Significant Quantitative Trait Loci Research Articles

Unraveling genetic underpinnings of purine content in pork

The significance of purine base content as an important nutrition indicator in foods arises from its potential to trigger hyperuricemia or gout via high-purine diet. Livestock meats, including pork, generally contain moderate to high total purine content (TP). Recent research revealed substantial variations within and across pig breeds, implying genetic factors influencing this trait. Thus, this study aimed to unravel the genetic underpinnings governing purine base content in pork. The heritability estimates (h2) for the four purine traits ranged from 0.14 to 0.35. A total of 14, 36, 19 and 25 quantitative trait loci (QTLs) were identified for guanine, adenine, hypoxanthine, and TP, respectively. Our comprehensive gene set enrichment analysis and gene network analysis revealed 15 promising candidate genes intricately interwoven within diverse purine metabolism pathways, such as purine ribonucleotide metabolic process, purine nucleotide metabolism and transport, and purine salvage pathways, all contributing to TP. Strikingly, most genetic variants significantly associated with TP displayed analogous effects on multiple purine bases. Two distinct and highly significant QTLs (P < 10-12) emerged on Sus Scrofa chromosome (SSC) 12: one impacting guanine content and the other concurrently influencing adenine and hypoxanthine levels. The peak of the guanine QTL on SSC12 resided 1.1 kb downstream of the transmembrane protein 238 like (TMEM238L) gene and is encapsulated within a genomic segment characterized by the histone modification H3K27me3. Focused fine-mapping for the SSC12 QTL associated with adenine and hypoxanthine levels narrowed its scope to around 172 kb, encompassing the growth arrest specific 7 (GAS7) and myosin heavy chain 13 (MYH13) genes. However, the observed QTL effect was not attributed to any missense mutations within the two genes. This pioneering study unveils the genetic variations and candidate genes associated with purine content in livestock, laying a robust foundation for the selective breeding of pig lines with reduced purine base content.

Identification of Quantitative Trait Loci Controlling Root Morphological Traits in an Interspecific Soybean Population Using 2D Imagery Data.

Roots are the hidden and most important part of plants. They serve as stabilizers and channels for uptaking water and nutrients and play a crucial role in the growth and development of plants. Here, two-dimensional image data were used to identify quantitative trait loci (QTL) controlling root traits in an interspecific mapping population derived from a cross between wild soybean 'PI366121' and cultivar 'Williams 82'. A total of 2830 single-nucleotide polymorphisms were used for genotyping, constructing genetic linkage maps, and analyzing QTLs. Forty-two QTLs were identified on twelve chromosomes, twelve of which were identified as major QTLs, with a phenotypic variation range of 36.12% to 39.11% and a logarithm of odds value range of 12.01 to 17.35. Two significant QTL regions for the average diameter, root volume, and link average diameter root traits were detected on chromosomes 3 and 13, and both wild and cultivated soybeans contributed positive alleles. Six candidate genes, Glyma.03G027500 (transketolase/glycoaldehyde transferase), Glyma.03G014500 (dehydrogenases), Glyma.13G341500 (leucine-rich repeat receptor-like protein kinase), Glyma.13G341400 (AGC kinase family protein), Glyma.13G331900 (60S ribosomal protein), and Glyma.13G333100 (aquaporin transporter) showed higher expression in root tissues based on publicly available transcriptome data. These results will help breeders improve soybean genetic components and enhance soybean root morphological traits using desirable alleles from wild soybeans.

Posttranscriptional regulation of FAN1 by miR-124-3p at rs3512 underlies onset-delaying genetic modification in Huntington’s disease

Many Mendelian disorders, such as Huntington's disease (HD) and spinocerebellar ataxias, arise from expansions of CAG trinucleotide repeats. Despite the clear genetic causes, additional genetic factors may influence the rate of those monogenic disorders. Notably, genome-wide association studies discovered somewhat expected modifiers, particularly mismatch repair genes involved in the CAG repeat instability, impacting age at onset of HD. Strikingly, FAN1, previously unrelated to repeat instability, produced the strongest HD modification signals. Diverse FAN1 haplotypes independently modify HD, with rare genetic variants diminishing DNA binding or nuclease activity of the FAN1 protein, hastening HD onset. However, the mechanism behind the frequent and the most significant onset-delaying FAN1 haplotype lacking missense variations has remained elusive. Here, we illustrated that a microRNA acting on 3'-UTR (untranslated region) SNP rs3512, rather than transcriptional regulation, is responsible for the significant FAN1 expression quantitative trait loci signal and allelic imbalance in FAN1 messenger ribonucleic acid (mRNA), accounting for the most significant and frequent onset-delaying modifier haplotype in HD. Specifically, miR-124-3p selectively targets the reference allele at rs3512, diminishing the stability of FAN1 mRNA harboring that allele and consequently reducing its levels. Subsequent validation analyses, including the use of antagomir and 3'-UTR reporter vectors with swapped alleles, confirmed the specificity of miR-124-3p at rs3512. Together, these findings indicate that the alternative allele at rs3512 renders the FAN1 mRNA less susceptible to miR-124-3p-mediated posttranscriptional regulation, resulting in increased FAN1 levels and a subsequent delay in HD onset by mitigating CAG repeat instability.

Genetic linkage mapping and quantitative trait locus (QTL) analysis of sweet basil (Ocimum basilicum L.) to identify genomic regions associated with cold tolerance and major volatiles.

Chilling sensitivity is one of the greatest challenges affecting the marketability and profitability of sweet basil (Ocimum basilicum L.) in the US and worldwide. Currently, there are no sweet basils commercially available with significant chilling tolerance and traditional aroma profiles. This study was conducted to identify quantitative trait loci (QTLs) responsible for chilling tolerance and aroma compounds in a biparental mapping population, including the Rutgers advanced breeding line that served as a chilling tolerant parent, 'CB15', the chilling sensitive parent, 'Rutgers Obsession DMR' and 200 F2 individuals. Chilling tolerance was assessed by percent necrosis using machine learning and aroma profiling was evaluated using gas chromatography (GC) mass spectrometry (MS). Single nucleotide polymorphism (SNP) markers were generated from genomic sequences derived from double digestion restriction-site associated DNA sequencing (ddRADseq) and converted to genotype data using a reference genome alignment. A genetic linkage map was constructed and five statistically significant QTLs were identified in response to chilling temperatures with possible interactions between QTLs. The QTL on LG24 (qCH24) demonstrated the largest effect for chilling response and was significant in all three replicates. No QTLs were identified for linalool, as the population did not segregate sufficiently to detect this trait. Two significant QTLs were identified for estragole (also known as methyl chavicol) with only qEST1 on LG1 being significant in the multiple-QTL model (MQM). QEUC26 was identified as a significant QTL for eucalyptol (also known as 1,8-cineole) on LG26. These QTLs may represent key mechanisms for chilling tolerance and aroma in basil, providing critical knowledge for future investigation of these phenotypic traits and molecular breeding.

Pooled DNA sequencing in hairy vetch (Vicia villosa Roth) reveals QTL for seed dormancy but not pod dehiscence.

Hairy vetch (Vicia villosa Roth) is a promising legume cover crop, but its use is limited by high rates of pod dehiscence and seed dormancy. We used phenotypically contrasting pooled DNA samples (n=24 with 29-74 individuals per sample) from an ongoing cover crop breeding program across four environments (site-year combinations: Maryland 2020, Maryland 2022, Wisconsin 2021, Wisconsin 2022) to find genetic associations and genomic prediction accuracies for pod dehiscence and seed dormancy. We also combined pooled DNA sample genetic association results with the results of a prior genome-wide association study. Genomic prediction resulted in positive predictive abilities for both traits between environments and with an independent dataset (0.34-0.50), but reduced predictive ability for DNA pools with divergent seed dormancy in the Maryland environments (0.07-0.15). The pooled DNA samples found six significant (false discovery rate q-value<0.01) quantitative trait loci (QTL) for seed dormancy and four significant QTL for pod dehiscence. Unfortunately, the minor alleles of the pod dehiscence QTL increased the rate of pod dehiscence and are not useful for marker-assisted selection. When combined with a prior association study, sixteen seed dormancy QTL and zero pod dehiscence QTL were significant. Combining the association studies did not increase the detection of useful QTL.

RNA m6a Methylation Regulator Expression in Castration-Resistant Prostate Cancer Progression and Its Genetic Associations.

N6-methyladenosine (m6A) methylation, a prevalent epitranscriptomic modification, plays a crucial role in regulating mRNA expression, stability, and translation in mammals. M6A regulators have gained attention for their potential implications in tumorigenesis and clinical applications, such as cancer diagnosis and therapeutics. The existing literature predominantly addresses m6A regulators in the context of primary prostate cancer (PCa). However, a notable gap in the knowledge emerges regarding the dynamic expression patterns of these regulators as PCa progresses towards the castration-resistant stage (CRPC). Employing sequential window acquisition of all theoretical mass spectra (SWATH-MS) and RNAseq analysis, we comprehensively profiled the expression of 27 m6A regulators in hormone/androgen-dependent and -independent PCa cell lines, revealing distinct clustering between tumor and adjacent normal prostate tissues. High-grade PCa tumors demonstrated the upregulation of METTL3, RBM15B, and HNRNAPA2B1 and the downregulation of ZC3H13, NUDT21, and FTO. Notably, we identified six m6A regulators associated with PCa survival. Additionally, association analysis of the PCa-associated risk loci in the cancer genome atlas program (TCGA) data unveiled genetic variations near the WTAP, HNRNPA2B1, and FTO genes as significant expression quantitative trait loci. In summary, our study unraveled abnormalities in m6A regulator expression in PCa progression, elucidating their association with PCa risk loci. Considering the heterogeneity within the PCa phenotypes and treatment responses, our findings suggest that prognostic stratification based on m6A regulator expression could enhance PCa diagnosis and prognosis.

Genome-Wide Association Studies on the Kernel Row Number in a Multi-Parent Maize Population.

Kernel row number (KRN) is a crucial trait in maize that directly influences yield; hence, understanding the mechanisms underlying KRN is vital for the development of high-yielding inbred lines and hybrids. We crossed four excellent panicle inbred lines (CML312, CML444, YML46, and YML32) with Ye107, and after eight generations of selfing, a multi-parent population was developed comprising four subpopulations, each consisting of 200 lines. KRN was accessed in five environments in Yunnan province over three years (2019, 2021, and 2022). The objectives of this study were to (1) identify quantitative trait loci and single nucleotide polymorphisms associated with KRN through linkage and genome-wide association analyses using high-quality genotypic data, (2) identify candidate genes regulating KRN by identifying co-localized QTLs and SNPs, and (3) explore the pathways involved in KRN formation and identify key candidate genes through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Our study successfully identified 277 significant Quantitative trait locus (QTLs) and 53 significant Single Nucleotide Polymorphism (SNPs) related to KRN. Based on gene expression, GO, and KEGG analyses, SNP-177304649, SNP-150393177, SNP-135283055, SNP-138554600, and SNP-120370778, which were highly likely to be associated with KRN, were identified. Seven novel candidate genes at this locus (Zm00001d022420, Zm00001d022421, Zm00001d016202, Zm00001d050984, Zm00001d050985, Zm00001d016000, and Zm00014a012929) are associated with KRN. Among these, Zm00014a012929 was identified using the reference genome Mo17. The remaining six genes were identified using the reference genome B73. To our knowledge, this is the first report on the association of these genes with KRN in maize. These findings provide a theoretical foundation and valuable insights into the genetic mechanisms underlying maize KRN and the development of high-yielding hybrids through heterosis.

Identification of additional body weight QTLs in the Berlin Fat Mouse BFMI861 lines using time series data

The Berlin Fat Mouse Inbred line (BFMI) is a model for obesity and metabolic syndrome. The sublines BFMI861-S1 and BFMI861-S2 differ in weight despite high genetic similarity and a shared obesity-related locus. This study focused on identifying additional body weight quantitative trait loci (QTLs) by analyzing weekly weight measurements in a male population of the advanced intercross line BFMI861-S1 x BFMI861-S2. QTL analysis, utilizing 200 selectively genotyped mice (GigaMUGA) and 197 males genotyped for top SNPs, revealed a genome-wide significant QTL on Chr 15 (68.46 to 81.40 Mb) for body weight between weeks 9 to 20. Notably, this QTL disappeared (weeks 21 to 23) and reappeared (weeks 24 and 25) coinciding with a diet change. Additionally, a significant body weight QTL on Chr 16 (3.89 to 22.79 Mb) was identified from weeks 6 to 25. Candidate genes, including Gpt, Cbx6, Apol6, Apol8, Sun2 (Chr 15) and Trap1, Rrn3, Mapk1 (Chr 16), were prioritized. This study unveiled two additional body weight QTLs, one of which is novel and responsive to diet changes. These findings illuminate genomic regions influencing weight in BFMI and emphasize the utility of time series data in uncovering novel genetic factors.

Unveiling the predominance of Saccharum spontaneum alleles for resistance to orange rust in sugarcane using genome-wide association.

Six QTLs of resistance to sugarcane orange rust were identified in modern interspecific hybrids by GWAS. For five of them, the resistance alleles originated from S. spontaneum. Altogether, they efficiently predict disease resistance. Sugarcane orange rust (SOR) is a threatening emerging disease in many sugarcane industries worldwide. Improving the genetic resistance of commercial cultivars remains the most promising solution to control this disease. In this study, an association panel of 568 modern interspecific sugarcane hybrids (Saccharum officinarum x S. spontaneum) from Réunion's breeding program was evaluated for its resistance to SOR under natural conditions of infection. Two genome-wide association studies (GWAS) were conducted between disease reactions and 183,842 single nucleotide polymorphism (SNP) markers obtained by targeted genotyping-by-sequencing. Five resistance quantitative trait loci (QTLs), named Oru1, Oru2, Oru3, Oru4 and Oru5, were identified using a single-locus GWAS (SL-GWAS). These five QTLs all originated from the species S. spontaneum. A multi-locus GWAS (ML-GWAS) uncovered an additional but less significant resistance QTL named Oru6, which originated from S. officinarum. All six QTLs had a moderate to major phenotypic effect on disease resistance. Prediction accuracy estimated with linear regression models based on each of the five QTLs identified by SL-GWAS was between 0.16-0.41. Altogether, these five QTLs provided a relatively high prediction accuracy of 0.60. In comparison, accuracies obtained with six genome-wide prediction models (i.e., GBLUP, Bayes-A, Bayes-B, Bayes-C, Bayesian Lasso and RKHS) reached only 0.65. The good prediction accuracy of disease resistance provided by the QTLs and the predominant S. spontaneum origin of their resistance alleles pave the way for effective marker-assisted breeding strategies.

Genetic Analyses of the Shape and Volume of Onion Bulbs and Daylength Effects on Bulbing

The formation of onion (Allium cepa) bulbs is affected by photoperiod length and onion germplasm is commonly classified as short- (SD), intermediate-, or long-day (LD) types. The objectives of this study were to develop a segregating family from a cross between doubled haploids (DHs) of LD and SD onions and complete genetic analyses of bulb shape and volume, as well as daylength effects on bulbing. DH parents and F1 and F2 progenies were grown in a greenhouse under lengthening days. The diameters of the neck constriction and pseudostems were measured weekly and their ratio was used as the determinant of bulbing. Bulbs were harvested when the foliage collapsed and the diameters and heights of individual bulbs were measured and used to calculate a shape index (diameter divided by height) and bulb volume. Single nucleotide polymorphisms (SNPs) were genotyped and a genetic map of 112 SNPs constructed. Genetic analysis revealed two highly significant quantitative trait loci (QTL) affected bulbing under increasing daylengths, and both QTL showed significant additive effects with no dominance. One highly significant QTL was detected for bulb-shape index and explained 30% of phenotypic variation for bulb shape. Three additional QTL were slightly above the significance threshold, and together these four QTL explained over 50% of the phenotypic variation for bulb shape. No significant QTL were detected for bulb volume. These results reveal that bulb-shape and daylength effects on bulbing are relatively simply inherited, and this research should facilitate introgression of traits between onion populations of different daylength sensitivities and efforts to modify bulb shape.

Genome-Wide Association Analysis of Seed Vigor-Related Traits in Wheat

Seed vigor is a crucial indicator comprehensively assessing the quality of seeds, reflecting the growth advantage and production potential of seeds, and has a significant effect on seeds’ stress resistance. Identifying and controlling loci related to wheat seed vigor is essential for accelerating genetic trait gains. Here, we performed a large genome-wide association study (GWAS) to identify several significant quantitative trait loci (QTLs) associated with seed vigor-related traits. A total of 404 wheat samples with diverse genetic backgrounds were used as experimental materials. Twenty-eight loci significantly associated with seed vigor-related traits in wheat were identified, distributed on chromosomes 3A, 4A, 5B, 7A, and 7B. Two potential novel loci controlling wheat seed vigor were discovered, with a total of 80 candidate genes associated with seed vigor located on these loci. Among them, TraesCS4A01G020000.1 encodes a late embryogenesis abundant (LEA) protein gene, and TraesCS5B01G298500.1 encodes a helicase gene, both showing specific expression in seeds and highly correlated with seed vigor. Overall, these findings provide valuable insights for the future application of these genes in wheat breeding.

Identification of Yellow Seed Color Genes Using Bulked Segregant RNA Sequencing in Brassica juncea L.

Yellow seed breeding is an effective method to improve oil yield and quality in rapeseed (Brassica napus L.). However, naturally occurring yellow-seeded genotypes have not been identified in B. napus. Mustard (Brassica juncea L.) has some natural, yellow-seeded germplasms, yet the molecular mechanism underlying this trait remains unclear. In this study, a BC9 population derived from the cross of yellow seed mustard "Wuqi" and brown seed mustard "Wugong" was used to analyze the candidate genes controlling the yellow seed color of B. juncea. Subsequently, yellow-seeded (BY) and brown-seeded (BB) bulks were constructed in the BC9 population and subjected to bulked segregant RNA sequencing (BSR-Seq). A total of 511 differentially expressed genes (DEGs) were identified between the brown and yellow seed bulks. Enrichment analysis revealed that these DEGs were involved in the phenylpropanoid biosynthetic process and flavonoid biosynthetic process, including key genes such as 4CL, C4H, LDOX/TT18, PAL1, PAL2, PAL4, TT10, TT12, TT4, TT8, BAN, DFR/TT3, F3H/TT6, TT19, and CHI/TT5. In addition, 111,540 credible single-nucleotide polymorphisms (SNPs) and 86,319 INDELs were obtained and used for quantitative trait locus (QTL) identification. Subsequently, two significant QTLs on chromosome A09, namely, qSCA09-3 and qSCA09-7, were identified by G' analysis, and five DEGs (BjuA09PAL2, BjuA09TT5, BjuA09TT6, BjuA09TT4, BjuA09TT3) involved in the flavonoid pathway were identified as hub genes based on the protein-to-protein network. Among these five genes, only BjuA09PAL2 and BjuA09F3H had SNPs between BY and BB bulks. Interestingly, the majority of SNPs in BjuA09PAL2 were consistent with the SNPs identified between the high-quality assembled B. juncea reference genome "T84-66" (brown-seed) and "AU213" (yellow-seed). Therefore, BjuA09PAL2, which encodes phenylalanine lyase, was considered as the candidate gene associated with yellow seed color of B. juncea. The identification of a novel gene associated with the yellow seed coloration of B. juncea through this study may play a significant role in enhancing yellow seed breeding in rapeseed.

Identification of a Branch Number Locus in Soybean Using BSA-Seq and GWAS Approaches.

The determination of the soybean branch number plays a pivotal role in plant morphogenesis and yield components. This polygenic trait is subject to environmental influences, and despite its significance, the genetic mechanisms governing the soybean branching number remain incompletely understood. To unravel these mechanisms, we conducted a comprehensive investigation employing a genome-wide association study (GWAS) and bulked sample analysis (BSA). The GWAS revealed 18 SNPs associated with the soybean branch number, among which qGBN3 on chromosome 2 emerged as a consistently detected locus across two years, utilizing different models. In parallel, a BSA was executed using an F2 population derived from contrasting cultivars, Wandou35 (low branching number) and Ruidou1 (high branching number). The BSA results pinpointed a significant quantitative trait locus (QTL), designated as qBBN1, located on chromosome 2 by four distinct methods. Importantly, both the GWAS and BSA methods concurred in co-locating qGBN3 and qBBN1. In the co-located region, 15 candidate genes were identified. Through gene annotation and RT-qPCR analysis, we predicted that Glyma.02G125200 and Glyma.02G125600 are candidate genes regulating the soybean branch number. These findings significantly enhance our comprehension of the genetic intricacies regulating the branch number in soybeans, offering promising candidate genes and materials for subsequent investigations aimed at augmenting the soybean yield. This research represents a crucial step toward unlocking the full potential of soybean cultivation through targeted genetic interventions.

Large scale sequence-based screen for recessive variants allows for identification and monitoring of rare deleterious variants in pigs.

Most deleterious variants are recessive and segregate at relatively low frequency. Therefore, high sample sizes are required to identify these variants. In this study we report a large-scale sequence based genome-wide association study (GWAS) in pigs, with a total of 120,000 Large White and 80,000 Synthetic breed animals imputed to sequence using a reference population of approximately 1,100 whole genome sequenced pigs. We imputed over 20 million variants with high accuracies (R2>0.9) even for low frequency variants (1-5% minor allele frequency). This sequence-based analysis revealed a total of 14 additive and 9 non-additive significant quantitative trait loci (QTLs) for growth rate and backfat thickness. With the non-additive (recessive) model, we identified a deleterious missense SNP in the CDHR2 gene reducing growth rate and backfat in homozygous Large White animals. For the Synthetic breed, we revealed a QTL on chromosome 15 with a frameshift variant in the OBSL1 gene. This QTL has a major impact on both growth rate and backfat, resembling human 3M-syndrome 2 which is related to the same gene. With the additive model, we confirmed known QTLs on chromosomes 1 and 5 for both breeds, including variants in the MC4R and CCND2 genes. On chromosome 1, we disentangled a complex QTL region with multiple variants affecting both traits, harboring 4 independent QTLs in the span of 5 Mb. Together we present a large scale sequence-based association study that provides a key resource to scan for novel variants at high resolution for breeding and to further reduce the frequency of deleterious alleles at an early stage in the breeding program.

Unravelling novel and pleiotropic genes for cannon bone circumference and bone mineral density in Yorkshire pigs.

Leg weakness is a prevalent health condition in pig farms. The augmentation of cannon bone circumference and bone mineral density can effectively improve limb strength in pigs and alleviate leg weakness. This study measured forelimb cannon bone circumference (fCBC) and rear limb cannon bone circumference (rCBC) using an inelastic tapeline and rear limb metatarsal area bone mineral density (raBMD) using a dual-energy X-ray absorptiometry bone density scanner. The samples of Yorkshire castrated boars were genotyped using a 50K single-nucleotide polymorphism (SNP) array. The SNP-chip data were imputed to the level of whole-genome sequencing data (iWGS). This study used iWGS data to perform genome-wide association studies and identified novel significant SNPs associated with fCBC on SSC6, SSC12, and SSC13, rCBC on SSC12 and SSC14, and raBMD on SSC7. Based on the high phenotypic and genetic correlations between CBC and raBMD, multi-trait meta-analysis was performed to identify pleiotropic SNPs. A significant potential pleiotropic quantitative trait locus (QTL) regulating both CBC and raBMD was identified on SSC15. Bayes fine mapping was used to establish the confidence intervals for these novel QTLs with the most refined confidence interval narrowed down to 56kb (15.11 to 15.17Mb on SSC12 for fCBC). Furthermore, the confidence interval for the potential pleiotropic QTL on SSC15 in the meta-analysis was narrowed down to 7.45kb (137.55 to137.56Mb on SSC15). Based on the biological functions of genes, the following genes were identified as novel regulatory candidates for different phenotypes: DDX42, MYSM1, FTSJ3, and MECOM for fCBC; SMURF2, and STC1 for rCBC; RGMA for raBMD. Additionally, RAMP1, which was determined to be located 23.68kb upstream of the confidence interval of the QTL on SSC15 in the meta-analysis, was identified as a potential pleiotropic candidate gene regulating both CBC and raBMD. These findings offered valuable insights for identifying pathogenic genes and elucidating the genetic mechanisms underlying CBC and BMD.

The genetic architecture of flowering in the Arabidopsis accession colonising southern Patagonia

AbstractFlowering is a critical developmental step impacting the survival of plant species, and a plethora of complex interdependent regulatory pathways have evolved to sense and integrate environmental with endogenous signals. Extensive explorations in Arabidopsis thaliana revealed some key genes controlling flowering time and associated phenotypic traits. However, quantitative and population genetics suggest that other less explored flowering players may also have relevant impact on the natural variability, explaining adaptive developmental responses to local environments. Here, we report on the flowering time genetic architecture in A. thaliana, using a Recombinant‐Inbred‐Line (RIL) population constructed with accessions bearing contrasting flowering phenotypes, such as the laboratory standard Col‐0 and the anthropically introduced southern Patagonia accession (Pat). Informative Quantitative‐Trait‐Loci (QTLs) mapping resulted in the recognition of a most significant QTL under vernalisation, localised at the end of chromosome V, and independently confirmed genetically by Heterogeneous‐Inbred‐Family lines (HIFs) and Near‐Isogenic‐Lines (NILs). Importantly, classical genes controlling flowering time and vernalisation, such as FLOWERING LOCUS T (FT), FLOWERING LOCUS C (FLC) and FRIGIDA (FRI) do not occur in this QTL. Furthermore, the associated Col‐0 allele contributed to delayed flowering with an increased number of leaves and correlated increase in leaf length; however, this was counterintuitive, as Col‐0 presented the contrary phenotypes when compared to Pat. We conclude that vernalisation contributing to local adaptation may be more diversely influenced across natural species variants than previously thought, with causative alleles having both positive and negative effects depending on genetic background.

Use of whole-genome sequence data for fine mapping and genomic prediction of sea louse resistance in Atlantic salmon.

Sea louse (Lepeophtheirus salmonis) infestation of Atlantic salmon (Salmo salar) is a significant challenge in aquaculture. Over the years, this parasite has developed immunity to medicinal control compounds, and non-medicinal control methods have been proven to be stressful, hence the need to study the genomic architecture of salmon resistance to sea lice. Thus, this research used whole-genome sequence (WGS) data to study the genetic basis of the trait since most research using fewer SNPs did not identify significant quantitative trait loci. Mowi Genetics AS provided the genotype (50k SNPs) and phenotype data for this research after conducting a sea lice challenge test on 3,185 salmon smolts belonging to 191 full-sib families. The 50 k SNP genotype was imputed to WGS using the information from 197 closely related individuals with sequence data. The WGS and 50k SNPs of the challenged population were then used to estimate genetic parameters, perform a genome-wide association study (GWAS), predict genomic breeding values, and estimate its accuracy for host resistance to sea lice. The heritability of host resistance to sea lice was estimated to be 0.21 and 0.22, while the accuracy of genomic prediction was estimated to be 0.65 and 0.64 for array and WGS data, respectively. In addition, the association test using both array and WGS data did not identify any marker associated with sea lice resistance at the genome-wide level. We conclude that sea lice resistance is a polygenic trait that is moderately heritable. The genomic predictions using medium-density SNP genotyping array were equally good or better than those based on WGS data.

Effect of the number of QTL of carcass weight on the accuracy of genomic estimated breeding values in Hanwoo

The objective of this study was to investigate the relationship between the number of QTL (Quantitative Trait Loci) affecting the carcass weight of Hanwoo and the accuracy of GEBV (Genomic Estimated Breeding Values). The study was performed simulation study to generate three populations comprising 100, 500 and 1,000 QTL. Each population consisted of 13,500 individuals which were selected from the 6th to 10th generations of all 10 generations, while keeping litter size constant (N=1) in population parameters. The genetic parameters for these populations were set at 0.45 trait heritability, 0.3 QTL heritability and 48,578 total markers. In order to confirm the number of significant QTL under the threshold of Bonferroni correction, both GWAS (Genome-wide association study) and GWAS based on Random Forest (RF) were performed. In each population, there were 33, 50 and 32 QTL identified by only GWAS, with 100, 500 and 1,000 QTL. Additionally, RF-based GWAS detected 65, 260 and 491. These results showed that the number of significant QTL is not related to the overall number of QTL in the population. Furthermore, machine learning based GWAS is more accurate than solely using GWAS. To estimate the accuracy of GEBV (Genomic Best Linear Unbiased Prediction) in each population, 5-fold cross-validation was employed using SNP effect of the QTL over p-value. In the result of the estimation, GEBV rose with an increase in the number of QTL in the population, and the correlation between TBV (True Breeding Values) followed the same tendency. As a result, this study suggested that the number of QTL related to carcass weight in Hanwoo could exert a significant influence on genetic improvement.

Genetic Architecture of Body Fat Composition in Mice

Body fat composition is a quantitative measure of obesity, a major health concern in humans. Laboratory mice are considered an excellent model for dissecting the genetic basis of obesity due to the genetic variation present in inbred strains for body size and fat composition, as well as their historical use as a model organism for human disease studies. To identify the loci controlling fat pad weights and body weight in mice, we performed a quantitative trait loci (QTL) analysis of 513 (SM/J x NZB/BINJ) F2 individuals fed a high-fat diet for 14 weeks. Our analysis separated fat composition genetic effects from those affecting overall body sizes in mouse. Composite interval mapping (CIM) results showed that body weight was conditioned by three major additive QTLs, explaining 3 to 30% of the phenotypic variation. One significant QTL on chromosome 19 conditioned all fat pads with the exception of the inguinal fat weight, which was controlled by a different QTL also on chromosome 19. Significant QTLs associated with fat compositions were detected on chromosomes 17 and 19 and differed from those of body weight. The fat pad QTLs also showed mainly additive gene effects and they explained 2 to 7% of variation in fat composition. Joint analysis of correlated traits detected five additional large effect QTLs on five different linkage groups. These findings have indicated that fat composition and body weight in mouse are conditioned by one to three major additive genes and can therefore be potentially manipulated in controlling obesity.

FGFs function in regulating myoblasts differentiation in spotted sea bass (Lateolabrax maculatus)

Fibroblast growth factors (FGFs) are a family of structurally related peptides that regulate processes such as cell proliferation, differentiation, and damage repair. In our previous study, fibroblast growth factor receptor 4 (fgfr4) was detected in the most significant quantitative trait loci (QTL), when identified of QTLs and genetic markers for growth-related traits in spotted sea bass. However, knowledge of the function of fgfr4 is lacking, even the legends to activate the receptor is unknown in fish. To remedy this problem, in the present study, a total of 33 fgfs were identified from the genomic and transcriptomic databases of spotted sea bass, of which 10 were expressed in the myoblasts. According to the expression pattern during myoblasts proliferation and differentiation, fgf6a, fgf6b and fgf18 were selected for further prokaryotic expression and purification. The recombinant proteins FGF6a, FGF6b and FGF18 were found to inhibit myoblast differentiation. Overall, our results provide a theoretical basis for the molecular mechanisms of growth regulation in economic fish such as spotted sea bass.