Genes of the RNASE5 pathway contain SNP associated with milk production traits in dairy cattle

Background

Identification of the processes and mutations responsible for the large genetic variation in milk production among dairy cattle has proved challenging. One approach is to identify a biological process potentially involved in milk production and to determine the genetic influence of all the genes included in the process or pathway. Angiogenin encoded by angiogenin, ribonuclease, RNase A family 5 (RNASE5) is relatively abundant in milk, and has been shown to regulate protein synthesis and act as a growth factor in epithelial cells in vitro. However, little is known about the role of angiogenin in the mammary gland or if the polymorphisms present in the bovine RNASE5 gene are associated with lactation and milk production traits in dairy cattle. Given the high economic value of increased protein in milk, we have tested the hypothesis that RNASE5 or genes in the RNASE5 pathway are associated with milk production traits. First, we constructed a “RNASE5 pathway” based on upstream and downstream interacting genes reported in the literature. We then tested SNP in close proximity to the genes of this pathway for association with milk production traits in a large dairy cattle dataset.

Results

The constructed RNASE5 pathway consisted of 11 genes. Association analysis between SNP in 1 Mb regions surrounding these genes and milk production traits revealed that more SNP than expected by chance were associated with milk protein percent (P < 0.05 significance). There was no significant association with other traits such as milk fat content or fertility.

Conclusions

These results support a role for the RNASE5 pathway in milk production, specifically milk protein percent, and indicate that polymorphisms in or near these genes explain a proportion of the variation for this trait. This method provides a novel way of understanding the underlying biology of lactation with implications for milk production and can be applied to any pathway or gene set to test whether they are responsible for the variation of complex traits.     

Phenotypic variation and natural selection at catsup, a pleiotropic quantitative trait gene in Drosophila

Quantitative traits are shaped by networks of pleiotropic genes . To understand the mechanisms that maintain genetic variation for quantitative traits in natural populations and to predict responses to artificial and natural selection, we must evaluate pleiotropic effects of underlying quantitative trait genes and define functional allelic variation at the level of quantitative trait nucleotides (QTNs). Catecholamines up (Catsup), which encodes a negative regulator of tyrosine hydroxylase , the rate-limiting step in the synthesis of the neurotransmitter dopamine, is a pleiotropic quantitative trait gene in Drosophila melanogaster. We used association mapping to determine whether the same or different QTNs at Catsup are associated with naturally occurring variation in multiple quantitative traits. We sequenced 169 Catsup alleles from a single population and detected 33 polymorphisms with little linkage disequilibrium (LD). Different molecular polymorphisms in Catsup are independently associated with variation in longevity, locomotor behavior, and sensory bristle number. Most of these polymorphisms are potentially functional variants in protein coding regions, have large effects, and are not common. Thus, Catsup is a pleiotropic quantitative trait gene, but individual QTNs do not have pleiotropic effects. Molecular population genetic analyses of Catsup sequences are consistent with balancing selection maintaining multiple functional polymorphisms.     

Genome-wide association study reveals the genetic architecture of flowering time in rapeseed (Brassica napus L.)

Flowering time adaptation is a major breeding goal in the allopolyploid species Brassica napus. To investigate the genetic architecture of flowering time, a genome-wide association study (GWAS) of flowering time was conducted with a diversity panel comprising 523 B. napus cultivars and inbred lines grown in eight different environments. Genotyping was performed with a Brassica 60K Illumina Infinium SNP array. A total of 41 single-nucleotide polymorphisms (SNPs) distributed on 14 chromosomes were found to be associated with flowering time, and 12 SNPs located in the confidence intervals of quantitative trait loci (QTL) identified in previous researches based on linkage analyses. Twenty-five candidate genes were orthologous to Arabidopsis thaliana flowering genes. To further our understanding of the genetic factors influencing flowering time in different environments, GWAS was performed on two derived traits, environment sensitivity and temperature sensitivity. The most significant SNPs were found near Bn-scaff_16362_1-p380982, just 13 kb away from BnaC09g41990D, which is orthologous to A. thaliana CONSTANS (CO), an important gene in the photoperiod flowering pathway. These results provide new insights into the genetic control of flowering time in B. napus and indicate that GWAS is an effective method by which to reveal natural variations of complex traits in B. napus.     

Genetics of fat tissue accumulation in pigs: a comparative approach

Fatness traits are important in pig production since they influence meat quality and fattening efficiency. On the other hand, excessive fat accumulation in humans has become a serious health problem due to worldwide spread of obesity. Since the pig is also considered as an animal model for numerous human diseases, including obesity and metabolic syndrome, comparative genomic studies may bring new insights into genetics of fatness/obesity. Input of genetic factors into phenotypic variability of these traits is rather high and the heritability coefficient (h ²) of these traits oscillates around 0.5. Genome scanning revealed the presence of more than 500 QTLs for fatness in the pig genome. In addition to QTL studies, many candidate gene polymorphisms have been analyzed in terms of their associations with pig fatness, including genes encoding leptin (LEP) and its receptor (LEPR), insulin-like growth factor 2 (IGF-2), fatty acid-binding proteins (FABP3 andFABP4), melanocortin receptor type 4 (MC4R), and theFTO (fat mass and obesity-associated) gene. Among them, a confirmed effect on pig fatness was found for a well-known polymorphism of theIGF-2 gene. In humans the strongest association with predisposition to obesity was shown for polymorphism of theFTO gene, while in pigs such an association seems to be doubtful. The development of functional genomics has revealed a large number of genes whose expression is associated with fat accumulation and lipid metabolism, so far not studied extensively in terms of the association of their polymorphism with pig fatness. Recently, epigenomic mechanisms, mainly RNA interference, have been considered as a potential source of information on genetic input into the fat accumulation process. The rather limited progress in studies focused on the identification of gene polymorphism related with fatness traits shows that their genetic background is highly complex.     

Linkage mapping of gene-associated SNPs to pig chromosome 11

Single nucleotide polymorphisms (SNPs) were discovered in porcine expressed sequence tags (ESTs) orthologous to genes from human chromosome 13 (HSA13) and predicted to be located on pig chromosome 11 (SSC11). The SNPs were identified as sequence variants in clusters of EST sequences from pig cDNA libraries constructed in the Sino-Danish pig genome project. In total, 312 human gene sequences from HSA13 were used for similarity searches in our pig EST database. Pig ESTs showing significant similarity with HSA13 genes were clustered and candidate SNPs were identified. Allele frequencies for 26 SNPs were estimated in a group of 80 unrelated pigs from Danish commercial pig breeds: Duroc, Hampshire, Landrace and Large White. Eighteen of the 26 SNPs genotyped in the PiGMaP Reference Families were mapped by linkage analysis to SSC11. The EST-based SNPs published here are new genetic markers useful for linkage and association studies in commercial and experimental pig populations. This study represents the first gene-associated SNP linkage map of pig chromosome 11 and adds new comparative mapping information between SSC11 and HSA13. Furthermore, our data facilitate future studies aimed at the identification of interesting regions on pig chromosome 11, positional cloning and fine mapping of quantitative trait loci in pig.     

Whole genome association study identifies regions of the bovine genome and biological pathways involved in carcass trait performance in Holstein-Friesian cattle

Background

Four traits related to carcass performance have been identified as economically important in beef production: carcass weight, carcass fat, carcass conformation of progeny and cull cow carcass weight. Although Holstein-Friesian cattle are primarily utilized for milk production, they are also an important source of meat for beef production and export. Because of this, there is great interest in understanding the underlying genomic structure influencing these traits. Several genome-wide association studies have identified regions of the bovine genome associated with growth or carcass traits, however, little is known about the mechanisms or underlying biological pathways involved. This study aims to detect regions of the bovine genome associated with carcass performance traits (employing a panel of 54,001 SNPs) using measures of genetic merit (as predicted transmitting abilities) for 5,705 Irish Holstein-Friesian animals. Candidate genes and biological pathways were then identified for each trait under investigation.

Results

Following adjustment for false discovery (q-value < 0.05), 479 quantitative trait loci (QTL) were associated with at least one of the four carcass traits using a single SNP regression approach. Using a Bayesian approach, 46 QTL were associated (posterior probability > 0.5) with at least one of the four traits. In total, 557 unique bovine genes, which mapped to 426 human orthologs, were within 500kbs of QTL found associated with a trait using the Bayesian approach. Using this information, 24 significantly over-represented pathways were identified across all traits. The most significantly over-represented biological pathway was the peroxisome proliferator-activated receptor (PPAR) signaling pathway.

Conclusions

A large number of genomic regions putatively associated with bovine carcass traits were detected using two different statistical approaches. Notably, several significant associations were detected in close proximity to genes with a known role in animal growth such as glucagon and leptin. Several biological pathways, including PPAR signaling, were shown to be involved in various aspects of bovine carcass performance. These core genes and biological processes may form the foundation for further investigation to identify causative mutations involved in each trait. Results reported here support previous findings suggesting conservation of key biological processes involved in growth and metabolism.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-837) contains supplementary material, which is available to authorized users.     

Association of the heart fatty acid-binding protein (FABP3) gene with milk traits in Manchega breed sheep

The ovine fatty acid-binding protein type 3 gene has been chosen as a functional candidate gene for milk traits. Two different single nucleotide polymorphisms (SNPs) of ovine FABP3 gene have been tested in a daughter design comprising 13 families. No association was found between estimated breeding values for milk yield, protein and fat contents (FC) and genotypes across families using anova and transmission disequilibrium test (TDT). In within-family analysis, one family showed a significant effect for FC. These results could indicate linkage disequilibrium between the FABP3 gene and a quantitative trait loci (QTL) for FC, with the heterozygous genotype associated with a positive effect in this trait.     

A stochastic expectation and maximization algorithm for detecting quantitative trait-associated genes

MOTIVATION: Most biological traits may be correlated with the underlying gene expression patterns that are partially determined by DNA sequence variation. The correlations between gene expressions and quantitative traits are essential for understanding the functions of genes and dissecting gene regulatory networks. RESULTS: In the present study, we adopted a novel statistical method, called the stochastic expectation and maximization (SEM) algorithm, to analyze the associations between gene expression levels and quantitative trait values and identify genetic loci controlling the gene expression variations. In the first step, gene expression levels measured from microarray experiments were assigned to two different clusters based on the strengths of their association with the phenotypes of a quantitative trait under investigation. In the second step, genes associated with the trait were mapped to genetic loci of the genome. Because gene expressions are quantitative, the genetic loci controlling the expression traits are called expression quantitative trait loci. We applied the same SEM algorithm to a real dataset collected from a barley genetic experiment with both quantitative traits and gene expression traits. For the first time, we identified genes associated with eight agronomy traits of barley. These genes were then mapped to seven chromosomes of the barley genome. The SEM algorithm and the result of the barley data analysis are useful to scientists in the areas of bioinformatics and plant breeding. Availability and implementation: The R program for the SEM algorithm can be downloaded from our website: http://www.statgen.ucr.edu.     

Porcine genomics delivers new tools and results: this little piggy did more than just go to market

The past decade has yielded new tools for pig geneticists and breeders thanks to the considerable developments resulting from efforts to map the pig genome. The pig genetic linkage map now has nearly 5000 loci including several hundred genes, microsatellites and amplified fragment length polymorphisms (AFLP) markers. Using tools that include somatic cell hybrid panels and radiation hybrid panels, the physical genetic map is also growing rapidly and has over 4000 genes and markers. Scientists using both exotic and commercial breeds for quantitative trait loci (QTL) scans and candidate gene analyses have identified a number of important chromosomal regions and individual genes associated with growth rate, leanness, feed intake, meat quality, litter size and disease resistance. Using marker-assisted selection (MAS) the commercial pig industry is actively incorporating these gene markers and traditional performance information to improve traits of economic importance in pig production. Researchers now have novel tools including pig gene arrays and advanced bioinformatics that are being exploited to find new candidate genes and to advance the understanding of gene function in the pig. Sequencing of the pig genome has been initiated and further sequencing is now being considered. Advances in pig genomics and directions for future research and the implications to both the pig industry and human health are reviewed.     

Genome-Wide Association Study for Wool Production Traits in a Chinese Merino Sheep Population

Genome-wide association studies (GWAS) provide a powerful approach for identifying quantitative trait loci without prior knowledge of location or function. To identify loci associated with wool production traits, we performed a genome-wide association study on a total of 765 Chinese Merino sheep (JunKen type) genotyped with 50 K single nucleotide polymorphisms (SNPs). In the present study, five wool production traits were examined: fiber diameter, fiber diameter coefficient of variation, fineness dispersion, staple length and crimp. We detected 28 genome-wide significant SNPs for fiber diameter, fiber diameter coefficient of variation, fineness dispersion, and crimp trait in the Chinese Merino sheep. About 43% of the significant SNP markers were located within known or predicted genes, including YWHAZ, KRTCAP3, TSPEAR, PIK3R4, KIF16B, PTPN3, GPRC5A, DDX47, TCF9, TPTE2, EPHA5 and NBEA genes. Our results not only confirm the results of previous reports, but also provide a suite of novel SNP markers and candidate genes associated with wool traits. Our findings will be useful for exploring the genetic control of wool traits in sheep.     

Investigation of Obesity Candidate Genes On Porcine Fat Deposition Quantitative Trait Loci Regions

Objectives: To investigate possible obesity candidate genes in regions of porcine quantitative trait loci (QTL) for fat deposition and obesity‐related phenotypes. Research Methods and Procedures: Chromosome mapping and QTL analyses of obesity candidate genes were performed using DNA panels from a reference pig family. Statistical association analyses of these genes were performed for fat deposition phenotypes in several other commercial pig populations. Results: Eight candidate genes were mapped to QTL regions of pig chromosomes in this study. These candidate genes also served as anchor loci to determine homologous human chromosomal locations of pig fat deposition QTL. Preliminary analyses of relationships among polymorphisms of individual candidate genes and a variety of phenotypic measurements in a large number of pigs were performed. On the basis of available data, gene‐gene interactions were also studied. Discussion: Comparative analysis of obesity‐related genes in the pig is not only important for development of marker‐assisted selection on growth and fat deposition traits in the pig but also provides for an understanding of their genetic roles in the development of human obesity.     

QTL analysis of white blood cell, platelet and red blood cell-related traits in an F2 intercross between Landrace and Korean native pigs

Haematological traits play important roles in disease resistance and defence functions. The objective of this study was to locate quantitative trait loci (QTL) and the associated positional candidate genes influencing haematological traits in an F₂ intercross between Landrace and Korean native pigs. Eight blood‐related traits (six erythrocyte traits, one leucocyte trait and one platelet trait) were measured in 816 F₂ progeny. All experimental animals were genotyped with 173 informative microsatellite markers located throughout the pig genome. We report that nine chromosomes harboured QTL for the baseline blood parameters: genomic regions on SSC 1, 4, 5, 6, 8, 9, 11, 13 and 17. Eight of twenty identified QTL reached genome‐wide significance. In addition, we evaluated the KIT locus, an obvious candidate gene locus affecting variation in blood‐related traits. Using dense single nucleotide polymorphism marker data on SSC 8 and the marker‐assisted association test, the strong association of the KIT locus with blood phenotypes was confirmed. In conclusion, our study identified both previously reported and novel QTL affecting baseline haematological parameters in pigs. Additionally, the positional candidate genes identified here could play an important role in elucidating the genetic architecture of haematological phenotype variation in swine and in humans.     

Genetic variation of wood chemical traits and association with underlying genes in Eucalyptus urophylla

We developed a quantitative and association genetic study with Eucalyptus urophylla using a progeny trial. Based on a sample of 831 trees distributed in 84 half-sib families whose wood was phenotyped by near-infrared spectroscopy, the results showed that traits related to lignin, cellulose, and wood extractives presented significant additive genetic variability with moderate to high narrow sense heritability (h ²?=?0.28 to 0.93). Genetic correlations varied with high standard error and showed low to moderate values. Using three cellulose synthase genes (EuCesA1, EuCesA2, and EuCesA3) and three candidate genes involved in the lignin pathway (EuC4H1, EuC4H2, and EuCAD2), an association study was performed for each of the gene action models (co-dominant, recessive, and dominant) using two methods. Firstly, single-marker association tests were done and 539 tests (49 single nucleotide polymorphisms (SNPs)?×?11 traits) were analyzed. After Bonferroni correction with a significance level of P?=?0.00102, only four SNPs presented significant association with syringyl and syringyl-to-guaiacyl ratio with an adjusted coefficient of determination varying between 2.6 and 4.4 %. Secondly, a model selection method, the backward approach, was implemented. Similar SNPs were detected by both the backward selection and the individual marker approaches. However, the latter detected new associations with other traits, genes, and SNPs and improved the quality of the model as shown by the BIC criteria and the higher adjusted determination coefficient (1.5 to 8.3 %). Our results reveal that cellulose genes can be associated with lignin traits (syringyl-to-guaiacyl ratio) and stress the possible pleiotropic effect of some genes.     

Genome-Wide Association Analysis for Blood Lipid Traits Measured in Three Pig Populations Reveals a Substantial Level of Genetic Heterogeneity

Serum lipids are associated with myocardial infarction and cardiovascular disease in humans. Here we dissected the genetic architecture of blood lipid traits by applying genome-wide association studies (GWAS) in 1,256 pigs from Laiwu, Erhualian and Duroc × (Landrace × Yorkshire) populations, and a meta-analysis of GWAS in more than 2,400 pigs from five diverse populations. A total of 22 genomic loci surpassing the suggestive significance level were detected on 11 pig chromosomes (SSC) for six blood lipid traits. Meta-analysis of GWAS identified 5 novel loci associated with blood lipid traits. Comparison of GWAS loci across the tested populations revealed a substantial level of genetic heterogeneity for porcine blood lipid levels. We further evaluated the causality of nine polymorphisms nearby or within the APOB gene on SSC3 for serum LDL-C and TC levels. Of the 9 polymorphisms, an indel showed the most significant association with LDL-C and TC in Laiwu pigs. But the significant association was not identified in the White Duroc × Erhualian F₂ resource population, in which the QTL for LDL-C and TC was also detected on SSC3. This indicates that population-specific signals may exist for the SSC3 QTL. Further investigations are warranted to validate this assumption.     

A whole-genome association study for pig reproductive traits

A whole-genome association study was performed for reproductive traits in commercial sows using the PorcineSNP60 BeadChip and Bayesian statistical methods. The traits included total number born (TNB), number born alive (NBA), number of stillborn (SB), number of mummified foetuses at birth (MUM) and gestation length (GL) in each of the first three parities. We report the associations of informative QTL and the genes within the QTL for each reproductive trait in different parities. These results provide evidence of gene effects having temporal impacts on reproductive traits in different parities. Many QTL identified in this study are new for pig reproductive traits. Around 48% of total genes located in the identified QTL regions were predicted to be involved in placental functions. The genomic regions containing genes important for foetal developmental (e.g. MEF2C) and uterine functions (e.g. PLSCR4) were associated with TNB and NBA in the first two parities. Similarly, QTL in other foetal developmental (e.g. HNRNPD and AHR) and placental (e.g. RELL1 and CD96) genes were associated with SB and MUM in different parities. The QTL with genes related to utero-placental blood flow (e.g. VEGFA) and hematopoiesis (e.g. MAFB) were associated with GL differences among sows in this population. Pathway analyses using genes within QTL identified some modest underlying biological pathways, which are interesting candidates (e.g. the nucleotide metabolism pathway for SB) for pig reproductive traits in different parities. Further validation studies on large populations are warranted to improve our understanding of the complex genetic architecture for pig reproductive traits.     

Characterization of the porcine acyl-CoA synthetase long-chain 4 gene and its association with growth and meat quality traits

Summary Long-chain acyl-CoA synthetase (ACSL) catalyses the formation of long-chain acyl-CoA from fatty acid, ATP and CoA, activating fatty acids for subsequent reactions. Long-chain acyl-CoA synthetase thus plays an essential role in both lipid biosynthesis and fatty acid degradation. The ACSL4 gene was evaluated as a positional candidate gene for the quantitative trait loci (QTL) located between SW2456 and SW1943 on chromosome X. We have sequenced 4906 bp of the pig ACSL4 mRNA. Sequence analysis allowed us to identify 10 polymorphisms located in the 3'-UTR region and to elucidate two ACSL4 haplotypes. Furthermore, a QTL and an association study between polymorphisms of the ACSL4 gene and traits of interest were carried out in an Iberian x Landrace cross. We report QTL that have not been previously identified, and we describe an association of the ACSL4 polymorphisms with growth and percentage of oleic fatty acid. Finally, we have determined allelic frequencies in 140 pigs belonging to the Iberian, Landrace, Large White, Meishan, Pietrain, Duroc, Vietnamese, Peccary and Babirusa populations.     

A genome-wide association study for feed efficiency-related traits in a crossbred pig population

Feed efficiency (FE) is one of the most important traits in pig production. However, it is difficult and costly to measure it, limiting the collection of large amount of data for an accurate selection for better FE. Therefore, the identification of single-nucleotide polymorphisms (SNPs) associated with FE-related traits to be used in the genetic evaluation is of great interest of pig breeding programs for increasing the prediction accuracy and the genetic progress of these traits. The objective of this study was to identify SNPs significantly associated with FE-related traits: average daily gain (ADG), average daily feed intake (ADFI) and feed conversion ratio (FCR). We also aimed to identify potential candidate genes for these traits. Phenotypic information recorded on a population of 2386 three-way crossbreed pigs that were genotyped for 51 468 SNPs was used. We identified three loci of quantitative trait (QTL) regions associated with ADG and three QTL regions associated with ADFI; however, no significant association was found for FCR. A false discovery rate (FDR) ≤ 0.005 was used as the threshold for declaring an association as significant. The QTL regions associated with ADG on Sus scrofa chromosome (SSC) 1 were located between 177.01 and 185.47 Mb, which overlaps with the QTL regions for ADFI on SSC1 (173.26 and 185.47 Mb). The other QTL region for ADG was located on SSC12 (2.87 and 3.22 Mb). The most significant SNPs in these QTL regions explained up to 3.26% of the phenotypic variance of these traits. The non-identification of genomic regions associated with FCR can be explained by the complexity of this trait, which is a ratio between ADG and ADFI. Finally, the genes CDH19, CDH7, RNF152, MC4R, PMAIP1, FEM1B and GAA were the candidate genes found in the 1 Mb window around the QTL regions identified in this study. Among them, the MC4R gene (SSC1) has a well-known function related to ADG and ADFI. In this study, we identified three QTL regions for ADG (SSC1 and SSC12) and three for ADFI (SSC1). These regions were previously described in purebred pig populations; however, to our knowledge, this is the first study to confirm the relevance of these QTL regions in a crossbred pig population. The potential use of the SNPs and genes identified in this study in prediction models that combine genomic selection and marker-assisted selection should be evaluated for increasing the prediction accuracy of these traits in this population.