Graham Plastow

A missense variant of the porcine melanocortin-4 receptor (MC4R) gene is associated with fatness, growth, and feed intake traits.

Abstract. Our knowledge of the genetic factors affecting obesity is increasing, but information about the individual gene effects remains limited in humans as well as in animal models. The melanocortin-4 receptor gene (MC4R) has been implicated in the regulation of feeding behavior and body weight in humans and mice. We have studied MC4R as a candidate gene for the control of economically important growth and performance traits in the pig. A missense mutation was identified in a region highly conserved among melanocortin receptor (MCR) genes. To determine whether there was an association of this MC4R polymorphism with phenotypic variation, we tested the mutation in a large number of individual animals from several different pig lines. Analyses of growth and performance test records showed significant associations of MC4R genotypes with backfat and growth rate in a number of lines as well as feed intake overall. It is probable that the variant amino acid residue of the MC4R mutation (or a closely linked mutation) causes a significant change of the MC4R function. These results support the functional significance of a pig MC4R missense mutation and suggest that comparative genomics based on model species may be equally important for application to farm animals as they are for human medicine.

Investigation of the retinol-binding protein 4 (RBP4) gene as a candidate gene for increased litter size in pigs

As pork production becomes more integrated, small to moderate gains in litter size can mean large returns in overall efficiency. Traditionally, pig geneticists have used the methods of crossbreeding and selection to make improvements in litter size (Rothschild 1996). However, identification of individual genes influencing litter size followed by marker-assisted selection (MAS) could potentially accelerate the rate of improvement in economically important traits. Considerable debate exists on the best method to identify individual genes or quantitative trait loci (QTL). The two methods used to identify genes or markers linked to these quantitative traits are genomic scans and the candidate gene approach. Genomic scans usually use crosses of divergent (exotic) breeds, and severalgeneration pedigrees are produced in which coinheritance of the trait and chromosomal markers can be followed. Several studies have been published on reproductive traits, but the population sizes of these crosses have generally been limited to a few hundred females (Rathje et al. 1997; Rohrer et al. 1999; Wilkie et al. 1999), and detection has been limited to chromosomal regions with moderate to large effects. Another problem with the genomic scan approach is the difficulty in identifying the gene or genes underlying any QTL that are found. Because of the use of exotic pig breeds in these crosses, these QTL may not be relevant in commercial lines of pigs used by pork producers. An alternative strategy is the candidate gene approach (Rothschild and Soller 1997) that employs information of the biology of individual genes and gene function from other species to select genes for investigation. Once a polymorphism has been identified in a candidate gene, association studies are completed to determine the relationship of the gene/marker with traits of interest. These studies do not require crosses of diverse breeds or lines, and they may be applied directly to commercial lines of pigs. This enables researchers to use larger populations with the candidate gene approach, and hence, because of the larger populations, there is considerably more statistical power to detect smaller effects. The major concern of the candidate gene approach is proving that the candidate gene is the causative trait gene. However, once an association is shown with the candidate gene approach, the gene test has immediate use in MAS for commercial pork production. Breeders use the existing linkage disequilibruim between the gene marker and trait, and it is straightforward to re-estimate the association after each generation. With the candidate gene approach, the estrogen receptor gene (ESR) has been demonstrated to have large allelic effects in both the unimproved Chinese Meishan breed (1.4 pigs per litter) and in several populations of improved Large White lines (.4 pigs/litter; Rothschild et al. 1996; Short et al. 1997). More recently, the prolactin receptor has been shown to be associated with differences in litter size in commercial lines and in selected populations (Vincent et al. 1998; D. Pomp, personal communication), and the follicle stimulating hormone beta ( FSHB) gene may also be associated with litter size differences (Li et al. 1998). A set of traits as complex as reproductive traits allows researchers to consider various aspects of the biology of each trait when considering possible candidate genes. Retinol-binding protein 4 (RBP4) is expressed during a critical time of pregnancy in the pig (Harney et al. 1993). Retinoic acid (vitamin A) is derived from endogenous retinol, which is delivered to target cells by RBP. The availability of retinol to the conceptuses is limited to that which can be transported from maternal capillaries across several boundaries of epithelium to the uterine lumen (Roberts et al. 1993). Brief and Chew (1985) showed that supplementing the diet of pregnant sows with vitamin A can increase litter size. Given that the conceptuses signal uterine secretion of RBP and that they express high levels of RBP around day 12 of pregnancy, this suggests an important function of retinol and RBP at this time (Trout et al. 1991). The most advanced conceptuses secrete the RBP necessary for their development. Less advanced conceptuses, however, are vulnerable to the rising level of retinol and its derivatives, and may fall victim to the embryotoxic properties of these compounds (Roberts et al. 1993). The transport and buffering ability of RBP at this critical time in pregnancy makes RBP4a strong candidate gene for litter size in pigs. The RBP4locus maps to Chr 14 (Messer et al. 1996). Preliminary results with RBP4as a candidate gene and limited numbers of sows showed an additive gene effect for the favorable allele of 0.52 ± 0.30 pigs per litter in a Large White Hyperprolific line and 0.45 ± 0.43 in the control (Ollivier et al. 1997). The objective of this research was to verify that RBP4is associated with litter size. A total of 2555 litter records from 1300 sows were used in the litter size analyses. Traits included total number born (TNB) and number born alive (NBA). The females were from six different PIC commercial lines. The lines examined were of Large White and Landrace origin (four sources), as well as a synthetic line consisting of Duroc and Large White. These lines were all housed in genetic nucleus farms owned by PIC Europe or PIC USA and were raised in accordance with approved farm management practices. Animals were genotyped for the RBP4 marker at a PIC r search lab (Cambridge, UK) or at Iowa State University. A MspI PCR-RFLP was developed for further testing of the effects of this gene. The designed primers were in exons 2 and 4 and were forward 5’-GAGCAAGATGGAATGGGTT-3’ and reverse 5’-CTCGGTGTCTGTAAAGGTG-3’. These were used with 25 ng of pig genomic DNA in a 25ml PCR reaction (50 mM KCl, 10 mM Tris-HCl, 0.1% Triton X-100), 200mM each dNTP, 1.5 mM MgCl2, 200mM each primer, and 0.6 UTaqpolymerase (Promega, Correspondence to: M.F. Rothschild Mammalian Genome 11, 75–77 (2000).

Coordinated international action to accelerate genome-to-phenome with FAANG, the Functional Annotation of Animal Genomes project

We describe the organization of a nascent international effort, the Functional Annotation of Animal Genomes (FAANG) project, whose aim is to produce comprehensive maps of functional elements in the genomes of domesticated animal species.

The Belt mutation in pigs is an allele at the Dominant white (I/KIT) locus

Abstract. A white belt is a common coat color phenotype in pigs and is determined by a dominant allele (Be). Here we present the result of a genome scan performed using a Hampshire (Belt)/Pietrain (non-Belt) backcross segregating for the white belt trait. We demonstrate that Belt maps to the centromeric region of pig Chromosome (Chr) 8 harboring the Dominant white (I/KIT) locus. Complete cosegregation between Belt and a single nucleotide polymorphism in the KIT gene was observed. Another potential candidate gene, the endothelin receptor type A gene (EDNRA), was excluded as it was assigned to a different region (SSC8q21) by FISH analysis. We argue that Belt is a regulatory KIT mutation on the basis of comparative data on mouse KIT mutants and our previous sequence analysis of the KIT coding sequence from a Hampshire pig. Quantitative PCR analysis revealed that Belt is not associated with a KIT duplication, as is the case for the Patch and Dominant white alleles. Thus, Belt is a fourth allele at the Dominant white locus, and we suggest that it is denoted IBe.

Genome Sequence and Assembly of Bos indicus

Cattle are divided into 2 groups referred to as taurine and indicine, both of which have been under strong artificial selection due to their importance for human nutrition. A side effect of this domestication includes a loss of genetic diversity within each specialized breed. Recently, the first taurine genome was sequenced and assembled, allowing for a better understanding of this ruminant species. However, genetic information from indicine breeds has been limited. Here, we present the first genome sequence of an indicine breed (Nellore) generated with 52X coverage by SOLiD sequencing platform. As expected, both genomes share high similarity at the nucleotide level for all autosomes and the X chromosome. Regarding the Y chromosome, the homology was considerably lower, most likely due to uncompleted assembly of the taurine Y chromosome. We were also able to cover 97% of the annotated taurine protein-coding genes.

Linkage disequilibrium in Angus, Charolais, and Crossbred beef cattle

Linkage disequilibrium (LD) and the persistence of its phase across populations are important for genomic selection as well as fine scale mapping of quantitative trait loci (QTL). However, knowledge of LD in beef cattle, as well as the persistence of LD phase between crossbreds (C) and purebreds, is limited. The objective of this study was to understand the patterns of LD in Angus (AN), Charolais (CH), and C beef cattle based on 31,073, 32,088, and 33,286 SNP in each population, respectively. Amount of LD decreased rapidly from 0.29 to 0.23 to 0.19 in AN, 0.22 to 0.16 to 0.12 in CH, 0.21 to 0.15 to 0.11 in C, when the distance range between markers changed from 0–30 kb to 30–70 kb and then to 70–100 kb, respectively. Breeds and chromosomes had significant effects (P < 0.001) on LD decay. There was significant interaction between breeds and chromosomes (P < 0.001). Correlations of LD phase were high between C and AN (0.84), C and CH (0.81), as well as between AN and CH (0.77) for distances less than or equal to 70 kb. These dropped when the distance increased. Estimated effective population sizes for AN and CH were 207 and 285, respectively, for 10 generations ago. Given a useful LD of at least 0.3 between pairs of SNPs, the LD phase between any pair of the three breed groups was highly persistent. The current SNP density would allow the capture of approximately 49% of useful LD between SNP and marker QTL in AN, and 38% in CH. A higher density SNP panel or redesign of the current panel is needed to achieve more of useful LD for the purpose of genomic selection beef cattle.

Genome-wide association analyses for growth and feed efficiency traits in beef cattle.

A genome-wide association study using the Illumina 50K BeadChip included 38,745 SNP on 29 BTA analyzed on 751 animals, including 33 purebreds and 718 crossbred cattle. Genotypes and 6 production traits: birth weight (BWT), weaning weight (WWT), ADG, DMI, midtest metabolic BW (MMWT), and residual feed intake (RFI), were used to estimate effects of individual SNP on the traits. At the genome-wide level false discovery rate (FDR < 10%), 41 and 5 SNP were found significantly associated with BWT and WWT, respectively. Thirty-three of them were located on BTA6. At a less stringent significance level (P < 0.001), 277 and 27 SNP were in association with single traits and multiple traits, respectively. Seventy-three SNP on BTA6 and were mostly associated with BW-related traits, and heavily located around 30 to 50Mb. Markers that significantly affected multiple traits appeared to impact them in same direction. In terms of the size of SNP effect, the significant SNP (P < 0.001) explained between 0.26 and 8.06% of the phenotypic variation in the traits. Pairs of traits with low genetic correlation, such as ADG vs. RFI or DMI vs. BWT, appeared to be controlled by 2 groups of SNP; 1 of them affected the traits in same direction, the other worked in opposite direction. This study provides useful information to further assist the identification of chromosome regions and subsequently genes affecting growth and feed efficiency traits in beef cattle.

Detailed characterization of the porcine MC4R gene in relation to fatness and growth

In contrast to the human MC4R gene, where multiple variants have been described, several of which are associated with appetite and obesity, few MC4R variants have been reported in the pig. The most interesting polymorphism reported to date in the pig is p.Asp298Asn, which is significantly associated with variation in growth and fatness traits in most breeds and crosses. However, some reports have seemingly failed to confirm this association. The discrepancy of p.Asp298Asn associations in some pig populations suggested that further discovery of SNPs in MC4R would be useful. Utilizing the recently released pig genome sequence information, we obtained the whole MC4R genome sequence and detected five additional SNPs, a variable (CA)(n) repeat and a C indel in the ISU Berkshire x Yorkshire pig resource family. Linkage disequilibrium (LD) analysis revealed that the additional five SNPs were not in strong LD with p.Asp298Asn, but single marker association analysis indicated that they were significantly (P < 0.05) associated with fatness measures and very highly significantly (P < 0.0001) associated with average daily gain on test (ADGTEST). Three major haplotypes were identified and the subsequent association analyses suggested that the two non-synonymous SNPs had different effects, e.g. p.Arg236His influenced back fat and growth on test while p.Asp298Asn was primarily associated with variation in growth rate in this population. An interaction effect between these two SNPs was found for ADGTEST, which may partly explain some of the previous discrepancies reported for MC4R in different pig populations. Examination of the p.Arg236His polymorphism in populations where the effect of p.Asp298Asn is limited is warranted.

A major SNP resource for dissection of phenotypic and genetic variation in Pacific white shrimp (Litopenaeus vannamei)

Bioinformatics and re-sequencing approaches were used for the discovery of sequence polymorphisms in Litopenaeus vannamei. A total of 1221 putative single nucleotide polymorphisms (SNPs) were identified in a pool of individuals from various commercial populations. A set of 211 SNPs were selected for further molecular validation and 88% showed variation in 637 samples representing three commercial breeding lines. An association analysis was performed between these markers and several traits of economic importance for shrimp producers including resistance to three major viral diseases. A small number of SNPs showed associations with test weekly gain, grow-out survival and resistance to Taura Syndrome Virus. Very low levels of linkage disequilibrium were revealed between most SNP pairs, with only 11% of SNPs showing an r(2)-value above 0.10 with at least one other SNP. Comparison of allele frequencies showed small changes over three generations of the breeding programme in one of the commercial breeding populations. This unique SNP resource has the potential to catalyse future studies of genetic dissection of complex traits, tracing relationships in breeding programmes, and monitoring genetic diversity in commercial and wild populations of L. vannamei.

Accuracy of genomic breeding values for residual feed intake in crossbred beef cattle.

The benefit of using genomic breeding values (GEBV) in predicting ADG, DMI, and residual feed intake for an admixed population was investigated. Phenotypic data consisting of individual daily feed intake measurements for 721 beef cattle steers tested over 5 yr was available for analysis. The animals used were an admixed population of spring-born steers, progeny of a cross between 3 sire breeds and a composite dam line. Training and validation data sets were defined by randomly splitting the data into training and testing data sets based on sire family so that there was no overlap of sires in the 2 sets. The random split was replicated to obtain 5 separate data sets. Two methods (BayesB and random regression BLUP) were used to estimate marker effects and to define marker panels and ultimately the GEBV. The accuracy of prediction (the correlation between the phenotypes and GEBV) was compared between SNP panels. Accuracy for all traits was low, ranging from 0.223 to 0.479 for marker panels with 200 SNP, and 0.114 to 0.246 for marker panels with 37,959 SNP, depending on the genomic selection method used. This was less than accuracies observed for polygenic EBV accuracies, which ranged from 0.504 to 0.602. The results obtained from this study demonstrate that the utility of genetic markers for genomic prediction of residual feed intake in beef cattle may be suboptimal. Differences in accuracy were observed between sire breeds when the random regression BLUP method was used, which may imply that the correlations obtained by this method were confounded by the ability of the selected SNP to trace breed differences. This may also suggest that prediction equations derived from such an admixed population may be useful only in populations of similar composition. Given the sample size used in this study, there is a need for increased feed intake testing if substantially greater accuracies are to be achieved.