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A genetic linkage map is a representation of the relative positions of genetic loci on the chromosomes. Its construction is based on knowledge of how often alleles of different loci are inherited together or become exchanged by genetic recombination. Therefore, it is important to understand how genetic recombination takes place during meiosis. In eukaryotic organisms, meiosis is a series of two special cell divisions, enabling the creation of new combinations of alleles. The alternative type of cell division, called mitosis, merely produces genetically identical copies of cells. This chapter contains a concise description of meiosis as far as is relevant for the understanding of genetic recombination. Recombination frequency is introduced as a measure of the rate of genetic recombination.
The life cycle and the ploidy level
A cell containing one basic set of chromosomes is called haploid. A cell containing two sets is called diploid. In some species, cells contain more than two sets of chromosomes; such species are called polyploid and will be discussed briefly in Section 2.7. The life cycle of eukaryotic species is characterized by successions of haploid and diploid phases. In both phases, mitosis enables vegetative growth leading to multicellular organisms or it enables multiplication leading to more, genetically identical individuals, as in microorganisms. Meiosis is responsible for the transition from the diploid to the haploid phase. At some stage, two haploid cells will merge to form the so-called zygote, thereby closing the life cycle at the diploid level. The lengths of the haploid and diploid phases of the life cycle vary greatly among and within higher and lower eukaryotes. For example, for most fungi, the zygote forms the entire diploid phase in the life cycle: the zygote goes directly into meiosis and the resulting haploid cells divide mitotically into multicellular haploid mycelia. In most animals, the four cells produced by meiosis develop into gametes without further cell divisions, and the gametes are ready to fuse with gametes of the opposite sex to form diploid zygotes. Here, the gametes represent the entire, but very short, haploid phase in the life cycle. In most higher plants, the four meiotic daughter cells undergo a few mitotic divisions before the gamete is produced. The life cycle can also be more complicated: for instance, in honey bees the drones (the males) are haploid, the worker bees (the females) are diploid but sterile, and the queen bee is diploid and fertile.
The rate of recombination between two loci is a measure of the distance these loci are apart on the chromosomes. Measuring these values for all loci is the starting point for estimating the map of the loci. In Chapter 2, the recombination frequency of two loci was introduced as the measure of genetic recombination: this is the proportion of gametes that are recombinant between the two loci in a single meiosis. A complication with higher plants and animals is that we do not observe the gametes but the diploid individuals in which two gametes are combined. Because in diploids two alleles are present for each locus, there is not always a one-to-one relationship between the observation of a locus and its genotype. Strictly speaking, we should use the term phenotype for observations regarding the genotype. In some cases, it is possible to determine exactly the genotype from the phenotype, for instance in the backcross. In such cases, estimates of recombination frequencies can be obtained by simple counting. We show that the same estimates are obtained by employing the maximum likelihood principle. This same principle can then also be employed in situations where there is no one-to-one relationship between phenotype and genotype. In yet other situations, recombinant genotypes are the result of several subsequent meioses. Here, the observed recombination must be translated into the probability of recombination in a single meiosis.
What do we observe? From separate loci to pairs, from phenotype to genotype
Recombination is a phenomenon that occurs with respect to pairs of genes or markers. In a regular linkage analysis, however, we start with observing the phenotypes of loci separately. Only as a next step are the observations combined into pairs, which allows the study of recombination. Let us first look at phenotypes: what exactly is a phenotype? The term phenotype was introduced by the Danish geneticist W. Johannsen back in 1909 in order to be able to make the distinction between what is observed of an organism and its genetic constitution. By definition, a phenotype is what we can observe of the genotype of an individual. Although somewhat confusing, the pleasant characteristic of many genetic markers is that the phenotype is equal to the genotype. This is why the words are regularly used as synonymous in the context of linkage analysis. However, there can be a major difference.
Please note that the calculations of some of the exercises in this book are demonstrated with MS-Excel spreadsheets (.xls). These are included in the set of files that are available for downloading at the Cambridge University Press web site at http://www.cambridge.org/9781107013216.
Exercise 3.1.
First determine, for each combination of markers, which genotype classes are recombinant and which non-recombinant. Here, combinations of AA with Aa are recombinant, whilst combinations of AA with AA and Aa with Aa are non-recombinant. Next, determine the total number of recombinant individuals and divide this by the total number of individuals with known genotype:
K–L: r̂ = (4 + 5 + 8 + 5)/100 = 0.22
K–M: r̂ = (0 + 5 + 8 + 1)/100 = 0.14
L–M: r̂ = (0 + 4 + 5 + 1)/100 = 0.10
Exercise 3.2.
First determine, for each combination of loci, which genotype classes are recombinant and which non-recombinant. Here, combinations of a with h are recombinant, whilst combinations of a with a and h withare non-recombinant. Any combination with an unknown observation, ‘–’, must be subtracted from the 164 individuals to determine the total number of individuals with known genotype, as these combinations cannot be classified as recombinant nor as non-recombinant. Next, determine the total number of recombinant individuals and divide this by the total number of individuals with known genotype. The easiest way to do this is tallying the individuals in a 3 × 3 contingency table. If you have access to the data in a computer spreadsheet, you can achieve this easily with pivot tables.
The genetic circumstances found in outbreeding species are more complicated than in inbreeding species: more than two alleles may be present at each locus and the linkage phases may vary across loci and between the parents of an experimental cross. The experimental design of inbreeding species often cannot be applied to outbreeding species. This chapter focuses on explaining in detail the genetic situations encountered in outbreeding species. It further describes the linkage analysis of a full-sib family of an outbreeding species.
Introduction
In the preceding chapters, we treated linkage mapping for experimental populations derived from a cross between two fully homozygous parents. Homozygosity is obtained by many generations of self-fertilization or sib mating, or by creating doubled haploids. For many plant species, self-pollination followed by self-fertilization (also called autogamy) is the normal mode of sexual reproduction. In some hermaphrodite species (i.e. species with organs of both sexes), a system of self-incompatibility or self-sterility causes an obstruction to self-fertilization. In dioecious species (dioecious means that individuals have organs of only one of the two sexes), self-fertilization is of course impossible. In outbreeding species, for which the normal mode of sexual reproduction is by crossing with other individuals (also called allogamy), forced inbreeding usually results in individuals with a poor viability. This phenomenon is called inbreeding depression. The consequence for such species is that linkage analysis can often only be performed using populations obtained by crossing relatively unrelated individuals.
Genetic linkage mapping is a very powerful tool, but it turns out to be quite sensitive to incomplete or erroneous information. In practice, it is often impossible to record data on all loci-individual combinations. Therefore, the mapping computations have to be done with some missing observations. It also turns out that a mapping experiment is prone to errors because of the huge number of observations. In this chapter, we address some of the common problems encountered in practice.
Introduction
The preceding chapters describe the theory underlying the construction of genetic linkage maps. With suitable computer software, map construction should be straightforward. We write should, because in practice this is not always the case. There are two reasons for this. As in statistical modelling, we view map construction as a method in which observations are fitted to a model. In practice, the fit between observations and model can be far from perfect. In this case, the observations may not behave according to the model or the model is an incorrect abstraction of the way the observations behave. In our specific case of genetic map construction, this means that a poor fit may either be caused by poor quality of the marker observations or by an imperfect model of the genetics, or both.
Six experiments were conducted to investigate the effect of a feed supplement on the performance of grazing Belgian Blue double-muscled (BBDM) heifers with an initial weight and age of 195 ± 43 kg and 190 ± 52 days. Treatments included were: Exp. 1: supplementation with beet pulp (BP): 2 kg/day per head v.ad libitum intake; Exp. 2: supplementation ad libitum with BP v. a mixture of BP and soybean meal (SBM; BP/SBM ratio of 80/20; FW (fresh weight) basis); Exp. 3: supplementation with 4 kg/day per head of a mixture of BP/SBM (80/20; FW basis) v. BP/formaldehyde-treated SBM (BP/FSBM); Exp. 4: supplementation with 4 kg/day per head of a mixture with a similar protein content (125 g DVE per kg dry matter (DM)), consisting of 80/20 BP/SBM v. 92/8 BP/FSBM; Exp. 5: supplementation with 3 kg/day per head of a mixture of BP/SBM (80/20; FW basis) v. BP/DDGS (dried distillers grains and solubles; 70/30, FW basis); and Exp. 6: supplementation with 3 kg/day per head of 80/20 BP/SBM v. maize silage (MS) and SBM, on the basis of a similar protein concentration in the DM as the 80/20 BP/SBM supplement, and fed at a similar amount of DM as in the BP/SBM group. Supplementing BP ad libitum did not affect daily gain (0.54 v. 0.48 kg) and partial feed conversion (3.62 kg on average) compared with 2 kg/day. Supplying SBM besides BP increased growth rate compared with BP (0.87 v. 0.62 kg/day; P < 0.001), but partial feed conversion was similar. Supplying FSBM did not affect growth rate and partial feed conversion (P > 0.10), but blood urea levels were reduced by FSBM (P < 0.05). DDGS tended to increase growth rate (0.77 v. 0.59 kg/day; P < 0.10) compared with BP/SBM, without effect on partial feed conversion. Replacing BP by MS did not affect daily gain, but partial feed conversion tended to be higher (3.21 v. 3.60 kg/kg body weight (BW) gain; P = 0.062). Increasing the supplement (80/20 BP/SBM) level from 3 to 4 kg daily, corresponding to 1.02% and 1.18% of the mean BW, respectively, resulted in a tendency (P = 0.121) for an increased growth rate. Grazing BBDM heifers of <1 year of age necessitate extra protein besides an energy supplement to improve their performance. DDGS can replace SBM and BP can be replaced by MS.
The growth dispersion of farmed fish is a subject of increasing interest and one of the most important factors in stocking density. On a duration of 60 days, the effect of stocking density on the growth, coefficient of variation and inter-individual variation of feed intake (CVFI) of juvenile Nile tilapia Oreochromis niloticus L. (14.9 ± 1.2 g) were studied in an experimental tank-based flow-through system. Groups of fish were stocked at four stocking densities: 200, 400, 600 and 800 fish/m3, corresponding to a density of ∼3, 6, 9 and 12 kg/m3 and referred to as D1, D2, D3 and D4, respectively. Each treatment was applied to triplicate groups in a completely randomized design. No treatment-related mortality was observed. The fish densities increased throughout the experiment from 3 to 23.5, 6 to 43.6, 9 to 56.6 and 12 to 69 kg/m3. Results show that mass gain and specific growth rate (SGR, %M/day) were negatively correlated with increased stocking density. Groups of the D1 treatment reached a mean final body mass (FBM) of 119.3 g v. 88.9 g for the D4 groups. Feed conversion ratios (FCRs) were 1.38, 1.54, 1.62 and 1.91 at D1, D2, D3 and D4 treatments, respectively. Growth heterogeneity, expressed by the inter-individual variations of fish mass (CVM), was significantly affected by time (P < 0.001), stocking density (P < 0.001) and their interaction (P < 0.05). The difference in CVM was particularly conspicuous towards the end of the experiment and was positively correlated with stocking density. Similarly, radiographic study shows that CVFI was also found to be significantly greater for groups reared at high stocking densities (D3 and D4) than the other treatments (D1 and D2). These differences in both CVM and CVFI related to the stocking density need to be taken into account by husbandry practices to assure the production of more homogeneous fish size. A simple economic analysis indicates a parabolic relationship between profit and density with optimal final density at the peak of the curve. Given reasonable assumptions about production costs, the optimal final density (Dopt) is 73.7 kg/m3. A sensitivity analysis shows that changes in the fixed cost have no effects on the optimal final density. However, small change in variable costs, such as feed and juvenile costs, may have substantial effect on the optimal density.
The high pre-weaning mortality in farm animal species and poor welfare conditions of reproductive females question modern industrial farming acceptability. A growing body of literature has been produced recently, investigating the impact of maternal stress during gestation on maternal and offspring physiology and behavior in farm animals. Until now, the possible impact of prenatal stress on neonatal health, growth and survival could not be consistently demonstrated, probably because experimental studies use small numbers of animals and thus do not allow accurate estimations. However, the data from literature synthesized in the present review show that in ungulates, maternal stress can sometimes alter important maternal parameters of neonatal survival such as colostrum production (ruminants) and maternal care to the newborn (pigs). Furthermore, maternal stress during gestation can affect maternal immune system and impair her health, which can have an impact on the transfer of pathogens from the mother to her fetus or neonate. Finally, prenatal stress can decrease the ability of the neonate to absorb colostral immunoglobulins, and alter its inflammatory response and lymphocyte functions during the first few weeks of life. Cortisol and reproductive hormones in the case of colostrogenesis are pointed out as possible hormonal mediators. Field data and epidemiological studies are needed to quantify the role of maternal welfare problems in neonatal health and survival.
The genomic breeding value accuracy of scarcely recorded traits is low because of the limited number of phenotypic observations. One solution to increase the breeding value accuracy is to use predictor traits. This study investigated the impact of recording additional phenotypic observations for predictor traits on reference and evaluated animals on the genomic breeding value accuracy for a scarcely recorded trait. The scarcely recorded trait was dry matter intake (DMI, n = 869) and the predictor traits were fat–protein-corrected milk (FPCM, n = 1520) and live weight (LW, n = 1309). All phenotyped animals were genotyped and originated from research farms in Ireland, the United Kingdom and the Netherlands. Multi-trait REML was used to simultaneously estimate variance components and breeding values for DMI using available predictors. In addition, analyses using only pedigree relationships were performed. Breeding value accuracy was assessed through cross-validation (CV) and prediction error variance (PEV). CV groups (n = 7) were defined by splitting animals across genetic lines and management groups within country. With no additional traits recorded for the evaluated animals, both CV- and PEV-based accuracies for DMI were substantially higher for genomic than for pedigree analyses (CV: max. 0.26 for pedigree and 0.33 for genomic analyses; PEV: max. 0.45 and 0.52, respectively). With additional traits available, the differences between pedigree and genomic accuracies diminished. With additional recording for FPCM, pedigree accuracies increased from 0.26 to 0.47 for CV and from 0.45 to 0.48 for PEV. Genomic accuracies increased from 0.33 to 0.50 for CV and from 0.52 to 0.53 for PEV. With additional recording for LW instead of FPCM, pedigree accuracies increased to 0.54 for CV and to 0.61 for PEV. Genomic accuracies increased to 0.57 for CV and to 0.60 for PEV. With both FPCM and LW available for evaluated animals, accuracy was highest (0.62 for CV and 0.61 for PEV in pedigree, and 0.63 for CV and 0.61 for PEV in genomic analyses). Recording predictor traits for only the reference population did not increase DMI breeding value accuracy. Recording predictor traits for both reference and evaluated animals significantly increased DMI breeding value accuracy and removed the bias observed when only reference animals had records. The benefit of using genomic instead of pedigree relationships was reduced when more predictor traits were used. Using predictor traits may be an inexpensive way to significantly increase the accuracy and remove the bias of (genomic) breeding values of scarcely recorded traits such as feed intake.
Transfer of sufficient immunoglobulin G (IgG) to the neonatal calf via colostrum is vital to provide the calf with immunological protection and resistance against disease. The objective of the present study was to determine the factors associated with both colostral IgG concentration and colostral weight in Irish dairy cows. Fresh colostrum samples were collected from 704 dairy cows of varying breed and parity from four Irish research farms between January and December 2011; colostral weight was recorded and the IgG concentration was determined using an ELISA method. The mean IgG concentration in the colostrum was 112 g/l (s.d. = 51 g/l) and ranged from 13 to 256 g/l. In total, 96% of the samples in this study contained >50 g/l IgG, which is considered to be indicative of high-quality colostrum. Mean colostral weight was 6.7 kg (s.d. = 3.6 kg) with a range of 0.1 to 24 kg. Factors associated with both colostral IgG concentration and colostral weight were determined using a fixed effects multiple regression model. Parity, time interval from calving to next milking, month of calving, colostral weight and herd were all independently associated with IgG concentration. IgG concentration decreased (P < 0.01) by 1.7 (s.e. = 0.6) g/l per kg increase in the colostral weight. Older parity cows, cows that had a shorter time interval from calving to milking, and cows that calved earlier in spring or in the autumn produced colostrum with higher IgG concentration. Parity (P < 0.001), time interval from calving to milking (P < 0.01), weight of the calf at birth (P < 0.05), colostral IgG concentration (P < 0.01) and herd were all independently associated with colostral weight at the first milking. Younger parity cows, cows milked earlier post-calving, and cows with lighter calves produced less colostrum. In general, colostrum quality of cows in this study was higher than in many previous studies; possible reasons include use of a relatively low-yielding cow type that produces low weight of colostrum, short calving to colostrum collection interval and grass-based nutritional management. The results of this study indicate that colostral IgG concentration can be maximised by reducing the time interval between calving and collection of colostrum.
This study aimed at assessing the effect of the observation method (direct or from video) and the effect of the presence of an observer on the behavioural results in veal calves kept on a commercial farm. To evaluate the effect of the observation method, 20 pens (four to five calves per pen) were observed by an observer for 60 min (two observation sessions of 30 min) and video-recorded at the same time. To evaluate the effect of the presence of the observer in front of the pen, 24 pens were video-recorded on 4 consecutive days and an observer was present in front of each pen for 60 min (two observation sessions of 30 min) on the third day. Behaviour was recorded using instantaneous scan sampling. For the study of the observer's effect, the analysis was limited to the posture, abnormal oral behaviour and manipulation of substrates. The two observation methods gave similar results for the time spent standing, but different results for all other behaviours. The presence of an observer did not affect the behaviour of calves at day level; however, their behaviour was affected when the observer was actually present in front of the pens. A higher percentage of calves were standing and were manipulating substrate in the presence of the observer, but there was no effect on abnormal oral behaviour. In conclusion, direct observations are a more suitable observation method than observations from video recordings for detailed behaviours in veal calves. The presence of an observer has a short-term effect on certain behaviours of calves that will have to be taken into consideration when monitoring these behaviours.
Tannins, polyphenolic compounds found in plants, are known to complex with proteins of feed and rumen bacteria. This group of substances has the potential to reduce methane production either with or without negative effects on digestibility and microbial yield. In the first step of this study, 10 tannin-rich extracts from chestnut, mimosa, myrabolan, quebracho, sumach, tara, valonea, oak, cocoa and grape seed, and four rapeseed tannin monomers (pelargonidin, catechin, cyanidin and sinapinic acid) were used in a series of in vitro trials using the Hohenheim gas test, with grass silage as substrate. The objective was to screen the potential of various tannin-rich extracts to reduce methane production without a significant effect on total gas production (GP). Supplementation with pelargonidin and cyanidin did not reduce methane production; however, catechin and sinapinic acid reduced methane production without altering GP. All tannin-rich extracts, except for tara extract, significantly reduced methane production by 8% to 28% without altering GP. On the basis of these results, five tannin-rich extracts were selected and further investigated in a second step using a Rusitec system. Each tannin-rich extract (1.5 g) was supplemented to grass silage (15 g). In this experiment, nutrient degradation, microbial protein synthesis and volatile fatty acid production were used as additional response criteria. Chestnut extract caused the greatest reduction in methane production followed by valonea, grape seed and sumach, whereas myrabolan extract did not reduce methane production. Whereas chestnut extract reduced acetate production by 19%, supplementation with grape seed or myrabolan extract increased acetate production. However, degradation of fibre fractions was reduced in all tannin treatments. Degradation of dry matter and organic matter was also reduced by tannin supplementation, and no differences were found between the tannin-rich extracts. CP degradation and ammonia-N accumulation in the Rusitec were reduced by tannin treatment. The amount and efficiency of microbial protein synthesis were not significantly affected by tannin supplementation. The results of this study indicated that some tannin-rich extracts are able to reduce methane production without altering microbial protein synthesis. We hypothesized that chestnut and valonea extract have the greatest potential to reduce methane production without negative side effects.
N-3 long-chain polyunsaturated fatty acids (n-3 LCPUFA) are subject of growing interest as they are of particular relevance for meat quality and human health. However, their content in the muscles of cattle is generally low probably as the complex result of their biosynthesis from dietary n-3 PUFA in the muscle and/or in other tissues/organs and of their subsequent uptake by the muscle. In view of this, this study aimed at understanding whether the changes in the muscle n-3 LCPUFA content, depending on the diet (maize silage v. grass) or the muscle type (Rectus abdominis, RA v.Semitendinosus, ST) in 12 Charolais steers, were related to variations in the gene expression of proteins involved in n-3 LCPUFA biosynthesis or cellular uptake. Tissue fatty acid composition was analysed by gas-liquid chromatography and mRNA abundance of proteins by quantitative real-time PCR. The grass-based diet resulted in a 2.3-fold (P < 0.0002) increase in both RA and ST n-3 LCPUFA content compared with the maize silage-based diet, whereas no difference in the expression of genes involved in n-3 LCPUFA biosynthesis and uptake was observed between diets. ST exhibited a 1.5-fold higher n-3 LCPUFA content than RA (P < 0.003), whereas the gene expression of proteins involved in n-3 LCPUFA biosynthesis and uptake was 1.3- to 18-fold higher in RA than in ST (P < 0.05). In conclusion, diet- or muscle type-dependent changes in the muscle n-3 LCPUFA content of Charolais steers did not seem to be mediated by the gene expression regulation of proteins involved in the biosynthesis or uptake of these fatty acids.
Genetic linkage maps are an increasingly important tool in both fundamental and applied research, enabling the study and deployment of genes that determine important biological traits. This concise introduction to genetic mapping in species with disomic inheritance enables life science graduate students and researchers to use mapping software to produce more reliable results. After a brief refresher on meiosis and genetic recombination, the steps in the map construction procedure are described, with explanations of the computations involved. The emphasis throughout is on the practical application of the methods described; detailed mathematical formulae are avoided and exercises are included to help readers consolidate their understanding. A chapter on recognising and solving problems provides valuable guidance for dealing with real-life situations. An extensive chapter dedicated to the more complex situation of outbreeding species offers a unique insight into the approach required for many economically important and model species, both plants and animals.
A lot has been written about the opportunities of the Internet for medicine, and lately, also for disease research specifically. Although it remains to be seen how significant and sustainable a change this will result in, some recent developments are highly relevant for the area of genetic research. User-friendly, low-threshold web-based tools do not only provide information to patients and other users, but they also supply user-generated data that can be utilized by both medical practice and medical research. Many of these developments have been below the radar of mainstream academic research so far. Issues related to data quality and standardization, as well as data protection and privacy, still need to be addressed. Dismissing these platforms as fads of a tiny privileged minority risks missing the opportunity to have our say in these debates.