Quantitative Trait Loci Research Articles

Salt tolerance candidate genes identified by QTL mapping, RNA-seq, and functional analysis in tilapia

Salt tolerance in fish is crucial for aquaculture as it enhances survival and productivity in varying salinity conditions, thus expanding the range of viable farming environments and improving economic sustainability. Through QTL mapping and GWAS in a hybrid F2 family of Mozambique and Nile tilapia, two large-effect QTL on chromosomes 11 and 18 were identified respectively. These two QTL explained a total of 39.9 % of the phenotypic variance. The identification of a QTL on LG11 suggests the presence of a previously unrecognized genetic factor contributing to salt tolerance. Comparative transcriptomic analysis of gill and kidney tissues between susceptible and tolerant tilapias highlights the importance of osmotic balance in regulation of salt tolerance in tilapia. Integration of QTL and RNA-seq data identified two candidate genes: acyl-coenzyme A thioesterase 5 (acot5) and sodium- and chloride-dependent taurine transporter (slc6a6) likely playing critical roles in such process. Functional analysis showed that over-expressing acot5 or slc6a6 in grouper kidney cells increased viability under salt stress by 4.46 % and 17.53 %, respectively. Subcellular localization revealed nuclear presence of ACOT5 and stress-induced nuclear translocation of SLC6A6. These findings highlight acot5 and slc6a6 as candidates for genetic manipulation and selection to enhance salt tolerance in tilapia, guiding genetic improvement efforts and promoting sustainable practices.

Implication of digestive functions and microbiota in the establishment of muscle glycogen differences between divergent lines for ultimate pH

Both the quality of chicken meat and the quality of chicks are influenced by the level of breast muscle glycogen reserves. In order to study the role of digestive metabolism in establishing this muscular phenotype, we compared two divergent chicken lines for the ultimate pH (pHu) of the breast meat, a proxy for glycogen reserves. Males aged 4 weeks had twice the breast muscle glycogen content in the pHu- line (low pHu) than in the pHu + line (high pHu). The increase in glycogen reserves (pHu-) was associated with a higher relative weight of the proventriculus and gizzard, as well as better apparent ileal digestibility of nitrogen and calcium. The diversity of the cecal microbiota was comparable, but three bacterial genera (Lachnospira, Lachnospiraceae UCG-010, Caproiciproducens) varied between the lines. The differences observed could lead to down-regulation of carbon fixation in prokaryotes and of the citrate cycle in the pHu + line. RNA-seq analysis of the jejunum, the major site of nutrient absorption, revealed 149 genes differentially expressed (DE) between the lines, including several genes linked to immunity, hormonal response and circadian rhythms that are less expressed in pHu + animals. Others involved in cell migration and proliferation, and more generally tissue morphogenesis, also differed between the lines. Among the DE genes, several co-localized with Quantitative Trait Loci (QTL) controlling pHu and selection signatures identified in the divergent lines, such as the gene coding for ghrelin, a hormone regulating appetite.

Discovery of key volatile compound and regulatory gene governing the ‘taro-like’ aroma in wax gourd (Benincasa hispida)

The distinctive ‘taro-like’ aroma in wax gourd has become a crucial flavor characteristic, significantly enhancing its market value. Despite its importance, the volatile compounds responsible for this aroma and their genetic regulatory mechanisms have not been sufficiently studied. Utilizing headspace-gas chromatography-quadrupole time of flight-mass spectrometry (HS-GC-QTOF-MS), this research examined the volatile compounds in wax gourd germplasms with and without the ‘taro-like’ aroma, identifying 2-acetyl-1-pyrroline (2-AP) as the primary volatile responsible for this aroma. To uncover the genetic basis of this trait, segregating populations and recombinant inbred lines (RILs) were developed from MY-1 x GX-71 germplasms, which exhibit significant differences in aroma expression. Genetic analysis revealed that a single recessive gene governs the ‘taro-like’ aroma trait in wax gourd. Through bulked segregant analysis sequencing (BSA-seq) and quantitative trait loci (QTL) mapping, the candidate region for this aroma gene was localized to a 285.29 kb interval on Chromosome 2. Further examination of candidate gene expression and sequence variations highlighted the BhBADH gene's critical role in the formation of the ‘taro-like’ aroma. Additionally, RNA-seq analysis revealed significant enrichment of differentially expressed genes (DEGs) associated with metabolic pathways involved in environmental information processing, which contribute to 2-AP synthesis in wax gourd with a ‘taro-like’ aroma. Our research provides a theoretical foundation for the molecular breeding to enhance the aroma quality of wax gourd.

Genetic dissection of Meloidogyne incognita resistance genes based on VIGS functional analysis in Cucumis metuliferus

The southern root-knot nematode, Meloidogyne incognita, is a highly serious plant parasitic nematode species that causes significant economic losses in various crops, including cucumber (Cucumis sativus L.). Currently, there are no commercial cultivars available with resistance to M. incognita in cucumber. However, the African horned melon (Cucumis metuliferus Naud.), a semi-wild relative of cucumber, has shown high resistance to M. incognita. In this study, we constructed an ultrahigh-density genetic linkage bin-map using low-coverage sequences from an F2 population generated through the cross between C. metuliferus inbred lines CM3 and CM27. Finally, we identified a QTL (quantitative trait locus, QTL3.1) with a LOD (logarithm of the odds) score of 3.84, explaining 8.4% of the resistance variation. Subsequently, by combining the results of qPCR (quantitative PCR) and VIGS (virus-induced gene silencing), we identified two genes, EVM0025394 and EVM0006042, that are potentially involved in the resistance to M. incognita in CM3. The identification of QTLs and candidate genes in this study serve as a basis for further functional analysis and lay the groundwork for harnessing this resistance trait.

Quantitative Trait Locus Mapping Combined with RNA Sequencing Identified Candidate Genes for Resistance to Powdery Mildew in Bitter Gourd (Momordica charantia L.)

Improving the powdery mildew resistance of bitter gourd is highly important for achieving high yield and high quality. To better understand the genetic basis of powdery mildew resistance in bitter gourd, this study analyzed 300 lines of recombinant inbred lines (RILs) formed by hybridizing the powdery mildew-resistant material MC18 and the powdery mildew-susceptible material MC402. A high-density genetic map of 1222.04 cM was constructed via incorporating 1,996,505 SNPs generated by resequencing data from 180 lines, and quantitative trait locus (QTL) positioning was performed using phenotypic data at different inoculation stages. A total of seven QTLs related to powdery mildew resistance were identified on four chromosomes, among which qPm-3-1 was detected multiple times and at multiple stages after inoculation. By selecting 18 KASP markers that were evenly distributed throughout the region, 250 lines and parents were genotyped, and the interval was narrowed to 207.22 kb, which explained 13.91% of the phenotypic variation. Through RNA-seq analysis of the parents, 11,868 differentially expressed genes (DEGs) were screened. By combining genetic analysis, gene coexpression, and sequence comparison analysis of extreme materials, two candidate genes controlling powdery mildew resistance in bitter gourd were identified (evm.TU.chr3.2934 (C3H) and evm.TU.chr3.2946 (F-box-LRR)). These results represent a step forward in understanding the genetic regulatory network of powdery mildew resistance in bitter gourd and lay a molecular foundation for the genetic improvement in powdery mildew resistance.

Parental assigned chromosomes for cultivated cacao provides insights into genetic architecture underlying resistance to vascular streak dieback.

Diseases of Theobroma cacao L. (Malvaceae) disrupt cocoa bean supply and economically impact growers. Vascular streak dieback (VSD), caused by Ceratobasidium theobromae, is a new encounter disease of cacao currently contained to southeast Asia and Melanesia. Resistance to VSD has been tested with large progeny trials in Sulawesi, Indonesia, and in Papua New Guinea with the identification of informative quantitative trait loci (QTLs). Using a VSD susceptible progeny tree (clone 26), derived from a resistant and susceptible parental cross, we assembled the genome to chromosome-level and discriminated alleles inherited from either resistant or susceptible parents. The parentally phased genomes were annotated for all predicted genes and then specifically for resistance genes of the nucleotide-binding site leucine-rich repeat class (NLR). On investigation, we determined the presence of NLR clusters and other potential disease response gene candidates in proximity to informative QTLs. We identified structural variants within NLRs inherited from parentals. We present the first diploid, fully scaffolded, and parentally phased genome resource for T. cacao L. and provide insights into the genetics underlying resistance and susceptibility to VSD.

Proteo-genomic analyses in relatively lean Chinese adults identify proteins and pathways that affect general and central adiposity levels

Adiposity is an established risk factor for multiple diseases, but the causal relationships of different adiposity types with circulating protein biomarkers have not been systematically investigated. We examine the causal associations of general and central adiposity with 2923 plasma proteins among 3977 Chinese adults (mean BMI = 23.9 kg/m²). Genetically-predicted body mass index (BMI), body fat percentage (BF%), waist circumference (WC), and waist-to-hip ratio (WHR) are significantly (FDR < 0.05) associated with 399, 239, 436, and 283 proteins, respectively, with 80 proteins associated with all four and 275 with only one adiposity trait. WHR is associated with the most proteins (n = 90) after adjusting for other adiposity traits. These associations are largely replicated in Europeans (mean BMI = 27.4 kg/m²). Two-sample Mendelian randomisation (MR) analyses in East Asians using cis-protein quantitative trait locus (cis-pQTLs) identified in GWAS find 30/2 proteins significantly affect levels of BMI/WC, respectively, with 10 showing evidence of colocalisation, and seven (inter-alpha-trypsin inhibitor heavy chain H3, complement factor B, EGF-containing fibulin-like extracellular matrix protein 1, thioredoxin domain-containing protein 15, alpha-2-antiplasmin, fibronectin, mimecan) are replicated in separate MR using different cis-pQTLs identified in Europeans. These findings identified potential novel mechanisms and targets, to our knowledge, for improved treatment and prevention of obesity and associated diseases.

HBI: a hierarchical Bayesian interaction model to estimate cell-type-specific methylation quantitative trait loci incorporating priors from cell-sorted bisulfite sequencing data

Methylation quantitative trait loci (meQTLs) quantify the effects of genetic variants on DNA methylation levels. However, most published studies utilize bulk methylation datasets composed of different cell types and limit our understanding of cell-type-specific methylation regulation. We propose a hierarchical Bayesian interaction (HBI) model to infer cell-type-specific meQTLs, which integrates a large-scale bulk methylation data and a small-scale cell-type-specific methylation data. Through simulations, we show that HBI enhances the estimation of cell-type-specific meQTLs. In real data analyses, we demonstrate that HBI can further improve the functional annotation of genetic variants and identify biologically relevant cell types for complex traits.

Breeding for flooding tolerance in rice: Advancements and future perspectives

Rice, the resilient grain, is cultivated in different types of ecosystems, from seacoast to hilly areas. Unfortunately, due to climate change, it frequently suffers from submergence stress during its growth period. Anoxic stress at the germination phase, flash flooding during the vegetative phase and water stagnation in low-lying areas are the major types of flooding in rice. When floodwaters rise, rice adapts. Some varieties stretch their stems toward the surface, gasping for air. Others remain dormant, conserving energy, like the FR13A landrace, SUB1 equips rice with underwater endurance. It orchestrates a genetic symphony, fine-tuning metabolic pathways and signalling survival. Some of the promising quantitative trait locus (QTLs) identified are qAG-9-2, which is responsible for anaerobic germination tolerance; qSUB1 for vegetative stage submergence tolerance, and qTIL12 for deep water adaptation. Identifying other novel QTLs and donors helps to breed varieties tolerant to different types of submergence stress. Along with Swarna SUB1, many mega-submergence-tolerant varieties have been developed and released for cultivation in Asia. As we cultivate these versatile survivors, we sow hope for a food-secure future.

A high-throughput protocol for testing heat-stress tolerance in pollen

AbstractViable pollen is crucial for fertilization, but pollen is generally highly susceptible to heat stress. A quick, reliable method for testing the heat-stress tolerance of pollen is needed to improve the heat-stress tolerance in plants, but current methods require considerable space and labor. In addition, many such methods only test tolerance to a single constant temperature, making it time-consuming to screen heat tolerance over a wide temperature range and to examine the dynamics of pollen viability at different temperatures. To address this issue, we aimed to: (1) develop an easy, reliable method for measuring pollen viability at different temperatures; and (2) identify the best temperature range for screening pollen with high heat-stress tolerance. We harvested mature pollen from wheat (Triticum aestivum) plants and transferred it to a 96-well plate filled with liquid medium containing sucrose. We placed the plate in a PCR machine operating under a gradient PCR program to simultaneously test a range of temperatures. After incubating the pollen for 4 h, at temperatures ranging from 21.9 to 47 °C, we examined the pollen grains under a light microscope and employed a specific image analysis pipeline to assess the effects of temperature on pollen morphology, germination, and tube growth. This method facilitated the high-throughput screening of many pollen samples, enabling rapid, reliable, and precise analysis of pollen viability in response to temperature. Our approach should be applicable to other plant species and could be used to identify quantitative trait loci or genes influencing heat stress tolerance in pollen for breeding programs.

Multiomics integration unravels genotype‐microbiome interactions shaping the conjunctival transcriptome

AbstractGene expression is a molecular phenotype shaped by the interplay between genotype and environment. The microbiome represents a critical environmental exposure for the host. However, the genotype‐microbiome interactions (GMIs) shaping the host transcriptome remain largely unexplored. Here, we integrated conjunctival multiomics data from 120 pairs of twins to investigate GMIs. We identified 272,972 expression quantitative trait loci associated with 5946 genes and 241,073 genotype‐controlled correlations between gene expression and microbial abundance. We developed a modeling approach, MicroGenix, that screens for GMIs from host genome, transcriptome, and microbiome data and identifies GMIs associated with the disease through gene‐based association tests. We applied MicroGenix to the conjunctival data set and found that incorporating GMIs into gene expression prediction models significantly increased prediction accuracy. The genes with increased accuracy were overrepresented by those encoding cell adhesion molecules. We further used MicroGenix to predict the transcriptome from conjunctival metagenomes and identified GMIs associated with ocular surface disease. Our work provides a resource for studying host‐microbiome interactions at the conjunctiva and a computational approach to investigate GMIs using multiomics data.

A network-based systems genetics framework identifies pathobiology and drug repurposing in Parkinson's disease.

Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder. However, current treatments are directed at symptoms and lack ability to slow or prevent disease progression. Large-scale genome-wide association studies (GWAS) have identified numerous genomic loci associated with PD, which may guide the development of disease-modifying treatments. We presented a systems genetics approach to identify potential risk genes and repurposable drugs for PD. First, we leveraged non-coding GWAS loci effects on multiple human brain-specific quantitative trait loci (xQTLs) under the protein-protein interactome (PPI) network. We then prioritized a set of PD likely risk genes (pdRGs) by integrating five types of molecular xQTLs: expression (eQTLs), protein (pQTLs), splicing (sQTLs), methylation (meQTLs), and histone acetylation (haQTLs). We also integrated network proximity-based drug repurposing and patient electronic health record (EHR) data observations to propose potential drug candidates for PD treatments. We identified 175 pdRGs from QTL-regulated GWAS findings, such as SNCA , CTSB , LRRK2, DGKQ , CD38 and CD44 . Multi-omics data validation revealed that the identified pdRGs are likely to be druggable targets, differentially expressed in multiple cell types and impact both the parkin ubiquitin-proteasome and alpha-synuclein (a-syn) pathways. Based on the network proximity-based drug repurposing followed by EHR data validation, we identified usage of simvastatin as being significantly associated with reduced incidence of PD (fall outcome: hazard ratio (HR) = 0.91, 95% confidence interval (CI): 0.87-0.94; for dementia outcome: HR = 0.88, 95% CI: 0.86-0.89), after adjusting for 267 covariates. Our network-based systems genetics framework identifies potential risk genes and repurposable drugs for PD and other neurodegenerative diseases if broadly applied.

Identification of candidate genes for endometrial cancer in multi-omics: a Mendelian randomization analysis

Endometrial cancer is the most common malignant tumor of the uterus, but the underlying genetic mechanisms of EC remain unclear. To identify candidate genes and investigate genetic mechanisms for endometrial cancer, we utilized the summary-data-based Mendelian randomization (SMR) method to investigate causal associations between genetic variants, gene expression, DNA methylation, and endometrial cancer. Three main analyses were conducted utilizing cis-expression and methylation quantitative trait loci (eQTLs and mQTLs) as instrumental variables to examine causal relationships with endometrial cancer, and assessing the causal relationship between DNA methylation and gene expression. Data sources included genetic association data from O'Mara et al. eQTL data from the GTEx database, and mQTL data from McRae et al. Analysis involved the HEIDI test to distinguish pleiotropy, SMR analysis with multiple testing correction, and colocalization analysis to assess associations driven by linkage disequilibrium. Functional enrichment analysis was performed by the Metascape tool. Our study showed that three genes, SNX11, LINC00243, and EVI2A, were identified as causally related to endometrial cancer. SNX11 exhibited a positive causal relationship, while LINC00243 and EVI2A showed negative ones. Furthermore, 24 CpG sites were identified as causally related to endometrial cancer, with cg14424631 (CYP19A1) being the most significant. The study revealed common genes implicated in endometrial cancer, gene expression, and methylation sites, with LINC00243 playing a key role. Colocalization analysis confirmed significant causal relationships between LINC00243, SNX11, and endometrial cancer. Enrichment analysis uncovered pathways like interferon gamma signaling enriched in both endometrial cancer GWAS and e/mQTL. These findings shed light on the molecular mechanisms underlying endometrial cancer development. The study identified candidate genes and DNA methylation loci causally associated with endometrial cancer, which are expected to serve as potential targets for treatment.

HMGCR as a promising molecular target for therapeutic intervention in aortic aneurisms: a mendelian randomization study

BackgroundDespite the exploration of the connections between serum low-density lipoprotein cholesterol (LDL-C) levels and aneurisms in epidemiological studies, causality remains unclear. Therefore, this study aimed to assess the causal impact of LDL-C-lowering targets (HMGCR, PCSK9, NPC1L1, CETP, APOB, and LDLR) on various forms of aneurisms using Mendelian Randomization (MR) analysis.MethodsTwo genetic instruments acted as proxies for exposure to LDL-C-lowering drugs: expression quantitative trait loci of drug target genes and genetic variants linked to LDL-C near drug target genes. Summary-data-based MR (SMR), inverse-variance-weighted MR (IVW-MR), and multivariable MR (MVMR) methods were employed to compute the effect estimates.ResultsThe SMR analysis revealed substantial associations between increased HMGCR expression and a heightened risk of aortic aneurism (odds ratio [OR] = 1.603, 95% confidence interval [CI] = 1.209–2.124), thoracic aortic aneurism (OR = 1.666, 95% CI = 1.122–2.475), and abdominal aortic aneurism (OR = 1.910, 95% CI = 1.278–2.856). Likewise, IVW-MR analysis demonstrated positive correlations between HMGCR-mediated LDL-C and aortic aneurism (OR = 2.228, 95% CI = 1.702–2.918), thoracic aortic aneurism (OR = 1.751, 95% CI = 1.191–2.575), abdominal aortic aneurism (OR = 4.784, 95% CI = 3.257–7.028), and cerebral aneurism (OR = 1.993, 95% CI = 1.277–3.110). Furthermore, in the MVMR analysis, accounting for body mass index, smoking, and hypertension, a significant positive relationship was established between HMGCR-mediated LDL-C levels and the development of aortic aneurisms, encompassing both thoracic and abdominal subtypes. Similarly, consistent positive associations were observed for PCSK9 and CETP genes, as well as PCSK9-mediated and CETP-mediated LDL-C levels, with the occurrence of aortic aneurism and abdominal aortic aneurism. Nonetheless, the evidence for potential associations between APOB, NPC1L1 and LDLR with specific subtypes of aortic aneurisms lacked consistent support from both SMR and IVW-MR analyses.ConclusionsOur MR analysis offered compelling evidence of a plausible causal link between HMGCR and an increased risk of aortic aneurism, encompassing both thoracic and abdominal types. These groundbreaking findings further bolster the case for the deployment of HMGCR inhibitors in the treatment of aortic aneurisms, including both thoracic and abdominal variants.

Validation of TYK2 and exploration of PRSS36 as drug targets for psoriasis using Mendelian randomization

Psoriasis is a chronic inflammatory skin disorder with multiple causes, including genetic and environmental factors. Despite advances in treatment, there remains a need to identify novel therapeutic targets. A Mendelian randomization (MR) analysis was conducted to identify therapeutic targets for psoriasis. Data on cis-expression quantitative trait loci were obtained from the eQTLGen Consortium (n = 31,684). Summary statistics for psoriasis (outcome) were sourced from the GWAS Catalog with a sample size of 484,598, including 5,427 cases and 479,171 controls. Colocalization analysis was used to assess whether psoriasis risk and gene expression were driven by shared single nucleotide polymorphisms. Drug prediction and molecular docking were utilized to validate the pharmacological value of the drug targets. The MR analysis found that 81 drug targets were significantly associated, and two (TYK2 and PRSS36) were supported by colocalization analysis (PP.H4 > 0.80). Phenome-wide association studies did not show any associations with other traits at the gene level. Biologically, these genes were closely related to immune function. Molecular docking revealed strong binding with drugs and proteins, as supported by available structural data. This study validated TYK2 as a drug target for psoriasis, in line with its existing clinical use, including the development of decucravacitinib. PRSS36 is a potential novel target requiring further investigation.

Pangenome – its Aspect and Prospect

Pangenome is a very new discipline showing a collection of unique and variable genomes of any species in one model. This discipline is a combination of three subjects of Biology like Computer Science and Applied Mathematics. All genes from all species or strains are called Pangenomes. Pangenome was first discovered during the preparation of protein-based vaccine of the G group of Streptococcus sp. which is the main cause of neonatal life-threatening disease by a team of scientists led by Tettelin. Conventional method of Vaccine preparation was not successful due to the existence of five different serotypes in this organism. Then Tettelin with groups of scientists tried to use the genome sequence information for preparing protein- based vaccine in G group of Streptococcus. During their studies, two gap free genomes were found in this G group of bacteria. After sequencing studies of many strains, they found several regions of diversity (genomic diversity) among different isolates of same strain. After sequencing a large number of genomes, they realized that genes belong to isolates present in the species but not present in genomes. Then the Pangenome concept came to their mind and genes were classified into Core genome, Accessory or Dispensable genome and Species-specific genomes. Bacteria genomes have a distinct number of genes but there are differences in the genomes of different strains of a single species that is mainly due to horizontal gene transfer of genes from other bacteria. The Pangenome concept and its discovery was discussed. Plant genomes are dynamic with whole genome duplication, tandem duplication, transposable element duplication, chromosomal rearrangements or structural variations like deletion, inversion, translocation and recombination between species. Several methods have already been tried for the improvement of crop plants like Marker Assisted Selection (MAS), QTL mapping, Single Nucleotide Polymorphisms (SNP), Copy number variations and Genome Wide Association Studies (GWAS) by comparing with Reference Genome Assemblies representing the genome sequence of the genome of an individual or organism. But the Reference genome is not able to locate all genetic variations of a species. To overcome this limitation the concept of Pangenomes may help in the crop improvement which has been discussed. Pangenomic studies have already been done in many crop plants like Rice, Wheat, Maize, Brassica, Soybean etc. Some of all these aspects have been discussed.

Dnajc3 (HSP40) Modulates Axon Regeneration in the Mouse Optic Nerve.

The present study is designed to identify the genes modulating optic nerve regeneration in the mouse. Using the BXD mouse strains as a genetic mapping panel, we examined differential responses to axon regeneration in order to map genomic loci modulating axonal regeneration. To study regeneration in the optic nerve, Pten was knocked down in the retinal ganglion cells using adeno-associated virus (AAV) delivery of an shRNA, followed by the induction of a mild inflammatory response by an intravitreal injection of Zymosan with CPT-cAMP. The axons of the retinal ganglion cells were damaged by optic nerve crush (ONC). Following a 12-day survival period, regenerating axons were labeled by Cholera Toxin B. Two days later, the regenerating axons within the optic nerve were examined to determine the number of regenerating axons and the distance traveled down the optic nerve. An integral genomic map was made using the regenerative response. Candidate genes were tested by knocking down expression using shRNA or by overexpressing the gene in AAV vectors. The analysis revealed a considerable amount of differential axonal regeneration across all 33 BXD strains, demonstrated by the number of axons regenerating and the length of the regenerating axons. Some strains (BXD99, BXD90, and BXD29) demonstrated significant axonal regeneration; while other strains (BXD13, BXD18, and BXD34) had very little axon regrowth. Within the regenerative data, there was a 4-fold increase in distance regenerated and a 7.5-fold difference in the number of regenerating axons. These data were used to map a quantitative trait locus modulating axonal regeneration to Chromosome 14 (115 to 119 Mb). Within this locus were 16 annotated genes. Subsequent testing revealed that one candidate gene, Dnajc3 , modulates axonal regeneration. Knocking down of Dnajc3 led to a decreased regeneration response in the high regenerative strains (BXD90), while overexpression of Dnajc3 resulted in an increased regeneration response in C57BL/6J and a low regenerative strain (BXD34). In this study, Dnajc3 (encodes Heat Shock Protein 40, HSP40, a molecular chaperone) was identified as a modulator of axon regeneration in mice. This is the first report defining the role of Dnajc3 (HSP40) in axon regeneration.

Integrating Quantitative Trait Loci (QTLs) through Meta-QTL (MQTL) Analysis to Facilitate Identification of Relationships between Phenotype and Genotype

Integrating Quantitative Trait Loci (QTLs) through Meta-QTL (MQTL) Analysis to Facilitate Identification of Relationships between Phenotype and Genotype

Development of a high-density genetic linkage map and identification of quantitativetrait loci (QTLs) associated with Botrytis cinerea resistance in strawberry(Fragaria × ananassa Duch.)

Development of a high-density genetic linkage map and identification of quantitativetrait loci (QTLs) associated with Botrytis cinerea resistance in strawberry(Fragaria × ananassa Duch.)

SNP-Based and Kmer-Based eQTL Analysis Using Transcriptome Data.

Traditional expression quantitative trait locus (eQTL) mapping associates single nucleotide polymorphisms (SNPs) with gene expression, where the SNPs are derived from large-scale whole-genome sequencing (WGS) data or transcriptome data. While WGS provides a high SNP density, it also incurs substantial sequencing costs. In contrast, RNA-seq data, which are more accessible and less expensive, can simultaneously yield gene expressions and SNPs. Thus, eQTL analysis based on RNA-seq offers significant potential applications. Two primary strategies were employed for eQTL in this study. The first involved analyzing expression levels in relation to variant sites detected between populations from RNA-seq data. The second approach utilized kmers, which are sequences of length k derived from RNA-seq reads, to represent variant sites and associated these kmer genotypes with gene expression. We discovered 87 significant association signals involving eGene on the basis of the SNP-based eQTL analysis. These genes include DYNLT1, NMNAT1, and MRLC2, which are closely related to neurological functions such as motor coordination and homeostasis, play a role in cellular energy metabolism, and function in regulating calcium-dependent signaling in muscle contraction, respectively. This study compared the results obtained from eQTL mapping using RNA-seq identified SNPs and gene expression with those derived from kmers. We found that the vast majority (23/30) of the association signals overlapping the two methods could be verified by haplotype block analysis. This comparison elucidates the strengths and limitations of each method, providing insights into their relative efficacy for eQTL identification.