From QTL to QTN: candidate gene set approach and a case study in porcine IGF1-FoxO pathway

Impact of genotype-calling methodologies on genome-wide association and genomic prediction in polyploids

Discovery and analysis of genetic variants underlying agriculturally important traits are key to molecular breeding of crops. Reduced representation approaches have provided cost-efficient genotyping using next-generation sequencing. However, accurate genotype calling from next-generation sequencing data is challenging, particularly in polyploid species due to their genome complexity. Recently developed Bayesian statistical methods implemented in available software packages, polyRAD, EBG, and updog, incorporate error rates and population parameters to accurately estimate allelic dosage across any ploidy. We used empirical and simulated data to evaluate the three Bayesian algorithms and demonstrated their impact on the power of genome-wide association study (GWAS) analysis and the accuracy of genomic prediction. We further incorporated uncertainty in allelic dosage estimation by testing continuous genotype calls and comparing their performance to discrete genotypes in GWAS and genomic prediction. We tested the genotype-calling methods using data from two autotetraploid species, Miscanthus sacchariflorus and Vaccinium corymbosum, and performed GWAS and genomic prediction. In the empirical study, the tested Bayesian genotype-calling algorithms differed in their downstream effects on GWAS and genomic prediction, with some showing advantages over others. Through subsequent simulation studies, we observed that at low read depth, polyRAD was advantageous in its effect on GWAS power and limit of false positives. Additionally, we found that continuous genotypes increased the accuracy of genomic prediction, by reducing genotyping error, particularly at low sequencing depth. Our results indicate that by using the Bayesian algorithm implemented in polyRAD and continuous genotypes, we can accurately and cost-efficiently implement GWAS and genomic prediction in polyploid crops.

Hunting for Genes Underlying Emotionality in the Laboratory Rat: Maps, Tools and Traps

Scientists have systematically investigated the hereditary bases of behaviors since the 19th century, moved by either evolutionary questions or clinically-motivated purposes. The pioneer studies on the genetic selection of laboratory animals had already indicated, one hundred years ago, the immense complexity of analyzing behaviors that were influenced by a large number of small-effect genes and an incalculable amount of environmental factors. Merging Mendelian, quantitative and molecular approaches in the 1990s made it possible to map specific rodent behaviors to known chromosome regions. From that point on, Quantitative Trait Locus (QTL) analyses coupled with behavioral and molecular techniques, which involved in vivo isolation of relevant blocks of genes, opened new avenues for gene mapping and characterization. This review examines the QTL strategy applied to the behavioral study of emotionality, with a focus on the laboratory rat. We discuss the challenges, advances and limitations of the search for Quantitative Trait Genes (QTG) playing a role in regulating emotionality. For the past 25 years, we have marched the long journey from emotionality-related behaviors to genes. In this context, our experiences are used to illustrate why and how one should move forward in the molecular understanding of complex psychiatric illnesses. The promise of exploring genetic links between immunological and emotional responses are also discussed. New strategies based on humans, rodents and other animals (such as zebrafish) are also acknowledged, as they are likely to allow substantial progress to be made in the near future.

Whole-genome resequencing from bulked-segregant analysis reveals gene set based association analyses for the Vibrio anguillarum resistance of turbot (Scophthalmus maximus)

Many achievements have been made to develop quantitative trait loci (QTLs) and gene-associated single nucleotide polymorphisms (SNPs) to facilitate practical marker-assisted selection (MAS) in aquatic animals. However, the systematic studies of SNPs associated with extreme threshold traits were poor in populations lacking of parental genomic information. Coupling next generation sequencing with bulked segregant analysis (BSA) should allow identification of numerous associated SNPs with extreme phenotypes. In the present study, using combination of SNP frequency difference and Euclidean distance, we conducted linkage analysis of SNPs located in genes involved in immune responses, and identified markers associated with Vibrio anguillarum resistance in turbot (Scophthalmus maximus). A total of 221 SNPs was found as candidate SNPs between resistant and susceptible individuals. Among these SNPs, 35 loci located in immune related genes were genotyped in verification population and 7 of them showed significant association with V. anguillarum resistance in both alleles and genotypes (P < 0.05). Among these 7 genes, PIK3CA-like, CYLD, VCAM1, RhoB and RhoGEF are involved in PI3K/Akt/mTOR pathway and NF-κB pathway, which influence the efficiency of bacteria entering the host and inflammation. SNP-SNP interaction analysis was performed by generalized multifactor dimensionality reduction (GMDR). The combination of SNP loci in RhoB, PIK3CA-like and ADCY3 showed a significant effect on V. anguillarum resistance with the verification rate in the sequencing population up to 70.8%. Taken all, our findings demonstrated the feasibility of BSA-seq approach in identifying genes responsible for the extreme phenotypes and will aid in performing MAS in turbot.

Transcriptome assembly and identification of genes and SNPs associated with growth traits in largemouth bass (Micropterus salmoides)

Growth is one of the most crucial economic traits of all aquaculture species, but the molecular mechanisms involved in growth of largemouth bass (Micropterus salmoides) are poorly understood. The objective of this study was to screen growth-related genes of M. salmoides by RNA sequencing and identify growth-related single-nucleotide polymorphism (SNP) markers through a growth association study. The muscle transcriptomes of fast- and slow-growing largemouth bass were obtained using the RNA-Seq technique. A total of 54,058,178 and 54,742,444 qualified Illumina read pairs were obtained for the fast-growing and slow-growing groups, respectively, giving rise to 4,865,236,020 and 4,926,819,960 total clean bases, respectively. Gene expression profiling showed that 3,530 unigenes were differentially expressed between the fast-growing and slow-growing phenotypes (false discovery rate ≤0.001, the absolute value of log₂ (fold change) ≥1), including 1,441 up-regulated and 2,889 down-regulated unigenes in the fast-growing largemouth bass. Analysis of these genes revealed that several signalling pathways, including the growth hormone-insulin-like growth factor 1 axis and signalling pathway, the glycolysis pathway, and the myostatin/transforming growth factor beta signalling pathway, as well as heat shock protein, cytoskeleton, and myofibril component genes might be associated with muscle growth. From these genes, 10 genes with putative SNPs were selected, and 17 SNPs were genotyped successfully. Marker-trait analysis in 340 individuals of Youlu No. 1 largemouth bass revealed three SNPs associated with growth in key genes (phosphoenolpyruvate carboxykinase 1, FOXO3b, and heat shock protein beta-1). This research provides information about key genes and SNPs related to growth, providing new clues to understanding the molecular basis of largemouth bass growth.

Gene set based association analyses for the WSSV resistance of Pacific white shrimp Litopenaeus vannamei

White Spot Syndrome Virus (WSSV) is regarded as a virus with the strongest pathogenicity to shrimp. For the threshold trait such as disease resistance, marker assisted selection (MAS) was considered to be a more effective approach. In the present study, association analyses of single nucleotide polymorphisms (SNPs) located in a set of immune related genes were conducted to identify markers associated with WSSV resistance. SNPs were detected by bioinformatics analysis on RNA sequencing data generated by Illimina sequencing platform and Roche 454 sequencing technology. A total of 681 SNPs located in the exons of immune related genes were selected as candidate SNPs. Among these SNPs, 77 loci were genotyped in WSSV susceptible group and resistant group. Association analysis was performed based on logistic regression method under an additive and dominance model in GenABEL package. As a result, five SNPs showed associations with WSSV resistance at a significant level of 0.05. Besides, SNP-SNP interaction analysis was conducted. The combination of SNP loci in TRAF6, Cu/Zn SOD and nLvALF2 exhibited a significant effect on the WSSV resistance of shrimp. Gene expression analysis revealed that these SNPs might influence the expression of these immune-related genes. This study provides a useful method for performing MAS in shrimp.

A Genome-Wide mRNA Screen and Functional Analysis Reveal FOXO3 as a Candidate Gene for Chicken Growth

Chicken growth performance provides direct economic benefits to the poultry industry. However, the underlying genetic mechanisms are unclear. The objective of this study was to identify candidate genes associated with chicken growth and investigate their potential mechanisms. We used RNA-Seq to study the breast muscle transcriptome in high and low tails of Recessive White Rock (WRR_h, WRR_l) and Xinghua chickens (XH_h, XH_l). A total of 60, 23, 153 and 359 differentially expressed genes were detected in WRR_h vs. WRR_l, XH_h vs. XH_l, WRR_h vs. XH_h and WRR_l vs. XH_l, respectively. GO, KEGG pathway and gene network analyses showed that CEBPB, FBXO32, FOXO3 and MYOD1 played key roles in growth. The functions of FBXO32 and FOXO3 were validated. FBXO32 was predominantly expressed in leg muscle, heart and breast muscle. After decreased FBXO32 expression, growth-related genes such as PDK4, IGF2R and IGF2BP3 were significantly down-regulated (P < 0.05). FBXO32 was significantly (P < 0.05) associated with carcass and meat quality traits, but not growth traits. FOXO3 was predominantly expressed in breast and leg muscle. In both of these tissues, the FOXO3 mRNA level in XH was significantly higher than that in WRR chickens with normal body weight (P < 0.05). In DF-1 cells, siRNA knockdown of FOXO3 significantly (P < 0.01) inhibited the MYOD expression and significantly up-regulated (P < 0.01 or P < 0.05) the expression of growth-related genes including CEBPB, FBXO32, GH, GHR, IGF1R, IGF2R, IGF2BP1, IGF2BP3, INSR, PDK1 and PDK4. Moreover, 18 SNPs were identified in FOXO3. G66716193A was significantly (P < 0.05) associated with growth traits. The sites C66716002T, C66716195T and A66716179G were significantly (P < 0.05) associated with growth or carcass traits. These results demonstrated that FOXO3 is a candidate gene influencing chicken growth. Our observations provide new clues to understand the molecular basis of chicken growth.

Association study between gene polymorphisms in PPAR signaling pathway and porcine meat quality traits

There is increasing evidence suggesting that fatty acids biosynthesis and metabolism are regulated by peroxisome proliferator-activated receptors (PPARs), mostly through the PPAR signaling pathway at the transcriptomic level. We hypothesized that the genetic variants of the enzymes in the PPAR signaling pathway may be associated with the traits of porcine meat quality (PMQ). We mined 77 potentially functional single nucleotide polymorphisms in the PPAR signaling pathway of the pig. There were 13 TagSNPs in 13 different genes mapped within the reported pig quantitative trait loci (QTLs) regions related to PMQ based on the Pig QTL database. Based on the association study with ten measured PMQ traits in both the pathway level and the SNP level, we tested eight significantly associated traits with additive effect in the PPAR signaling pathway and explored only one significant TagSNP in gene RXRB, which is directly associated with the trait of skin weight. Moreover, several interactions of TagSNPs were also significantly related to some of PMQ traits. In this large and comprehensive candidate gene set study, we found a modest association of genes and SNPs in the PPAR signaling pathway with PMQ. Further investigation of these gene polymorphisms jointly with fatty acid measures and other genetic factors would help us better understand the regulation mechanisms of PMQ.