Molecular genetics of behaviour: research strategies and perspectives for animal production

Genetic factors are undoubtedly involved in inter-individual variability of the behaviours that may be important for livestock production, as shown by pedigree studies, comparison of genetic stocks raised in the same environment, and selection experiments. The knowledge of gene polymorphisms responsible for genetic variability would increase the efficiency of selection, as shown for instance by the identification of the ryanodine receptor gene that harbours the mutations responsible for the porcine stress syndrome, that allows the eradication of the susceptibility allele. One strategy is to screen systematically the genes that are known to be involved in regulation of behaviour (functional candidate genes). This strategy is however very difficult for most behavioural traits, since behaviour is an emerging function from the whole brain/body and the molecular pathways involved in genetic variability are very poorly understood. Another strategy is to investigate linkage between trait variation and genetic markers in a segregating population (usually an intercross or backcross between two strains or breeds contrasting for the trait under study). It allows the detection of genomic regions influencing that trait (quantitative trait loci or QTL), and further investigation aims at the identification of the gene(s) located in each of these regions and the molecular polymorphisms involved in phenotypic variation. Although many QTL have been published for behavioural traits in experimental animals, very few examples are available where strong candidate genes have been identified. Further progress will be very much dependent upon the careful definition of behavioural traits to be studied (including their importance for animal production), on the reliability of their measurement in a large number of animals and on the efficient mastering of environmental factors of variability. The fast increase in the knowledge of genome sequence in several species will undoubtedly facilitate the application to farm animal species of the knowledge obtained in model organisms, as well as the use of model organisms to explore candidate genes detected by QTL studies in farm animals.     

A review on prolificacy genes in sheep

Ovulation rate and litter size are important reproduction traits in sheep and are of high economic value. Reproduction traits typically have low to medium heritabilities and do not exhibit a noticeable response to phenotypic selection. Therefore, inclusion of genetic information of the genes associated with reproductive ability could efficiently enhance the selection response. The most important major genes affecting prolificacy and their genetic diversities in different sheep breeds were reviewed. Different causative mutations with major effects on reproductive traits including ovulation rate and litter size have been found in various sheep breeds around the world. A general overview of the studies on main prolificacy genes showed that some alleles may express different phenotypic effects in different breeds, and thus, further studies on epistatic effects are necessary for more understanding of genetic control of reproductivity in sheep. Regarding the polygenic control of fertility traits, application of new high‐throughput technologies to find new variants is essential for future studies. Moreover, genomewide association studies and genomic best linear unbiased predictions of breeding values are likely to be effective tools for genetic improvement of sheep reproductive performance traits.     

QTL基因型值对标记辅助导入的影响

在标记辅助导入QTL的过程中,利用指数法进行背景选择。研究结果表明基因型值的大小对于导入QTL的频率和两个背景QTL的频率以及受体遗传背景的恢复影响很小,对于两个背景性状来说,基因型值较大时要比较小时获得更大的遗传进展。对于前景性状来说,在横交阶段,基因型值较大时要比较小时获得更大的遗传进展;而在回交阶段,结果正好相反。     

Candidate gene markers associated with somatotropic axis and milk selection.

One of the obstacles to progress in dairy cattle selection is that milk production traits are only expressed after the first calving. However, the use of the quantitative trait loci (QTL) technology will improve the efficiency of dairy industry with a positive image for the consumers. QTL are part of the genome showing a preponderant action and explaining the major part of variation of the trait production. At the present time, the two major strategies developed to detect such QTL are the candidate gene approach and the positional genetics approach. The somatotropic axis contains the most promising candidates in this respect, as it strongly regulates milk production. Then, the identification of favorable QTL associated with the somatotropic axis that are significantly correlated with genetic merits for milk production could lead to more effective selection programs.     

Exclusion of the swine leukocyte antigens as candidate region and reduction of the position interval for the Sus scrofa chromosome 7 QTL affecting growth and fatness

Pig chromosome 7 (SSC 7) has been shown to be rich in QTL affecting performance and quality traits. Most studies mapped the QTL close to the swine leukocyte antigens (SLA), which has a large effect on adaptability and natural selection. Previous comparative mapping studies suggested that the 15-cM region limited by markers LRA1 (mapped at 55 cM) and S0102 (mapped at 70 cM) contains hundreds of genes. To decrease the number of candidate genes, we improved the mapping resolution with a genetic chromosome dissection through a backcross recombinant progeny test program between Meishan (MS) and European (EU; i.e., Large White or Landrace) breeds. Three first-generation backcross--(EU x MS) x EU--and two second-generation backcross--([EU x MS] x EU) x EU--sires carrying a recombination in the QTL mapping interval were progeny-tested (i.e., measured for a total of 44 growth, fatness, carcass and meat quality traits). Progeny family size varied from 29 to 119 pigs. Animals were genotyped for markers covering the region of interest. Progeny-test results allowed the QTL interval to be decreased from 15 to 20 cM down to 10 cM, and even less than 6 cM if we assumed that the EU pigs used in this study share only one QTL allele. Except for a putative QTL affecting some carcass composition traits, the SLA is excluded as a candidate region, suggesting that it might be possible to apply a marker-assisted selection strategy for this QTL, while controlling SLA allele diversity. The strong QTL effects remaining in animals with only 12.5% (issued from first-generation backcross boars) and 6.25% (issued from second-generation back-cross boars) Meishan genetic background shows that epistatic interactions are likely to be limited. Finally, the QTL does not have strong effects on meat quality traits.     

Building phenotype networks to improve QTL detection: a comparative analysis of fatty acid and fat traits in pigs

Models in QTL mapping can be improved by considering all potential variables, i.e. we can use remaining traits other than the trait under study as potential predictors. QTL mapping is often conducted by correcting for a few fixed effects or covariates (e.g. sex, age), although many traits with potential causal relationships between them are recorded. In this work, we evaluate by simulation several procedures to identify optimum models in QTL scans: forward selection, undirected dependency graph and QTL-directed dependency graph (QDG). The latter, QDG, performed better in terms of power and false discovery rate and was applied to fatty acid (FA) composition and fat deposition traits in two pig F2 crosses from China and Spain. Compared with the typical QTL mapping, QDG approach revealed several new QTL. To the contrary, several FA QTL on chromosome 4 (e.g. Palmitic, C16:0; Stearic, C18:0) detected by typical mapping vanished after adjusting for phenotypic covariates in QDG mapping. This suggests that the QTL detected in typical mapping could be indirect. When a QTL is supported by both approaches, there is an increased confidence that the QTL have a primary effect on the corresponding trait. An example is a QTL for C16:1 on chromosome 8. In conclusion, mapping QTL based on causal phenotypic networks can increase power and help to make more biologically sound hypothesis on the genetic architecture of complex traits.     

Genome-wide mapping and identification of new quantitative trait loci affecting meat production, meat quality, and carcass traits within a Duroc purebred population

Most QTL detection studies in pigs have been carried out in experimental F(2) populations. However, segregation of a QTL must be confirmed within a purebred population for successful implementation of marker-assisted selection. Previously, QTL for meat quality and carcass traits were detected on SSC 7 in a Duroc purebred population. The objectives of the present study were to carry out a whole-genome QTL analysis (except for SSC 7) for meat production, meat quality, and carcass traits and to confirm the presence of segregating QTL in a Duroc purebred population. One thousand and four Duroc pigs were studied from base to seventh generation; the pigs comprised 1 closed population of a complex multigenerational pedigree such that all individuals were related. The pigs were evaluated for 6 growth traits, 7 body size traits, 8 carcass traits, 2 physiological traits, and 11 meat quality traits, and the number of pigs with phenotypes ranged from 421 to 953. A total of 119 markers were genotyped and then used for QTL analysis. We utilized a pedigree-based, multipoint variance components approach to test for linkage between QTL and the phenotypic values using a maximum likelihood method; the logarithm of odds score and QTL genotypic heritability were estimated. A total of 42 QTL with suggestive linkages and 3 QTL with significant linkages for 26 traits were detected. These included selection traits such as daily BW gain, backfat thickness, loin eye muscle area, and intramuscular fat content as well as correlated traits such as body size and meat quality traits. The present study disclosed QTL affecting growth, body size, and carcass, physiological, and meat quality traits in a Duroc purebred population.     

Identification of genomic regions related to age at first calving and first calving interval in water buffalo using single-step GBLUP

In Brazil, water buffaloes have been used to produce milk for mozzarella cheese production. Consequently, the main selection criterion applied for the buffalo genetic improvement is the estimated mozzarella yield as a function of milk, fat and protein production. However, given the importance of reproductive traits in production systems, this study aimed to use techniques for identifying genomic regions that affect the age at first calving (AFC) and first calving interval (FCI) in buffalo cows and to select candidate genes for the identification of QTL and gene expression studies. The single-step GBLUP method was used for the identification of genomic regions. Windows of 1 Mb containing single-nucleotide polymorphisms were constructed and the 10 windows that explained the greatest proportion of genetic variance were considered candidate regions for each trait. Genes present into the selected windows were identified using the UOA_WB_1 assembly as the reference, and their ontology was defined with the Panther tool. Candidate regions for both traits were identified on BBU 3, 12, 21 and 22; for AFC, candidates were detected on BBU 6, 7, 8, 9 and 15 and for first calving interval on BBU 4, 14 and 19. This study identified regions with great contribution to the additive genetic variance of age at first calving and first calving interval in the population of buffalo cows studied. The ROCK2, PMVK, ADCY2, MAP2K6, BMP10 and GFPT1 genes are main candidates for reproductive traits in water dairy buffaloes, and these results may have future applications in animal breeding programs or in gene expression studies of the species.     

Whole-genome association analyses for lifetime reproductive traits in the pig

Profits for commercial pork producers vary in part because of sow productivity or sow productive life (SPL) and replacement costs. During the last decade, culling rates of sows have increased to more than 50% in the United States. Both SPL and culling rates are influenced by genetic and nongenetic factors. A whole-genome association study was conducted for pig lifetime reproductive traits, including lifetime total number born (LTNB), lifetime number born alive (LNBA), removal parity, and the ratio between lifetime nonproductive days and herd life. The proportion of phenotypic variance explained by markers was 0.15 for LTNB and LNBA, 0.12 for removal parity, and 0.06 for the ratio between lifetime nonproductive days and herd life. Several informative QTL regions (e.g., 14 QTL regions for LTNB) and genes within the regions (e.g., SLC22A18 on SSC2 for LTNB) were associated with lifetime reproductive traits in this study. Genes associated with LTNB and LNBA were similar, reflecting the high genetic correlation (0.99 ± 0.003) between these traits. Functional annotation revealed that many genes at the associated regions are expressed in reproductive tissues. For instance, the SLC22A18 gene on SSC2 associated with LTNB has been shown to be expressed in the placenta of mice. Many of the QTL regions showing associations coincided with previously identified QTL for fat deposition. This reinforces the role of fat regulation for lifetime reproductive traits. Overall, this whole-genome association study provides a list of genomic locations and markers associated with pig lifetime reproductive traits that could be considered for SPL in future studies.     

Genetic Improvement of Dairy Cow Reproductive Performance

The welfare of cow along with profitability in production are important issues in sustainable animal breeding programmes. Along with an intense/intensive selection for increased milk yield, reproductive performance has declined in many countries, in part due to an unfavourable genetic relationship. The largely unchanged genetic trend in female fertility and calving traits for Scandinavian Red breeds shows that it is possible to avoid deterioration in these traits if they are properly considered in the breeding programme. Today's breeding is international with a global selection and extensive use of the best bulls. The Nordic countries have traditionally recorded and performed genetic evaluation for a broad range of functional traits including reproduction. In recent years many other countries have also implemented genetic evaluation for these traits. Thus, the relative emphasis of dairy cattle breeding objectives has gradually shifted from production to functional traits such as reproduction. Improved ways of recording traits, e.g. physiological measures, early indicator traits, assisted reproductive techniques and increased knowledge of genes and their regulation may improve the genetic selection strategies and have large impact on present and future genetic evaluation programmes. Extensive data bases with phenotypic recordings of traits for individuals and their pedigree are a prerequisite. Quantitative trait loci have been associated to the reproductive complex. Most important traits, including reproduction traits are regulated by a multitude of genes and environmental factors in a complex relationship, however. Genomic selection might therefore be important in future breeding programmes. Information on single nucleotide polymorphism has already been introduced in the selection programmes of some countries.     

Evaluation of association between polymorphism within the thyroglobulin gene and milk production traits in dairy cattle

In dairy cattle, many studies have reported quantitative trait loci (QTL) on the centromeric end of chromosome 14 that affect milk production traits. One of the candidate genes in this QTL region – thyroglobulin (TG) – was previously found to be significantly associated with marbling in beef cattle. Thus, based on QTL studies in dairy cattle and because of possible effects of this gene on fat metabolism, we investigated the association of TG with milk yield and composition in Holstein dairy cattle. A total of 1279 bulls from the Cooperative Dairy DNA Repository Holstein population were genotyped for a single nucleotide polymorphism in TG used previously in beef cattle studies. Analysis of 29 sire families showed no significant association between TG variants and milk production traits. Within‐sire family analysis suggests that TG is neither the responsible gene nor a genetic marker in association with milk production traits.     

Progress of genome wide association study in domestic animals

ABSTRACT: Domestic animals are invaluable resources for study of the molecular architecture of complex traits. Although the mapping of quantitative trait loci (QTL) responsible for economically important traits in domestic animals has achieved remarkable results in recent decades, not all of the genetic variation in the complex traits has been captured because of the low density of markers used in QTL mapping studies. The genome wide association study (GWAS), which utilizes high-density single-nucleotide polymorphism (SNP), provides a new way to tackle this issue. Encouraging achievements in dissection of the genetic mechanisms of complex diseases in humans have resulted from the use of GWAS. At present, GWAS has been applied to the field of domestic animal breeding and genetics, and some advances have been made. Many genes or markers that affect economic traits of interest in domestic animals have been identified. In this review, advances in the use of GWAS in domestic animals are described.     

玉米持绿相关ＱＴＬ整合图谱构建及一致性ＱＴＬ区域内候选基因发掘

 以玉米高密度遗传连锁图谱ＩＢＭ２２００８Ｎｅｉｇｈｂｏｒｓ为参考图谱,收集来自不同实验中的１７３个玉米持绿相关数量性状位点（ｑｕａｎｔｉｔａｔｉｖｅｔｒａｉｔｌｏｃｕｓ,ＱＴＬ）信息,利用ＢｉｏＭｅｒｃａｔｏｒ２．１软件,构建出玉米持绿相关ＱＴＬ 整合图谱;采用元分析技术,在１,４,５,９号染色体上发掘出５个持绿“一致性”ＱＴＬ 区间。根据“一致性”ＱＴＬ 区间两端标记在玉米物理图谱Ｂ７３ＲｅｆＧｅｎ＿ｖ２上的位置,将“一致性”ＱＴＬ 区间进行物理图谱定位,利用ＰｌａｎｔＧＤＢ（ｈｔｔｐ：／／ｗｗｗ．ｐｌａｎｔｇｄｂ．ｏｒｇ／）在线区段批量下载工具（ｄｏｗｎｌｏａｄｒｅｇｉｏｎｄａｔａ）下载“一致性”区间的１４４５个预测基因序列并进行生物信息学分析,发现预测基因主要参与具体的细胞过程,执行结合功能,催化、转移酶活性和氧化还原酶活性等分子功能。根据“一致性”ＱＴＬ 区间的基因位点名称,在ＮＣＢＩ中下载相关基因序列,与所在“一致性”ＱＴＬ 区 间所有预测基因保守结构域进行比对,在５个“一致性”持绿ＱＴＬ 区间内初步确定８个持绿相关候选基因。利用ＧＲＡＭＥＮＥ 网站（ｈｔｔｐ：／／ｗｗｗ．ｇｒａｍｅｎｅ．ｏｒｇ／）的Ｃｍａｐ功能,将水稻持绿基因狊犵狉（ｓｔａｙｇｒｅｅｎ）转定位于玉米物理图谱Ｂ７３ＲｅｆＧｅｎ＿ｖ２上,找到与其同源的玉米候选基因ＧＲＭＺＭ２Ｇ０９１８３７＿Ｔ０１,其序列与已发表的玉米衰老诱导叶绿体持绿蛋白基因狊犵狉１序列一致。     

Theoretical efficiency of multiple-trait quantitative trait loci-assisted selection

The effectiveness of five selection methods for genetic improvement of net merit comprising trait 1 of low heritability (h² = 0.1) and trait 2 of high heritability (h² = 0.4) was examined: (i) two‐trait quantitative trait loci (QTL)‐assisted selection; (ii) partial QTL‐assisted selection based on trait 1; (iii) partial QTL‐assisted selection based on trait 2; (iv) QTL‐only selection; and (v) conventional selection index without QTL information. These selection methods were compared under 72 scenarios with different combinations of the relative economic weights, the genetic correlations between traits, the ratio of QTL variance to total genetic variance of the trait, and the ratio of genetic variances between traits. The results suggest that the detection of QTL for multiple‐trait QTL‐assisted selection is more important when the index traits are negatively correlated than when they are positively correlated. In contrast to literature reports that single‐trait marker‐assisted selection (MAS) is the most efficient for low heritability traits, this study found that the identified QTL of the low heritability trait contributed negligibly to total response in net merit. This is because multiple‐trait QTL‐assisted selection is designed to maximize total net merit rather than the genetic response of the individual index trait as in the case of single‐trait MAS. Therefore, it is not economical to identify the QTL of the low heritability traits for the improvement of total net merit. The efficient, cost‐effective selection strategy is to identify the QTL of the moderate or high heritability traits of the QTL‐assisted selection index to facilitate total economic returns. Detection of the QTL of the low h² traits for the QTL‐assisted index selection is justified when the low h² traits have high negative genetic correlation with the other index traits and/or when both economic weights and genetic variances of the low h² traits are larger as compared to the other index traits of higher h². This study deals with theoretical efficiency of QTL‐assisted selection, but the same principle applies to SNP‐based genomic selection when the proportion of the genetic variance ‘explained by the identified QTLs’ in this study is replaced by ‘explained by SNPs’.     

Understanding the use of Bayes factor for testing candidate genes

After quantitative trait loci (QTL) detection, one of the main objectives of research is to identify the causal mutation explaining phenotypic differences. Candidate genes are usually selected according to the physiological mechanism of the trait and their location within the same region of the QTL. After detection of any polymorphism at the candidate gene sequence, it is important to determine whether the detected mutation is the one that causes the phenotypic variation. This is not, however, an easy task, because of the linkage disequilibrium between the genes located in the same region. Several methods have been proposed that consider the neutral marker information in validating the involvement of candidate genes. However, some statistical information may be lost because of the presence of both the QTL and candidate gene effects in the model of analysis. Here, the Bayes factor is suggested as an alternative and a procedure for its calculation between candidate gene and QTL models is presented. The procedure is illustrated with a simulation study and with an example consisting of three SNPs detected at the leptin receptor (LEPR) in an experimental intercross between Iberian and Landrace pigs. The results indicate that the Bayes factor procedure is more powerful than the classical approach.     

大白猪基因组选择在不同应用策略下的比较研究

目前,基因组选择（genomic selection,GS）技术已经在种猪育种中开展,但为获得较高的收益,还需研究一些应用策略,如确定仔猪基因分型个体比例和早期仔猪留种比例。本试验选择温氏集团出生于2011—2016年的大白种猪作为研究对象,共有超过4.5万条的生长测定记录,超过7万条繁殖记录,和2 090个个体的简化基因组测序（GBS）数据,其中,出生于2016年7~12月的440个体作为候选群体。研究性状包括两个生长性状（校正100 kg日龄和校正100 kg背膘厚）和一个繁殖性状（总产仔数）。为对比预测效果,在候选群体进行育种值预测时,按照是否利用其基因型或表型信息分为4种预测方案,比较不同方案的预测可靠性和个体选择指数的排名情况。结果显示,在预测候选群育种值时,利用其表型或基因型信息均比不利用时的预测结果更加可靠。对生长性状终测前、后进行基因组选择指数计算,发现,终测后指数排名前30%的个体都位于终测前指数排名前60%内。若仔猪出生后仅选择常规BLUP预测指数排名前60%的个体,会导致有接近15%的具有优秀潜力的个体被遗漏。本研究建议,对所有新生健康仔猪都进行基因分型并计算基因组选择指数,然后对指数排名靠前60%的个体进行性能测定。     

Genetic correlations between growth and reproductive traits in Zandi sheep

For the first time, the current study reports the genetic and phenotypic correlations between growth and reproductive traits in Zandi sheep. The data were comprised of 4,309 records of lamb growth traits from 1,378 dams and 273 sires plus 2,588 records of reproductive traits from 577 ewes. These data were extracted from available performance records at Khojir Breeding Station of Zandi sheep in Tehran, Iran, from 1993 to 2008. Correlations were estimated from two animal models in a bivariate analysis using restricted maximum likelihood procedure between lamb growth traits [birth weight (BW), weaning weight at 3 months of age (WW), as well as six-month weight (6 MW)] and ewe reproductive traits [litter size at birth (LSB), litter size at weaning (LSW), total litter weight at birth (TLWB), and total litter weight at weaning (TLWW)]. The genetic correlations between BW and reproductive traits varied from low to high ranges from 0.10 for BW–LSB to 0.86 for BW–TLWB. WW was moderately (0.37) to highly (0.96) correlated with all the reproductive traits. Moreover, the genetic correlations were observed between 6 MW and reproductive traits, varied from 0.19 to 0.95. Relationships between growth and reproductive traits ranged from 0.01 for BW–LSW to 0.28 for BW–TLWB in phenotypic effects. Results indicated that selection to improve WW would have high effect on genetic response in TLWW, and also, these results could be effective for all of the reproductive traits in Zandi sheep.     

Comparative Analysis of Different Implementation Strategies on Genomic Selection in Large White Pigs

At present, genomic selection (GS) has been applied in pigs breeding, but some implementation strategies, such as the determination of genotyping ratios or early selection rates for piglets, are required to obtain a higher benefit using this technology. The Large White pigs born from 2011 to 2016 at WENS Foodstuff Group Co.,Ltd were chose as the research objects, including more than 45 000 growth measurement records, more than 70 000 reproduction records and 2 090 individuals with genotyping-by-sequencing (GBS) data. The 440 individuals born from July to December in 2016 were used as the candidate individuals. The traits included two growth traits, age at 100 kg and backfat thickness at 100 kg, and one reproduction trait, number of total born. To compare the prediction effects, four prediction scenarios were designed according to including or ignoring the phenotypic or genotypic information of candidate individuals when predicting their breeding values. The predictive reliability of different scenarios and rankings of selection indices of individuals would be compared. The results showed that the results using the phenotypic and genotypic information was more reliable than ignoring them to predict the breeding values of candidate individuals. When genomic selection indices were calculated before and after performances testing for the growth traits, the individuals ranking in the top 30% of indices after testing were all found in the individuals ranking in the top 60% of indices before testing. If the piglets with the top 60% of traditional BLUP indices were only selected, around 15% of individuals with good genetic potentials would be omitted. This study suggests that all healthy piglets after birth are genotyped and their genomic selection indices are calculated, and then the individuals ranking in the top 60% of indices are chose to perform growth measurement.