the case-only design is a powerful method to detect interactions, prevalences for some complex traits are too high to implement this method without increasing type I error rates.

Background: Livestock, particularly cattle, are crucial for biotechnology fields, such as genetic breeding, infectious diseases, bioreactors, and specific disease models. However, genetic engineering in cattle has lagged due to long gestation periods, single embryo pregnancies, and high rearing costs. Additionally, the slow validation of germline transmission and the absence of germline-competent embryonic stem cells hinder progress. With the development of genome editing technologies like ZFN, TALEN, and CRISPR-Cas9, recent advancements have shown that Cas9-expressing pigs and chickens have been successfully produced. We hypothesize that generating CRISPR/Cas9-expressing cattle and their resources will provide a powerful resource for bovine genome editing, advancing our understanding of bovine genetics and disease resistance.

Results: In this study, two types of Cas9-expressing cattle were successfully produced: Cas9-RFP-fatty acid dehydrogenase I (FatI), Cas9-GFP-sgRNA for the prion protein (sgPRNP). Somatic cells from these cattle were induced to mutate multiple target genes when single-guide RNAs (sgRNAs) were transfected into the somatic cells. Additionally, semen from Cas9 expressing male cattle was frozen and used to fertilize wild-type oocytes, successfully transmitting the transgene (Cas9, reporter genes, FatI), and sgPRNP) to the next generation. Furthermore, the gene editing capabilities of Cas9, including knockout and high-efficiency knock-in, were confirmed in embryos derived from F1 semen through in vitro production.

Conclusion: These data demonstrate, for the first time, that Cas9-expressing cattle were successfully born, and this transgene was transmitted to the next-generation calves (F1) and F2 embryos. In addition, somatic and germ cells derived from F0 and F1generations were used to evaluate the potential for gene editing (knockout and knock-in) in multiple genes. PRNP-mutated F1 cattle are currently being raised as a resistance model for bovine spongiform encephalopathy. These transgenic bovine models and their derivatives will serve as a valuable resource for both in vitro and in vivo genome editing, advancing our genetic understanding of bovine genomics and diseases.

Background: With the rapid development of high-throughput sequencing technology, high-throughput sequencing data has grown on a massive scale, leading to the emergence of multiple public databases, such as EBI and GEO. Conducting secondary mining of high-throughput sequencing data in these databases can yield more valuable insights. Meta-analysis can quantitatively combine high-throughput sequencing data from the the same topic. It increases the sample size for data analysis, enhances statistical power, and results in more consistent and reliable conclusions.

Results: This study proposes a new between-study variance estimator $E_m$ . We prove that $E_m$ is non-negative and $E_m\left({\hat{\tau }}_m^2\right)$ increases with the increase of ${\hat{\tau }}_m^2$ , satisfying the general conditions of the between-study variance estimator. We get the DSLE2 (two-step estimation starting with the DSL estimate and the $E_m$ in the second step) random-effects meta-analysis model based on the between-study variance estimator Em. The accuracy and a series of evaluation metrics of the DSLE2 model are better than those of the other 6 meta-analysis models. DSLE2 model is applied to lung cancer and Parkinson's methylation data. Significantly differentially methylated sites identified by DSLE2 model and the genes with significantly differentially methylated sites are closely related to two diseases, indicating the effectiveness of DSLE2 random-effects model.

Conclusions: This paper propose the DSLE2 random-effects meta-analysis model based on new between-study variance estimator Em. The DSLE2 model performs well for methylation data.

Background: To explore the mitochondrial genes that play a key role in the occurrence and development of age-related hearing loss (ARHL) and provide a basis for the study of the mechanism of ARHL.

Results: 503 differentially expressed genes (DEGs) were detected in the GSE49543 dataset. 233 genes were up-regulated, and 270 genes were down-regulated. There are 1140 genes in the mitochondrial gene bank, and 28 differentially expressed genes related to ARHL. These genes are mainly involved in mitochondrial respiratory chain complex assembly, small molecule catabolism, NADH dehydrogenase complex assembly, organic acid catabolism, precursor metabolites and energy production, and mitochondrial span Membrane transport, metabolic processes of active oxygen species. Then, Cytoscape software identified the three key genes: Aco2, Bcs1l and Ndufs1. Immunofluorescence and Western blot experiments confirmed that the protein content of three key genes in aging cochlear hair cells decreased.

Conclusion: We employed bioinformatics analysis to screen 503 differentially expressed genes and identified three key genes associated with ARHL. Subsequently, we conducted in vitro experiments to validate their significance, thereby providing a valuable reference for further elucidating the role of mitochondrial function in the pathogenesis and progression of ARHL.

Background: Integrating germplasm populations genotyped by different genotyping platforms via genotype imputation is a way to utilize accumulated genetic resources. In this study, we used 278 canola samples genotyped via whole-genome sequencing (WGS) at 10× coverage to evaluate the imputation accuracy of three imputation approaches. The optimal imputation methods were used to impute and integrate two Canola genotype datasets: a diverse canola collection genotyped by genotyping-by-sequencing via transcriptome (GBS-t) and a double haploid (DH) line collection genotyped with low-coverage WGS (skim-WGS). The genomic predictive ability (GP) and detection power of marker‒trait association (GWAS) of the combined population for blackleg resistance were evaluated.

Results: The empirical imputation accuracy (r²) measured as the squared correlation between observed and imputed genotypes was moderate for Minimac3 when imputing from the GBS-t density to the WGS. The accuracy dramatically improved from 0.64 to 0.82 by removing SNPs with poor Minimac3-reported Rsq (Rsq < 0.2) quality statistics. The r² for GLIMPSE was higher than that for Beagle when imputing from different low-coverage to full-coverage WGS. We imputed and integrated the diverse canola collection and the DH lines, and the combined population showed similar or slightly greater predictive ability (PA) for blackleg resistance traits than did each of the single populations with ~ 921 K SNPs. Higher marker-trait association (MTA) detection powers were indicated with the combined population; however, similar numbers of MTAs were discovered when each single population was combined in a meta-GWAS.

Conclusion: It is feasible to impute and integrate germplasms from different sequencing platforms for downstream analyses. However, genetic heterogeneity across populations could add complexity to the analysis. Increasing the sample size by combining datasets showed slightly greater predictive ability and greater detection power in GWASs in the present study.

Background: Yunnan Province, located in Southwestern China, the intricate geography, variable climate, and abundant vegetation of the region have collectively contributed to shaping the distinctive germplasm characteristics observed in Yunnan indigenous cattle through prolonged domestication. The different breeds of Yunnan cattle exhibit distinct advantageous characteristics and traits, which are an important source of genetic variation because they might carry alleles that enable them to adapt to local environment and tough feeding conditions. However, a comprehensive genomic landscape of genetic resources has yet to be delineated.

Results: Herein, we employed 140 whole-genome sequencing data from Yunnan indigenous cattle across eight breeds to elucidate their genetic diversity and population structure. Utilizing both uniparental and biparental markers, we elucidated the intricate genetic composition of Yunnan indigenous cattle, which is closely correlated with the geographic environment. A predominant East Asian indicine ancestry which gradually diminishes towards the north. The analysis revealed a high genetic diversity among populations and a low-to-moderate inbreeding coefficient, underscoring the rich genetic reservoir of Yunnan cattle breeds. Additionally, gene flow between Yunnan indicine and wild Bos species in and around Yunnan was verified, highlighting localized introgression from Yunnan Gayal as a critical factor in the successful adaptation of Yunnan indicine cattle to the local hot and humid environments.

Conclusions: Our findings established the SNPs database for facilitating resource conservation and selective breeding. Moreover, these valuable insights into the genomic diversity and adaptive history of Yunnan indigenous cattle breeds contribute significantly to our understanding of their evolutionary dynamics and offer a foundation for future genetic improvement and conservation strategies.

The present study was undertaken to elucidate the population structure and differentiation of Indian yak from Chinese and wild cohorts on genome-wide scale by identifying the selection sweeps and genomic basis of their adaptation across different comparisons while analyzing whole genome sequencing (WGS) data using latest bioinformatics tools. The study included 105 individuals from three distinct yak populations i.e., Indian yak (n = 29); Chinese yak (n = 61) and wild yak (n = 15), hypothesized to be related along the evolutionary timescale. Efficient variant calling and quality control in GATK and PLINK programs resulted in around 1 million (1,002,970) high-quality (LD-independent) SNPs with an average genotyping rate of 96.55%. The PCA, ADMIXTURE and TREEMIX analysis revealed stratification of the yak groups into three distinct clusters. The empirical distribution pattern of minor allele frequency (MAF) of SNPs on genome-wide scale was also elucidated for three yak cohorts revealing unique distribution across five different bins. The selection signature analysis revealed candidate genes that are important for the adaptation of Indian yak against harsh environmental conditions in their habitats. Under iHS analysis, several genes were identified to be under selection pressure in Indian yak including ABCA12, EXOC1, JUNB, KLF1, PRDX2, NANOS3, RFX1, RFX2, and CACNG7. On the other hand, across population analysis revealed the genes like NR2F2, OSBPL10, CIDEC, WFIKKN2, ADCY, THSD7A, ADGRB3, TRPC1, VASH2, and ABHD5 to be part of selective sweeps under these comparisons. A total of 53 genes were found common between intra- and inter-population selection signature analysis of Indian yak. Notably, the genes harbouring the SNPs under selection pressure were significant for adaptation traits including lipidogenesis, energy metabolism, thermogenesis, hair follicle formation, oxidation-reduction reactions, hypoxia and reproduction. These genes may be evaluated as candidate genes for livestock adaptation to harsh environmental conditions and to further the research and application in the present era of climate change.

Background: Porcine parvoviruses (PPVs) are widespread worldwide in the swine population. PPV1 is a significant infectious agent in pig production, causing porcine reproductive failure. The pathogenic potential of novel PPVs has been poorly studied. Since wild boars are a reservoir for PPVs, the aim of this study was to investigate their prevalence and genetic diversity in the wild boar population. Tissue samples (spleen, lungs, and lymph nodes) collected from 108 wild boars from three regions of Russia during 2021-2024 were analyzed.

Results: PPV1-7 were found in wild boar populations in Russia, and the most abundant species were PPV7 (59.3%) and PPV3 (49.1%). The research did not reveal any significant relationship between the gender and age of the animals and the prevalence of PPVs. A comparison between the detection rates of PPVs and PCV2/PCV3 revealed the random nature of coinfections. For phylogenetic analysis, complete VP1/VP2 gene sequences of 17 PPV1 isolates were obtained. Most of them belonged to the 27a-like group. Two isolates were in the same cluster as the highly virulent Kresse strain. Isolate BelWB57 had amino acid substitutions that were specific to both the Kresse and 27a-like strains, but it was not classified in either group. Additionally, three sequences for PPV2, PPV3, and PPV7, and one sequence for PPV5 and PPV6 VP1/VP2 genes were obtained. PPV2, PPV3, and PPV7 isolates demonstrated distribution across various clusters with strains from domestic pigs and wild boars from different countries. PPV6 isolate was included in the same clade as the Russian isolate from a domestic pig, whereas PPV5 did not enter any clade with representatives from our country.

Conclusions: This is the first work devoted to the study of the PPV1-7 prevalence, as well as the genetic characteristics of isolates circulating among wild boars in various regions of Russia. Our data showed that PPV1-7 is widespread in wild boar populations. Phylogenetic analysis of PPV1 demonstrates a significant prevalence of 27a-like isolates.