Genome-wide association analysis to identify novel candidate genes and genomic model optimization to predict acute low-temperature stress resilience in olive flounder (Paralichthys olivaceus).

Genome-wide association and genomic prediction of thermal tolerance in olive flounders (Paralichthys olivaceus): A validation study

Genome-wide association study of tolerance to acute hypoxia in the olive flounder (Paralichthys olivaceus) using individual blood cortisol levels as a physiological phenotype.

BackgroundEdwardsiella tarda causes acute symptoms with ascites in Japanese flounder (Paralichthys olivaceus) and is a major problem for China’s aquaculture sector. Genomic selection (GS) has been widely adopted in breeding industries because it shortens generation intervals and results in the selection of individuals that have great breeding potential with high accuracy. Based on an artificial challenge test and re-sequenced data of 1099 flounders, the aims of this study were to estimate the genetic parameters of resistance to E. tarda in Japanese flounder and to evaluate the accuracy of single-step GBLUP (ssGBLUP), weighted ssGBLUP (WssGBLUP), and BayesB for improving resistance to E. tarda by using three subsets of pre-selected single nucleotide polymorphisms (SNPs). In addition, SNPs that are associated with this trait were identified using a single-SNP genome-wide association study (GWAS) and WssGBLUP.ResultsWe estimated a heritability of 0.13 ± 0.02 for resistance to E. tarda in Japanese flounder. One million SNPs at fixed intervals were selected from 4,978,724 SNPs that passed quality controls. GWAS identified significant SNPs on chromosomes 14 and 24. WssGBLUP revealed that the putative quantitative trait loci on chromosomes 1 and 14 contained SNPs that explained more than 1% of the genetic variance. Three 50 k-SNP subsets were pre-selected based on different criteria. Compared with pedigree-based prediction (ABLUP), the three genomic methods evaluated resulted in at least 7.7% greater accuracy of predictions. The accuracy of these genomic prediction methods was almost unchanged when pre-selected trait-related SNPs were used for prediction.ConclusionsResistance to E. tarda in Japanese flounder has a low heritability. GWAS and WssGBLUP revealed that the genetic architecture of this trait is polygenic. Genomic prediction of breeding values performed better than ABLUP. It is feasible to implement genomic selection to increase resistance to E. tarda in Japanese flounder with 50 k SNPs. Based on the criteria used here, pre-selection of SNPs was not beneficial and other criteria for pre-selection should be considered.

Genomic prediction (GP) has emerged an effective tool for addressing the many shortcomings of traditional selective breeding, thereby enhancing the selection process. In this study, we optimized GP methods using 5-fold cross-validation to estimate genome-estimated breeding values for the weight traits of olive flounder (Paralichthys olivaceus). To accomplish our goal, we determined the parentage of the target broodstock and the ability of 11 prediction models to predict the weight traits of 1.8-year-old olive flounders, which were genotyped using a 70 K single nucleotide polymorphism (SNP) array. Moreover, our optimization efforts toward the predictive ability of genomic best linear unbiased prediction (GBLUP), Bayesian B (BB), and random forest (RF) methods encompassed changes in various aspects such as fixed effects, SNP quantity, population size, and phenotypic data collected at different fish ages. Additionally, we assessed the predictive ability for the total length and body depth of fish using GBLUP, BB, and RF. Among the 11 prediction methods used in this study, the BB (0.675), Elastic Net (0.679), and RF (0.698) methods exhibited the highest predictive abilities, whereas the GBLUP (0.637) method demonstrated the lowest. Incorporating information regarding fish sex as a fixed effect substantially improved the predictive ability of GBLUP and BB. For mean models, utilizing 3000–5000 random SNP markers resulted in a higher predictive ability, similar to that obtained using 50,000 SNPs. Increasing the population size reduced the standard deviation of the predictive ability. Notably, phenotypic records from 1.8-year-old fish exhibited a significantly higher predictive ability than those from the other age groups. Furthermore, GBLUP, BB, and RF provided higher predictive abilities for length (0.655–0.852) and body depth (0.665–0.861). These findings may significantly shape future olive flounder genomic selection programs and offer valuable insights into GP in aquaculture.

Genome-wide association study identifies novel candidate genes linked to acute and chronic thermal stress resilience in olive flounder (Paralichthys olivaceus).

Prediction of genetic values plays a central role in quantitative genetics and breeding. Genomic prediction making use of genome-wide single nucleotide polymorphisms (SNPs) was widely adopted to predict breeding values in animal and plant breeding, and to accurately quantify individual disease risk early in human genetics. In the multi-omics era, as omics data (genome, transcriptome, proteome, metabolome, epigenome etc.) increasingly became available during recent years, exploring multi-layer omics data to be predictors in prediction models has been an accessible way to improve predictive abilities in phenotype prediction. Gene expression profiles potentially hold valuable information for the prediction of breeding values and phenotypes. The Drosophila melanogaster Genetic Reference Panel (DGRP) is a community resource for analysis of population genomics and quantitative traits. It consists of more than 200 fully sequenced inbred lines (include 185 lines with whole genome gene expression data) derived from the Raleigh population, USA. In Chapter 2, the utility of transcriptome data for phenotype prediction was tested with 185 inbred lines of Drosophila melanogaster for 9 traits in two sexes. In total, 2,863,909 SNPs and 18,140 genome-wide annotated genes and novel transcribed regions (NTRs) were used for all the analyses. We incorporated the transcriptome data into genomic prediction via two kernel methods: GTBLUP and GRBLUP, both combining single nucleotide polymorphisms and transcriptome data. The genotypic data was used to construct the common additive genomic relationship, which was used in genomic best linear unbiased prediction (GBLUP) or jointly in a linear mixed model with a transcriptome-based linear kernel (GTBLUP), or with a transcriptome-based Gaussian kernel (GRBLUP). We studied the predictive ability of the models and discuss a concept of “omics-augmented broad sense heritability” for the multi-omics era. There was one trait (olfactory perceptions to Ethyl Butyrate in females) in which the predictive ability of GRBLUP was significantly higher (0.23) than the predictive ability of GBLUP (0.21). Nonetheless, for most traits, GRBLUP and GBLUP provided similar predictive abilities, while GRBLUP explained more of the phenotypic variance. The better goodness of fit of GRBLUP in general did not translate into a better predictive ability. A possible explanation was suggested that sample size was small and gene expression was not measured at one time point and in one specific tissue which is functionally linked to the trait of interest. It is well known that gene expression and regulation may extensively vary among different tissues. However, the transcripts abundance of Drosophila melanogaster used was quantified from the entire flies. To test whether tissue-specific transcriptome data can substantially improve predictive abilities, in Chapter 3, we used tissue-specific transcriptome data from the three mice brain tissues: hippocampus (HIP), prefrontal cortex (PFC), and striatum (STR) for phenotype prediction on four novel behavioral traits and four muscle weight traits with low to medium heritability. There were 1063 mice individuals with pedigree information from a multigenerational outbred population which had been sequenced with the reduced-representation genotyping method genotyping-by-sequencing (GBS). After quality control, 523,028 SNPs were used in the analyses. All analyses were conducted in three groups of mice with pedigree, genotype, gene expression and phenotype data, which contained 208 (HIP), 185 (PFC) and 169 (STR) individuals, respectively. The abundances of RNA products from three tissues encompassed 16,533 genes in HIP, 16,249 genes in PFC and 16,860 genes in STR. For the muscle weight traits, the tissue-specific transcriptome data-based prediction (TBLUP) showed high predictive abilities, and the predictive abilities overall were remarkably higher than the pedigree-based prediction (BLUP) and the SNP-based prediction (GBLUP). For the four behavioral traits, the increase of predictive abilities of the transcriptome data-based prediction (TBLUP) were lower than that for the muscle weight traits. When combining transcriptome data with SNPs or pedigree information as predictors, predictive abilities overall were not improved. To study whether the numbers of genes has impact on transcriptome-based prediction, we randomly chose different number of genes for the prediction with TBLUP. The differences among predictive abilities were negligible. Our results suggested that making use of transcriptome data has the potential to improve phenotype predictions if transcriptome data can be sampled in a specific tissue. In contrast to phenotype prediction, multi-omics data are not ideal candidates for prediction of genetic value and estimation of heritability, since they are not causal variants but intermediate products between causal variants and phenotypes. During the transfer process of genetic information from DNA to phenotype, multi-omics data are inevitably affected by genetic and environmental effects, and the interaction between both. The ‘pan-genome’ denotes the set of all genes or open reading frames (ORFs) present in the genomes of a group of organisms. Pan-genomic open reading frames potentially carry genome-wide protein-coding genes or causal variant information in a population. The 1002 Yeast Genome project comprised 1,011 S. cerevisiae isolates that maximized the breadth of their ecological and geographical origins. In Chapter 4, we used 787 diploid S. cerevisiae isolates with 1,625,809 high-quality reference-based SNPs, 7,796 ORFs, copy number of ORFs (CNO) and 35 traits with linear models in the genomic prediction and estimation of heritability. Our results showed that compared to SNP-based genomic prediction (GBLUP), pan-genomic ORF-based genomic prediction (OBLUP) was distinctly more accurate for all the traits, and the predictive abilities were improved by 132% on average across all traits. In addition, the ORF-based heritability can capture more additive effects than SNP-based heritability for all traits. When we combined two subsets of total SNP data (MAF ≥ 0.01 and MAF ≥ 0.05) which contained 311,447 SNPs and 102,253 SNPs, respectively, to pan-genomic ORFs with GOBLUP, the predictive abilities remained the same with OBLUP only using pan-genomic ORFs data. For the second combined method GCBLUP, the predictive abilities remained the same as with CBLUP for all traits, suggesting that ORF data or CNO data covered all causal variant information which SNP data carried. When using three different numbers of isolates in training sets in ORF-based prediction, the predictive abilities of all traits increased as the number of isolates in the training set increased, showing that increasing the training set size could more accurately estimate ORF effects. We demonstrated that pan-genomic ORFs have the potential to be a substitution of single nucleotide polymorphisms in estimation of heritability and genomic prediction under certain conditions. However, in our study there was still a big gap between traits’ heritability estimates and prediction accuracy for all the traits. We provide evidence that if larger sample sizes can be used in training set, the prediction accuracy will be further improved.

Genome-wide association mapping of scuticociliatosis resistance in a vaccinated population of olive flounder (Paralichthys olivaceus).

Genome-wide association study for high-temperature tolerance in the Japanese flounder

Genetic diversity and signatures of selection in the mito-gynogenetic olive flounder Paralichthys olivaceus revealed by genome-wide SNP markers

Genome-wide association study for the detection of genetic variants associated with the antibody response upon viral hemorrhagic septicemia virus vaccination in Paralichthys olivaceus.

Early leaf spot (ELS) and late leaf spot (LLS) diseases are the two most destructive groundnut diseases in Ghana resulting in ≤ 70% yield losses which is controlled largely by chemical method. To develop leaf spot resistant varieties, the present study was undertaken to identify single nucleotide polymorphism (SNP) markers and putative candidate genes underlying both ELS and LLS. In this study, six multi-locus models of genome-wide association study were conducted with the best linear unbiased predictor obtained from 294 African groundnut germplasm screened for ELS and LLS as well as image-based indices of leaf spot diseases severity in 2020 and 2021 and 8,772 high-quality SNPs from a 48K SNP array Axiom platform. Ninety-seven SNPs associated with ELS, LLS and five image-based indices across the chromosomes in the 2 two sub-genomes. From these, twenty-nine unique SNPs were detected by at least two models for one or more traits across 16 chromosomes with explained phenotypic variation ranging from 0.01 - 62.76%, with exception of chromosome (Chr) 08 (Chr08), Chr10, Chr11, and Chr19. Seventeen potential candidate genes were predicted at ± 300 kbp of the stable/prominent SNP positions (12 and 5, down- and upstream, respectively). The results from this study provide a basis for understanding the genetic architecture of ELS and LLS diseases in African groundnut germplasm, and the associated SNPs and predicted candidate genes would be valuable for breeding leaf spot diseases resistant varieties upon further validation.

No Evidence in a Large UK Collection for Celiac Disease Risk Variants Reported by a Spanish Study

Proper quality control of data prior to downstream analyses is fundamental to ensure integrity of results; quality control of genomic data is no exception. While many metrics of quality control of genomic data exist, the objective of the present study was to quantify the genotype and allele concordance rate between called single nucleotide polymorphism (SNP) genotypes differing in GenCall (GC) score; the GC score is a confidence measure assigned to each Illumina genotype call. This objective was achieved using Illumina beadchip genotype data from 771 cattle (12428767 genotypes in total post-editing) and 80 sheep (1557360 SNPs genotypes in total post-editing) each genotyped in duplicate. The called genotype with the lowest associated GC score was compared to the genotype called for the same SNP in the same duplicated animal sample but with a GC score of >0.90 (assumed to represent the true genotype). The mean genotype concordance rate for a GC score of <0.300, 0.300-0.549, and ≥0.550 in the cattle (sheep in parenthesis) was 0.9467 (0.9864), 0.9707 (0.9953), and 0.9994 (0.99997) respectively; the respective allele concordance rate was 0.9730 (0.9930), 0.9849 (0.9976), and 0.9997 (0.99998). Hence, concordance eroded as the GC score of the called genotype reduced, albeit the impact was not dramatic and was not very noticeable until a GC score of <0.55. Moreover, the impact was greater and more consistent in the cattle population than in the sheep population. Furthermore, an impact of GC score on genotype concordance rate existed even for the same SNP GenTrain value; the GenTrain value is a statistical score that depicts the shape of the genotype clusters and the relative distance between the called genotype clusters.

BackgroundA quantitative trait is controlled both by major variants with large genetic effects and by minor variants with small effects. Genome-wide association studies (GWAS) are an efficient approach to identify quantitative trait loci (QTL), and genomic selection (GS) with high-density single nucleotide polymorphisms (SNPs) can achieve higher accuracy of estimated breeding values than conventional best linear unbiased prediction (BLUP). GWAS and GS address different aspects of quantitative traits, but, as statistical models, they are quite similar in their description of the genetic mechanisms that underlie quantitative traits.MethodsHere, we propose a stepwise linear regression mixed model (StepLMM) to unify GWAS and GS in a single statistical model. First, the variance components of the genomic-BLUP (GBLUP) model are estimated. Then, in the SNP selection step, the linear mixed model (LMM) for GWAS is equivalently transformed into a simple linear regression to improve computation speed, and the most significant SNP is selected and included into the evaluation model. In the SNP dropping step, the SNPs in the evaluation model are tested according to the standard errors of their estimated effects. If non-significant SNPs are present, the least significant one is dropped from the model and variance components are re-estimated. We used extended Bayesian information criteria (eBIC) to evaluate the model optimization, i.e. the model with the smallest eBIC is the final one and includes only significant SNPs.ResultsWe simulated scenarios with different heritabilities with 100 QTL. StepLMM estimated heritability accurately and mapped QTL precisely. Genomic prediction accuracy was much higher with StepLMM than with GBLUP. The comparison of StepLMM with other GWAS and GS methods based on a dataset from the 16th QTLMAS Workshop showed that StepLMM had medium mapping power, the lowest rate of false positives for QTL mapping, and the highest accuracy for genomic prediction.ConclusionsStepLMM is a combination of GWAS and GBLUP. GWAS and GBLUP are beneficial to each other in a single statistical model, GWAS improves genomic prediction accuracy, while GBLUP increases mapping precision and decreases the rate of false positives of GWAS. StepLMM has a high performance in both GWAS and GS and is feasible for agricultural breeding programs and human genetic studies.

Genome-wide association mapping to identify genetic loci governing agronomic traits and genomic prediction prospects in tetraploid potatoes