- freely available
Microarrays 2015, 4(4), 570-595; https://doi.org/10.3390/microarrays4040570
Abstract: The genetic markers associated with economic traits have been widely explored for animal breeding. Among these markers, single-nucleotide polymorphism (SNPs) are gradually becoming a prevalent and effective evaluation tool. Since SNPs only focus on the genetic sequences of interest, it thereby reduces the evaluation time and cost. Compared to traditional approaches, SNP genotyping techniques incorporate informative genetic background, improve the breeding prediction accuracy and acquiesce breeding quality on the farm. This article therefore reviews the typical procedures of animal breeding using SNPs and the current status of related techniques. The associated SNP information and genotyping techniques, including microarray and Lab-on-a-Chip based platforms, along with their potential are highlighted. Examples in pig and poultry with different SNP loci linked to high economic trait values are given. The recommendations for utilizing SNP genotyping in nimal breeding are summarized.
In recent years, breeding programs for farm animals have dramatically evolved from visual and phenotypic evaluations with subjective judgments to quantitative selections with genetic technology. While the breeding selection based on phenotypic evaluation can be slow, difficult and often not completely accurate for quantitative traits (also “polygenic” or “complex” traits), marker-assisted selection (MAS) techniques, which allow breeders to select farm animals with high breeding values early and effectively, have gained high popularity. It is a selection process that focuses on the traits of interest based on the linked genetic markers . When it is coupled with quantitative trait loci (QTL) analysis, MAS assists breeders to locate genetic loci associated with quantitative traits to select the individuals with desirable combinations of the genes [2,3,4].
MAS typically utilizes three types of genetic markers: (i) direct marker, for causative mutations, (ii) linkage disequilibrium (LD) maker, for population-wide linkage disequilibrium with the QTL, and (iii) linkage equilibrium maker (LE), for population-wide equilibrium with the QTL within pedigree . The MAS aims to maximize the rate of improvement in quantitative characters under different schemes of MAS information in molecular genetic polymorphism with data on phenotypic variations among individuals . Genetic markers used in MAS include random amplified polymorphic DNA (RAPD), single sequence repeat (SSR), amplified fragment length polymorphism (AFLP), and single nucleotide polymorphism (SNP). As they show their applicability in animal breeding to select qualitative and quantitative traits [6,7,8,9,10], SNP markers have increasingly attracted great attention, particularly for germplasm diversity evaluation due to the highly qualitative nature of data (QND) and the highly effective marker index (EMI) [10,11,12].
Moreover, SNP markers are abundant in the genome, genetically stable, capable of effectively distinguishing different alleles, and amenable for automated high-throughput analysis [9,13]. The combination of SNP genotyping techniques and large-scale association study can further identify the relationships between genes and SNP markers of single-gene traits, QTL or genomic regions affecting quantitative traits [3,4]. Therefore, effective SNP genotyping with high accuracy is important to the success of highly efficient breeding selection.
A variety of SNP genotyping techniques have been developed to improve their detection accuracy, time, and cost . These genotyping procedures typically involve the amplification of allele-specific products for the SNP of interest. This is followed by the detection techniques, such as enzymatic ligation [15,16], enzymatic cleavage [17,18], primer extension [19,20], split DNA enzymes G-quadruplex [21,22], sequencing [23,24], and mass spectroscopy . These detection techniques utilize enzymes, molecular beacon, or fluorescent dyes to label the DNA probes, thereby leading to the requirement of high reagent cost or complex procedures.
Utilizing SNP genotyping techniques to select the species of farm animals with high economic trait values, including reproduction, growth rate, milking yield, and egg production, have become increasingly popular . Recently, great efforts have been devoted to improving the SNP detection efficiency, reducing reagent consumption, minimizing sample volume and increasing sensitivity based on microfabrication techniques [27,28,29]. The application of genotyping procedures in farm animals should be simple and affordable, while they can simultaneously generate a vast amount of genotyping data [13,30,31]. A miniaturized version of the genotyping procedures in farm animals can further reduce the associated cost and make point-of-care gene detection/diagnosis possible . Particularly, in recent years, miniaturized devices, which can integrate a series of laboratory functions on a single tiny chip (or so-called Lab-on-a-Chip), have many advantages over their analogues at the macroscale, including portability, reduced sample consumption, rapid reaction times, and high throughput [33,34,35,36,37,38]. For instance, SNP discrimination of swine in reproduction traits has been demonstrated . Hence, the potential of Lab-on-a-Chip technology combined with SNP genotyping provides a number of advantages, which include decreasing the amount of reagents for the experiments and shortening the time for genetic selection, allowing breeders to conduct SNP detection on-site and to ad-hoc select candidates for high value breeding.
The purpose of this article is to discuss the procedures of SNP discovery and genotyping techniques in regard to selective animal breeding. The associated SNP information and genotyping techniques, including microarray and Lab-on-a-Chip based platforms, are highlighted. The recommendations of utilizing SNP genotyping for animal breeding are summarized.
2. Discovery of Novel SNP (Single Nucleotide Polymorphism) for Animal Breeding
SNP markers have been utilized by breeders to seek for high breeding values of economic traits . They are used to predict the potential genes associated with economic traits, the linkage disequilibrium (LD) extent between markers at the genome level, as well as the livestock genetic diversity [41,42,43,44,45]. For example, researchers have used the SNP panel to estimate the LD levels of the pig breeds in Finland and United States . The results show the SNP markers not only can serve as a powerful tool in MAS in breeds, but also reveal the phylogenetic relationships [41,46,47]. Further, more than 4000 QTL for production traits, with their associated SNP and SSR markers, are reported in chicken . However, among a variety of available SNP markers and the sequence information of farm animals, only a few SNP markers contribute to the genetic variation for economic traits. The capability to effectively select critical SNP markers contributing to high breeding values is extremely relevant for efficient animal breeding schemes. Figure 1 illustrates a typical SNP selection procedure for animal breeding in three steps: (1) SNP discovery from a SNP marker pool, (2) SNP primary selection to validate the SNP in population and (3) secondary selection for routine SNP detection in nucleus farms.
In the first step of SNP discovery, SNP marker information is obtained through sequencing techniques, targeting local lesions in genomes (TILLING) analysis or in silico study on sequenced species. A SNP marker pool, from public database or Single Nucleotide Polymorphism Database (dbSNP), to represent informative SNP markers, is found at a given representative population. Based on the phenotypic differences and genetic background from the population with the traits of interest, the candidate SNPs are selected. Next, the primary selection is conducted to identify the association of the SNP markers and breeding germplasms. Bead-based assay and microarray are popular tools during this selection step. Finally, in the secondary selection step, the SNP markers validated from the primary selection are used during the association analysis. Additional samples from farms are exploited to evaluate their SNP genotypes. Commercial tools of TaqMan® and Invader® assays are commonly employed.
3. Strategies for SNP Discovery
To establish the large numbers of SNPs, two strategies have been employed in a given population. The first strategy is sequence-independent, and it detects unknown sequence variants or those species whose whole genome sequence are not available . This strategy include restriction enzyme-based [50,51], DNA conformational changes, and target induced local lesions in genomes (TILLING) . The traditional sequencing techniques such as shotgun sequencing or Sanger’s method are labor-intensive, time-consuming, and with relatively high error rates. Once a species’ genome has been sequenced and assembled, the re-sequencing of other individual species allows users to discover sequence variations on a genome-wide scale . This strategy can provide indirect evidence, with solid evidence of sequence information still necessary to determine the actual genetic code.
The second strategy is sequence-dependent, and it detects known sequence variants, in which whole genome sequence information is utilized to discover SNPs of interest. Commercial genotyping assays can simultaneously genotype up to 2 million SNP per DNA sample (e.g., Illumina SNP chips). Other genotyping platforms involving multiplex, loci-specific PCR or whole genome amplification followed by primer extension or allele-specific hybridization can distinguish between bi-allelic SNPs in different fluorescence colors.
To interpret the sequencing data and to accurately identify the SNPs of interest, bioinformatics algorithms for searching SNPs have been developed, including Tablet , Pyrobayes , SOAP , VarScan , MAQ , MagicViewer , Atlas-SNP2 . Hence, SNP discovery can also result from DNA resequencing analysis using novel deep-sequencing strategies. Although the sequence-dependent strategy is powerful, it relies on available sequence information and assembly of the sequencing contigs with a high rate of accuracy. On the other hand, the traditional sequence-independent strategy is irreplaceable for the species with limited sequence information available. In addition, the overlapping sequencing peaks of heterozygous alleles prove challenging in distinguishing these from sequencing errors and ambiguities, which can be easily distinguished using traditional strategies. These traditional methods are more feasible in genetic association mapping and diversity analysis by means of SNPs.
4. SNP Genotyping Technology
A variety of SNP genotyping techniques have been developed and reviewed [13,14,61,62,63,64,65,66,67,68]. These methods comprise a single or multiple allele-discrimination principles, along with signal detection mechanisms. One key feature in these SNP genotyping techniques, apart from those based on direct hybridization, is the two-step separation, which involves the first step in generation of allele-specific molecular reaction products and the second step in separation and detection of the allele specific products for SNP identification. Based on the molecular reaction system, SNP detection methods can be divided into four categories: primer extension-based, sequencing-based, restriction enzyme-based, and hybridization-based systems.
In this section, SNP genotyping techniques will be briefly discussed according to three categories, based on the number of samples or SNP sites to be genotyped. “Large throughput” genotyping is defined as genotyping of thousands of individuals for hundreds/thousands of SNP sites, or even conducting genome-wide association studies (GWAS). Likewise, “medium throughput” genotyping is an intermediary option, which detects hundreds of samples for hundreds to thousands of SNP sites. “Small throughput” methods only detect one to tens of samples for tens of targets [61,69]. Depending on the number of SNP sites and sample size, researchers and breeders can design and choose appropriate genotyping techniques. For example, in a genetic-mapping study that requires surveying many SNP markers along the whole genome to identify the candidate alleles, large throughput methods should be implemented. In a phylogenetic analysis, which demands a large sample size but only a few SNP markers, small and medium throughput methods are to be utilized with a few SNP primers or probes as an economical approach.
Table 1 lists the common SNP genotyping methods based on their throughputs in three categories: large, medium and small throughput. These intend to give breeders a general guideline to implement SNP genotyping techniques in farm animal breeding.
|Genotyping Method||Proudct Name||Detection Mechanism||Platform||Throughput||Error Rate||Price||Reference|
|Large Throughput (more than hundreds or thousands SNPs/reaction)|
|OpenArray® (Applied Biosystems. Foster City, CA, USA)||TaqMAN||Primer extension||Fluorescence||>1000 samples/SNPs||Low||$$$|||
|Infinium® II (Illumina, San Diego, CA, USA)||Illumina Infinium assay||Primer extension||Fluorescence||>1000 SNPs/sample||Medium to High||$$$$$|||
|GoldenGate® (Illumina, San Diego, CA, USA)||GoldenGate®||Hybridization||Fluorescence||>1000 SNPs/sample/reaction||Low||$$$$$|||
|Genome Wide SNP Array (Affymetrix, Santa Clara, CA, USA)||Affymetrix®||Hybridization||Fluorescence||>40 K SNPs/sample/reaction||Low||$$$$$|||
|RAD sequencing||Illumina||Sequencing||Capillary electrophoresis||>13 K SNPs/sample/reaction||Low||$$$|||
|Medium Throughput (hundreds to low thousands SNPs/reaction)|
|TaqMan assay (Applied Biosystems. Foster City, CA, USA)||TaqMan||Primer extension||Fluorescence||Up to 384 samples/SNP||Low||$$|||
|MassARRAY® system (Agena Bioscience, San Diego, CA, USA)||iPLEX||Primer extension||Mass spectrometer||60 SNPs/sample/reaction||Low||$$$|||
|SNPstream genotyping system (Beckman Coulter, Brea, CA, USA)||48-plex GenomeLab SNPstream||Primer extension||Fluorescence||48 SNPs/sample/reaction||N.A.||$$$|||
|SNaPshot® multiplex system (Applied Biosystems)||SNaPshot||Primer extension||Capillary electrophoresis||10 SNPs/sample/reaction||Low||$$$|||
|PCR-APEX||Genorama®||Primer extension||Fluorescene||Up to 384 samples/SNP||Low||$$$||[78,79]|
|Luminex xMAP technology (Luminex, Austin, TX, USA )||Luminex100™||Ligation||Flow cytometer||Up to 100 samples/SNP||Medium to high||$$$|||
|Small Throughput (less than hundred SNPs/reaction)|
|Invader assay||Laboratory use||Endonuclease cleavage||Fluorescence||1 SNP/sample/reaction||Low||$|||
|PCR-RFLP||Laboratory use||Restriction enzyme||Gel electrophoresis||1 SNP/sample/reaction||Low||$|||
|DASH||Laboratory use||Hybridization||Fluorescence||1 SNP/sample/reaction||Low||$|||
4.1. Large Throughput Methods
Large throughput genotyping methods deal with a large-scale project, where hundreds to thousands SNP sites on a few individuals or a few SNP sites on many individuals are simultaneously queried. These genotyping methods provide a great deal of SNP genotyping data, thereby enabling conducting whole-genome association studies in a given population.
Microarray technology is an ideal method for large throughput analysis of multiple SNP sites. Two detection principles are widely used in microarray-based methods, which include allele-specific oligonucleotide (ASO) hybridization and allele-specific primer (ASP) extension, as shown in Figure 2. In Figure 2A, allele-specific oligonucleotides are separately immobilized onto the glass plate using the photolithography method. Fluorescent signal is detected upon the probe and target hybridized. The mismatched target is removed from the probe after a stringent washing procedure. An ASP extension-based microarray uses the primers pre-synthesized in an array to conduct PCR extension, as shown in Figure 2B, with extended product shown when 3′ end of primer perfectly bound to the sample target. By contrast, no extended product could be found when a mismatched base pair occurred at the 3′ end of the primer. Two types of microarray are used when determining multiple SNP genotypes by measuring fluorescence intensity or mass to charge ratio, as shown in Figure 2C.
Commercial tools derived from the concept of microarray have been developed; they include Infinium® II (Illumina), GoldenGate® (Illumina), Genome Wide SNP Array (Affymetrix, Santa Clara, CA, USA) and OpenArray® (Applied Biosystems). Although these tools can provide massive information for SNP discrimination, two issues need attention: (i) the assay may have a high failure rate and (ii) it can only be conducted on model species because of the requirement of oligonucleotide probes or primers attached physically on the array [61,68]. In other words, lack of available oligonucleotide information may impede the use of large throughput microarray methods on the farm animals.
Despite the limitation in using microarray technologies, many advantages still exist, including large throughput data analysis and improving accuracy in accelerating animal breeding. For example, Jiang et al.  used BovineSNP50 BeadChip (Illumina) to conduct genome-wide association study (GWAS) for obtaining cattle milk production traits. They consequently screened 105 SNPs out of 54K SNPs, which are significantly associated genome-wise with one or multiple milk production on 2093 daughters from 14 paternal half-sib families.
Other large throughput SNP genotyping techniques include TaqMan® OpenArray®, Restriction site-associated DNA sequencing (RAD sequencing) and Pyrosequencing. These techniques are flexible, high throughput and low cost. TaqMan® OpenArray® is the derivative of TaqMan product to discriminate multiple SNP genotypes . It has been implemented in QTL mapping the pig meat quality gene, PHKG1 with SNP markers. A total of 53 SNPs are first identified from Illumina PorcineSNP60 chip and further applied as an OpenArray® for genotyping 140 pigs. A SNP site is found to affect PHKG1 gene splicing during post-transcription and the mutant causes high glycogen content and low meat quality in skeletal muscle . RAD sequencing and pyrosequencing are both developed for genotyping-by-sequencing (GBS) to discriminant sequence variant. RAD sequencing use fragmenting pooled samples’ genome for sequencing, which reduces the complexity across target genomes and delivers high resolution genomic data. The sequenced tags, RADSeqs, are used as the custom probes for massive parallel SNP genotyping . A total of 50 QTL linked RADseqs are found to be highly associated with salmon resistance against Infectious Pancreatic Necrosis challenge .
4.2. Medium Throughput Methods
The second category is medium throughput SNP genotyping tools for a slightly smaller number of individual or SNP sites—hundreds of samples for hundreds to thousands of SNP sites. The commercial products in this category are iPLEX, SNPstream, SNaPshot, PCR-APEX (Arrayed primer extension), Luminex100™ and TaqMan assay. The first four methods are based on primer extension (iPLEX, SNPstream and SNaShot), while the latter two employ oligonucleotide ligation and exonuclease mechanism to achieve multiplexing in a single tube PCR. Similar concerns with large throughput methods remain—including limited applicability and high failure rate [61,68].
4.3. Small Throughput Methods
For small throughput projects, the technical platform for DNA molecular markers detection includes PCR-free genotyping methods, single-step homogenous method, homogeneous detection with fluorescence polarization, DNA chip/Array based assays, bead-based methods, mass spectrum based genotyping assays and dynamic allele-specific hybridization (DASH) [9,81]. Among the genetic markers used in these techniques, SNP markers are one of the preferred genotyping approaches due to their genetic stability and applicability to effectively distinguish heterozygote from homozygote alleles along with their co-dominances and amenable to high-throughput automated analysis [9,13].
PCR-RFLP is another technique for small throughput projects in which individual SNPs are differentiated by analyzing the patterns derived from cleavage of their amplified DNA. If a sample with different nucleotides in the same SNP site differs in the distance between sties of cleavage of a particular restriction endonuclease, then the length of the fragments produced differ when the DNA is digested with a restriction enzyme.
5. Miniaturizing Platforms of SNP Genotyping
5.1. Lab-on-a-Chip Platform
Recently, miniaturized devices have brought many advantages over their analogues at the macroscale, including portability, reduced sample consumption, rapid reaction times, and high throughput [33,34,35,36,37]. In particular, Lab-on-a-Chip (LOC) involves the miniaturization of a series of sample preparation, genome amplification, and SNP detection onto a single chip, thereby simplifying the whole SNP genotyping process, as shown in Figure 3. It has been broadly used in biochemical fields, including genomics, proteomics, drug discovery, and infectious disease diagnostics. Such a miniaturization platform shows many advantages, including high surface-to-volume ratio, low reagent volumes, low background noise, and low reaction time.
Modern livestock production requires powerful technology for a fast, reliable and effective breeding protocol. For example, superior sows are selected based on their genetic background for high-yield traits. Once the piglets or poultry are selected at early stage, the breeders could save more money and increase the economic values. Lab-on-a-Chip technology, which implements genetic biotechnology onto a monolithic platform, enables the automation of the complete laboratory procedures of the genetic analysis protocol [48,85]. Use of this technology can simplify the analysis protocol, expedite the assay time, and reduce the risk of sample contamination.
For example, a Lab-on-a-Chip genotyping system, which integrated the functions of PCR and FRET-based melting curve analysis onto a microchip was developed to genotype SNPs—CYP3A4*15B allele and CYP3A5*3 allele . Saliva samples from participants were directly used without any purification process, showing the potential of point-of-care SNP genotyping. A few more examples include the identification of plant pathogens , Escherichia coli (E. coli) and hepatitis B virus , human disease of non-syndromic sensorineural hearing loss (NSSNHL) , and many others.
5.2. Microfluidics for SNP Detection
The development of the Lab-on-a-Chip technology for SNP detection has been rapidly progressing. Various working principles had been utilized. A digital microfluidic system was developed for SNP detection . A droplet containing magnetic beads and probes was actuated with addressable electrodes with the magnetic beads-probe used for SNP detection via the fluorescence signal due to oligonucleotide ligation . Moreover, DNA in droplets or on beads within the channel had been developed for SNP detection. Recently, a hydrogel array for SNP detection was proposed with different probes separately incorporated in multiple channels to achieve multiplexing SNP detection . This technique provided users with an opportunity for a large throughput analysis using a microfluidic-based system. An improved microarray device, made of a polyacrylamide gel-based microarray, was shown to overcome the drawbacks of the signal-to-noise ratio and diffusion-limited kinetics between the reaction liquids in which PCR or hybridization is conducted [90,91].
Due to its simplicity and low cost, melting curve analysis were widely applied for SNP genotyping in Lab-on-a-Chip. A rapid melting curve analysis system was demonstrated via immobilizing microbeads on the surface of a microheater chip with rapid temperature controller capabilities as shown in Figure 4A . This method was based on random bead immobilization using a micro contact printing technique. A visual SNP genotyping system in Figure 4B was demonstrated via asymmetric PCR process and split DNA enzymes, G-quadruplex . Only when both α-probe and β-probe were perfectly hybridized to the target single-stranded DNA, the G-quadruplex could be assembled. A hydrodynamic microbead array on a single chip combined with multiple bio-molecules detecting system was created . Four types of samples, including (1) perfect-match sample, (2) one-mismatch sample (SNP), (3) totally mismatch sample and (4) no sample control, were injected to the microfluidic and mixed with microbeads, as shown in Figure 4C. A significant difference in fluorescence intensity was observed among four types of samples. While the system provided a multiplex detection microarray, it required fluorescence-labeled molecular beacons. In addition, a droplet-based SNP genotyping system with silica superparamagnetic beads in aqueous droplets was developed [93,94]. The device, whose schematics was shown in Figure 4B, utilized silica superparamagnetic beads to extract and carry sample DNA from mammalian. It performed sample preparation in droplet followed by real time PCR and employed melt curve apparatus for SNP detection. Different versions of bead-based SNP genotyping, which employed microbeads as solid carrier to conduct DNA melting analysis, were demonstrated for high signal-to-noise ratios [39,95]. The SNP genotyping based on melting curves was facilitated in microfluidic platform either being heated in a confined space or passing through in a rapid temperature gradient inside microchannels for conducting conduct DNA melting analysis, as shown in Figure 4C,D [39,95]. Discrimination of ataxia telangiectasia mutated (ATM) gene from Landrace sow was successfully shown. Another bead-based method was shown in a micromachined chip combined with multiple bio-molecules detecting system . Four types of samples, including (1) perfect-match sample, (2) one-mismatch sample (SNP), (3) totally mismatch sample and (4) no sample control, were injected to the microfluidic and mixed with microbeads, as shown in Figure 4E. Finally, a visual SNP genotyping system in Figure 4F was demonstrated via asymmetric PCR process and split DNA enzymes, G-quadruplex . Only when both α-probe and β-probe were perfectly hybridized to the target single-stranded DNA, the G-quadruplex could be assembled as a simple SNP genotyping approach.
6. Application of Trait Selection in Farm Animals by Using SNPs
The utilization of SNP genotyping techniques in promoting QTL analysis among farm animals will permit breeders and farmers to predict traits and estimate breeding values. The traits of economic interest in general include growth, body composition, carcass, meat quality, reproduction, and disease resistance capability [3,13,97,98]. These traits are mostly quantitative traits, and regulated by SNP. This section thus will review these popular traits, the associated genes, as well as their SNP markers. Two major farm animals with high economic values—swine and poultry—will exemplify our discussion.
6.1. SNP Discovery in Swine
In swine, (1) reproduction and (2) carcass and meat quality are traits with high economic values.
The traits related to swine reproduction usually include litter size, litter number, number weaned, age at puberty, weaning to estrus interval, farrowing interval, and total number of pigs born (TNB) . Among them, TNB is one of the most important reproductive traits. It includes (i) number born alive (NBA), (ii) number of stillborn piglets, and (iii) number of mummies . The recent studies show that the development of early embryos, such as the period from morula, blastocyst for embryo implant, fetus development, and placental efficiency, is critical in determining the litter size of sow [101,102]. Landrace sows with specific SNPs located on the regulatory regions of ATM gene has been discovered by using GoldenGate® in differentially expressed genes between morula and blastocyst . These genes play an important role in TNB, NBA, and the average birth weight of piglets due to their differential expressions between the morula and blastocyst stages. Recently, the SNP associated to ATM genes had been detected by using Lab-on-a-Chip platforms [39,95].
Another important trait for reproduction is litter size, which is highly influenced by ovarian follicular growth. Two estrogens receptors (ESR) (e.g., ESR1 and ESR2) are found to be involved in ovarian follicular growth [104,105]. The associated SNP has been genotyped using PCR-RFLP (Hsp92 II) method in two Iberian pig populations . No significant association is found between litter size and ESR2 polymorphism. Furthermore, the Polish sow with AA genotype is found to have the largest litter size compared to other genotypes of AB and BB . Statistically insignificant trait difference in the results implies the utilization of a different SNP genotyping strategy with larger populations or more SNP markers may be necessary to identify the SNPs for the traits of interest. Up to now, all the SNPs identified in these two gene have failed to be validated in large populations or across breeds.
On the other hand, the polymorphism in Prolactin receptor (PRLR) gene, which controls luteal and follicular steroidogenesis of Large White sows (n = 301), has been tested by using the PCR-RFLP method. The results suggest that PRLR/AluI gene improves reproductive performance traits of sows . The medium throughput genotyping techniques are adapted to validate multiple candidate SNPs and to decipher how PRLR and ESR play different roles in sow reproduction, without knowing the correlation with the traits of interests. In addition, a commercial PCR-APEX chip, which contains 45 SNPs, is used to determinate PSS-porcine stress syndrome (a single-gene disease) in four QTL genes, including protein kinase adenosine monophosphate-activated γ3-subunit (PRKAG3), calpastatin (CAST), Melanocortin 4 receptor (MC4R) and ESR—these four genes are exploited for the marker-assisted selection. The SNPs’ genotyping technique used in this selection procedure is PCR-APEX (Arrayed primer extension) technique—a medium throughput method [78,79].
Besides, there have been significant interest lately in several populations and in different approaches for leptin receptor (LEPR), MC4R, Phosphatidylinositol 3-kinase catalytic subunit type 3 (PIK3C3) and vertnin (VRTN) genes analyzed [98,99,100,101,102,103,104,105,106,107,108,109,110]. Associations of SNPs in inter-alpha-trypsin inhibitor heavy chains (ITIH) genes as well as retinol binding protein 4 (RBP4) and follicle stimulating hormone (FSHB) genes with porcine reproductive traits have been reported [111,112].
6.1.2. Meat Quality Traits
Meat quality is important in swine-breeding programs. The related traits, including muscle growth, tenderness, color and oxidative stability, are mostly quantitative . They are affected by many loci that have a wide degree of effects on phenotypic variations. For instance, muscle growth is found to be regulated by MSTN gene [4,45,113]. To estimate the LD, 351 animals in 117 sire/dam/offspring trios across four breeds of pigs (Duroc, Hampshire, Landrance, Yorkshire) using Illumina PorcineSNP60 BeadChip were used. Two SNP markers (g.435 and g.447) have been reported to associate with controlling myostatin expression in pigs. These two SNPs, located in the promoter region of MSTN gene, are vital functional genetic markers . Insulin-like-growth factor 2 (IGF2) gene, located in chromosome 2 of pig, has been found to positively control skeletal muscle growth, which is discovered by using RFLP. An SNP (G>A) of IGF2 intron 3 (g.3072) disturbs a repressor zinc finger BED-type containing 6 (ZBED6) binding onto IGF2 and causes a three-fold overexpression of postnatal skeletal muscle IGF2 mRNA. While this mutant leads to the increase of carcass lean yields, it reduces backfat deposition [114,115]. Furthermore, IGF2 plays an important role in the pig industry due to the 15%~30% and 10%~20% of phenotypic variation in muscle mass and backfat thickness, respectively .
SNP marker of pituitary-specific transcription factor (PIT1) has been reported to be associated with growth and carcass traits in pigs . The ryanodine receptor (RYR1) mutant pigs induce a high rate of glycolysis, leading to a low pH value of meat after slaughtering. The low pH value of meat could produce pale, soft, and exudative muscles, which have an unacceptable meat appearance for customers. RYR1 gene was reported to be associated with porcine stress syndrome (PSS) and functions at regulating calcium transport across skeleton muscle . Muscle regulatory factor (MRF) gene family, which encoded a basic helix-loop helix protein and was responsible for myotubular transformation. Two genes, Myf5 and Myf6, in MRF family were found to regulate the development of skeletal muscle fibers and postnatal growth hypertrophy, which affected meat quality [118,119,120].
Poultry has become the leading meat consumed in the United States and many other countries. It has been bred for two purposes: egg laying and meat production . Therefore, the reproduction trait (e.g., egg production, egg quality and egg shell) and the meat production traits (e.g., muscle growth, tenderness and water holding capacity (WHC)) are of considerable economic importance [121,122]. The first SNP genotyping array for chicken has been demonstrated by Affymetrix® lately, showing its importance in research and practical applications .
Genetic selection in superior chicken improves the breeding speed and accuracy. Several important traits have been addressed in Taiwan chicken breeding programs, which include improvement of egg production, egg quality, and eggshell. The traits of egg production, egg quality and eggshell are possibly controlled by multiple genes. For instance, low density lipoprotein receptor-related protein 8 (LRP8) gene has a 6.5 kb receptor transcript and is expressed in the brain and ovary. The LRP8 gene plays a role in cholesterol supply for steroid biosynthesis, thereby enabling folliculogenesis .
In birds, melatonin is found in ovarian follicular fluid, suggesting the role of melatonin on ovarian functions [125,126,127]. Two high-affinity melatonin receptors from MSTN family, MTNR1A and MTNR1B, have been cloned in numerous species, but there is an additional receptor subtype, MTNR1C, have been identified only in amphibians and birds. Direct PCR-sequencing, PCR-SSCP and PCR-RFLP are used to genotype these genes [125,126].
Also, the reproduction trait of poultry can be related to the laying performance. Due to low heritability, the selection in laying performance imperatively requires the MAS strategy. Yu et al. has 139,013 SNPs obtained from 42,291,356 RADseq tags by using RAD sequencing method . Five novel genes, including membrane associated guanylate kinase (MAGI-1), KIAA1462, Rho GTPase activating protein 21 (ARHGAP21), acyl-CoA synthetase family member 2 (ACSF2) and astrotactin 2 (ASTN2) are found to be promising candidate MAS markers for egg number in geese.
The haplotype of the PIT1 gene has shown a significant association with growth traits in chicken. PIT1 affects the expression of growth hormone, thyroid-stimulating hormone chain, and prolactin. The PIT1 SNP marker has been used to determine the genotypes of 10 chicken populations (n = 662), including six Chinese indigenous breeds, White Leghorn, paternal/maternal lines of brown egg layer and a paternal line of broiler by using the PCR-SSCP method. Two PIT1 SNP haplotypes, AA and TT types, have a significant difference between their body weights at eight weeks, making the PIT1 SNP marker a potential marker for MAS selection of early growth rates in chicken . Similar results have also been obtained by using PCR-RFLP in the cross of White Recessive Rock (WRR) and Chinese Xinghua .
In addition, the melanocortin 4 receptor (MC4R) belongs to G protein–coupled receptors (GPCR) super family and is a transmembrane neuron receptor, which controls the appetite, body weight and energy metabolism. The polymorphism of MC4R in the population of crossing Broiler and Chinese Silky chicken has been genotyped for the carcass traits in body weight and growth by using the PCR-SSCP technique .
Finally, all these candidate genes with SNP markers discussed in this section for economic traits of swine and poultry are summarized in Table 2.
|Species||Traits||Gene 1||Chromosome Location||Putative Functions||Refs.|
|Reproduction||ESR||1||Effect of follicular growth and litter size||[104,105].|
|Reproduction||PRLR||16||Control luteal and follicular steroidogenesis|||
|Meat Quality||MSTN||15||Negative regulator for muscle mass||[4,45,113]|
|Swine||Meat Quality||IGF-2||2||Growth-promoting peptidesStructurally homologous with insulin Producing uniformity of pork leanness||[114,115]|
|Meat Quality||RYR1||6||Known as Halothane gene, Ryanodine receptor causing Ca2+ release|||
|Meat Quality||Myf5, Myf6||5||Transcription regulator of skeleton muscle development and increase of meat mass||[118,119,120]|
|Poultry||Reproduction||LRP8||1||Cholesterol supply for steroid biosynthesis, which enables folliculogenesis, melatonin in ovarian|||
|Reproduction||MTNR family||8||MTNR binds melatonin, affecting growth and reproduction||[125,126]|
|Reproduction (geese)||(MAGI-1), KIAA1462, ARHGAP21, ACSF2, ASTN2||MAGI-1: 12 |
|MAGI-1: cell proliferation and apoptosis |
KIAA1462: Meiotic recombination
ARHGAP21: cell-cell adhesion formation and cellular migration
ACSF2: fatty acid synthesis
ASTN2: cell adhesion
|Growth||PIT1||4||Secretion of growth hormone, prolactin and thyroid-stimulating hormone||[128,129]|
|Growth||MC4R||18||Appetite, growth and weight gain|||
1 ATM: ataxia telangiectasia mutated protein, ESR: Estrogen receptor, PRLR: prolactin receptor, MSTN: myostatin, IGF-2: Insulin-like-growth factor 2, CRC: calcium release channel, MYF6: myogenic factor 6, LRP8: low density lipoprotein receptor-related protein 8, MTNR: melatonin receptors, MAGI-1: membrane associated guanylate kinase 1, KIAA1462: KIAA1462, ARHGAP: Rho-GTP activating protein, ACSF2: acyl-CoA synthetase family member 2, ASTN2: astrotactin 2, PIT1: pituitary specific transcription factor gene 1, MC4R: Melanocortin 4 receptor.
As the traits described in the last section are generally lowly heritable, traditional breeding selections with phenotypic evaluations are insufficient. Quantitative MAS techniques with reliable markers constitute a feasible and effective approach. In practice, depending on the number of individuals and SNP sites, choosing appropriate SNP genotyping techniques for animal breeding will involve tradeoffs between reliability, sample preparation, reagent/sample expense, instrument depreciation and procedure complexity. For instance, large throughput SNP methods require a series of complicated steps and special instruments. They are high cost, labor intensive and time-consuming.
Moreover, even though the rapid progress of sequencing technology and the “$1000 genome” project may be soon possible and can significantly reduce the cost , it may still be an unlikely ask to solely conduct whole genome sequencing (WGS) for breeding in farm animals. Instead, a feasible approach will be to utilize the WGS of a small population of the individuals and to use a SNP chip for the genotyping of the majority of the individuals should be collectively adapted. In other words, WGS is adapted for high accuracy during the first and second stages of the breeding (e.g., SNP discovery and primary selection in Figure 1), and a SNP chip is exploited for lower costs in the third stage (e.g., secondary selection in Figure 1). For example, during the stage of the secondary selection, successfully promoting the use of genetic selection in swine and poultry requires elevated animal breeding efficiency while maintaining high accuracy with high long range LD. In addition, using 10% of the SNP markers from the original panel can still result in good accuracy for genotype imputation , suggesting small or medium throughput techniques will be sufficient for SNP genotyping of certain traits of interest in breeding. Although it is at early stages, the Lab-on-a-Chip technique has shown its potential for low density panels in SNP genotyping and can become a feasible tool with practical demonstrations and industrial commercialization. This approach is particularly useful for farm animals whereby individuals are not as valuable as cattle or swine. However, breeding at the top levels (e.g., great grandparent (GGP) or grandparent (GP) lines) should be profitable because the profits can come from a large population descending from the genotyped and breed parent animals.
SNP genotyping techniques have received considerable attention in selective breeding. We have discussed the procedures of SNP discovery and genotyping techniques for animal breeding. The associated SNP information and various genotyping techniques are presented. General findings may be summarized as follows:
Animal breeding by using phenotypic selection is insufficient.
Utilization of genetic markers for breeding has become mainstream in the livestock industry.
In particular, breeding using SNP markers is an effective and economical approach as it possesses the advantages of abundance and wide distribution in genome, and is easy to analyze.
SNP markers can be discovered by using sequence-dependent and sequence-independent methods. The former one is large throughput but expensive, while the latter one is small throughput but robust.
Appropriate SNP genotyping techniques for animal breeding will be adapted addressing concerns regarding throughput, reliability, associated expense, and procedure complexity.
A combination of whole-genome sequencing for a small population of individuals and a SNP chip for the genotyping of the majority of individuals will be a feasible and economical approach for animal breeding.
The authors thank T. Kirk for manuscript editing and financial support from Ministry of Science and Technology, Taiwan (MOST 102-2221-E-002-084-MY3 and 103-2321-B-002-076).
Conflicts of Interest
The authors declare no conflict of interest.
- Lande, R.; Thompson, R. Efficiency of marker-assisted selection in the improvement of quantitative traits. Genetics 1990, 124, 743–756. [Google Scholar] [PubMed]
- Collard, B.C.Y.; Jahufer, M.Z.Z.; Brouwer, J.B.; Pang, E.C.K. An introduction to markers, quantitative trait loci (QTL) mapping and marker-assisted selection for crop improvement: The basic concepts. Euphytica 2005, 142, 169–196. [Google Scholar] [CrossRef]
- Revilla, M.; Ramayo-Caldas, Y.; Castelló, A.; Corominas, J.; Puig-Oliveras, A.; Ibáñez-Escriche, N.; Muñoz, M.; Ballester, M.; Folch, J.M. New insight into the SSC8 genetic determination of fatty acid composition in pigs. Genet. Sel. Evol. 2014, 46. [Google Scholar] [CrossRef] [PubMed]
- Tu, P.-A.; Shiau, J.-W.; Ding, S.-T.; Lin, E.-C.; Wu, M.-C.; Wang, P.-H. The association of genetic variations in the promoter region of myostatin gene with growth traits in Duroc pigs. Anim. Biotechnol. 2012, 23, 291–298. [Google Scholar] [CrossRef] [PubMed]
- Dekkers, J.C. Commercial application of marker-and gene-assisted selection in livestock: Strategies and lessons. J. Anim. Sci. 2004, 82, E313–E328. [Google Scholar] [PubMed]
- Vos, P.; Hogers, R.; Bleeker, M.; Reijans, M.; van de Lee, T.; Hornes, M.; Friters, A.; Pot, J.; Paleman, J.; Kuiper, M.; et al. AFLP: A new technique for DNA fingerprinting. Nucleic Acids Res. 1995, 23, 4407–4414. [Google Scholar] [CrossRef] [PubMed]
- Williams, J.G.; Kubelik, A.R.; Livak, K.J.; Rafalski, J.A.; Tingey, S.V. DNA polymorphisms amplified by arbitrary primers are useful as genetic markers. Nucleic Acids Res. 1990, 18, 6531–6535. [Google Scholar] [CrossRef] [PubMed]
- Tautz, D. Hypervariability of simple sequences as a general source for polymorphic DNA markers. Nucleic Acids Res. 1989, 17, 6463–6471. [Google Scholar] [CrossRef] [PubMed]
- Yang, W.; Kang, X.; Yang, Q.; Lin, Y.; Fang, M. Review on the development of genotyping methods for assessing farm animal diversity. J. Anim. Sci. Biotechnol. 2013, 4. [Google Scholar] [CrossRef] [PubMed]
- Hayes, B.J.; Lewin, H.A.; Goddard, M.E. The future of livestock breeding: Genomic selection for efficiency, reduced emissions intensity, and adaptation. Trends Genet. 2013, 29, 206–214. [Google Scholar] [CrossRef] [PubMed]
- Varshney, R.K.; Chabane, K.; Hendre, P.S.; Aggarwal, R.K.; Graner, A. Comparative assessment of EST-SSR, EST-SNP and AFLP markers for evaluation of genetic diversity and conservation of genetic resources using wild, cultivated and elite barleys. Plant Sci. 2007, 173, 638–649. [Google Scholar] [CrossRef]
- Gao, Y.; Zhang, R.; Hu, X.; Li, N. Application of genomic technologies to the improvement of meat quality of farm animals. Meat Sci. 2007, 77, 36–45. [Google Scholar] [CrossRef] [PubMed]
- Vignal, A.; Milan, D.; SanCristobal, M.; Eggen, A. A review on SNP and other types of molecular markers and their use in animal genetics. Genet. Sel. Evol. 2002, 34, 275–306. [Google Scholar] [CrossRef] [PubMed]
- Kim, S.; Misra, A. SNP genotyping: Technologies and biomedical applications. Ann. Rev. Biomed. Eng. 2007, 9, 289–320. [Google Scholar] [CrossRef] [PubMed]
- Landegren, U.; Kaiser, R.; Sanders, J.; Hood, L. A ligase-mediated gene detection technique. Science 1988, 241, 1077–1080. [Google Scholar] [CrossRef] [PubMed]
- Tong, A.K.; Li, Z.; Jones, G.S.; Russo, J.J.; Ju, J. Combinatorial fluorescence energy transfer tags for multiplex biological assays. Nat. Biotechnol. 2001, 19, 756–759. [Google Scholar] [CrossRef] [PubMed]
- Botstein, D.; White, R.L.; Skolnick, M.; Davis, R.W. Construction of a genetic-linkage map in man using restriction fragment length polymorphisms. Am. J. Hum. Genet. 1980, 32, 314–331. [Google Scholar] [PubMed]
- Lyamichev, V.; Mast, A.L.; Hall, J.G.; Prudent, J.R.; Kaiser, M.W.; Takova, T.; Kwiatkowski, R.W.; Sander, T.J.; de Arruda, M.; et al. Polymorphism identification and quantitative detection of genomic DNA by invasive cleavage of oligonucleotide probes. Nat. Biotechnol. 1999, 17, 292–296. [Google Scholar] [PubMed]
- Sokolov, B.P. Primer extension technique for the detection of single nucleotide in genomic DNA. Nucleic Acids Res. 1990, 18. [Google Scholar] [CrossRef]
- Takatsu, K.; Yokomaku, T.; Kurata, S.; Kanagawa, T. A FRET-based analysis of SNPs without fluorescent probes. Nucleic Acids Res. 2004, 32. [Google Scholar] [CrossRef] [PubMed]
- Kolpashchikov, D.M. Split DNA enzyme for visual single nucleotide polymorphism typing. J. Am. Chem. Soc. 2008, 130, 2934–2935. [Google Scholar] [CrossRef] [PubMed]
- Neo, J.L.; Aw, K.D.; Uttamchandani, M. Visual SNP genotyping using asymmetric PCR and split DNA enzymes. Analyst 2011, 136, 1569–1572. [Google Scholar] [CrossRef] [PubMed]
- Li, Q.; Liu, Z.; Monroe, H.; Culiat, C.T. Integrated platform for detection of DNA sequence variants using capillary array electrophoresis. Electrophoresis 2002, 23, 1499–1511. [Google Scholar] [CrossRef]
- Ronaghi, M.; Uhlen, M.; Nyren, P. A sequencing method based on real-time pyrophosphate. Science 1998, 281, 363–365. [Google Scholar] [CrossRef] [PubMed]
- Tost, J.; Gut, I.G. Genotyping single nucleotide polymorphisms by MALDI mass spectrometry in clinical applications. Clin. Biochem. 2005, 38, 335–350. [Google Scholar] [CrossRef] [PubMed]
- Yang, K.-T.; Lin, C.Y.; Liou, J.S.; Fan, Y.H.; Chiou, S.H.; Huang, C.W.; Wu, W.-P.; Lin, E.-C.; Chen, C.-F.; Lee, Y.-P.; et al. Differentially expressed transcripts in shell glands from low and high egg production strains of chickens using cDNA microarrays. Anim. Reprod. Sci. 2007, 101, 113–124. [Google Scholar] [CrossRef] [PubMed]
- Chowdhury, J.; Kagiala, G.V.; Pushpakom, S.; Lauzon, J.; Makin, A.; Atrazhev, A.; Stickel, A.; Newman, W.G.; Backhouse, C.J.; Pilarski, L.M. Microfluidic platform for single nucleotide polymorphism genotyping of the thiopurine S-methyltransferase gene to evaluate risk for adverse drug events. J. Mol. Diagn. 2007, 9, 521–529. [Google Scholar] [CrossRef] [PubMed]
- Jung, Y.K.; Kim, J.; Mathies, R.A. Microfluidic Linear Hydrogel Array for Multiplexed Single Nucleotide Polymorphism (SNP) Detection. Anal. Chem. 2015, 87, 3165–3170. [Google Scholar] [CrossRef] [PubMed]
- Schmalzing, D.; Belenky, A.; Novotny, M.A.; Koutny, L.; Salas-Solano, O.; El-Difrawy, S.; Adourian, A.; Matsudaira, P.; Ehrlich, D. Microchip electrophoresis: a method for high-speed SNP detection. Nucleic Acids Res. 2000, 28. [Google Scholar] [CrossRef]
- Rege, J. Biotechnology options for improving livestock production in developing countries, with special reference to sub-Saharan Africa. In Proceedings of the Third Biennial Conference of the African Small Ruminant Research Network, UICC, Kampala, Uganda, 5–9 December 1994.
- Teale, A.; Tan, S.; Tan, J.-H. Applications of molecular genetic and reproductive technologies in the conservation of domestic animal diversity. In Proceedings of the 5th World Congress on Genetics Applied to Livestock Production, Guelph, ON, Canada, 7–12 August 1994.
- Girkin, J.M.; Mohammed, M.-I.; Ellis, E.M. A miniaturised integrated biophotonic point-of-care genotyping system. Faraday Discuss. 2011, 149, 115–123. [Google Scholar] [CrossRef] [PubMed]
- Horejsh, D.; Martini, F.; Poccia, F.; Ippolito, G.; Di Caro, A.; Capobianchi, M.R. A molecular beacon, bead-based assay for the detection of nucleic acids by flow cytometry. Nucleic Acids Res. 2005, 33. [Google Scholar] [CrossRef] [PubMed]
- Ng, J.; Liu, W.-T. Miniaturized platforms for the detection of single-nucleotide polymorphisms. Anal. Bioanal. Chem. 2006, 386, 427–434. [Google Scholar] [CrossRef] [PubMed]
- Zhang, Y.; Park, S.; Liu, K.; Tsuan, J.; Yang, S.; Wang, T.-H. A surface topography assisted droplet manipulation platform for biomarker detection and pathogen identification. Lab Chip 2011, 11, 398–406. [Google Scholar] [CrossRef] [PubMed]
- Nilsson, J.; Evander, M.; Hammarström, B.; Laurell, T. Review of cell and particle trapping in microfluidic systems. Anal. Chim. Acta 2009, 649, 141–157. [Google Scholar] [CrossRef] [PubMed]
- Riahi, R.; Mach, K.E.; Mohan, R.; Liao, J.C.; Wong, P.K. Molecular Detection of Bacterial Pathogens Using Microparticle Enhanced Double-Stranded DNA Probes. Anal. Chem. 2011, 83, 6349–6354. [Google Scholar] [CrossRef] [PubMed]
- Ramji, R.; Wang, M.; Bhagat, A.A.S.; Tan Shao Weng, D.; Thakor, N.V.; Teck Lim, C.; Chen, C.-H. Single cell kinase signaling assay using pinched flow coupled droplet microfluidics. Biomicrofluidics 2014, 8. [Google Scholar] [CrossRef] [PubMed]
- Kao, P.-C.; Ding, S.-T.; Lin, E.-C.; Li, K.-C.; Wang, L.; Lu, Y.-W. A bead-based single nucleotide polymorphism (SNP) detection using melting temperature on a microchip. Microfluid. Nanofluidics 2014, 17, 477–488. [Google Scholar] [CrossRef]
- Seidel, G.E. Brief introduction to whole-genome selection in cattle using single nucleotide polymorphisms. Reprod. Fertil. Dev. 2010, 22, 138–144. [Google Scholar] [CrossRef] [PubMed]
- Ai, H; Huang, L.; Ren, J. Genetic diversity, linkage disequilibrium and selection signatures in chinese and Western pigs revealed by genome-wide SNP markers. PLoS ONE 2013, 8, e56001. [Google Scholar]
- Edea, Z.; Bhuiyan, M.S.; Dessie, T.; Rothschild, M.F.; Dadi, H.; Kim, K.S. Genome-wide genetic diversity, population structure and admixture analysis in African and Asian cattle breeds. Animal 2015, 9, 218–226. [Google Scholar] [CrossRef] [PubMed]
- Muir, W.M.; Wong, G.K.S.; Zhang, Y.; Wang, J.; Groenen, M.A.; Crooijmans, R.P. Genome-wide assessment of worldwide chicken SNP genetic diversity indicates significant absence of rare alleles in commercial breeds. Proc. Natl. Acad. Sci. 2008, 105, 17312–17317. [Google Scholar] [CrossRef] [PubMed]
- Lee, T.; Shin, D.H.; Cho, S.; Kang, H.S.; Kim, S.H.; Lee, H.K.; Kim, H.; Seo, K.S. Genome-wide Association Study of Integrated Meat Quality-related Traits of the Duroc Pig Breed. Asian Australas. J. Anim. Sci. 2014, 27, 303–309. [Google Scholar] [CrossRef] [PubMed]
- Tu, P.A.; Lo, L.L.; Chen, Y.C.; Hsu, C.C.; Shiau, J.W.; Lin, E.C.; Lin, R.S.; Wang, P.H. Polymorphisms in the promoter region of myostatin gene are associated with carcass traits in pigs. J. Anim. Breed. Genet. 2014, 131, 116–122. [Google Scholar] [CrossRef] [PubMed]
- Uimari, P.; Tapio, M. Extent of linkage disequilibrium and effective population size in Finnish Landrace and Finnish Yorkshire pig breeds. J. Anim. Sci. 2011, 89, 609–614. [Google Scholar] [CrossRef] [PubMed]
- Badke, Y.M.; Bates, R.O.; Ernst, C.W.; Schwab, C.; Steibel, J.P. Estimation of linkage disequilibrium in four US pig breeds. BMC Genomics 2012, 13. [Google Scholar] [CrossRef] [PubMed]
- Van Heirstraeten, L.; Spang, P.; Schwind, C.; Drese, K.S.; Ritzi-Lehnert, M.; Nieto, B.; Camps, M.; Landgraf, B.; Guasch, F.; Corbera, A.H.; et al. Integrated DNA and RNA extraction and purification on an automated microfluidic cassette from bacterial and viral pathogens causing community-acquired lower respiratory tract infections. Lab Chip 2014, 14, 1519–1526. [Google Scholar] [CrossRef] [PubMed]
- Beaulieu, M.; Larson, G.P.; Geller, L.; Flanagan, S.D.; Krontiris, T.G. PCR candidate region mismatch scanning: Adaptation to quantitative, high-throughput genotyping. Nucleic Acids Res. 2001, 29, 1114–1124. [Google Scholar] [CrossRef] [PubMed]
- Haliassos, A.; Chomel, J.C.; Grandjouan, S.; Kruh, J.; Kaplan, J.C.; Kitzis, A. Detection of minority point mutations by modified PCR technique: A new approach for a sensitive diagnosis of tumor-progression markers. Nucleic Acids Res. 1989, 17, 8093–8099. [Google Scholar] [CrossRef] [PubMed]
- Haliassos, A.; Chomel, J.C.; Tesson, L.; Baudis, M.; Kruh, J.; Kaplan, J.C.; Kitzis, A. Modification of enzymatically amplified DNA for the detection of point mutations. Nucleic Acids Res. 1989, 17, 3606. [Google Scholar] [CrossRef] [PubMed]
- Comai, L.; Henikoff, S. TILLING: Practical single-nucleotide mutation discovery. Plant J. 2006, 45, 684–694. [Google Scholar] [CrossRef] [PubMed]
- Pareek, C.S.; Smoczynski, R.; Tretyn, A. Sequencing technologies and genome sequencing. J. Appl. Genet. 2011, 52, 413–435. [Google Scholar] [CrossRef] [PubMed]
- Milne, I.; Bayer, M.; Cardle, L.; Shaw, P.; Stephen, G.; Wright, F.; Marshall, D. Tablet—Next generation sequence assembly visualization. Bioinformatics 2010, 26, 401–402. [Google Scholar] [CrossRef] [PubMed]
- Quinlan, A.R.; Stewart, D.A.; Stromberg, M.P.; Marth, G.T. Pyrobayes: An improved base caller for SNP discovery in pyrosequences. Nat. Methods 2008, 5, 179–181. [Google Scholar] [CrossRef] [PubMed]
- Li, R.; Li, Y.; Fang, X.; Yang, H.; Wang, J.; Kristiansen, K.; Wang, J. SNP detection for massively parallel whole-genome resequencing. Genome Res. 2009, 19, 1124–1132. [Google Scholar] [CrossRef] [PubMed]
- Koboldt, D.C.; Chen, K.; Wylie, T.; Larson, D.E.; McLellan, M.D.; Mardis, E.R.; Weinstock, G.M.; Wilson, R.K.; Ding., L. VarScan: Variant detection in massively parallel sequencing of individual and pooled samples. Bioinformatics 2009, 25, 2283–2285. [Google Scholar] [CrossRef] [PubMed]
- Li, H.; Ruan, J.; Durbin, R. Mapping short DNA sequencing reads and calling variants using mapping quality scores. Genome Res. 2008, 18, 1851–1858. [Google Scholar] [CrossRef] [PubMed]
- Hou, H.; Zhao, F.; Zhou, L.; Zhu, E.; Teng, H.; Li, X.; Bao, Q.; Wu, J.; Sun, Z. MagicViewer: Integrated solution for next-generation sequencing data visualization and genetic variation detection and annotation. Nucleic Acids Res. 2010, 38, W732–W736. [Google Scholar] [CrossRef] [PubMed]
- Shen, Y.; Wan, Z.; Coarfa, C.; Drabek, R.; Chen, L.; Ostrowski, E.A.; Liu, Y.; Weinstock, G.M.; Wheeler, D.A.; Gibbs, R.A.; Yu, F. A SNP discovery method to assess variant allele probability from next-generation resequencing data. Genome Res. 2010, 20, 273–280. [Google Scholar] [CrossRef] [PubMed]
- Tsuchihashi, Z.; Dracopoli, N. Progress in high throughput SNP genotyping methods. Pharmacogenomics J. 2002, 2, 103–110. [Google Scholar] [CrossRef] [PubMed]
- Sobrino, B.; Brion, M.; Carracedo, A. SNPs in forensic genetics: A review on SNP typing methodologies. Forensic Sci. Int. 2005, 154, 181–194. [Google Scholar] [CrossRef] [PubMed]
- Giancola, S.; McKhann, H.I.; Bérard, A.; Camilleri, C.; Durand, S.; Libeau, P.; Roux, F.; Reboud, X.; Gut, I.G.; Brunel, D. Utilization of the three high-throughput SNP genotyping methods, the GOOD assay, Amplifluor and TaqMan, in diploid and polyploid plants. Theor. Appl. Genet. 2006, 112, 1115–1124. [Google Scholar] [CrossRef] [PubMed]
- Gupta, P.K.; Rustgi, S.; Mir, R.R. Array-based high-throughput DNA markers for crop improvement. Heredity 2008, 101, 5–18. [Google Scholar] [CrossRef] [PubMed]
- Ragoussis, J. Genotyping technologies for genetic research. Ann. Rev. Genomics Hum. Genet. 2009, 10, 117–133. [Google Scholar] [CrossRef] [PubMed]
- Garvin, M.; Saitoh, K.; Gharrett, A. Application of single nucleotide polymorphisms to non-model species: A technical review. Mol. Ecol. Res. 2010, 10, 915–934. [Google Scholar] [CrossRef] [PubMed]
- Bagge, M.; Xia, X.; Lubberstedt, T. Functional markers in wheat. Curr. Opin. Plant Biol. 2007, 10, 211–216. [Google Scholar] [CrossRef] [PubMed]
- Syvänen, A.-C. Accessing genetic variation: Genotyping single nucleotide polymorphisms. Nat. Rev. Genet. 2001, 2, 930–942. [Google Scholar] [CrossRef] [PubMed]
- Oraguzie, N.C.; Rikkerink, E.H.A.; Gardiner, S.E.; de Silva, H.N. Association Mapping in Plants; Springer: Berlin, Germany, 2007. [Google Scholar]
- Gunderson, K.L.; Steemers, F.J.; Lee, G.; Mendoza, L.G.; Chee, M.S. A genome-wide scalable SNP genotyping assay using microarray technology. Nat. Genet. 2005, 37, 549–554. [Google Scholar] [CrossRef] [PubMed]
- Fan, J.B.; Chee, M.S.; Gunderson, K.L. Highly parallel genomic assays. Nat. Rev. Genet. 2006, 7, 632–644. [Google Scholar] [CrossRef] [PubMed]
- Fernandez, A.I.; Pérez-Montarelo, D.; Barragán, C.; Ramayo-Caldas, Y.; Ibáñez-Escriche, N.; Castelló, A.; Noguera, J.L.; Silió, L.; Folch, J.M.; Rodríguez, M.C. Genome-wide linkage analysis of QTL for growth and body composition employing the PorcineSNP60 BeadChip. BMC Genet. 2012, 13. [Google Scholar] [CrossRef] [PubMed]
- Baird, N.A.; Etter, P.D.; Atwood, T.S.; Currey, M.C.; Shiver, A.L.; Lewis, Z.A.; Selker, E.U.; Cresko, W.A.; Johnson, E.C. Rapid SNP discovery and genetic mapping using sequenced RAD markers. PLoS ONE 2008, 3, e3376. [Google Scholar] [CrossRef] [PubMed]
- Ahmadian, A.; Gharizadeh, B.; Gustafsson, A.C.; Sterky, F.; Nyren, P.; Uhlen, M.; Lundeberg, J. Single-nucleotide polymorphism analysis by pyrosequencing. Anal. Biochem. 2000, 280, 103–110. [Google Scholar] [CrossRef] [PubMed]
- Gabriel, S.; Ziaugra, L.; Tabbaa, D. SNP genotyping using the Sequenom MassARRAY iPLEX platform. Curr. Protoc. Hum. Genet. 2009, 2. [Google Scholar] [CrossRef]
- Bell, P.A.; Chaturvedi, S.; Gelfand, C.A.; Huang, C.Y.; Kochersperger, M.; Kopla, R.; Modica, F.; Pohl, M.; Varde, S.; Zhao, R.; et al. SNPstream UHT: Ultra-high throughput SNP genotyping for pharmacogenomics and drug discovery. Biotechniques 2002, 74, 76–77. [Google Scholar]
- Bouakaze, C.; Keyser, C.; de Martino, S.J.; Sougakoff, W.; Veziris, N.; Dabernat, H.; Ludes, B. Identification and genotyping of mycobacterium tuberculosis complex species by use of a SNaPshot Minisequencing-based assay. J. Clin. Microbiol. 2010, 48, 1758–1766. [Google Scholar] [CrossRef] [PubMed]
- Kamińiski, S.; Brym, P.; Ruść, A.; Wojcik, E. Microarray of SNPs for diverse applications in commercial pig breeding. Pol. J. Vet. Sci. 2008, 12, 69–74. [Google Scholar]
- Kurg, A.; Tõnisson, N.; Georgiou, I.; Shumaker, J.; Tollett, J.; Metspalu, A. Arrayed primer extension: Solid-phase four-color DNA resequencing and mutation detection technology. Genet. Test. 2000, 4, 1–7. [Google Scholar] [CrossRef] [PubMed]
- Bruse, S.E.; Moreau, M.P.; Azaro, M.A.; Zimmerman, R.; Brzustowicz, L.M. Improvements to bead-based oligonucleotide ligation SNP genotyping assays. Biotechniques 2008, 45, 559–571. [Google Scholar] [CrossRef] [PubMed]
- Prince, J.A.; Feuk, L.; Howell, W.M.; Jobs, M.; Emahazion, T.; Blennow, K.; Brookes, A.J. Robust and accurate single nucleotide polymorphism genotyping by dynamic allele-specific hybridization (DASH): Design criteria and assay validation. Genome Res. 2001, 11, 152–162. [Google Scholar] [CrossRef] [PubMed]
- Jiang, L.; Liu, J.; Sun, D.; Ma, P.; Ding, X.; Yu, Y.; Zhang, Q. Genome wide association studies for milk production traits in Chinese Holstein population. PLoS ONE 2010, 5, e13661. [Google Scholar] [CrossRef] [PubMed]
- Ma, J.; Yang, J.; Zhou, L.; Ren, J.; Liu, X.; Zhang, H.; Yang, B.; Zhang, Z.; Ma, H.; Xie, X. A splice mutation in the PHKG1 gene causes high glycogen content and low meat quality in pig skeletal muscle. PLoS Genet. 2014, 10, e1004710. [Google Scholar] [CrossRef] [PubMed]
- Houston, R.D.; Davey, J.W.; Bishop, S.C.; Lowe, N.R.; Mota-Velasco, J.C.; Hamilton, A.; Guy, D.R.; Tinch, A.E.; Thomson, M.L.; Blaxter, M.L.; et al. Characterisation of QTL-linked and genome-wide restriction site-associated DNA (RAD) markers in farmed Atlantic salmon. BMC Genomics 2012, 13. [Google Scholar] [CrossRef] [PubMed]
- Lo, R.C.; Ugaz, V.M. Microchip DNA electrophoresis with automated whole-gel scanning detection. Lab Chip 2008, 8, 2135–2145. [Google Scholar] [CrossRef] [PubMed]
- Julich, S.; Riedel, M.; Kielpinski, M.; Urban, M.; Kretschmer, R.; Wagner, S.; Fritzsche, W.; Henkel, T.; Möller, R.; Werres, S. Development of a Lab-on-a-Chip device for diagnosis of plant pathogens. Biosens. Bioelectron. 2011, 26, 4070–4075. [Google Scholar] [CrossRef] [PubMed]
- Lee, J.G.; Cheong, K.H.; Huh, N.; Kim, S.; Choi, J.W.; Ko, C. Microchip-based one step DNA extraction and real-time PCR in one chamber for rapid pathogen identification. Lab Chip 2006, 6, 886–895. [Google Scholar] [CrossRef] [PubMed]
- Marasso, S.L.; Mombello, D.; Cocuzza, M.; Casalena, D.; Ferrante, I.; Nesca, A.; Poiklik, P.; Rekker, K.; Aaspollu, A.; Ferrero, S. A polymer Lab-on-a-Chip for genetic analysis using the arrayed primer extension on microarray chips. Biomed. Microdevices 2014, 16, 661–670. [Google Scholar] [CrossRef] [PubMed]
- Shen, H.H.; Su, T.Y.; Liu, Y.J.; Chang, H.Y.; Yao, D.J. Single-Nucleotide Polymorphism Detection Based on a Temperature-Controllable Electrowetting on Dielectrics Digital Microfluidic System. Sens. Mater. 2013, 25, 643–651. [Google Scholar]
- Kolchinsky, A.; Mirzabekov, A. Analysis of SNPs and other genomic variations using gel-based chips. Hum. Mutat. 2002, 19, 343–360. [Google Scholar] [CrossRef] [PubMed]
- Dubiley, S.; Kirillov, E.; Mirzabekov, A. Polymorphism analysis and gene detection by minisequencing on an array of gel-immobilized primers. Nucleic Acids Res. 1999, 27. [Google Scholar] [CrossRef]
- Russom, A.; Haasl, S.; Brookes, A.J.; Andersson, H.; Stemme, G. Rapid melting curve analysis on monolayered beads for high-throughput genotyping of single-nucleotide polymorphisms. Anal. Chemis. 2006, 78, 2220–2225. [Google Scholar] [CrossRef] [PubMed]
- Shin, D.; Zhang, Y.; Wang, T.-H. A droplet microfluidic approach to single-stream nucleic acid isolation and mutation detection. Microfluid. Nanofluidics 2014, 17, 425–430. [Google Scholar] [CrossRef] [PubMed]
- Zhang, Y.; Shin, D.J.; Wang, T.H. Detecting genetic variations in a droplet. In Proceedings of the 15th International Conference on Miniaturized Chemical and Biochemical Analysis Systems (Micro-TAS 2011), Seattle, WA, USA, 2–6 October 2011; Volume 978, pp. 4–5.
- Li, K.C.; Ding, S.T.; Lin, E.C.; Wang, L.A.; Lu, Y.W. Melting analysis on microbeads in rapid temperature-gradient inside microchannels for single nucleotide polymorphisms detection. Biomicrofluidics 2014, 8. [Google Scholar] [CrossRef] [PubMed]
- Sochol, R.D.; Casavant, B.P.; Dueck, M.E.; Lee, L.P.; Lin, L. A dynamic bead-based microarray for parallel DNA detection. J. Micromech. Microeng. 2011, 21, 054019. [Google Scholar] [CrossRef]
- Koopaee, H.K.; Koshkoiyeh, A.E. SNPs genotyping technologies and their applications in farm animals breeding programs: Review. Braz. Arch. Biol. Technol. 2014, 57, 87–95. [Google Scholar] [CrossRef]
- Schroyen, M.; Stinckens, A.; Verhelst, R.; Niewold, T.; Buys, N. The search for the gene mutations underlying enterotoxigenic Escherichia coli F4ab/ac susceptibility in pigs: A review. Vet. Res. 2012, 43. [Google Scholar] [CrossRef] [PubMed]
- Rothschild, M.F. Genetics and reproduction in the pig. Anim. Reprod. Sci. 1996, 42, 143–151. [Google Scholar] [CrossRef]
- Onteru, S.; Fan, B.; Du, Z.Q.; Garrick, D.; Stalder, K.; Rothschild, M. A whole-genome association study for pig reproductive traits. Anim. Genet. 2012, 43, 18–26. [Google Scholar] [CrossRef] [PubMed]
- Cao, S.; Han, J.; Wu, J.; Li, Q.; Liu, S.; Zhang, W.; Pei, Y.; Ruan, X.; Liu, Z.; Wang, X.; et al. Specific gene-regulation networks during the pre-implantation development of the pig embryo as revealed by deep sequencing. BMC Genomics 2014, 15. [Google Scholar] [CrossRef] [PubMed]
- Wilson, M.E.; Biensen, N.J.; Ford, S.P. Novel insight into the control of litter size in pigs, using placental efficiency as a selection tool. J. Anim. Sci. 1999, 77, 1654–1658. [Google Scholar] [PubMed]
- Chang, P. Using High-Throughput Single Nucleotide Polymorphism Genotyping to Reveal Litter Size-Related Molecular Markers in Landrace Sows. In Animal Science; National Taiwan University: Taipei, Taiwan, 2010. [Google Scholar]
- Rothschild, M.; Jacobson, C.; Vaske, D.; Tuggle, C.; Wang, L.; Short, T.; Eckardt, G.; Sasaki, S.; Vincent, A.; McLaren, D.; et al. The estrogen receptor locus is associated with a major gene influencing litter size in pigs. Proc. Natl. Acad. Sci. USA 1996, 93, 201–205. [Google Scholar] [CrossRef] [PubMed]
- Munoz, G.; Ovilo, C.; Amills, M.; Rodriguez, C. Mapping of the porcine oestrogen receptor 2 gene and association study with litter size in Iberian pigs. Anim. Genet. 2004, 35, 242–244. [Google Scholar] [CrossRef] [PubMed]
- Terman, A.; Kumalska, M. The effect of a SNP in ESR gene on the reproductive performance traits in Polish sows. Russ. J. Genet. 2012, 48, 1260–1263. [Google Scholar] [CrossRef]
- Judyma, D. Polymorphism in the PRLR/AluI gene and its effect on litter size in Large White sows. Anim. Sci. Pap. Rep. 2004, 22, 523–527. [Google Scholar]
- Hirose, K.; Ito, T.; Fukawa, K.; Arakawa, A.; Mikawa, S.; Hayashi, Y.; Tanaka, K. Evaluation of effects of multiple candidate genes (LEP, LEPR, MC4R, PIK3C3, and VRTN) on production traits in Duroc pigs. Anim. Sci. J. 2014, 85, 198–206. [Google Scholar] [CrossRef] [PubMed]
- Muñoz, G.; Alcázar, E.; Fernández, A.; Barragán, C.; Carrasco, A.; de Pedro, E.; Silió, L.; Sánchez, J.L.; Rodríguez, M.C. Effects of porcine MC4R and LEPR polymorphisms, gender and Duroc sire line on economic traits in Duroc × Iberian crossbred pigs. Meat Sci. 2011, 88, 169–173. [Google Scholar] [CrossRef] [PubMed]
- Pérez-Montarelo, D.; Rodríguez, M.C.; Fernández, A.; Benítez, R.; García, F.; Silió, L.; Fernández, A. Haplotypic diversity of porcine LEP and LEPR genes involved in growth and fatness regulation. J. Appl. Genet. 2015, 56, 525–533. [Google Scholar] [CrossRef] [PubMed]
- Balcells, I.; Castelló, A.; Noguera, J.L.; Fernández-Rodríguez, A.; Sánchez, A.; Tomás, A. Sequencing and gene expression of the porcine ITIH SSC13 cluster and its effect on litter size in an Iberian × Meishan F2 population. Anim. Reprod. Sci. 2011, 128, 85–92. [Google Scholar] [CrossRef] [PubMed]
- Rothschild, M.F. Porcine genomics delivers new tools and results: This little piggy did more than just go to market. Genet. Res. 2004, 83, 1–6. [Google Scholar] [CrossRef] [PubMed]
- Bongiorni, S.; Tilesi, F.; Bicorgna, S.; Iacoponi, F.; Willems, D.; Gargani, M.; D'Andrea, M.; Pilla, F.; Valentini, A. Promoter polymorphisms in genes involved in porcine myogenesis influence their transcriptional activity. BMC Genet. 2014, 15. [Google Scholar] [CrossRef] [PubMed]
- Van Laere, A.-S.; Nguyen, M.; Braunschweig, M.; Nezer, C.; Collette, C.; Moreau, L.; Archibald, A.L.; Haley, C.S.; Buys, N.; Tally, M. A regulatory mutation in IGF2 causes a major QTL effect on muscle growth in the pig. Nature 2003, 425, 832–836. [Google Scholar] [CrossRef] [PubMed]
- Vykoukalova, Z.; Knoll, A.; Dvořák, J.; Čepica, S. New SNPs in the IGF2 gene and association between this gene and backfat thickness and lean meat content in Large White pigs. J. Anim. Breed. Genet. 2006, 123, 204–207. [Google Scholar] [CrossRef] [PubMed]
- Jeon, J.-T.; Carlborg, O.; Törnsten, A.; Giuffra, E.; Amarger, V.; Chardon, P.; Andersson-Eklund, L.; Andersson, K.; Hansson, I.; Lundström, K.; et al. A paternally expressed QTL affecting skeletal and cardiac muscle mass in pigs maps to the IGF2 locus. Nat. Genet. 1999, 21, 157–158. [Google Scholar] [PubMed]
- Tuggle, C.; Yu, T.P.; Helm, J.; Rothschild, M. Cloning and restriction fragment length polymorphism analysis of a cDNA for swine PIT-1, a gene controlling growth hormone expression. Anim. Genet. 1993, 24, 17–21. [Google Scholar] [CrossRef] [PubMed]
- Klont, R.E.; Lambooy, E.; van Logtestijn, J.G. Effect of dantrolene treatment on muscle metabolism and meat quality of anesthetized pigs of different halothane genotypes. J. Anim. Sci. 1994, 72, 2008–2016. [Google Scholar] [PubMed]
- Liu, M.; Peng, J.; Xu, D.Q.; Zheng, R.; Li, F.G.; Li, J.L.; Zuo, B.; Lei, M.G.; Xiong, Y.Z.; Deng, C.Y.; et al. Associations of MYF5 gene polymorphisms with meat quality traits in different domestic pig (Sus scrofa) populations. Genetics Mol. Biol. 2007, 30, 370–374. [Google Scholar]
- Stupka, R.; Citek, J.; Sprysl, M.; Okrouhla, M.; Brzobohaty, L. The impact of MYOG, MYF6 and MYOD1 genes on meat quality traits in crossbred pigs. Afr. J. Biotechnol. 2012, 11, 15405–15409. [Google Scholar]
- Wright, D.; Kerje, S.; Lundström, K.; Babol, J.; Schütz, K.; Jensen, P.; Andersson, L. Quantitative trait loci analysis of egg and meat production traits in a red junglefowl × White Leghorn cross. Anim. Genet. 2006, 37, 529–534. [Google Scholar] [CrossRef] [PubMed]
- Rubin, C.-J.; Zody, M.C.; Eriksson, J.; Meadows, J.R.S.; Sherwood, E.; Webster, W.T.; Jiang, L.; Ingman, M.; Sharpe, T.; Ka, S.; et al. Whole-genome resequencing reveals loci under selection during chicken domestication. Nature 2010, 464, 587–591. [Google Scholar] [CrossRef] [PubMed]
- Kranis, A.; Gheyas, A.A.; Boschiero, C.; Turner, F.; Yu, L.; Smith, S.; Talbot, R.; Pirani, A.; Brew, F.; Kaiser, P.; et al. Development of a high density 600K SNP genotyping array for chicken. BMC Genomics 2013, 14. [Google Scholar] [CrossRef] [PubMed]
- Yao, J.; Chen, Z.; Xu, G.; Wang, X.; Ning, Z.; Zheng, J.; Qu, L.; Yang, N. Low-density lipoprotein receptor-related protein 8 gene association with egg traits in dwarf chickens. Poult. Sci. 2010, 89, 883–886. [Google Scholar] [CrossRef] [PubMed]
- Sundaresan, N.; Marcus Leo, M.D.; Subramani, J.; Anish, D.; Sudhagar, M.; Ahmed, K.A.; Saxena, M.; Tyagi, J.S.; Sastry, K.V.; Saxena, V.K. Expression analysis of melatonin receptor subtypes in the ovary of domestic chicken. Vet. Res. Commun. 2009, 33, 49–56. [Google Scholar] [CrossRef] [PubMed]
- Li, D.; Zhang, L.; Smith, D.; Xu, H.; Liu, Y.; Zhao, X.; Wang, Y.; Zhu, Q. Genetic effects of melatonin receptor genes on chicken reproductive traits. Czech J. Anim. Sci. 2013, 58, 58–64. [Google Scholar]
- Yu, S.; Chu, W.; Zhang, L.; Han, H.; Zhao, R.; Wu, W.; Zhu, J.; Dodson, M.V.; Wei, W.; Liu, H.; Chen, J. Identification of Laying-Related SNP Markers in Geese Using RAD Sequencing. PLoS ONE 2015, 10, e0131572. [Google Scholar] [CrossRef] [PubMed]
- Jiang, R.; Li, J.; Qu, L.; Li, H.; Yang, N. A new single nucleotide polymorphism in the chicken pituitary-specific transcription factor (POU1F1) gene associated with growth rate. Anim. Genet. 2004, 35, 344–346. [Google Scholar] [CrossRef] [PubMed]
- Nie, Q.; Fang, M.; Xie, L.; Zhou, M.; Liang, Z.; Luo, Z.; Wang, G.; Bi, W.; Liang, C.; Zhang, W. The PIT1 gene polymorphisms were associated with chicken growth traits. BMC Genet. 2008, 9. [Google Scholar] [CrossRef] [PubMed]
- Qiu, X.; Li, N.; Deng, X.; Zhao, X.; Meng, Q.; Wang, X. The single nucleotide polymorphisms of chicken melanocortin-4 receptor (MC4R) gene and their association analysis with carcass traits. Sci. China C Life Sci. 2006, 49, 560–566. [Google Scholar] [CrossRef] [PubMed]
- Ansorge, W.J. Next-generation DNA sequencing techniques. New Biotechnol. 2009, 25, 195–203. [Google Scholar] [CrossRef] [PubMed]
© 2015 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/).