Interaction among individuals is universal, both in animals and in plants, and substantially affects evolution of natural populations and responses to artificial selection in agriculture. Although quantitative genetics has successfully been applied to many traits, it does not provide a general theory accounting for interaction among individuals and selection acting on multiple levels. Consequently, current quantitative genetic theory fails to explain why some traits do not respond to selection among individuals, but respond greatly to selection among groups. Understanding the full impacts of heritable interactions on the outcomes of selection requires a quantitative genetic framework including all levels of selection and relatedness. Here we present such a framework and provide expressions for the response to selection. Results show that interaction among individuals may create substantial heritable variation, which is hidden to classical analyses. Selection acting on higher levels of organization captures this hidden variation and therefore always yields positive response, whereas individual selection may yield response in the opposite direction. Our work provides testable predictions of response to multilevel selection and reduces to classical theory in the absence of interaction. Statistical methodology provided elsewhere enables empirical application of our work to both natural and domestic populations.

The objective of this study was to estimate genetic parameters for major milk fatty acids and milk production traits. One morning milk sample was collected from 1,918 Holstein-Friesian heifers located in 398 commercial herds in The Netherlands. Each sample was analyzed for total percentages of fat and protein, and for detailed fatty acid percentages (computed as fatty acid weight as a proportion of total fat weight). Intraherd heritabilities were high for C4:0 to C16:0, ranging from 0.42 for C4:0 to 0.71 for C10:0. Saturated and unsaturated C18 fatty acids had intraherd heritability estimates of approximately 0.25, except for C18:2 cis-9, trans-11, which was 0.42. Standard errors of the heritabilities were between 0.07 and 0.12. Genetic correlations were high and positive among C4:0 to C14:0, as well as among unsaturated C18, but correlations of C4:0 to C14:0 with unsaturated C18 were generally weak. The genetic correlation of C16:0 with fat percentage was positive (0.65), implying that selection for fat percentage should result in a correlated increase of C16:0, whereas unsaturated C18 fatty acids decreased with increasing fat percentage (-0.74). Milk fat composition can be changed by means of selective breeding, which offers opportunities to meet consumer demands regarding health and technological aspects.

Interactions among individuals are universal, both in animals and in plants and in natural as well as domestic populations. Understanding the consequences of these interactions for the evolution of populations by either natural or artificial selection requires knowledge of the heritable components underlying them. Here we present statistical methodology to estimate the genetic parameters determining response to multilevel selection of traits affected by interactions among individuals in general populations. We apply these methods to obtain estimates of genetic parameters for survival days in a population of layer chickens with high mortality due to pecking behavior. We find that heritable variation is threefold greater than that obtained from classical analyses, meaning that two-thirds of the full heritable variation is hidden to classical analysis due to social interactions. As a consequence, predicted responses to multilevel selection applied to this population are threefold greater than classical predictions. This work, combined with the quantitative genetic theory for response to multilevel selection presented in an accompanying article in this issue, enables the design of selection programs to effectively reduce competitive interactions in livestock and plants and the prediction of the effects of social interactions on evolution in natural populations undergoing multilevel selection.

With regard to human health aspects of milk fat, increasing the amount of unsaturated fatty acids in milk is an important selection objective. The cow's diet has an influence on the degree of unsaturation, but literature suggests that genetics also plays a role. To estimate genetic variation in milk fatty acid unsaturation indices, milk fatty acid composition of 1,933 Dutch Holstein Friesian heifers was measured and unsaturation indices were calculated. An unsaturation index represents the concentration of the unsaturated product proportional to the sum of the unsaturated product and the saturated substrate. Intraherd heritabilities were moderate, ranging from 0.23 +/- 0.07 for conjugated linoleic acid (CLA) index to 0.46 +/- 0.09 for C16 index. We genotyped the cows for the SCD1 A293V and DGAT1 K232A polymorphisms, which are known to alter milk fatty acid composition. Both genes explain part of the genetic variation in unsaturation indices. The SCD1 V allele is associated with lower C10, C12, and C14 indices, and with higher C16, C18, and CLA indices in comparison to the SCD1 A allele, with no differences in total unsaturation index. In comparison to the DGAT1 K allele, the DGAT1 A allele is associated with lower C10, C12, C14, and C16 indices and with higher C18, CLA, and total indices. We conclude that selective breeding can contribute to higher unsaturation indices, and that selective breeding can capitalize on genotypic information of both the SCD1 A293V and the DGAT1 K232A polymorphism.

Dietary fat may play a role in the aetiology of many chronic diseases. Milk and milk-derived foods contribute substantially to dietary fat, but have a fat composition that is not optimal for human health. We measured the fat composition of milk samples in 1918 Dutch Holstein Friesian cows in their first lactation and estimated genetic parameters for fatty acids. Substantial genetic variation in milk-fat composition was found: heritabilities were high for short- and medium-chain fatty acids (C4:0-C16:0) and moderate for long-chain fatty acids (saturated and unsaturated C18). We genotyped 1762 cows for the DGAT1 K232A polymorphism, which is known to affect milk-fat percentage, to study the effect of the polymorphism on milk-fat composition. We found that the DGAT1 K232A polymorphism has a clear influence on milk-fat composition. The DGAT1 allele that encodes lysine (K) at position 232 (232K) is associated with more saturated fat; a larger fraction of C16:0; and smaller fractions of C14:0, unsaturated C18 and conjugated linoleic acid (P < 0.001). We conclude that selective breeding can make a significant contribution to change the fat composition of cow's milk.

The effects of beta-lactoglobulin (beta-LG), beta-casein (beta-CN), and kappa-CN variants and beta-kappa-CN haplotypes on the relative concentrations of the major milk proteins alpha-lactalbumin (alpha-LA), beta-LG, alpha(S1)-CN, alpha(S2)-CN, beta-CN, and kappa-CN and milk production traits were estimated in the milk of 1,912 Dutch Holstein-Friesian cows. We show that in the Dutch Holstein-Friesian population, the allele frequencies have changed in the past 16 years. In addition, genetic variants and casein haplotypes have a major impact on the protein composition of milk and explain a considerable part of the genetic variation in milk protein composition. The beta-LG genotype was associated with the relative concentrations of beta-LG (A >> B) and of alpha-LA, alpha(S1)-CN, alpha(S2)-CN, beta-CN, and kappa-CN (B > A) but not with any milk production trait. The beta-CN genotype was associated with the relative concentrations of beta-CN and alpha(S2)-CN (A(2) > A(1)) and of alpha(S1)-CN and kappa-CN (A(1) > A(2)) and with protein yield (A(2) > A(1)). The kappa-CN genotype was associated with the relative concentrations of kappa-CN (B > E > A), alpha(S2)-CN (B > A), alpha-LA, and alpha(S1)-CN (A > B) and with protein percentage (B > A). Comparing the effects of casein haplotypes with the effects of single casein variants can provide better insight into what really underlies the effect of a variant on protein composition. We conclude that selection for both the beta-LG genotype B and the beta-kappa-CN haplotype A(2)B will result in cows that produce milk that is more suitable for cheese production.

The effects of lactation stage, negative energy balance (NEB), and milk fat depression (MFD) were estimated on detailed milk fat composition in primiparous Holstein-Friesian cows. One morning milk sample was collected from each of 1,933 cows from 398 commercial Dutch herds in winter 2005. Milk fat composition was measured using gas chromatography, and fat and protein percentage were measured using infrared spectrometry. Each fatty acid changed 0.5 to 1 phenotypic standard deviation over lactation, except odd-chain C5:0 to C15:0, branched-chain fatty acids, and trans-10, cis-12 conjugated linoleic acid (CLA). The greatest change was an increase from 31.2 to 33.3% (wt/wt) for C16:0 from d 80 to 150 of lactation. Energy status was estimated for each cow as the deviation from each average lactation fat-to-protein ratio (FPdev). A high FPdev (>0.12) indicated NEB. Negative energy balance was associated with an increase in C16:0 (0.696 +/- 0.178) and C18:0 (0.467 +/- 0.093), which suggested mobilization of body fat reserves. Furthermore, NEB was associated with a decrease in odd-chain C5:0 to C15:0 (-0.084 +/- 0.020), which might reflect a reduced allocation of C3 components to milk fat synthesis. A low FPdev indicated MFD (<-0.12) and was associated with a decrease in C16:0 (-0.681 +/- 0.255) and C18:0 (-0.128 +/- 0.135) and an increase in total unsaturated fatty acids (0.523 +/- 0.227). The study showed that both lactation stage and energy balance significantly contribute to variation in milk fat composition and alter the activity of different fatty acid pathways.

BackgroundIdentifying genomic regions, and preferably individual genes, responsible for genetic variation in milk fat composition of bovine milk will enhance the understanding of biological pathways involved in fatty acid synthesis and may point to opportunities for changing milk fat composition via selective breeding. An association study of 50,000 single nucleotide polymorphisms (SNPs) was performed for even-chain saturated fatty acids (C4:0-C18:0), even-chain monounsaturated fatty acids (C10:1-C18:1), and the polyunsaturated C18:2cis9,trans11 (CLA) to identify genomic regions associated with individual fatty acids in bovine milk.ResultsThe two-step single SNP association analysis found a total of 54 regions on 29 chromosomes that were significantly associated with one or more fatty acids. Bos taurus autosomes (BTA) 14, 19, and 26 showed highly significant associations with seven to ten traits, explaining a relatively large percentage of the total additive genetic variation. Many additional regions were significantly associated with the fatty acids. Some of the regions harbor genes that are known to be involved in fat synthesis or were previously identified as underlying quantitative trait loci for fat yield or content, such as ABCG2 and PPARGC1A on BTA 6; ACSS2 on BTA 13; DGAT1 on BTA 14; ACLY, SREBF1, STAT5A, GH, and FASN on BTA 19; SCD1 on BTA26; and AGPAT6 on BTA 27.ConclusionsMedium chain and unsaturated fatty acids are strongly influenced by polymorphisms in DGAT1 and SCD1. Other regions also showed significant associations with the fatty acids studied. These additional regions explain a relatively small percentage of the total additive genetic variance, but they are relevant to the total genetic merit of an individual and in unraveling the genetic background of milk fat composition. Regions identified in this study can be fine mapped to find causal mutations. The results also create opportunities for changing milk fat composition through breeding by selecting individuals based on their genetic merit for milk fat composition.