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Montmayeur JP, le Coutre J, editors. Fat Detection: Taste, Texture, and Post Ingestive Effects. Boca Raton (FL): CRC Press/Taylor & Francis; 2010.

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Fat Detection: Taste, Texture, and Post Ingestive Effects.

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Chapter 16Heritable Variation in Fat Preference



For humans, eating is often a group rather than a solitary activity, and it is inevitable when eating with others that individual differences in food preferences become obvious. These food preferences form early in life (Mennella et al., 2001) and persist into adulthood (Nicklaus et al., 2004). People like to eat familiar foods that are safe and avoid foods associated, even indirectly, with illness. However, pleasant experiences and time also help to form food preferences. For instance, the ability to tolerate and even like bitterness increases as children grow to adulthood, and the liking for sweet and sour decreases (Desor and Beauchamp, 1987; Liem and Mennella, 2003). Over a lifetime, new foods are tried, rejected, or incorporated into the diet. Against this backdrop of development and environment, there are inborn differences in food likes and dislikes which may be due to genetic constitution. There is a genetic basis to bitter detection in humans (Bufe et al., 2005) and given that fat intake is moderately to highly heritable, it is likely that genotype contributes to food selection and, by extension, to fat preference.

The focus here is on how individual differences in fat preference are formed and, in particular, the evidence that the liking for fat is influenced by genotype. The interest in dietary fat arises because its intake is tied to metabolic syndrome, a constellation of disorders that feature obesity, diabetes, and hypertension. An adage is that everything that tastes good is bad for you, and the liking for fat fits well into this viewpoint: fat is desirable and when people are given the opportunity to do so, many will adopt a high-fat diet. Two aspects of fat make it attractive, its sensory qualities (Reed et al., 1992b) and postingestive consequences (i.e., feelings of satiety). Fat is sensed in the mouth and although the texture is a key feature of its sensory properties, fat itself may be a legitimate taste stimulus. The evidence for this assertion is recent and reviewed below, but it is useful to know that fat has been considered a taste by some through the ages. For instance, Fernel wrote “There are nine classes of flavors, and the sense of taste recognizes no others: acrid, tart, fatty; salty, sour, and sweet; bitter, pungent, and insipid” (Fernelius, 1581). While controversies arise when applying the term “taste” to fat, and the issue is dealt with elsewhere in this book, “umami” as a taste was equally controversial but it was readily adopted as a fifth basic taste once the receptor(s) was identified (Chaudhari et al., 2000; Nelson et al., 2002). Likewise, when the oral receptors for fat are unequivocally identified, its place as a basic taste will probably become equally well accepted. What is known about fat as a taste is outlined below.

Taste is both the gatekeeper and advance messenger of ingestion, keeping out bad food and warning the gastrointestinal system about the impending rush of nutrients. One of the effects of fat stimuli in the mouth is to prepare the body for calories, setting off a cephalic phase response. This cascade of events may be a general response to incoming dietary fat in mammals because it is found in rats (Ramirez, 1985) as well as people (Mattes, 2001; Crystal and Teff, 2006). Under normal circumstances, once fat is ingested, it is briefly held in the stomach and then absorbed in the intestines. From here it is either oxidized for energy or stored, primarily in adipose tissue. In some abnormal states, such as untreated diabetes, fat is more easily oxidized than carbohydrate and is thus preferred, at least in experiments using animal models (Tordoff et al., 1987). In addition to the other benefits of fat, it contains pharmacologically active substances, for instance, olive oil has an anti-inflammatory agent (Beauchamp et al., 2005). These compounds may also contribute to the human liking for fat.


The details of how fat is sensed in the mouth are not well understood but it is worth reviewing what is currently known to help put the potential for genetic influences into perspective. The chemical properties of fat (as opposed to its texture) are probably sensed after it is hydrolyzed to free fatty acids rather than sensed as triglycerides. This conclusion is drawn from a study in rats which demonstrated that when lingual lipase (the oral enzyme responsible for breaking down triglyceride to free fatty acids) is reduced, triglycerides become less preferred while the preference for free fatty acids remain unchanged (Kawai and Fushiki, 2003). There is a belief that humans do not have as much lingual lipase as rats (Pritchard et al., 1967) and therefore probably do not rely on fat “taste” to the same extent, but this widely held belief is due for reconsideration, perhaps using other methods to measure lingual lipase, such as proteomics. From rat studies, we know that lingual lipase acts quickly on the tongue to liberate free fatty acids but if oral fat perception requires an enzymatic step, it may explain why fat-containing foods are often savored. Perhaps, keeping the fat-containing food in the mouth for a few extra seconds enhances its enjoyment. Lingual lipase is also most highly concentrated around the circumvallate papillae (in the rear of the mouth) and so the path to fat perception may start there (Hamosh and Scow, 1973). Although the majority of studies on oral free fatty acid detection are done using rats and mice as experimental subjects, humans can detect them in solution, and people differ markedly in their threshold and perceptions of intensity for free fatty acids (Chale-Rush et al., 2007a).

How are free fatty acids sensed in the mouth so that information about their presence can be relayed to the brain? The current hypothesis is that they are sensed in the same way that some other tastes are sensed in the mouth, i.e., they are detected by G-protein-coupled receptors (GPCRs) in taste receptor cells on the tongue, which in turn signal through a common second-messenger cascade involving G-proteins, phospholipase C beta2, and the transient receptor potential M5 (TRPM5) ion channel. This signal is then relayed to the brain by gustatory nerves and decoded.

Many lines of evidence support this model of fat perception. Investigators have recorded from or cut the gustatory nerves (as opposed to trigeminal sensory nerves) and demonstrated that information about the presence of fat in the mouth is conveyed to the brain (Stratford et al., 2006; Gaillard et al., 2007; Stratford et al., 2008). Furthermore, free fatty acids elicit an increase in intracellular calcium in about 30% of rat taste receptor cells (Liu et al., 2008a) and this event is associated with cell signaling through the release of neurotransmitters. In addition, several GPCRs have been suggested to be the “fat taste receptor”: one such is GPR120, which is expressed in taste tissue but not nonsensory epithelium (Cartoni et al., 2007; Tsuzuki, 2007; Matsumura et al., 2008). When introduced into a cell-based assay, GPR120 causes the cell to respond to fatty acids by increasing intracellular calcium (Tsuzuki, 2007; Eguchi, 2008), similar to the response seen by other GPCRs specific to classic taste stimuli (Adler et al., 2000; Chandrashekar et al., 2000; Nelson et al., 2001, 2002). GPR40 has also been implicated in fat taste but this finding is controversial; some investigators find that receptor is expressed in taste receptor cells (Cartoni et al., 2007) while others do not (Matsumura et al., 2008). However, mice genetically engineered to be null for this receptor (GPR40) have reduced preferences for fats like corn oil, which supports the role of this receptor in oral fat perception (although this effect could also come from other tissues since GPR40 is also expressed in pancreatic β cells) (Itoh et al., 2003).

Another membrane-bound protein that is related to fat taste is CD36. Several lines of evidence support its role in taste: (a) it is detected at the apical surface of taste receptor cells (where chemical stimuli in the mouth and receptors would interact), (b) it colocalizes in taste papillae with known signaling molecules (Laugerette et al., 2005), (c) the normal increase in intracellular calcium of taste receptor cells in response to free fatty acids is blocked in CD36 knockout mice, and (d) the activation of brain areas associated with fatty acid stimulation is eliminated in these knockout mice (Gaillard et al., 2007). Furthermore, (e) CD36 knockout mice are indifferent to fat solutions that mice with intact alleles prefer (Fushiki and Kawai, 2005; Laugerette et al., 2005; Sclafani et al., 2007a). However, CD36 and GPR120 are not often found in the same taste receptor cells (Matsumura et al., 2008), so whether they reflect two mechanisms of fat sensing or work together through cell-to-cell signaling is not known. There are a few more available pieces of information about the signaling cascade involved in fatty acid perception. Gustducin (a G-protein found in taste receptor cells) does not seem to be involved in fat taste because knockout of this gene does not affect fat preference in mice (Sclafani et al., 2007a,b). However, the transient receptor potential cation channel found in taste cells (TRPM5) is important in fat perception because knockout of this gene abolishes fat intake (Sclafani et al., 2007a,b). Taken together, free fatty acids are detected in taste receptor cells, and this information is conveyed through gustatory nerves where the information is interpreted by the brain. Although several genes and their associated proteins involved in the transduction pathway have been identified, the details are not well understood. However, the genes that are known to be involved are polymorphic in humans (see “Electronic Resources”), and therefore, alleles of these receptors and other signaling molecules would be a natural target of genetic investigation.


A brief review of the advances in knowledge through animal research will be helpful in interpreting the human studies. Research on fat preferences has focused on mice and rats, mostly because they are commensal with humans and have similar food preferences. In fact, one way to make mice and rats fat is to feed them human “junk-food” (Sclafani and Springer, 1976), which is typically high in fat. However, research in the area of taste and food preferences is developing using flies and worms as model systems and these approaches will also add to our knowledge of the molecular aspects of fat preference (Gordesky-Gold et al., 2008). One of the important messages from animal research is the power of both genetics and experience to change fat preference. Rats given a sample of pure fat to eat for several days later select a diet very high in fat when offered a choice between fat, protein, and carbohydrate (Reed et al., 1992a,b). Rats offered only a high-fat diet become avid fat consumers and will drink large amounts of pure oil (Reed et al., 1991). Although these experiments demonstrate the power of experience on fat preference, the results of genetic studies have also reinforced the point that fat preference is inborn. Inbred strains of rats and mice treated similarly differ markedly in their willingness to ingest fat and they also differ in how much weight they gain when they do (Schemmel et al., 1970; West et al., 1992; Lewis et al., 2007; Svenson et al., 2007). Specific strains have been identified and studied because of their disparate fat preference or response to a high-fat diet (West et al., 1995; Levin et al., 1997; Smith Richards et al., 2002; Almind and Kahn, 2004; Collins et al., 2004; Ehrich et al., 2005; Kumar et al., 2007; Svenson et al., 2007; Tordoff et al., 2008).

With the advent of new methods in molecular genetics, it is possible to identify the specific genes that contribute to individual differences among mice (and humans, see below) in fat preference. One such study was conducted by crossing inbred strains of mice that had either a high- or low-fat preference, and evaluating the offspring for macronutrient preference. By comparing the parents, grandparents, and offspring from three generations, the genetic effects on fat preference could be established (h2 = 0.19) (Smith Richards et al., 2002), an estimate strikingly similar to the heritability of fat preference in humans (see below). The investigators conducted a genome-wide scan and found three chromosomal regions where fat-preferring mice shared DNA in common more often than could be expected by chance. These loci were on chromosomes 8 and 18, and also on the X-chromosome. The genes that contribute to the fat preference within these regions are not known, but this landmark study demonstrates that specific genetic regions are associated with fat preference and that it may be possible soon to identify the specific genes involved. This work is likely to advance further, because one of the benefits to studying model systems for fat preference is the tighter control over experience and learning and the wealth of molecular resources, e.g., genetic engineering, microarray, proteomics, and breeding techniques available.


Animal models are useful because they may point the way to understand human behavior toward high-fat foods. The study of fat preference in animal models is simpler than for humans because it is assumed that when animals are offered a choice, the item selected is the one preferred. However, this is not the case in humans, who are motivated by complex thoughts and constraints such as health beliefs, price, advertising, and social embarrassment. To make the problem even harder, humans are faced with multiple choices and rarely encounter the simple two-food choice offered to rats or mice. Therefore, to understand fat preference in humans, it is important to explicitly consider how it is measured and the limitations of these methods. This issue of measurement is also of particular concern for genetic studies because accurate estimates of heritability and genotype–phenotype associations depend upon the ability to assess a large number of people using methods that detect stable individual differences, and thus, even the most rudimentary measures in a controlled laboratory study are often out of reach.


Because of the impracticality of testing thousands of people in the lab, most genetic studies have relied on self-reported fat intake as a proxy measure of fat preference, with the expectation that in the broadest terms, people eat more of the food they prefer. A common way to collect data is through food diaries, in which the subject tracks food intake. Advantages of this method are that it is readily available, the subject is in their habitual environment and eating as usual—or nearly so, because there will be effects of recordkeeping on food intake. The limitation is that even for subjects who are motivated and honest in their reports, errors are introduced because of estimates of food composition or portion size, and there is also the risk that subjects may try to mislead the investigator by failing to record foods that are undesirable. The underreporting of food intake by the diary method is a well-known and often studied phenomenon, and to make matters worse in the realm of fat preference, these types of foods are sometimes selectively underreported (Goris et al., 2000). To circumvent this bias, creative investigators have tried to reduce recording errors, by asking subjects to photograph their meals immediately before they are eaten (de Castro, 2000). Another pencil and paper method to assess food intake is through food frequency questionnaires, which require that subjects recall their food intake and give information about how many times they have consumed a certain food during the past. The benefit of this method is that the time spent in collecting data is reduced and in contrast to food diaries, it does not rely on a subject’s sustained attention and continuous participation. The disadvantage is that this method is a less sensitive way to measure food intake.

Another way to measure human food intake is to have subjects live in structured environments in which their food choices and intake can be monitored more precisely—these types of studies can be conducted in a cafeteria where subjects come for meal time (Levitsky and Youn, 2004), a restaurant (Wansink et al., 2007), or in environments where subjects come to live (for short periods of time) so that their food intake can be closely monitored (Larson et al., 1995). Another method to measure fat intake and preference is to invite subjects into the laboratory and give them an opportunity to select food for a single meal or to ask subjects to taste and rate foods that differ in fat content (Mela and Sacchetti, 1991). The advantage of these types of laboratory studies is that the amount and type of food consumed can be more accurately monitored, but the limitation is that the subjects’ eating behavior is unlikely to be the same as it would be were the subjects living in their normal environment. The benefit of these short-term laboratory-based methods is that they can be tailored to ask specific research questions, but there are limitations because short-term tests are less likely to generalize to food preferences and intake outside of the laboratory (Pangborn and Giovanni, 1984). Furthermore, subjects are not adept at discerning the fat content of food in short-term tests (Mela and Christensen, 1987).


With these limitations in mind, the liking for a diet high or low in fat may be a stable trait (phenotype) of human subjects (Geiselman et al., 1998; Blundell and Cooling, 1999) and identifying a stable trait is the first step in establishing heritability. However, it is surprisingly rare for investigators to include reliability data in food preference studies with a genetic focus. Therefore, the genetic studies reviewed below need to be evaluated in this context: although fat preference may be a stable individual trait, methods to assess it are imperfect and thus, true heritability may be higher than reported due to measurement errors in the methods used to assess fat preference. It is also important to remember that exposure, experience, learning, and culture shape fat preference. Studies in rats suggest that maternal exposure to a high-fat diet results in offspring or even grandchildren that have an increased fat preference (Wu and Suzuki, 2006; Bayol et al., 2007). Other studies have suggested that maternal diet can affect the methylation of gene promoters which can in turn change gene regulation in the offspring (Waterland and Jirtle, 2003; Burdge et al., 2007). Given these observations, it is a short step to speculate that women who eat a high-fat diet during pregnancy and lactation might pass this trait on to their offspring through epigenetic mechanisms. In addition to exposure during development, fat preference may be due in part to cultural learning and the most obvious example is the changes that occur when people immigrate. However, something as simple as an enforced change of routine can change preference too, for instance, subjects asked to eat a low-fat diet report less liking for some high-fat foods (Mattes, 1993; Ledikwe et al., 2007), indicating that fat preference is at least partly affected by recent dietary experience.

Since learning and experience affect fat preference, it is hard to parse the inborn effects of genotype from these closely allied influences. There is cross cultural research using populations that differ in both genotype and culture and there have been some attempts to measure fat preference in different groups. One such study was focused on the fat preference of Pima Indians and Caucasians. Pima Indians are a native population in the United States with a high prevalence of obesity. They traditionally lived in a frugal desert climate and were of normal weight but as circumstances changed they have adopted a diet higher in calories and fat, and have become one of the most obese populations in the world. Although obesity is often associated with elevated fat preferences (Drewnowski et al., 1985), on average, the Pima Indians have lower preferences than do Caucasians (Salbe et al., 2004). Although genetics and environment probably both contribute to this group difference, the design of the experiment does not allow us to estimate the contribution of each factor.


To try to untangle the effects of environment, learning, and culture, the study of twins provides a useful methodology. Monozygotic (MZ) twins are genetically identical (although recent studies challenge this notion (Fraga et al., 2005); see below) whereas dizygotic (DZ) twins are no more alike genetically than siblings. Thus, the behavior of MZ and DZ twins can be compared and heritability assessed. An alternative to twin studies are family studies, which follow a similar principle. The degree of genetic sharing between family members is compared with the degree of phenotypic similarity and the contribution of genotype to the trait is evaluated. The scientific literature about twins and fat preference was reviewed several years ago and the heritability estimates ranged widely from study to study (Reed et al., 1997). This conclusion has not changed when data from more recent studies are considered (Stafleu et al., 1994; Feunekes et al., 1997; Yeo et al., 1997; Vachon et al., 1998).


As mentioned above, although twins are thought to be genetically identical, several lines of evidence suggest that this is not strictly accurate: the mutation rate for DNA replication, while low, is not zero, and so for a given cell lineage (including the germ cells), twins can differ in genotype based on spontaneous mutation. In addition, the results of recent studies suggest two other sources of differences in the genomes of twins. First, one of the surprises about the human genome is the extent to which small patches of the genome are duplicated, giving rise to multiple copies of genes (Sharp et al., 2005; Wong et al., 2007). While this type of duplication was known and appreciated as a part of a sequence of events (duplication and diversification), giving birth to new genes, the degree of duplication, especially among sensory genes, was unexpected (Nozawa et al., 2007). Even more surprising is that studies of MZ twins have revealed instances where genetically “identical” twins differ in gene copy number (Bruder et al., 2008). Second, the other source of differences in the genome of MZ twins is the degree of epigenetic modification, as measured by the methylation of particular parts of the genome. This methylation is thought to affect gene expression, and so twins that differ in methylation status at a particular location will presumably differ in the rate of transcription of a given gene (Fraga et al., 2005).

These observations about the genome of human twins have implications for fat preference. First, MZ twins that differ in body weight have been described and a key difference between these twins who are discordant for body weight is their preference for dietary fat (Rissanen et al., 2002), which raises the possibility that mutation, copy number variation, or epigenetic events might have a detectable effect on fat preference. The second implication is more general, which is that heritability estimates depend upon the assumption that MZ twins are genetically identical—if they are not, current estimates of heritability for the fat preference phenotype (as well as other traits) are underestimates.


Family and twin studies suggest fat intake and preference are heritable traits but individual studies differ in the strength of this effect. Table 16.1 contains a summary of the relevant statistics from studies that measured the percent of calories ingested as fat, which we will refer to as fat preference for simplicity. Some studies compute the percentage of the phenotype that can be accounted for by the additive effect of genes (heritability, h2) and some studies report the correlation among relative pairs, e.g., similarity among siblings. No attempt was made in these family correlation studies to estimate the additive effect of genes relative to household or unshared environmental effects. When these data are considered as a whole, the most obvious point is that the values range from no heritability at all to strikingly high values for a behavioral trait (>h2 = 0.48). Likewise, family similarity ranged from none (r = 0.0) to strong (r > 0.6). Other points also emerge from these studies. There was one study of twins reared apart, an experimental genetic design often considered the most informative (since twins are not reared in the same environment and the degree of similarity is thought to be genetic in origin). For this study, the heritability (h2) of fat intake was 0.35. This study makes the useful point that living in the same household is not a necessary prerequisite for family members to resemble each other in fat intake (Hur et al., 1998).

TABLE 16.1

TABLE 16.1

Family Correlations or Heritability Estimates of Percent of Calories as Fat

Examining the pattern of family correlations for fat intake revealed several other results. First, the method of data collection affected the strength of the family correlations. Values from food diary methods were generally higher (mean = 0.33, range = −0.23 to 0.69) than from food frequency questionnaires (mean = 0.16, range = −0.02 to 0.42). Age-related effects were also apparent because family correlations among people of the same generation, e.g., siblings, were higher (mean = 0.40, range = 0.04–0.69) than for people of different generations, e.g., parent–offspring (mean = 0.24, range = −0.23 to 0.49). Sex effects were not apparent. Family correlations computed for a same-sex pairing, e.g., mothers and daughters, were similar to those computed for opposite sex pairings, e.g., mothers and sons (meansame-sex = 0.38, range = −0.02 to 0.69 versus meanopposite-sex = 0.36, range = 0.12–0.54). The twin studies typically reported heritability (rather than family correlations) but the same trend as for the family correlations was observed: the food diary method associated with higher heritability (h2 = 0.48) than other methods of measuring fat intake (h2 = 0.0–0.42). In the twin studies, generation effects do not apply because all twins are of the same generation and sex effects could not be evaluated (because the studies did not typically report heritability separately for opposite sex DZ twin pairs). Overall the highest family correlations or heritability estimates of fat intake were obtained using food diaries from people of the same generation.


When studying genetics of fat preference, sometimes investigators choose to focus on the intake of individual high-fat foods, e.g., cheese or ice cream rather than the total amount of fat consumed or the percent of calories from fat. A recent study of food preferences of twins reports a high heritability for foods in the category of meat and fish (Breen et al., 2006) and another found similarly high heritability for specific foods like hamburgers (Falciglia and Norton, 1994). Other investigators have used factor analysis to group foods into categories, finding that additive genetic effects accounted for about 44% of the variance in the intake of “high-fat” foods (Keskitalo et al., 2008a), or have asked questions about liking and food use frequency for sweet high-fat foods (e.g., ice cream) and salty high-fat foods (French fries), finding that depending on the twins samples, heritability estimates ranged from 0% to 71% (Keskitalo et al., 2008a,b). There is a heritable component to high-fat food consumption, but like the analysis of total fat intake, the degree of genetic contribution varies widely based on the population studied and how the phenotype is measured.


Twin and family studies are a classic method to study human heritability for a given trait but the genetic contribution to fat preference can be studied with other types of experimental designs. In the case of the studies above, the focus is on trying to assess the relative contribution of genes and environment whereas with the genetic methods described below, the focus is on identifying which genes might be particularly influential. To identify these genes, there are several types of approaches, the most common of which is an association study. In this type of design, biologically unrelated subjects are grouped by genotype and their intake or preference for dietary fat is compared. There are two types of association studies, a “candidate gene” approach in which only genes selected for some prior association with the trait are genotyped, or a “genome-wide association” approach in which alleles are densely genotyped throughout the genome. Both types of studies are called “association studies” because the association between genotype and phenotype is evaluated, but the designs differ in the scope of the genes considered.

In contrast to association studies which involve unrelated individuals, linkage studies incorporate people who are biologically related. In this type of experimental design, the degree of genetic sharing between relatives as well as their similarity or lack of similarly in fat preference and intake is calculated and compared. Linkage methods do not precisely localize the effect to a particular gene but rather identify a large chunk of DNA (that would contain many genes) which is shared by family members who are similar in phenotype. In the next section, the relationship of specific genotypes to fat intake in humans will be reviewed from both candidate gene association and linkage studies. As of now, no genome-wide association (GWA) studies of fat preference have been completed although their potential utility is discussed at the end of this chapter.

Candidate gene association studies focus on alleles from only one or a few genes and their association with fat intake or preference. An example of this type was a study of variation in the agouti-related protein (AgRP) gene and fat intake. The motivation to examine this particular gene comes from the study of obese mice. In the early days of mouse genetics, obese yellow mice were discovered (Danforth, 1927). The obesity was later determined to be due to the inappropriate expression of a gene involved in coat color (agouti) (Bultman et al., 1992). Investigators reasoned that the coat color gene was not likely to be normally involved in obesity but that it could be related to a different gene that did have a natural role in obesity. The discovery of agouti “related” protein confirmed this hypothesis (Shutter et al., 1997). This second gene is expressed in brain regions that regulate feeding and obesity and has several alleles in the human population, one of which is more common in Caucasians and one of which is more common in African-Americans. Each of these “most-common” alleles is associated with reduced fat intake (as a percent of total calories) in their respective racial population (Loos et al., 2005).

A potential target for candidate gene studies is CD36 because of its role in the sensory transduction of fatty acid perception (see above). If the CD36 gene is involved in fat sensing in humans as it is in mice, these differences could partially account for differences among people in oral fat perception. The human homologue of the mouse CD36 gene is located in the middle of human chromosome 7, and during the last several years, many alleles of CD36 have been discovered (Ma et al., 2004) (see Table 16.2). The CD36 gene and its alleles in humans have been studied by investigators who noticed its relationship with diet-induced disease such as diabetes. One hypothesis to explain these associations is that CD36 alleles might change fat perception and intake and thereby contribute to metabolic diseases. The discovery of a putative CD36 pseudogene allele (Table 16.2), which is analogous to a natural human “gene-knockout,” suggests that if this gene indeed codes for an oral fat receptor, there might be large differences among people with working and nonworking copies of the gene.

TABLE 16.2

TABLE 16.2

Alleles of CD36 in Humans and Known Health Relationships

There are two linkage studies that have examined fat intake as a phenotype, and both studies used food frequency questionnaires to assess the amount of fat habitually consumed by the subject (Cai et al., 2004; Collaku et al., 2004). One study reported linkage to fat intake on chromosome 2, a region which was also associated with body fatness. The other study used Caucasian and African-American families recruited at five locations in the United States. The results from this study were different: the main linkage finding for fat intake (irrespective of total calories) was on chromosome 12. These two studies were similar in design and methodology but the regions of the genome implicated were different and it is worth noting that neither of these regions overlapped genetic regions associated with fat preference in the mouse (Smith Richards et al., 2002). The individual results may be valid and the difference may be due to the genetic background of the populations studied, or the disparate results may be due to the relatively low heritability or low power (which led to noise and false positive results in one or both studies).


Candidate gene association studies such as the one described above are undertaken because there is evidence that a particular gene may be involved in a particular biological pathway associated with a genetic trait, but these types of studies have limitations. The results are often difficult to replicate and they do not assess every gene in the genome. However, there has been a recent change in the direction of human genetics of complex traits, with the introduction of GWA studies, which use a dense or ultradense mapping panel (so that nearly every gene can be included in the analysis). Most genes are tested for their association with the trait and therefore the design gives investigators the ability to discover new effects of genes that were not previously understood. This approach is done at a more fine-grained resolution than a linkage study, which then requires substantial followup to find a particular gene (within the broadly linked region). Given the advantages of GWA studies, progress in the study of other complex genetic traits has been swift. For instance, GWA studies suggest that alleles of the fat mass and obesity-associated (FTO) gene are related to obesity (Frayling et al., 2007), a finding that is replicable across many human populations (Frayling et al., 2007; Scott et al., 2007; Scuteri et al., 2007; Andreasen et al., 2008; Chang et al., 2008; Do et al., 2008; Freathy et al., 2008; Liu et al., 2008a,b; Marvelle et al., 2008; Ng et al., 2008; Qi et al., 2008; Tan et al., 2008). GWA studies have started to encompass traits similar to fat preference, like sweet taste-related traits (Hansen et al., submitted) and studies of fat intake and fat preference may be just around the corner.


Human food intake is subject to all manner of vicissitudes and this is equally true for particular aspects, like fat preference. Although fat preference has normally been studied from a metabolic or neuroscience perspective, the recent understanding that fat may be a taste quality in the same way as sweet or bitter has led to renewed interested in how and why fat is liked and consumed. Several lines of evidence point to genetic influences on fat preference. In mice and rats, inbred strains differ markedly in preference and since their environment is precisely controlled, these influences are ascribed mostly to genotype and certain regions have already been identified as harboring fat preference genes. Genetic studies in humans also suggest a strong streak of genetic influence but the manner in which fat preference is measured (food diaries versus food frequency questionnaires) influences the outcome of twin and family studies. Several investigators have begun the long march to discover genes that influence fat preference. Although the initial studies are few and inconsistent, new GWA methods providing greater stability are on the horizon. If these methods can be used to study fat preference in thousands (or tens of thousands) of individuals, progress might be quick in understanding the contribution of genotype to the preference for a high-fat diet.

Electronic Resources



The author’s research was supported by a National Institute of Diabetes and Digestive and Kidney Diseases Grant DK58797. Michael G. Tordoff and Brian Gantick commented on this work prior to publication. Discussions with Carol Christensen, Julie A Mennella, and Marcia Levin Pelchat enhanced the quality of this work.


  1. Adler E, Hoon MA, Mueller KL, Chandrashekar J, Ryba NJP, Zuker CS. A novel family of mammalian taste receptors. Cell. 2000;100:693–702. [PubMed: 10761934]
  2. Almind K, Kahn CR. Genetic determinants of energy expenditure and insulin resistance in diet-induced obesity in mice. Diabetes. 2004;53:3274–3285. [PubMed: 15561960]
  3. Andreasen CH, Stender-Petersen KL, Mogensen MS, Torekov SS, Wegner L, Andersen G, Nielsen AL, et al. Low physical activity accentuates the effect of the FTO rs9939609 polymorphism on body fat accumulation. Diabetes. 2008;57:95–101. [PubMed: 17942823]
  4. Bachmanov AA, Reed DR, Tordoff MG, Price RA, Beauchamp GK. Nutrient preference and diet-induced adiposity in C57BL/6ByJ and 129P3/J mice. Physiol Behavior. 2001;72:603–613. [PMC free article: PMC3341942] [PubMed: 11282146]
  5. Bayol SA, Farrington SJ, Stickland NC. A maternal ‘junk food’ diet in pregnancy and lactation promotes an exacerbated taste for ‘junk food’ and a greater propensity for obesity in rat offspring. Br J Nutr. 2007;98:843–851. [PubMed: 17697422]
  6. Beauchamp GK, Keast RS, Morel D, Lin J, Pika J, Han Q, Lee CH, Smith AB, Breslin PA. Phytochemistry: Ibuprofen-like activity in extra-virgin olive oil. Nature. 2005;437:45–46. [PubMed: 16136122]
  7. Blundell JE, Cooling J. High-fat and low-fat (behavioural) phenotypes: Biology or environment? Proc Nutr Soc. 1999;58:773–777. [PubMed: 10817143]
  8. Breen FM, Plomin R, Wardle J. Heritability of food preferences in young children. Physiol Behav. 2006;88:443–447. [PubMed: 16750228]
  9. Bruder CE, Piotrowski A, Gijsbers AA, Andersson R, Erickson S, de Stahl TD, Menzel U, et al. Phenotypically concordant and discordant monozygotic twins display different DNA copy-number-variation profiles. Am J Hum Genet. 2008;82:763–771. [PMC free article: PMC2427204] [PubMed: 18304490]
  10. Bufe B, Breslin PA, Kuhn C, Reed DR, Tharp CD, Slack JP, Kim UK, Drayna D, Meyerhof W. The molecular basis of individual differences in phenylthiocarbamide and propylthiouracil bitterness perception. Curr Biol. 2005;15:322–327. [PMC free article: PMC1400547] [PubMed: 15723792]
  11. Bultman SJ, Michaud EJ, Woychik RP. Molecular characterization of the mouse agouti locus. Cell. 1992;71:1195–1204. [PubMed: 1473152]
  12. Burdge GC, Slater-Jefferies J, Torrens C, Phillips ES, Hanson MA, Lillycrop KA. Dietary protein restriction of pregnant rats in the F0 generation induces altered methylation of hepatic gene promoters in the adult male offspring in the F1 and F2 generations. Br J Nutr. 2007;97:435–439. [PMC free article: PMC2211514] [PubMed: 17313703]
  13. Cai G, Cole SA, Bastarrachea RA, Maccluer JW, Blangero J, Comuzzie AG. Quantitative trait locus determining dietary macronutrient intakes is located on human chromosome 2p22. Am J Clin Nutr. 2004;80:1410–1414. [PubMed: 15531694]
  14. Cartoni C, Yasumatsu K, le Coutre J, Ninomiya Y, Damak S. Diminished taste responses to fatty acids and oils in GPR40 knockout mice. The Fifth International Symposium on Molecular and Neural Mechanisms of Taste and Olfactory Perception; Fukuoka. 2007.
  15. Chale-Rush A, Burgess JR, Mattes RD. Evidence for human orosensory (taste?) sensitivity to free fatty acids. Chem Senses. 2007a;32:423–431. [PubMed: 17361006]
  16. Chale-Rush A, Burgess JR, Mattes RD. Multiple routes of chemosensitivity to free fatty acids in humans. Am J Physiol Gastrointest Liver Physiol. 2007b;292:G1206–1212. [PubMed: 17234892]
  17. Chandrashekar J, Mueller KL, Hoon MA, Adler E, Feng L, Guo W, Zuker CS, Ryba JP. T2Rs function as bitter taste receptors. Cell. 2000;100:703–711. [PubMed: 10761935]
  18. Chang YC, Liu PH, Lee WJ, Chang TJ, Jiang YD, Li HY, Kuo SS, Lee KC, Chuang LM. Common variation in the fat mass and obesity-associated (FTO) gene confers risk of obesity and modulates BMI in the Chinese population. Diabetes. 2008;57:2245–2252. [PMC free article: PMC2494679] [PubMed: 18487448]
  19. Chaudhari N, Landin AM, Roper SD. A metabotropic glutamate receptor variant functions as a taste receptor. Nat Neurosci. 2000;3:113–119. [PubMed: 10649565]
  20. Collaku A, Rankinen T, Rice T, Leon AS, Rao DC, Skinner JS, Wilmore JH, Bouchard C. A genome-wide linkage scan for dietary energy and nutrient intakes: The Health, Risk Factors, Exercise Training, and Genetics (HERITAGE) Family Study. Am J Clin Nutr. 2004;79:881–886. [PubMed: 15113729]
  21. Collins S, Martin TL, Surwit RS, Robidoux J. Genetic vulnerability to diet-induced obesity in the C57BL/6J mouse: Physiological and molecular characteristics. Physiol Behav. 2004;81:243–248. [PubMed: 15159170]
  22. Corpeleijn E, van der Kallen CJ, Kruijshoop M, Magagnin MG, de Bruin TW, Feskens EJ, Saris WH, Blaak EE. Direct association of a promoter polymorphism in the CD36/FAT fatty acid transporter gene with Type 2 diabetes mellitus and insulin resistance. Diabet Med. 2006;23:907–911. [PubMed: 16911630]
  23. Crystal SR, Teff KL. Tasting fat: Cephalic phase hormonal responses and food intake in restrained and unrestrained eaters. Physiol Behav. 2006;89:213–220. [PubMed: 16846622]
  24. Danforth C. Hereditary adiposity in mice. J Hered. 1927;18:153–162.
  25. de Castro JM. Eating behavior: Lessons from the real world of humans. Nutrition. 2000;16:800–813. [PubMed: 11054584]
  26. Desor JA, Beauchamp GK. Longitudinal changes in sweet preferences in humans. Physiol Behav. 1987;39:639–641. [PubMed: 3588712]
  27. Do R, Bailey SD, Desbiens K, Belisle A, Montpetit A, Bouchard C, Perusse L, Vohl MC, Engert JC. Genetic variants of FTO influence adiposity, insulin sensitivity, leptin levels, and resting metabolic rate in the Quebec Family Study. Diabetes. 2008;57:1147–1150. [PubMed: 18316358]
  28. Drewnowski A, Brunzell JD, Sande K, Iverius PH, Greenwood MR. Sweet tooth reconsidered: Taste responsiveness in human obesity. Physiol Behav. 1985;35:617–622. [PubMed: 4070436]
  29. Eguchi A. Long-chain fatty acids induce intracellular Ca2+ via G-protein coupled receptor 120 (GPR120). Fifteenth International Symposium on Olfaction and Taste; San Francisco, CA. 2008.
  30. Ehrich TH, Hrbek T, Kenney-Hunt JP, Pletscher LS, Wang B, Semenkovich CF, Cheverud JM. Fine-mapping gene-by-diet interactions on chromosome 13 in a LG/J × SM/J murine model of obesity. Diabetes. 2005;54:1863–1872. [PubMed: 15919810]
  31. Fabsitz RR, Garrison RJ, Feinleib M, Hjortland M. A twin analysis of dietary intake: Evidence for a need to control for possible environmental differences in MZ and DZ twins. Behav Genet. 1978;8:15–25. [PubMed: 565201]
  32. Faith MS, Rha SS, Neale MC, Allison DB. Evidence for genetic influences on human energy intake: Results from a twin study using measured observations. Behav Genet. 1999;29:145–154. [PubMed: 10547920]
  33. Falciglia GA, Norton PA. Evidence for a genetic influence on preference for some foods. J Am Diet Assoc. 1994;94:154–158. [PubMed: 8300990]
  34. Fernelius I. Therapeutices universalis seu medendi rationis, libri septem. Frankfurt: Andream Wechelum; 1581. p. 133.
  35. Feunekes GI, Stafleu A, de Graaf C, van Staveren WA. Family resemblance in fat intake in the Netherlands. Eur J Clin Nutr. 1997;51:793–799. [PubMed: 9426352]
  36. Fraga MF, Ballestar E, Paz MF, Ropero S, Setien F, Ballestar ML, Heine-Suner D, et al. Epigenetic differences arise during the lifetime of monozygotic twins. Proc Natl Acad Sci U S A. 2005;102:10604–10609. [PMC free article: PMC1174919] [PubMed: 16009939]
  37. Frayling TM, Timpson NJ, Weedon MN, Zeggini E, Freathy RM, Lindgren CM, Perry JR, et al. A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science. 2007;316:889–894. [PMC free article: PMC2646098] [PubMed: 17434869]
  38. Freathy RM, Timpson NJ, Lawlor DA, Pouta A, Ben-Shlomo Y, Ruokonen A, Ebrahim S, et al. Common variation in the FTO gene alters diabetes-related metabolic traits to the extent expected given its effect on BMI. Diabetes. 2008;57:1419–1426. [PMC free article: PMC3073395] [PubMed: 18346983]
  39. Fushiki T, Kawai T. Chemical reception of fats in the oral cavity and the mechanism of addiction to dietary fat. Chem Senses. 2005;30(Suppl 1):i184–i185. [PubMed: 15738104]
  40. Gaillard D, Laugerette F, Darcel N, El-Yassimi A, Passilly-Degrace P, Hichami A, Akhtar Khan N, Montmayeur JP, Besnard P. The gustatory pathway is involved in CD36-mediated orosensory perception of long-chain fatty acids in the mouse. FASEB J. 2007;22:1458–1468. [PubMed: 18162488]
  41. Geiselman PJ, Anderson AM, Dowdy ML, West DB, Redmann SM, Smith SR. Reliability and validity of a macronutrient self-selection paradigm and a food preference questionnaire. Physiol Behav. 1998;63:919–928. [PubMed: 9618017]
  42. Gordesky-Gold B, Rivers N, Ahmed OM, Breslin PA. Drosophila melanogaster prefers compounds perceived sweet by humans. Chem Senses. 2008;33:301–309. [PubMed: 18234713]
  43. Goris AH, Westerterp-Plantenga MS, Westerterp KR. Undereating and underrecording of habitual food intake in obese men: Selective underreporting of fat intake. Am J Clin Nutr. 2000;71:130–134. [PubMed: 10617957]
  44. Hamosh M, Scow RO. Lingual lipase and its role in the digestion of dietary lipid. J Clin Invest. 1973;52:88–95. [PMC free article: PMC302230] [PubMed: 4682389]
  45. Hansen JL, Breslin PA, Martin NG, Wright MJ, Reed DR. Heritability of taste intensity for four sweeteners and suggestive linkage and association to chromosome. :3. submitted.
  46. Heller RF, O’Connell DL, Roberts DC, Allen JR, Knapp JC, Steele PL, Silove D. Lifestyle factors in monozygotic and dizygotic twins. Genet Epidemiol. 1988;5:311–321. [PubMed: 3215506]
  47. Hur YM, Bouchard Jr. TJ, Eckert E. Genetic and environmental influences on self-reported diet: A reared-apart twin study. Physiol Behav. 1998;64:629–636. [PubMed: 9817574]
  48. Itoh Y, Kawamata Y, Harada M, Kobayashi M, Fujii R, Fukusumi S, Ogi K, et al. Free fatty acids regulate insulin secretion from pancreatic beta cells through GPR40. Nature. 2003;422:173–176. [PubMed: 12629551]
  49. Kawai T, Fushiki T. Importance of lipolysis in oral cavity for orosensory detection of fat. Am J Physiol Regul Integr Comp Physiol. 2003;285:R447–R454. [PubMed: 12702486]
  50. Keskitalo K, Silventoinen K, Tuorila H, Perola M, Pietilainen KH, Rissanen A, Kaprio J. Genetic and environmental contributions to food use patterns of young adult twins. Physiol Behav. 2008a;93:235–242. [PMC free article: PMC3639380] [PubMed: 17897688]
  51. Keskitalo K, Tuorila H, Spector TD, Cherkas LF, Knaapila A, Kaprio J, Silventoinen K, Perola M. The Three-Factor Eating Questionnaire, body mass index, and responses to sweet and salty fatty foods: A twin study of genetic and environmental associations. Am J Clin Nutr. 2008b;88:263–271. [PubMed: 18689360]
  52. Kumar KG, Poole AC, York B, Volaufova J, Zuberi A, Richards BK. Quantitative trait loci for carbohydrate and total energy intake on mouse chromosome 17: Congenic strain confirmation and candidate gene analyses (Glo1, Glp1r) Am J Physiol Regul Integr Comp Physiol. 2007;292:R207–R216. [PubMed: 16946080]
  53. Kuriki K, Hamajima N, Chiba H, Kanemitsu Y, Hirai T, Kato T, Saito T, et al. Increased risk of colorectal cancer due to interactions between meat consumption and the CD36 gene A52C polymorphism among Japanese. Nutr Cancer. 2005;51:170–177. [PubMed: 15860439]
  54. Larson DE, Tataranni PA, Ferraro RT, Ravussin E. Ad libitum food intake on a “cafeteria diet” in Native American women: Relations with body composition and 24-h energy expenditure. Am J Clin Nutr. 1995;62:911–917. [PubMed: 7572735]
  55. Laskarzewski P, Morrison JA, Khoury P, Kelly K, Glatfelter L, Larsen R, Glueck CJ. Parent-child nutrient intake interrelationships in school children ages 6 to 19: The Princeton School District Study. Am J Clin Nutr. 1980;33:2350–2355. [PubMed: 7435415]
  56. Laugerette F, Passilly-Degrace P, Patris B, Niot I, Febbraio M, Montmayeur JP, Besnard P. CD36 involvement in orosensory detection of dietary lipids, spontaneous fat preference, and digestive secretions. J Clin Invest. 2005;115:3177–3184. [PMC free article: PMC1265871] [PubMed: 16276419]
  57. Ledikwe JH, Ello-Martin J, Pelkman CL, Birch LL, Mannino ML, Rolls BJ. A reliable, valid questionnaire indicates that preference for dietary fat declines when following a reduced-fat diet. Appetite. 2007;49:74–83. [PubMed: 17275138]
  58. Levin BE, Dunn-Meynell AA, Balkan B, Keesey RE. Selective breeding for diet-induced obesity and resistance in Sprague–Dawley rats. Am J Physiol. 1997;273:R725–R730. [PubMed: 9277561]
  59. Levitsky DA, Youn T. The more food young adults are served, the more they overeat. J Nutr. 2004;134:2546–2549. [PubMed: 15465745]
  60. Lewis SR, Dym C, Chai C, Singh A, Kest B, Bodnar RJ. Genetic variance contributes to ingestive processes: A survey of eleven inbred mouse strains for fat (Intralipid) intake. Physiol Behav. 2007;90:82–94. [PubMed: 17028044]
  61. Liem DG, Mennella JA. Heightened sour preferences during childhood. Chem Senses. 2003;28:173–180. [PMC free article: PMC2789429] [PubMed: 12588738]
  62. Liu P, Yu T, Shah BP, Hansen DR, Gilbertson TA. Fatty acids elicit membrane depolarization and a rise in intracellular calcium in rodent taste cells. International Symposium on Olfaction and Taste; San Francisco, CA. 2008a.
  63. Liu YJ, Liu XG, Wang L, Dina C, Yan H, Liu JF, Levy S, et al. Genome-wide association scans identified CTNNBL1 as a novel gene for obesity. Hum Mol Genet. 2008b;17:1803–1813. [PMC free article: PMC2900891] [PubMed: 18325910]
  64. Loos RJ, Rankinen T, Rice T, Rao DC, Leon AS, Skinner JS, Bouchard C, Argyropoulos G. Two ethnic-specific polymorphisms in the human Agouti-related protein gene are associated with macronutrient intake. Am J Clin Nutr. 2005;82:1097–1101. [PubMed: 16280444]
  65. Ma X, Bacci S, Mlynarski W, Gottardo L, Soccio T, Menzaghi C, Iori E, et al. A common haplotype at the CD36 locus is associated with high free fatty acid levels and increased cardiovascular risk in Caucasians. Hum Mol Genet. 2004;13:2197–2205. [PubMed: 15282206]
  66. Marvelle AF, Lange LA, Qin L, Adair LS, Mohlke KL. Association of FTO with obesity-related traits in the Cebu Longitudinal Health and Nutrition Survey (CLHNS) Cohort. Diabetes. 2008;57:1987–1991. [PMC free article: PMC2453620] [PubMed: 18426866]
  67. Matsumura S, Mizushife T, Yoneda T, Eguchi A, Manabe Y, Tsuzuki S, Inoue K, Iwanaga T, Fushiki T. GPR expression in the rat taste bud relating to fatty acid sensing. Fifteenth International Symposium on Olfaction and Taste; San Francisco, CA. 2008.
  68. Mattes RD. Fat preference and adherence to a reduced-fat diet. Am J Clin Nutr. 1993;57:373–381. [PubMed: 8438771]
  69. Mattes RD. The taste of fat elevates postprandial triacylglycerol. Physiol Behav. 2001;74:343–348. [PubMed: 11714498]
  70. Mela DJ, Christensen CM. Sensory assessment of oiliness in a low moisture food. J Sens Stud. 1987;2:273–281.
  71. Mela DJ, Sacchetti DA. Sensory preferences for fats: Relationships with diet and body composition. Am J Clin Nutr. 1991;53:908–915. [PubMed: 2008871]
  72. Mennella JA, Jagnow CP, Beauchamp GK. Prenatal and postnatal flavor learning by human infants. Pediatrics. 2001;107:E88. [PMC free article: PMC1351272] [PubMed: 11389286]
  73. Nelson G, Hoon MA, Chandrashekar J, Zhang Y, Ryba NJ, Zuker CS. Mammalian sweet taste receptors. Cell. 2001;106:381–390. [PubMed: 11509186]
  74. Nelson G, Chandrashekar J, Hoon MA, Feng L, Zhao G, Ryba NJ, Zuker CS. An amino-acid taste receptor. Nature. 2002;416:199–202. [PubMed: 11894099]
  75. Ng MC, Park KS, Oh B, Tam CH, Cho YM, Shin HD, Lam VK, et al. Implication of genetic variants near TCF7L2, SLC30A8, HHEX, CDKAL1, CDKN2A/B, IGF2BP2, and FTO in Type 2 diabetes and obesity in 6,719 Asians. Diabetes. 2008;57:2226–2233. [PMC free article: PMC2494677] [PubMed: 18469204]
  76. Nicklaus S, Boggio V, Chabanet C, Issanchou S. A prospective study of food preferences in childhood. Food Qual Prefer. 2004;15:805–818.
  77. Nozawa M, Kawahara Y, Nei M. Genomic drift and copy number variation of sensory receptor genes in humans. Proc Natl Acad Sci U S A. 2007;104:20421–20426. [PMC free article: PMC2154446] [PubMed: 18077390]
  78. Oliveria SA, Ellison RC, Moore LL, Gillman MW, Garrahie EJ, Singer MR. Parent-child relationships in nutrient intake: The Framingham Children’s Study. Am J Clin Nutr. 1992;56:593–598. [PubMed: 1503074]
  79. Pangborn RM, Giovanni ME. Dietary intake of sweet foods and of dairy fats and resultant gustatory responses to sugar in lemonade and to fat in milk. Appetite. 1984;5:317–327. [PubMed: 6549376]
  80. Patterson TL, Rupp JW, Sallis JF, Atkins CJ, Nader PR. Aggregation of dietary calories, fats, and sodium in Mexican-American and Anglo families. Am J Prev Med. 1988;4:75–82. [PubMed: 3395494]
  81. Perusse L, Tremblay A, Leblanc C, Cloninger CR, Reich T, Rice J, Bouchard C. Familial resemblance in energy intake: Contribution of genetic and environmental factors. Am J Clin Nutr. 1988;47:629–635. [PubMed: 3354487]
  82. Pritchard ET, Dawes C, Philips SR. Apparent lipase activity of human saliva. Arch Oral Biol. 1967;12:1217–1219. [PubMed: 5233944]
  83. Qi L, Kang K, Zhang C, van Dam RM, Kraft P, Hunter D, Lee CH, Hu FB. FTO gene variant is associated with obesity: Longitudinal analyses in two cohort studies and functional test. Diabetes. 2008;57:3145–3151. [PMC free article: PMC2570413] [PubMed: 18647953]
  84. Ramirez I. Oral stimulation alters digestion of intragastric meals in rats. Am J Physiol. 1985;248:R459–R463. [PubMed: 3985188]
  85. Reed DR, Tordoff MG, Friedman MI. Enhanced acceptance and metabolism of fats by rats fed a high-fat diet. Am J Physiol. 1991;261:R1084–R1088. [PubMed: 1951757]
  86. Reed DR, Friedman MI, Tordoff MG. Experience with a macronutrient source influences subsequent macronutrient selection. Appetite. 1992a;18:223–232. [PubMed: 1510464]
  87. Reed DR, Mela DJ, Friedman MI. Sensory and metabolic influences on fat intake. In: Mela DJ, editor. Dietary Fats: Determinants of Preference, Selection and Consumption. London, New York: Elsevier Applied Science; 1992b. pp. 117–137.
  88. Reed DR, Bachmanov AA, Beauchamp GK, Tordoff MG, Price RA. Heritable variation in food preferences and their contribution to obesity. Behav Genet. 1997;27:373–387. [PMC free article: PMC3647229] [PubMed: 9519563]
  89. Rissanen A, Hakala P, Lissner L, Mattlar CE, Koskenvuo M, Ronnemaa T. Acquired preference especially for dietary fat and obesity: A study of weight-discordant monozygotic twin pairs. Int J Obes Relat Metab Disord. 2002;26:973–977. [PubMed: 12080452]
  90. Salbe AD, DelParigi A, Pratley RE, Drewnowski A, Tataranni PA. Taste preferences and body weight changes in an obesity-prone population. Am J Clin Nutr. 2004;79:372–378. [PubMed: 14985209]
  91. Schemmel R, Mickelsen O, Gill JL. Dietary obesity in rats: Body weight and body fat accretion in seven strains of rats. J Nutr. 1970;100:1041–1048. [PubMed: 5456549]
  92. Sclafani A, Springer D. Dietary obesity in adult rats: Similarities to hypothalamic and human obesity syndromes. Physiol Behav. 1976;17:461–471. [PubMed: 1013192]
  93. Sclafani A, Ackroff K, Abumrad NA. CD36 gene deletion reduces fat preference and intake but not post-oral fat conditioning in mice. Am J Physiol Regul Integr Comp Physiol. 2007a;293:R1823–R1832. [PubMed: 17804586]
  94. Sclafani A, Zukerman S, Glendinning JI, Margolskee RF. Fat and carbohydrate preferences in mice: The contribution of alpha-gustducin and Trpm5 taste-signaling proteins. Am J Physiol Regul Integr Comp Physiol. 2007b;293:R1504–R1513. [PMC free article: PMC2375390] [PubMed: 17652359]
  95. Scott LJ, Mohlke KL, Bonnycastle LL, Willer CJ, Li Y, Duren WL, Erdos MR, et al. A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science. 2007;316:1341–1345. [PMC free article: PMC3214617] [PubMed: 17463248]
  96. Scuteri A, Sanna S, Chen WM, Uda M, Albai G, Strait J, Najjar S, et al. Genome-wide association scan shows genetic variants in the FTO gene are associated with obesity-related traits. PLoS Genet. 2007;3:e115. [PMC free article: PMC1934391] [PubMed: 17658951]
  97. Sharp AJ, Locke DP, McGrath SD, Cheng Z, Bailey JA, Vallente RU, Pertz LM, et al. Segmental duplications and copy-number variation in the human genome. Am J Hum Genet. 2005;77:78–88. [PMC free article: PMC1226196] [PubMed: 15918152]
  98. Shutter JR, Graham M, Kinsey AC, Scully S, Luthy R, Stark KL. Hypothalamic expression of ART, a novel gene related to agouti, is up-regulated in obese and diabetic mutant mice. Genes Dev. 1997;11:593–602. [PubMed: 9119224]
  99. Smith Richards BK, Belton BN, Poole AC, Mancuso JJ, Churchill GA, Li R, Volaufova J, Zuberi A, York B. QTL analysis of self-selected macronutrient diet intake: Fat, carbohydrate, and total kilocalories. Physiol Genomics. 2002;11:205–217. [PubMed: 12388789]
  100. Stafleu A, Van Staveren WA, de Graaf C, Burema J, Hautvast JG. Family resemblance in energy, fat, and cholesterol intake: A study among three generations of women. Prev Med. 1994;23:474–480. [PubMed: 7971875]
  101. Stratford JM, Curtis KS, Contreras RJ. Chorda tympani nerve transection alters linoleic acid taste discrimination by male and female rats. Physiol Behav. 2006;89:311–319. [PubMed: 16963089]
  102. Stratford JM, Curtis KS, Contreras RJ. Linoleic acid increases chorda tympani nerve responses to and behavioral preferences for monosodium glutamate by male and female rats. Am J Physiol Regul Integr Comp Physiol. 2008;295:R764–R772. [PMC free article: PMC2536858] [PubMed: 18635450]
  103. Svenson KL, Von Smith R, Magnani PA, Suetin HR, Paigen B, Naggert JK, Li R, Churchill GA, Peters LL. Multiple trait measurements in 43 inbred mouse strains captures the phenotypic diversity characteristic of human populations. J Appl Physiol. 2007;1021:2369–2378. [PubMed: 17317875]
  104. Tan JT, Dorajoo R, Seielstad M, Sim X, Rick OT, Seng CK, Yin WT, et al. FTO variants are associated with obesity in the Chinese and Malay populations in Singapore. Diabetes. 2008;57:2851–2857. [PMC free article: PMC2551698] [PubMed: 18599522]
  105. Tordoff MG, Tepper BJ, Friedman MI. Food flavor preferences produced by drinking glucose and oil in normal and diabetic rats: Evidence for conditioning based on fuel oxidation. Physiol Behav. 1987;41:481–487. [PubMed: 3432403]
  106. Tordoff MG, Alarcon LK, Lawler MP. Preferences of 14 rat strains for 17 taste compounds. Physiol Behav. 2008;95:308–332. [PMC free article: PMC2642481] [PubMed: 18639567]
  107. Tsuzuki S. Mechanisms on the oral chemoreception of fats: The possible participation of FAT/CD36 and GPR120. Fifth International Symposium on Molecular and Neural Mechanisms of Taste and Olfactory Perception; Fukuoka. 2007.
  108. Vachon CM, Sellers TA, Kushi LH, Folsom AR. Familial correlation of dietary intakes among postmenopausal women. Genet Epidemiol. 1998;15:553–563. [PubMed: 9811418]
  109. Valdes AM, Van Oene M, Hart DJ, Surdulescu GL, Loughlin J, Doherty M, Spector TD. Reproducible genetic associations between candidate genes and clinical knee osteoarthritis in men and women. Arthritis Rheum. 2006;54:533–539. [PubMed: 16453284]
  110. Vauthier JM, Lluch A, Lecomte E, Artur Y, Herbeth B. Family resemblance in energy and macronutrient intakes: The Stanislas Family Study. Int J Epidemiol. 1996;25:1030–1037. [PubMed: 8921491]
  111. Wade J, Milner J, Krondl M. Evidence for a physiological regulation of food selection and nutrient intake in twins. Am J Clin Nutr. 1981;34:143–147. [PubMed: 7193970]
  112. Wansink B, Payne CR, North J. Fine as North Dakota wine: Sensory expectations and the intake of companion foods. Physiol Behav. 2007;90:712–716. [PubMed: 17292930]
  113. Waterland RA, Jirtle RL. Transposable elements: Targets for early nutritional effects on epigenetic gene regulation. Mol Cell Biol. 2003;23:5293–5300. [PMC free article: PMC165709] [PubMed: 12861015]
  114. West DB, Boozer CN, Moody DL, Atkinson RL. Dietary obesity in nine inbred mouse strains. Am J Physiol. 1992;262:R1025–R1032. [PubMed: 1621856]
  115. West DB, Waguespack J, McCollister S. Dietary obesity in the mouse: Interaction of strain with diet composition. Am J Physiol. 1995;268:R658–R665. [PubMed: 7900908]
  116. Wong KK, de Leeuw RJ, Dosanjh NS, Kimm LR, Cheng Z, Horsman DE, MacAulay C, Ng RT, Brown CJ, Eichler EE. A comprehensive analysis of common copy-number variations in the human genome. Am J Hum Genet. 2007;80:91–104. [PMC free article: PMC1785303] [PubMed: 17160897]
  117. Wu Q, Suzuki M. Parental obesity and overweight affect the body-fat accumulation in the offspring: The possible effect of a high-fat diet through epigenetic inheritance. Obes Rev. 2006;7:201–208. [PubMed: 16629875]
  118. Yeo M, Treloar S, Marks G, Heath AC, Martin N. What are the causes of individual differences in food consumption and are they modified by personality. Pers Individ Diff. 1997;23:535–542.
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