Genetics and genomics of animal welfare

Genetics and genomics of animal welfare

Genetics and genomics of animal welfare 2 Per Jensen IFM Biology, Linko¨ping University, Linko¨ping, Sweden 2.1 Introduction Behavior is a produc...

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Genetics and genomics of animal welfare


Per Jensen IFM Biology, Linko¨ping University, Linko¨ping, Sweden



Behavior is a product of the genes carried by an individual in close interaction with the environment (Jensen, 2006). Genes contain the instructions for forming the sensory organs, nervous system, and muscular apparatus of an individual, and therefore determine the foundation and boundaries of behavior. Yet most behavioral research in the area of animal welfare has focused on the role of the environment, and only a small fraction of scientific efforts have been devoted to behavior genetics. Not only is it possible to assess the welfare of animals by means of their behavior, but the ability to perform proper behavior is a major determinant of animals’ welfare. Hence, the interaction between genes and behavior is of central importance to animal welfare science. It is therefore the topic of the present chapter. It will discuss basic principles relevant for assessing and improving farm animal behavior and welfare, but to do so, it will also provide examples from studies using dogs and laboratory animals. However, the fundamentals of genetics are of course independent of species.

2.1.1 Domestication as a model Although not a primary topic for the present chapter, domestication is a central process with high relevance for the welfare of animals. Domestication, the process during which populations of wild animals become adapted to a life under human reproductive control (Price, 2002), may be the largest genetic experiment in human history. Darwin realized the power of this as a proof-of-concept for his theory of evolution, and devoted a large part of “The Origin of Species” to domestication of animals and plants. He realized that if organisms could change so drastically in only relatively few generations of directed selection imposed by humans, then similar changes could happen in nature under the pressure of natural selection. Indeed, domestication can be viewed primarily as an evolutionary process, where some of the natural selection pressures have been replaced by human controlled selection. But much of natural selection remains even among domesticated animals. For example, parasites and disease affect individual fitness beyond human control and food shortage and predation can exert major selection pressures even under conditions of human protection. A central aspect of domestication is the fact that a wide range of traits in domesticated animals adapt so as to maximize the fitness of the individuals while under Advances in Agricultural Animal Welfare. DOI: Copyright © 2018 Elsevier Ltd. All rights reserved.


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human auspices. This includes them becoming easier to tame and increasingly tolerant of close proximity of conspecifics and able to interact with humans. This is perhaps most obvious in the case of our oldest domesticated animal, the dog. Compared to their ancestors, gray wolves, dogs easily become tame and affiliate closely with humans, they can thrive in large groups of conspecifics (as seen, for example, in stray dogs) and they excel in comprehending human signals and ostensive cues (Miklosi, 2008). Domestication of animals has been going on for about 15,000 years (Diamond, 2002), but a sudden change in the conditions for the coexistence between domesticated animals and humans has occurred during the last 100 years, with a dramatic escalation over the last 50 years. This escalation is caused by the introduction of heavily increased selection pressures for increased growth and reproduction in farm animals. Since about 1960, average production levels of the common farm animals have more than doubled, and in some cases more than tripled, a process associated with several side effects with potential impacts on animal welfare (Rauw and Kanis, 1998). Hence, domestication has for most of its history caused animals to be better adapted, and probably to have improved welfare, when under human control. However, contemporary selection may sometimes have the opposite effects on welfare, as will be seen later in this chapter.


Behavior and welfare—defining the concepts

This chapter will be focused on the effects of genetic selection on animal behavior and welfare. A definition of the concepts may therefore be in order. Animal welfare has been defined in many different ways by different researchers, but in this chapter I will use the one put forward by Donald Broom: welfare is the state of an individual with respect to its attempts to cope with its environment (Broom, 2008). This includes health and emotional responses, and can range from poor to good. Coping is in itself a broad and somewhat imprecise term, but it generally includes all biological measures an animal takes in order to avoid damage from stress (Broom, 2008). Partly, coping involves physiological adjustments (Koolhaas et al., 2011) (for example, as a response to stressful ambient temperature conditions, or limited food supply), but will very often also consists of behavioral reactions. In response to crowding and social instability, animals may perform aggression, and lack of opportunities for species-specific behavior such as foraging can induce behavioral disorders such as stereotypies (Morgan and Tromborg, 2007). Behavior is the observable response of an animal to external and internal stimuli. Obviously the behavioral response will therefore be intimately linked to the physiological state of an individual and thus represents a direct window to its inner state. In assessing welfare, behavior therefore plays a central role, since it is the primary means by which an animal responds to environmental challenges. Behavioral expression is also under strong genetic influence, so selection can drastically alter the way animals respond to stimuli and hence their ability to cope (Jensen et al., 2008).

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Resource allocation theory

On a conceptual level, it is relatively easy to understand why one-sided selection for increased production may affect welfare. Growth and reproduction are energyand resource-demanding biological processes, and an animal is always under resource constraints. Other processes require their share of energy, for example the immune system and the motor apparatus. Input of energy is limited by intake and digestive capacity, which is in turn limited by food quality. Hence there is a consistent intra-individual competition for energy and other resources by the different biological processes, and thus selection acting on one of them will inevitably affect all the others. These thoughts have been formally conceptualized in the so-called resource allocation theory (Beilharz et al., 1993). In a simple, nonmathematical version, this theory explains that when an animal is genetically adapted to a particular ecological niche, its fitness is totally limited by the environment. If artificial selection then favors only one trait over others (e.g., rapid growth), the animal needs to modify resource allocation so that less priority is given to those life processes which are not under similarly strong selection pressure. This inevitably causes correlated selection responses, meaning that traits which are not specifically selected for are modified as well, and in a manner which can be difficult to predict. Broiler chickens are perhaps the most extreme example of intense selection during the last decades. Since about 1960, the average growth rate has increased from 25 g per day to 100 g per day (Knowles et al., 2008). As a consequence, a number of traits have developed as side effects of the directed selection. For example, broilers have relatively longer, wider, and heavier small intestines, and relatively smaller brains and leg bones compared to their ancestors, the Red Junglefowl (Jackson and Diamond, 1996). While these can be seen as adaptations to more efficient food conversion and therefore indirect consequences of the imposed selection, other correlated traits are less adaptive and seriously compromise welfare. For example, in a large survey of British broilers, over 27% had locomotor problems and more than 3% were almost unable to walk, problems specifically associated with the high growth rate (Knowles et al., 2008). Furthermore, there is a general negative association between fertility and growth in animals as a consequence of resource allocation (Rauw and Kanis, 1998). In broilers, this means that in the breeding animals (i.e., the parents of the meatproducing birds) one finds reduced fertility in males and decreased egg production in females (Dawkins and Layton, 2012). To enable production of meat-producing broilers, breeder parents are therefore usually kept on limited food intake, often about 25% 50% of their voluntary intake, to maintain a reasonable rate of reproduction. It goes without saying that this is a huge welfare problem, since these breeders are constantly hungry. Similar lines of reasoning can be applied to other species as well. In fastgrowing pigs, leg problems are common with locomotor disturbances as a consequence, and in dairy cows with high milk production there is a greatly increased risk for mastitis and leg disorders (Rauw and Kanis, 1998). Tail biting, a serious


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damaging behavior in fattening pigs, has been increased by selection for leanness (Breuer et al., 2005), and feather pecking in laying hens increases with a lower age of sexual maturity (Jensen et al., 2005). Results such as these suggest a complex genetic architecture underlying fundamental biological traits such as growth and reproduction. It may in fact be difficult, if not impossible, to select for one trait without affecting others through indirect and correlated mechanisms. Therefore we need an increased understanding of the genetics underlying basic biology, including behavior.


Fundamental genetics

2.4.1 The basic genetic laws The fundamental laws of genetics, as discovered by Mendel in the 19th century, are still largely valid in spite of revolutionary insights into the molecular basis of heredity in the last century (Hartl, 2011). Without knowing about the existence of chromosomes and genes, Mendel postulated that traits are affected by two heritable units (known today as alleles), which segregate during sex cell formation and unite at fertilization. Every trait is therefore affected by alleles inherited from both the mother and the father. However, Mendel thought that alleles generally segregate independently of each other, so the likelihood of acquiring a particular trait from the mother was not correlated with the likelihood of acquiring another trait. In that sense, it should be perfectly possible to select separately for, for example, increased growth, without affecting other traits. However, as we have seen, traits tend to be functionally correlated, and this suggests that genes and alleles are not independent units as Mendel tended to believe. Mendel’s great luck (or skillful research design, if we so wish) was that he studied seven different traits in the pea, an organism with seven pair of chromosomes (which he, of course, did not know), and he chose traits controlled by genes on separate chromosomes (which again, he did not know, since the existence of chromosomes was unknown to him). Therefore the traits he studied always segregated independently.

2.4.2 Beyond one gene—one trait Today we know that the units of heredity are sequences of DNA, which we refer to as genes, organized into chromosomes where the paternal and maternal ones segregate during meiosis, causing the effects observed by Mendel (Hartl, 2011). However, paternal and maternal chromosomes exchange parts during meiosis. So although there is some segregation within a chromosome, the closer together two genes are situated the less chance that they will segregate. This is referred to as linkage, and two linked genes will normally be inherited as a unit. Hence, correlated selection responses can be caused by the fact that genes linked to the selected ones are “dragged along” in the selection process, simply because they are located

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close to each other in the genome. On a population genetics level, this causes what is referred to as “selective sweeps,” where regions around a selected locus become fixated in the selected population (Andersson and Georges, 2004). Two other important facts unknown to Mendel need to be taken into account. The first is that any single gene often affects many different traits, which is referred to as pleiotropy. Therefore selection for a gene related to a particular phenotype will normally also affect all other traits controlled by that same gene. The second is that a particular trait is rarely affected by only one gene. The rule is rather that multiple genes interact in more or less complex ways, such as in the genetics of different color patterns in animals (Kaelin and Barsh, 2013). This is referred to as epistasis, meaning that the effect of a particular gene depends on the presence and action of one or several other genes. Correlated selection responses may therefore also be a result of epistatic effects. A gene affecting, for example, growth, may simultaneously interact with one or many other genes related to other traits. The combined effects of pleiotropy, epistasis, and linkage are often referred to as genetic architecture. For example, many domestication-related traits in chickens appear to be controlled by genes which are organized in many large, pleiotropic “blocks,” and selection for one trait in such a block will therefore simultaneously select for all other traits affected by the same block (Wright et al., 2010).

2.4.3 Quantitative genetics Another feature of Mendel’s original findings is that he worked with discrete characters of his flowers—large versus small plants, wrinkled or smooth peas, etc. Such traits can often depend on single genes, but most qualities of organisms are not discrete. As a direct consequence of epistasis and pleiotropy, the majority of traits are continuous. For example, the size and shape of animals in a population vary on a continuous scale. This is of course fundamentally true for behavior, where for example, levels of aggression, activity, and cognitive ability often show a normal distribution. Such traits are called quantitative, and call for other methods of analysis than Mendel’s original ones. A central concept used to deal with quantitative genetic variation is heritability (Visscher et al., 2008). This defines the proportion of phenotypic variation in a population that can be attributed to genetic variation (most calculations of heritability take mainly the additive effects into account). This measure therefore acknowledges the fact that the environment also plays a central role in shaping the phenotype. A heritability of 0 means that all phenotypic variation can be explained by variation in the environment, while 1 means that all variation depends on genetic differences between the individuals. (Of course, heritability of 0 does not mean that genes do not contribute to the trait. Quite the opposite, it simply means that there is no genetic variation for the trait in the population, so all genes contributing to that trait are fixed). For example, heritability of tail biting in Landrace pigs has been estimated in one population to be 0.27 (Breuer et al., 2005), meaning that 27% of the variation can be explained by genetic variation, and for feather pecking in chickens, a heritability of 0.2 has been estimated (Kjaer et al., 2001).


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Heritability estimates do not help us to understand which genes are involved, but they are of great aid in developing breeding programs. For example, in a chicken population with heritabilities of 0.2 for feather pecking, a selection program aiming at decreasing the abnormal behavior can expect that 20% of the difference between the parents on one hand, and the average of the population on the other, is transmitted to the offspring. On a conceptual level, heritability estimates are also important to disentangle effects of genes from those of environment.


Behavior genetics—finding genes for behavior

We now live in the genomic era, where many species have had their genomes sequenced and de novo sequencing is becoming cheaper and simpler every year. Therefore we are no longer content with estimating heritabilities and patterns of inheritance of discrete and complex behavior. Behavior genetics is now increasingly occupied with finding the actual genes and the mutations causing a particular variation. This has the potential of bringing us closer than ever to determining the detailed mechanisms regulating behavior, from proteins to neurons to muscles. While research in this area has just begun, we are already seeing some important results. Two of the first individual genes found to be associated with specific variations in behavior were npr-1 in C. elegans, which causes the animals to be either solitary or social foragers (De Bono and Bargmann, 1998), and the gene for, which is a cGMP-dependent protein kinase underlying a distinct phenotypic difference in foraging styles in Drosophila larvae (Osborne, 1997). In chickens, we found that the propensity to be victims of feather pecking is closely related to a genetic polymorphism in PMEL17 (Keeling et al., 2004). This gene encodes a protein essential for maturation of melanosomes, and the identified mutation inhibits expression of black pigment in the bird. The same mutation also has pleiotropic effects on exploration and aggression (Karlsson et al., 2010a,b) (Fig. 2.1). Another example is the identification of mutations in the promoters of the arginine vasopressin receptor (AVPR1a), which has important effects on pair bonding and social behavior in a range of species, including humans (Donaldson and Young, 2008; Walum and Westberg, 2008). In chickens, the gene is located on chromosome 1, and we have found that it is most likely involved in some of the domestication-induced modifications of social behavior in this species (Wiren et al., 2009, 2013). Another recent example concerns a mutation in the thyroid stimulating hormone receptor gene (TSHR), where all domestic chickens carry a nine-base pair insert in the transmembrane region which is not present in the wild ancestor, the Red Junglefowl (Rubin et al., 2010). This mutation has probably been selected during domestication for its effects on increasing the reproductive capacity of chickens, but also has clear pleiotropic effects on aggression and fear of humans (Karlsson et al., 2015, 2016).

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Fig. 2.1 (A) Feather pecking is a detrimental behavior, common in laying hens in intensive housing systems. (B) In a genetic mapping study, it was found to be closely associated with the gene PMEL17; the diagram shows the LOD score (the probability of association) for genetic association along the whole chromosome 28, and indicates the position of the gene. (C) This gene is truly pleiotropic, and a specific mutation causes the loss of black pigments in feathers of the carrier in addition to decreasing the risk of being the victim of feather pecking. Source: Data from Keeling et al. (2004).

Recently, we showed evidence for a number of genes being involved in affecting anxiety behavior in chickens, of which the GABA receptor GABRB2 and the serine/ threonine kinase STK17A were two major examples (Johnsson et al., 2016). Interestingly, the same genes are involved in similar behavior phenotypes in other species, ranging from flies to humans. This indicates the potential broad applications of findings from behavior genetics.


Methodological problems in behavior genetics

Apart from the obvious problems with mastering the technology and statistics involved in behavior genetics, a number of more generic problems need to be mentioned. These relate to the populations studied and measures taken.


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2.6.1 Measuring behavior The first problem for a behavior scientist is to obtain reliable and meaningful phenotypes. Whereas ethology has a long tradition of measuring behavior and a strong quantitative tool box, many of the methods are difficult to use in connection with behavior genetics. The reason is that behavior genetics usually needs large samples of animals for reliable statistics while ethologists are used to spending a long time observing each individual. Behavior geneticists depend on behavior assays, and of course there are many such around, but their biological relevance is not always well assessed. For example, the meaning of behavior responses in the widely used open field test—also known as the novel arena test—has been heavily debated, in particular when applied to different species with different ecological life styles (Forkman et al., 2007). Attempting to localize genes and genetic architectures relating to domestication and welfare of chickens, we approached the problem by first carrying out fundamental ethological studies of the wild ancestor and domesticated chickens in seminatural environments (Schu¨tz, 2001). From the differences observed under these conditions, we developed an assay which could be used with automatic recording equipment, and validated that the assay reliably measured the relevant differences between wild and domestic birds. With this assay, we were then able in a relatively limited time to record the behavior of over 700 birds in an intercross for genetic mapping, and reported a series of loci associated with domestication-related behavior modifications (Schu¨tz et al., 2004).

2.6.2 Standardizing recordings The next major problem has to do with limiting the sample size in order to have a reasonable number of animals to phenotype. In general, the larger the environmental influence is on a trait (i.e., the lower the heritability), the more animals are needed to find the proper genetic associations. A common approach is therefore to try to standardize the environment, since reducing environmental variation will increase the heritability of a trait. For example, in a recent study of the genetics of human-directed social behavior in dogs, we used a population of laboratory raised beagles where human contact and handling were highly standardized for all individuals. The behavior assay used showed heritabilities of between 0.23 and 0.32 for dogs’ propensity to make contact with humans (Persson et al., 2015), allowing an efficient further mapping of causative genes using about 200 dogs. When it is not possible to standardize conditions, considerably larger samples are needed. Working with privately owned dogs, we needed a sample of about 2500 Labrador and golden retrievers to obtain reliable heritability measures for various behavioral traits (Sundman et al. 2016).

2.6.3 Relatedness of subjects The last problem to be addressed is that of genealogy. Genetic analysis requires as detailed knowledge as possible of the relatedness between the individuals included.

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Behavioral data should preferably be available from several generations, but at the very least it is necessary to have full information on siblingship in the recorded population. In laboratory populations this is the normal standard, and pedigree records are available for most farm animals as well. But even when all information is available, complex family structures can complicate genetic analysis and may call for the use of advanced statistical methods (Bendesky and Bargmann, 2011). Once these generic problems have been addressed, it is possible to use all the available tools of modern genetics to search for genes underlying behavior. A description of these tools goes beyond the scope of the present chapter. Many fundamental genetic techniques such as sequencing, large-scale genotyping, and bioinformatics analysis often require specialized skills of scientists trained in the field, and thus are performed in core facilities. However, most laboratories have the primary equipment and skills for performing small-scale genotyping (assessing genotypes on a few markers in a moderately large population), gene expression of target genes by means of real-time quantitative polymerase chain reaction, and sequencing of small DNA fragments (up to perhaps 100 200 base pairs) by means of, for example, pyrosequencing. Basic competence in bioinformatics is also becoming a common requirement and software development makes this part increasingly accessible for the general biologist. As technology develops rapidly in the field the situation is likely to change in the coming years, and the technology available even in small laboratories will become increasingly advanced and complex. In the following parts of the chapter, I will structure and give examples of the strategies used to find genes related to behavior and welfare. These strategies can broadly be divided into two different approaches: top-down and bottom-up.


From welfare to genes: top-down approaches

The top-down approach starts by identifying the welfare or behavior variation to be studied. Using genetic mapping techniques, the genetic regions affecting the traits are then identified with as much precision as possible. When successful, this approach can provide lists of putatively involved genes and their interactions, but usually stops at the correlational level. For determining the causative nature of any gene, a bottom-up approach is typically necessary, and this will be dealt with in the next section.

2.7.1 Mapping populations In order to find a genetic region, we first need to identify a mapping population. As already mentioned, the size and nature of this population should be adapted to the known or expected heritabilities, but most of all it needs to be selected based on the variation in the trait to be investigated. There is really no use in trying to map the genetic structure of a trait in a population where all individuals share the same level of the trait. If, for example, all chickens in a population perform feather


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pecking to the same extent, there is no phenotypic variation, and thus the genetic variation will not be possible to map. There are several ways to identify proper mapping populations. For example, in laboratory rodents, strains often differ considerably in many behavioral- and welfare-related aspects, such as the propensity to perform abnormal social behavior (Kalueff et al., 2006). Dog breeds vary extensively in behavior and disease susceptibility, traits which are highly suitable for genetic mapping (Dodman et al., 2010). Briefly, such mapping aims at finding genetic variants, which correlate with the phenotypes observed. For example, do mouse strains that are more liable to perform whisker-barbering share some genetic variants not found to the same extent in strains less prone to this behavior? Another common method is to selectively breed strains for different traits over a number of generations, causing a close-to-fixation of alleles affecting the traits concerned (Bendesky and Bargmann, 2011). The strains thus created can serve the same role as the dog breeds or mouse strains mentioned earlier (Flint, 2002). Closely related species with divergent selection pressures may serve a similar purpose. In a recent study, different species of Darwin’s finches of the Galapagos islands were used as mapping populations to search for genes relating to morphological adaptations, such as beak form and size (Lamichhaney et al., 2015). In the same vein, domestication offers a powerful basis for study populations, particularly when the wild ancestors are still alive and available for comparison with modern domesticates (Zeder, 2015). It is also possible to mimic early domestication by selectively breeding for domestication-related traits to create divergent populations. For example, in a famous experiment by the Russian geneticist Belyayev, farm foxes were selected for reduced fear of humans over many generations to study correlated selection effects (Trut et al., 2009), and the divergently selected populations have then been used for mapping tameness-related genes (Kukekova et al., 2010). We have used similar methods to breed populations of Red Junglefowl with divergent levels of fear of humans, with the intention of creating suitable mapping populations for traits related to chicken domestication (Agnvall et al., 2012, 2015).

2.7.2 Precision phenotyping As should be clear by now, successful genetic mapping depends on careful and exact phenotyping. I discussed the particular problems with behavior phenotyping earlier, and when welfare-related traits are in focus, this is no less important. An example of suitable welfare-related traits could be, for example, leg disorders in chickens, pigs, and cattle (Dunn et al., 2007; Boettcher et al., 1998). Of course, many behaviors are closely related to welfare, but damaging activities such as tail biting in pigs and feather pecking in chickens are particularly interesting (Keeling et al., 2004; Brunberg et al., 2012; Kjaer, 2009). Once a mapping population has been identified and the proper phenotypes decided upon, a genetic mapping approach can be applied. As mentioned, the purpose is to find associations between specific genetic loci and the phenotypes

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measured. A common method, which has produced many interesting results up to now, is so-called quantitative trait locus (QTL) analysis. A quantitative locus is defined as a genomic region which has a significant association with a quantitative trait, and it is mostly expected that any causative gene and mutation will be situated within the QTL region (Broman and Sen, 2009). There are several methods for generating a suitable mapping population, and one of the most common is the F2intercross design. A few individuals from one of the two divergent populations are mated with individuals from the other to generate an F1 generation. These animals will be heterozygous for any gene that affects the quantitative traits to be examined. By intercrossing the F1 animals with each other, a segregating F2 generation is produced. Due to recombinations during meiosis in the F1 alleles from both of the divergent original populations are mixed together, and at the loci affecting the traits of interest, there are now animals that are homozygous for either of the parental alleles, or heterozygous. This will be the case for all loci, and if a sufficient number of animals are bred there will be samples of individuals with different genotypes at all loci we want to consider. Therefore the F2 generation represents our mapping population. These animals are then genotyped for markers (usually single nucleotide polymorphisms, SNPs) throughout the genome, and then carefully phenotyped for the welfare trait considered, followed by a statistical analysis to find the association between every marker genotype and the phenotypes. QTL analysis has produced many results of high relevance for animal welfare. In quail, extensive mapping identified several QTL for social and emotional behavior (Recoquillay et al., 2015), and in chickens, important QTL associated with bone strength and osteoporosis have been found (Dunn et al., 2007; Johnsson et al., 2014). QTL associated with fear and stress responses have been identified in chickens (Schu¨tz et al., 2004), trout (Drew et al., 2007), and pigs (Larzul et al., 2015). However, as mentioned previously, QTL analysis can only produce correlations, and furthermore, its precision is limited. Often a QTL consists of a region containing dozens or even hundreds of genes, and it is usually only guesswork to identify putative candidates among them. Nevertheless, QTL offer a first and important step toward identifying causal genes, and a properly conducted experiment can often produce highly interesting biological results even if individual genes are not identified. For example, by performing a QTL analysis on an F2 intercross between ancestral Red Junglefowl and domesticated White Leghorn chickens, it was found that genes affecting domestication-related traits formed a number of linkage blocks, shedding light on the genetic architecture of domestication (Wright et al., 2010).

2.7.3 Combining expression data with phenotypes A powerful method to move from correlative associations to putative candidate genes has recently been developed, consisting of combining QTL analysis with expression QTL (eQTL). An eQTL is a locus which is statistically associated with the expression of a specific gene. In this analysis, expression levels of genes in particular tissues (e.g., in the brain) are used as the phenotype in the QTL analysis. Gene expression can be assessed for specific genes of interest, or a microarray


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approach can be used to determine expression levels of thousands of genes at the same time. Where an eQTL for a particular gene has a confidence interval overlapping that of a phenotypic QTL, this is a strong indication for that gene being a candidate gene causing the phenotype. For example, in an analysis of anxiety-related behavior in chickens, a QTL was found for the time spent in the center of an open field arena (Johnsson et al., 2016). In the same population, expression of thousands of genes in hypothalamus was assessed with microarray technology, and the expression level of each gene was used as phenotype in an eQTL analysis. The expression of STK17A overlapped that of the time spent in the center of the arena, indicating that this gene could be causative. As a last step, the expression level of the gene was correlated with the time spent in the center, and a significant positive correlation was found. These three steps are sufficient to conclude that STK17A probably is a major candidate for being a causative gene affecting open field behavior in chickens.

2.7.4 Genome Wide Association Closely related to QTL analysis is the widely used Genome Wide Association Study (GWAS). Here, we do not rely on any specific mapping population, all that is needed is a sufficiently large population in which the trait to be mapped shows appreciable variation and heritability. It is often used for discrete phenotypes (e.g., “sick” vs “healthy”), but can also be applied to continuous traits. Just as in a QTL analysis, phenotyping is essential, and the individuals are genotyped on markers throughout the genome. With adequate statistical methods, it is possible to find markers associated with the phenotype. For example, we studied a population of laboratory beagles and phenotyped their propensity to interact with humans. We then genotyped almost 200 dogs on 700 SNP-markers and found a significant association between behavior and markers situated close to five different genes which therefore constitute putative candidates for human-directed social behavior in dogs (Fig. 2.2). Gene expression is partly affected by genotype, as mentioned earlier, but also reacts dynamically to events in the environment. For example, when exposed to acute stress, a cascade of modified gene expression in the hypothalamus causes synthesis of the major enzymes and proteins involved in the hypothalamic pituitary adrenal axis (Sapolsky et al., 2000). We have examined the effects of stress in different life phases in chickens, and found that hypothalamic gene expression is chronically modified even by short stress experiences early in life (Elfwing et al., 2015; Ericsson et al., 2016). Gene expression profiles can therefore serve as a blueprint of previous stress experiences, a topic I will return to later in this chapter.


From genes to welfare: bottom-up approaches

Top-down methods usually produce only correlative results and possible candidate genes and mutations. The conclusive evidence that a particular gene or mutation

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Fig. 2.2 An unsolvable problem, shown on the picture, causes most dogs to turn to a nearby human and seemingly “ask for help.” The propensity of interacting with humans in this situation was genetically mapped in a population of beagles with a Genome Wide Association Study. A strong effect of a marker situated in the gene SEZ6L was found, as shown in the Manhattan plot at the bottom. For each genetic marker, the plot shows the strength of its association with the phenotype. Hence, this gene is therefore considered a strong candidate causing the variation in human interactions. Source: Data from Persson et al. (2016).

affects a particular trait requires bottom-up approaches. In these, the genotype at a suspected locus is manipulated with the purpose of testing specific hypotheses about its role in shaping the phenotype. Of course, this approach requires a specific idea or hypothesis to start with. Hence, some prior information is necessary. This can be acquired from an association study, as described in the previous section, or be based on independent biological insights concerning the role of particular genes. There are a number of possible methods to manipulate the genotype, some more and some less technically sophisticated.


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2.8.1 Identifying candidate genes The simplest way of implementing bottom-up methods is probably to use the normal variation occurring in a population, and then compare the phenotypes of animals with alternative genotypes on the gene of interest. For example, in a study of the genetic basis for separation anxiety in dogs, four genes were selected based on their previously demonstrated roles in attachment in rodents (van Rooy et al., 2016). In a sample of golden retrievers, the genotypes at these loci were compared between dogs showing anxiety and control dogs, and a variant in the dopamine receptor D2, drd2, was found to be strongly associated with the behavior. Similarly, a Hungarian research group identified the tyrosine hydroxylase gene (TH) as a possible candidate for inactivity and inattention in dogs based on its known involvement in mood disorders in humans (Kubinyi et al., 2012). German shepherds were genotyped for a polymorphism in the intron of the gene, and the genotype was found to affect the behavior of the dogs in an attention-impulsivity assay. Although using natural variation does offer interesting possibilities, proper hypothesis testing requires experimental manipulation and control. The most straightforward method may be to use classical breeding methods in order to produce animals with specific genotypes. We have developed a method called “locus controlled advanced intercross.” The starting point of this is the F2 intercross described previously, where two populations differing in the trait of interest are crossed to produce an F2 generation with recombined chromosomes. If the F2animals are intercrossed again, to produce an F3, this is referred to as an advanced intercross. For each new generation, more recombination occurs, and the chromosomes consist of smaller and smaller haplotype blocks. After a few generations, say F8, haplotype blocks contain relatively few genes. We can then select individuals with alternative genotypes on the locus we want to investigate, and breed them to produce offspring which have either of the original parental alleles on the locus while the rest of the chromosomes consist of random combinations of parental alleles. The effect of genotype on the selected locus can then be assessed against a background of random effects. Using this method, we have shown that the vasopressin receptor gene (AVPR1a) is probably involved in modifying social behavior during chicken domestication (Wiren et al., 2013) and a domestication selected mutation in the thyroid stimulating receptor gene (TSHR) affects reproduction and fear of humans (Karlsson et al., 2016). Another variant of the method uses an iterated back-cross procedure to breed birds with a particular gene variant against a uniform background, but the effects are essentially the same.

2.8.2 Experimental manipulations of genomes More technically advanced methods are used in medical research. This involves using molecular methods to either modify or inactivate a particular gene and investigating its function in development and disease. These methods are referred to as knockout (complete silencing of a gene), knockdown (partial silencing), and

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knockin (insertion of a gene in a particular position) (Kazdoba et al., 2016). This has become a basic method in medical research using laboratory animals, and genetically modified mice and rats are ubiquitous today. As we have seen previously in this chapter, most genes are pleiotropic and epistatic, so modifying a gene for studying a particular trait may simultaneously affect numerous other traits. This may have consequences for animal welfare, and extensive research has been devoted to develop methods and protocols to evaluate the welfare aspects of genetic manipulations (Jegstrup et al., 2003). The same methods can be used to improve production in farm animals, for example by manipulating genes involved in reproduction or growth. This raises similar concerns over possible side effects and there is a need for methods to continuously evaluate the welfare of genetically modified farm animals (van Reenen, 2009). As for the scientific use of genetic manipulation to increase the knowledge of the genetic underpinning of behavior, completely new methods have recently been launched and will probably take over a great deal of the techniques in research laboratories over the world. In particular, this is the case for the novel CRISPR-Cas9 technique (Konermann et al., 2015). It can be used to target any DNA sequence and specifically modify it as required, and the method is technically simple and cheap enough to be implemented even in relatively small laboratories. The method may also soon become a useful tool to practically improve animal welfare. For example, if a specific mutation is associated with some detrimental behavior or a disease, the technique could enable breeders to modify this mutation in the parental stock animals, and thereby breed only individuals without the harmful mutation without affecting the rest of the genome.


Beyond genetics and genomics: epigenetics

Until relatively recently, the genome was believed to be set and fixed from fertilization, and the idea that the environment could contribute information to the DNA, which could even be heritable, was dismissed as Lamarckian biology (Jablonka and Lamb, 1998). However, it has become clear that the DNA is considerably more flexible and responsive than previously thought, and the regulation of its function is referred to as “epigenetics.” It concerns different chemical modifications of the DNA and its close surroundings, which change the timing and extent of gene expression. In particular, two such modifications have received a lot of scientific interest: the methylation of cytosine bases (“DNA methylation”) and chemical modifications of histones, affecting the “packaging” of DNA in the cell nucleus. Both modify gene expression, and DNA methylation is usually associated with downregulation of the genes. DNA methylation is responsive to environmental challenges, for example, exposure to toxicants and stress, and can affect the risk of various diseases (Skinner et al., 2010; Franklin and Mansuy, 2010). For example, maternal style in rats changes the methylation of genes involved in the stress response, and offspring


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receiving more intense care are more stress resistant as adults (Kappeler and Meaney, 2010). Although much is still to be learnt about the mechanisms, evidence is amounting that epigenetics may be the key to understanding the dynamics of the interaction between genes and environment (Eichler et al., 2010). Most intriguing is perhaps the fact that some of the acquired epigenetic modifications can be transferred across generations, leading to what has been called “soft inheritance” (Richards, 2006). For example, exposure to environmental toxicants (endocrine disruptors) can affect male reproductive biology for generations after the exposure through sperm-transmitted epigenetic modifications (Guerrero-Bosagna and Skinner, 2014). The same is true for stress exposure. We studied chickens experimentally exposed to stress either shortly after hatch or chronically during maturation. This affected several aspects of their behavior as well as the gene expression profile in the hypothalamus, both of which were transferred to the offspring in the next generation (N¨att et al., 2009; Goerlich et al., 2012) (Fig. 2.3). Epigenetics truly opens completely new perspectives in behavior genetics and may even challenge fundamental paradigms in evolutionary biology. For animal welfare, it is an intriguing insight that stress vulnerability is affected by epigenetic mechanisms which may sometimes reflect the experiences of previous generations (Zannas and West, 2014). We are still in the early phases of research in epigenetics, and the field is likely to develop rapidly.

Fig. 2.3 Parent chickens were exposed to a brief period of stress during their first weeks of life. The diagram shows the FC difference in hypothalamic gene expression in the stressed fathers compared to unstressed fathers plotted against the expression differences of the same genes, comparing the unstressed offspring of the stressed fathers with the offspring of unstressed fathers. SUCLG2 and UMPS are two genes with particularly consistent transgenerational gene expression changes. FC 5 fold change. Source: Data from Goerlich et al. (2012).

Genetics and genomics of animal welfare



Future perspectives

Behavior genetics is not a novel science. In fact, the insight that behavior is under genetic control much in the same way as any other phenotype could possibly be the largest achievement of 20th-century ethology (Jensen, 2006). However, only recently have we begun to explore and understand the detailed mechanisms, and with that comes novel possibilities and challenges (Jensen et al., 2008). The new techniques of contemporary genomics offer new tools for assessing and improving welfare, but can also potentially pose large welfare risks if used without thorough consideration. The entire history of animal domestication relies on gradual selection of animals with preferred traits, so genetic modification of animals is by no means novel to our coexistence. However, the new technologies offer possibilities for highprecision breeding and rapid changes. Selection based on genotype (genomic selection), rather than phenotype as in standard breeding, has been in use for a while, and will probably increase in importance. It has an appeal mainly for increasing production by selecting alleles affecting, for example, milk production in dairy cows (Hayes et al., 2009). The risks associated with this are that selection for single genes can cause unpredicted and unwanted pleiotropic effects, and that there is less of balancing selection in other genes which could counteract the directed selection. On the other hand, the same method can be used to select genes that improve animal welfare. One of the oldest examples is the identification of the gene and the mutation causing malignant hyperthermia in pigs, a trait with detrimental effects for both welfare and meat quality (Vo¨geli et al., 1994). With relatively simple genomic selection, this mutation can be more or less eliminated. Hence, as always, the technology can be used for different purposes. Another field that may become important for animal welfare in the future is the possibility of “personalized treatment.” It has long been known that human patients respond differently to treatments depending on genetic differences, and genotyping individuals can help to increase the efficiency of many medical interventions (Bonter et al., 2011). This would be possible to translate to animals, and the possibility of genotyping individual animals to determine susceptibility to stress and vulnerability to disease could potentially mean a major leap forward for animal welfare. As I mentioned earlier, epigenetic modifications are important aspects of animal welfare, since they develop as a response to environmental stress. In principle, therefore, it should be possible to assess the welfare history of an individual by examining, for example, its DNA methylation profile. A major problem here is the tissue specificity of the epigenetic effects, and stress predominantly acts on genes in the brain, which is usually not available for assessment. However, recent research has shown that many brain-related epigenetic modifications are mirrored in blood cells (Provencal et al., 2012, 2013). An exciting future possibility is to determine an animal’s welfare history by means of a blood sample and methylation profiling of selected blood cells, which would provide unprecedented possibilities for retrospective welfare assessment (Fig. 2.4).


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Fig 2.4 Methylation profiles from red blood cells were obtained from egg laying chickens reared either in aviary floor systems or in cages, and then subjected to cluster analysis. Each column represents one individual bird, and each row represents one gene. Darker blue shows low methylation levels and darker red high levels. The clustering of methylation patterns of these genes has a clear profile representing the rearing conditions of the birds, showing that epigenetic blood profiles could possibly be used to assess previous experiences. Source: Data from Pertille et al. (2016).

Finally, we should mention the novel insights in transgenerational effects of stress as described earlier. This opens a novel perspective on animal breeding, where we may need to account for the rearing environments not just of the production animals themselves but also of their parents. For example, at this point it remains unknown how broilers are affected by the fact that their parents are kept chronically hungry for extended period in their lives.


Conclusions and implications

Animal welfare science has developed rapidly and increased in precision and methods over the last few decades. We can assess motivation with high precision, determine environmental preferences of animals with sophisticated quantitative methods, and even determine moods and emotions (Briefer et al., 2015). Physiological responses have for a long time been standard variables and are considered selfevident and integrated parts of any welfare assessment. However, when it comes to genetics and genomics, the research is remarkably scarce. Given its importance, as outlined in this chapter, one may wonder why.

Genetics and genomics of animal welfare


One possibility is that the field is considered difficult and the technologies are difficult to access for the average biologist. However, as should be obvious from the present chapter, behavior and welfare genetics should be considered an important and central aspect of animal welfare science, and both challenges and possibilities offered by the field are immense. There is a great need for increased efforts in the area. It is a field that lends itself to cross-disciplinary interactions. A trained ethologist can offer phenotyping skills not possessed by the average geneticist, who on the other hand can contribute all the technology needed for the necessary genetic analyses. Perhaps as more and more novel insights are published and the importance of the field becomes obvious, more welfare scientists may become interested in pursuing this research field. The fact that genes with different expressions in the brains of feather-pecking chickens compared to their victims are related to intestinal inflammation (Brunberg et al., 2011) should excite anyone interested in the mechanisms underlying this behavior disorder!

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