Genetic mapping of loci responsible for milk quality parameters in dairy cattle

The review presents a definition of loci controlling quantitative traits (quantitative trait loci, QTLs) and localization of all currently known QTLs responsible for milk production traits in dairy cattle. The QTL number and chromosome localization are verified, with special reference to chromosomes 1, 3, 6, 14, 20, and 23. In a number of cases, close location of QTLs for mastitis and for milk production traits was found. Some aspects of QTL pleiotropy and epistasis are discussed and mapping methods of major QTLs are listed.     

Genetic mapping of loci responsible for milk production traits in dairy cattle

The review presents a definition of loci controlling quantitative traits (quantitative trait loci, QTLs) and localization of all currently known QTLs responsible for milk production traits in dairy cattle. The QTL number and chromosome localization are verified, with special reference to chromosomes 1, 3, 6, 14, 20, and 23. In a number of cases, close location of QTLs for mastitis and for milk production traits was found. Some aspects of QTL pleiotropy and epistasis are discussed and mapping methods of major QTLs are listed. Original Russian Text Sc M.G. Smaragdov, 2006, published in Genetika, 2006, Vol. 42, No. 1, pp. 5–21.     

Quantitative trait loci mapping in dairy cattle: review and meta-analysis

From an extensive review of public domain information on dairy cattle quantitative trait loci (QTL), we have prepared a draft online QTL map for dairy production traits. Most publications (45 out of 55 reviewed) reported QTL for the major milk production traits (milk, fat and protein yield, and fat and protein concentration (%)) and somatic cell score. Relatively few QTL studies have been reported for more complex traits such as mastitis, fertility and health. The collated QTL map shows some chromosomal regions with a high density of QTL, as well as a substantial number of QTL at single chromosomal locations. To extract the most information from these published records, a meta-analysis was conducted to obtain consensus on QTL location and allelic substitution effect of these QTL. This required modification and development of statistical methodologies. The meta-analysis indicated a number of consensus regions, the most striking being two distinct regions affecting milk yield on chromosome 6 at 49 cM and 87 cM explaining 4.2 and 3.6 percent of the genetic variance of milk yield, respectively. The first of these regions (near marker BM143) affects five separate milk production traits (protein yield, protein percent, fat yield, fat percent, as well as milk yield).     

A search for quantitative trait loci for milk production traits on chromosome 6 in Finnish Ayrshire cattle

Cattle chromosome 6 was scanned with 11 markers, ten microsatellites and the casein haplotype, to identify quantitative trait loci (QTLs) affecting the following milk production traits: milk yield, fat percentage, fat yield, protein percentage and protein yield. Twelve Finnish Ayrshire half-sib families with a total of 480 sons were genotyped and used in a grand-daughter design. Interval mapping was performed with a multiple-marker regression approach with a one-QTL and a two-QTL model, and the significance threshold values were determined empirically using a permutation test. Across-family analysis with the one-QTL model revealed an effect on protein percentage (P < 0.05) and on milk yield (P < 0.05). The analysis with the two-QTL model identified significant effects (P < 0.05) on protein percentage, milk yield, and fat yield. Comparing these two cases, the results suggest the existence of two QTLs on chromosome 6 with an effect on milk production traits. One of the QTLs was located around the casein genes. As the other QTL was similar in location and effect to a QTL found previously in Holstein-Friesians, an identity-by-descent approach could be applied to fine map this region.     

Identification of QTLs for Yield and Associated Traits in F₂ Population of Rice

Identification of quantitative trait loci (QTLs) controlling yield and yield-related traits in rice was performed in the F₂ mapping population derived from parental rice genotypes DHMAS and K343. A total of 30 QTLs governing nine different traits were identified using the composite interval mapping (CIM) method. Four QTLs were mapped for number of tillers per plant on chromosomes 1 (2 QTLs), 2 and 3; three QTLs for panicle number per plant on chromosomes 1 (2 QTLs) and 3; four QTLs for plant height on chromosomes 2, 4, 5 and 6; one QTL for spikelet density on chromosome 5; four QTLs for spikelet fertility percentage (SFP) on chromosomes 2, 3 and 5 (2 QTLs); two QTLs for grain length on chromosomes 1 and 8; three QTLs for grain width on chromosomes1, 3 and 8; three QTLs for 1000-grain weight (TGW) on chromosomes 1, 4 and 8 and six QTLs for yield per plant (YPP) on chromosomes 2 (3 QTLs), 4, 6 and 8. Most of the QTLs were detected on chromosome 2, so further studies on chromosome 2 could help unlock some new chapters of QTL for this cross of rice variety. Identified QTLs elucidating high phenotypic variance can be used for marker-assisted selection (MAS) breeding. Further, the exploitation of information regarding molecular markers tightly linked to QTLs governing these traits will facilitate future crop improvement strategies in rice.     

Detection of loci affecting milk production and health traits in an elite US Holstein population using microsatellite markers

Quantitative trait loci (QTL) affecting health and milk production traits were studied in seven large half-sib US Holstein families by using the granddaughter design. Genotyping for 16 markers was completed and marker allele differences within and pooled-across families were analysed. Potential QTL were identified for somatic cell score (SCS), fat yield, fat percentage, protein yield and protein percentage. Three markers (BM203, BM4505 and BM2078) were associated with significant effects for different traits and, after further analysis, may be useful in marker-assisted selection in specific families. Comparisons between these data and previously identified QTL support the location of a QTL for milk yield and protein yield on chromosome 21.     

Mapping quantitative trait loci (QTL) in sheep. II. Meta-assembly and identification of novel QTL for milk production traits in sheep

An (Awassi × Merino) × Merino backcross family of 172 ewes was used to map quantitative trait loci (QTL) for different milk production traits on a framework map of 200 loci across all autosomes. From five previously proposed mathematical models describing lactation curves, the Wood model was considered the most appropriate due to its simplicity and its ability to determine ovine lactation curve characteristics. Derived milk traits for milk, fat, protein and lactose yield, as well as percentage composition and somatic cell score were used for single and two-QTL approaches using maximum likelihood estimation and regression analysis. A total of 15 significant (P < 0.01) and additional 25 suggestive (P < 0.05) QTL were detected across both single QTL methods and all traits. In preparation of a meta-analysis, all QTL results were compared with a meta-assembly of QTL for milk production traits in dairy ewes from various public domain sources and can be found on the ReproGen ovine gbrowser http://crcidp.vetsci.usyd.edu.au/cgi-bin/gbrowse/oaries_genome/. Many of the QTL for milk production traits have been reported on chromosomes 1, 3, 6, 16 and 20. Those on chromosomes 3 and 20 are in strong agreement with the results reported here. In addition, novel QTL were found on chromosomes 7, 8, 9, 14, 22 and 24. In a cross-species comparison, we extended the meta-assembly by comparing QTL regions of sheep and cattle, which provided strong evidence for synteny conservation of QTL regions for milk, fat, protein and somatic cell score data between cattle and sheep.     

Yield-enhancing quantitative trait loci (QTLs) from wild species

Wild species of crop plants are increasingly being used to improve various agronomic traits including yield in cultivars. Dense molecular maps have enabled mapping of quantitative trait loci (QTLs) for complex traits such as yield. QTLs for increased yield have been identified from wild relatives of several crop plants. Advanced backcross QTL analysis has been used to identify naturally occurring favorable QTL alleles for yield and minimize the effect of unwanted alleles from wild species. Yield QTLs from wild species are distributed on almost all chromosomes but more often in some regions. Many QTLs for yield and related traits derived from different wild accessions or species map to identical chromosomal regions. QTLs for highly correlated yield associated traits are also often co-located implying linkage or pleiotropic effects. Many QTLs have been detected in more than one environment and in more than one genetic background. The overall direction of effect of some QTLs however, may vary with genetic context. Thus, there is evidence of stable and consistent major effect yield-enhancing QTLs derived from wild species in several crops. Such QTLs are good targets for use in marker assisted selection though their context-dependency is a major constraint. Literature on yield QTLs mapped from wild species is summarized with special reference to rice and tomato.     

Identification of QTLs for some agronomic traits in rice using an introgression line from Oryza minuta

Wild progenitor species provide potential gene sources for complex traits such as yield and multiple resistances to biotic and abiotic stresses, and thus are expected to contribute to sustainable food supplies. An introgression line 'IR71033-121-15' was derived from a wild species Oryza minuta (2n = 48, BBCC, Acc No. 101141) at IRRI. Introgression analysis using 530 SSR and STS markers revealed that at least 14 chromosomal segments distributed over 12 chromosomes had been introgressed from O. minuta. An F2:3 population from the cross between IR71033 and Junambyeo (a Korean japonica cultivar) consisting of 146 lines was used for quantitative trait loci (QTL) analysis of 16 agronomic traits. A total of 36 single-locus QTLs (S-QTLs) and 45 digenic epistasis (E-QTLs) were identified. In spite of it's inferiority of O. minuta for most of the traits studied, its alleles contributed positively to 57% of the QTLs. The other QTLs originated from either parent, IR71033 or Junambyeo. QTLs for phenotypically correlated traits were mostly detected on introgressed segments. Fourteen QTLs corresponded to QTLs reported earlier, indicating that these QTLs are stable across genetic backgrounds. Twenty-two QTLs controlling yield and its components had not been detected in previous QTL studies. Of these, thirteen consisted of potentially novel alleles from O. minuta. QTLs from O. minuta introgression could be new sources of natural variation for the genetic improvement of rice.     

Testing for the presence of previously identified QTL for milk production traits in new populations

Five chromosomal regions previously associated with milk production traits were tested in 16 families of Black and White cattle from the UK. The traits were also linearly transformed into genetically and phenotypically independent variables normalized by phenotypic variances ('canonical traits'). Significant associations with the untransformed and canonical traits were found for bovine chromosome 6. There was also evidence that chromosome 9 influenced these traits. The linear transformation clarified the effects of chromosomal regions; regions with effects on all three untransformed traits (milk, fat and protein yields) were generally condensed into an effect on a single canonical trait. Comparison of our results with those reported previously for American Holstein cattle suggested that the QTL may not be the same.     

Identification of SNPs in Interferon Gamma,Interleukin-22, and Their Receptors and Associations with Health and Production-Related Traits in Canadian Holstein Bulls

Genetic variants in a number of immunoregulatory genes have been previously associated with health and production traits in dairy cattle. Therefore, in the following study, the genes coding interferon gamma (IFNG), IFNG receptor 1 and 2 domains, interleukin-22 (IL22), and IL22 receptor alpha 1, were investigated for single nucleotide polymorphisms (SNPs) in Holstein bulls. These SNPs, along with SNPs previously identified in IL10, IL10 receptor, and transforming growth factor beta 1 (TGFB1) genes, were evaluated for statistical associations to estimated breeding values for milk somatic cell score (SCS), a trait highly correlated to mastitis incidence, and various production-related traits, including milk yield, protein yield, fat yield, and lactation persistency. While no significant associations were found between these SNPs and SCS, SNPs in IL10 receptor beta subunit showed a significant effect on protein yield and lactation persistency. While there is evidence that IL10 plays an important role during lactation, it is also likely that the effects of SNPs in IL10 receptor beta subunit on protein yield and lactation persistency are due to linkage disequilibrium with a neighboring QTL.     

Identification of SNPs in interferon gamma, interleukin-22, and their receptors and associations with health and production-related traits in Canadian Holstein bulls

Genetic variants in a number of immunoregulatory genes have been previously associated with health and production traits in dairy cattle. Therefore, in the following study, the genes coding interferon gamma (IFNG), IFNG receptor 1 and 2 domains, interleukin-22 (IL22), and IL22 receptor alpha 1, were investigated for single nucleotide polymorphisms (SNPs) in Holstein bulls. These SNPs, along with SNPs previously identified in IL10, IL10 receptor, and transforming growth factor beta 1 (TGFB1) genes, were evaluated for statistical associations to estimated breeding values for milk somatic cell score (SCS), a trait highly correlated to mastitis incidence, and various production-related traits, including milk yield, protein yield, fat yield, and lactation persistency. While no significant associations were found between these SNPs and SCS, SNPs in IL10 receptor beta subunit showed a significant effect on protein yield and lactation persistency. While there is evidence that IL10 plays an important role during lactation, it is also likely that the effects of SNPs in IL10 receptor beta subunit on protein yield and lactation persistency are due to linkage disequilibrium with a neighboring QTL.     

Quantitative trait locus-by-environment interaction for milk yield traits on Bos taurus autosome 6

Genotype-by-environment interactions for production traits in dairy cattle have often been observed, while QTL analyses have focused on detecting genes with general effects on production traits. In this study, a QTL search for genes with environmental interaction for the traits milk yield, protein yield, and fat yield were performed on Bos taurus autosome 6 (BTA6), also including information about the previously investigated candidate genes ABCG2 and OPN. The animals in the study were Norwegian Red. Eighteen grandsires and 716 sires were genotyped for 362 markers on BTA6. Every marker bracket was regarded as a putative QTL position. The effects of the candidate genes and the putative QTL were modeled as a regression on an environmental parameter (herd year), which is based on the predicted herd-year effect for the trait. Two QTL were found to have environmentally dependent effects on milk yield. These QTL were located 3.6 cM upstream and 9.1 cM downstream from ABCG2. No environmentally dependent QTL was found to significantly affect protein or fat yield.     

Fine mapping quantitative trait loci affecting milk production traits on bovine chromosome 6 in a Chinese Holstein population

To fine map the previously detected quantitative trait loci （QTLs） affecting milk production traits on bovine chromosome 6 （BTA6）, 15 microsatellite markers situated within an interval of 14.3 cM spanning from BMS690 to BM4528 were selected and 918 daughters of 8 sires were genotyped. Two mapping approaches, haplotype sharing based LD mapping and single marker regression mapping, were used to analyze the data. Both approaches revealed a quantitative trait locus （QTL） with significant effects on milk yield, fat yield and protein yield located in the segment flanked by markers BMS483 and MNB209, which spans a genetic distance of 0.6 cM and a physical distance of 1.5 Mb. In addition, the single marker regression mapping also revealed a QTL affecting fat percentage and protein percentage at marker DIK2291. Our fine mapping work will facilitate the cloning of candidate genes underlying the QTLs for milk production traits.     

Genetic parameters for milk urea concentration and milk traits in Polish Holstein-Friesian cows

Milk urea concentration (MU) used by dairy producers for management purposes can be affected by selection for milk traits. To assess this problem, genetic parameters for MU in Polish Holstein-Friesian cattle were estimated for the first three lactations. The genetic correlation of MU with milk production traits, lactose percentage, fat to protein ratio (FPR) and somatic cell score (SCS) were computed with two 5-trait random regression test-day models, separately for each lactation. Data used for estimation (159,044 daily observations) came from 50 randomly sampled herds. (Co)variance components were estimated with the Bayesian Gibbs sampling method. The coefficient of variation for MU in all three parities was high (40–41 %). Average daily heritabilities of MU were 0.22 for the first parity and 0.21 for the second and third lactations. Average genetic correlations for different days in milk in the first three lactations between MU and other traits varied. They were small and negative for protein percentage (from ?0.24 to ?0.11) and for SCS (from ?0.14 to ?0.09). The weakest genetic correlation between MU and fat percentage, and between MU and lactose percentage were observed (from ?0.10 to 0.10). Negative average genetic correlation with the fat to protein ratio was observed only in the first lactation (?0.14). Genetic correlations with yield traits were positive and ranged from low to moderate for protein (from 0.09 to 0.33), fat (from 0.16 to 0.35) and milk yield (from 0.20 to 0.42). These results suggest that the selection on yield traits and SCS tends to increase MU slightly.     

Quantitative trait loci associated with body weight and abdominal fat traits on chicken chromosomes 3, 5 and 7

Body weight and abdominal fat traits in meat-type chickens are complex and economically important factors. Our objective was to identify quantitative trait loci (QTL) responsible for body weight and abdominal fat traits in broiler chickens. The Northeast Agricultural University Resource Population (NEAURP) is a cross between broiler sires and Baier layer dams. We measured body weight and abdominal fat traits in the F(2) population. A total of 362 F(2) individuals derived from four F(1) families and their parents and F(0) birds were genotyped using 29 fluorescent microsatellite markers located on chromosomes 3, 5 and 7. Linkage maps for the three chromosomes were constructed and interval mapping was performed to identify putative QTLs. Nine QTL for body weight were identified at the 5% genome-wide level, while 15 QTL were identified at the 5% chromosome-wide level. Phenotypic variance explained by these QTL varied from 2.95 to 6.03%. In particular, a QTL region spanning 31 cM, associated with body weight at 1 to 12 weeks of age and carcass weight at 12 weeks of age, was first identified on chromosome 5. Three QTLs for the abdominal fat traits were identified at the 5% chromosome-wide level. These QTLs explained 3.42 to 3.59% of the phenotypic variance. This information will help direct prospective fine mapping studies and can facilitate the identification of underlying genes and causal mutations for body weight and abdominal fat traits.     

Merino sheep: a further look at quantitative trait loci for wool production

A quantitative trait loci (QTL) analysis of wool traits from experimental half-sib data of Merino sheep is presented. A total of 617 animals distributed in 10 families were genotyped for 36 microsatellite markers on four ovine chromosomes OAR1, OAR3, OAR4 and OAR11. The markers covering OAR3 and OAR11 were densely spaced, at an average distance of 2.8 and 1.2 cM, respectively. Body weight and wool traits were measured at first and second shearing. Analyses were conducted under three hypotheses: (i) a single QTL controlling a single trait (for multimarker regression models); (ii) two linked QTLs controlling a single trait (using maximum likelihood techniques) and (iii) a single QTL controlling more than one trait (also using maximum likelihood techniques). One QTL was identified for several wool traits on OAR1 (average curvature of fibre at first and second shearing, and clean wool yield measured at second shearing) and on OAR11 (weight and staple strength at first shearing, and coefficient of variation of fibre diameter at second shearing). In addition, one QTL was detected on OAR4 affecting weight measured at second shearing. The results of the single trait method and the two-QTL hypotheses showed an additional QTL segregating on OAR11 (for greasy fleece weight at first shearing and clean wool yield trait at second shearing). Pleiotropic QTLs (controlling more than one trait) were found on OAR1 (clean wool yield, average curvature of fibre, clean and greasy fleece weightand staple length, all measured at second shearing).     

Population-specific quantitative trait loci mapping for functional stay-green trait in rice (Oryza sativa L.)

The functional stay-green trait gives leaves a longer duration of greenness and photosynthetic capacity during the grain-filling period. We developed two independent recombinant inbred line populations from the intra- and intersubspecific crosses of Oryza sativa L. subsp. japonica 'Suweon490' (japonica) × O. sativa subsp. japonica 'SNU-SG1' (japonica) and O. sativa subsp. indica 'Andabyeo' (indica) × O. sativa subsp. japonica 'SNU-SG1' (japonica), respectively. The common parental line 'SNU-SG1' was the functional source for the stay-green trait. Quantitative trait locus (QTL) mapping based on simple sequence repeat markers identified a total of six QTLs associated with two stay-green traits across two populations. The two traits were cumulative chlorophyll content (SPAD value) of flag leaf (CSFL) and total cumulative SPAD value of the four upper leaves (TCS). Four QTLs, tcs4, csfl6, csfl9 (or tcs9), and csfl12, located on chromosomes 4, 6, 9, and 12, respectively, were detected simultaneously in both populations. The remaining two QTLs, csfl2 (or tcs2) and tcs5, on chromosomes 2 and 5, respectively, were found to be population specific. Moreover, the functional stay-green trait of 'SNU-SG1' positively correlated with grain yield performance. Two yield QTLs, yld6 and yld9, on chromosomes 6 and 9 found in both populations were positioned at the same locations with the csfl6 and tcs9 QTLs for stay-green traits. Thus, the identified chromosomal regions can be promising targets of marker-assisted introgression of the functional stay-green trait into breeding materials for improvement of rice yield.     

RFLP Mapping in Soybean: Association between Marker Loci and Variation in Quantitative Traits

Quantitative trait loci (QTLs) have been mapped to small intervals along the chromosomes of tomato (Lycopersicon esculentum), by a method we call substitution mapping. The size of the interval to which a QTL can be mapped is determined primarily by the number and spacing of previously mapped genetic markers in the region surrounding the QTL. We demonstrate the method using tomato genotypes carrying chromosomal segments from Lycopersicon chmielewskii, a wild relative of tomato with high soluble solids concentration but small fruit and low yield. Different L. chmielewskii chromosomal segments carrying a common restriction fragment length polymorphism were identified, and their regions of overlap determined using all available genetic markers. The effect of these chromosomal segments on soluble solids concentration, fruit mass, yield, and pH, was determined in the field. Many overlapping chromosomal segments had very different phenotypic effects, indicating QTLs affecting the phenotype(s) to lie in intervals of as little as 3 cM by which the segments differed. Some associations between different traits were attributed to close linkage between two or more QTLs, rather than pleiotropic effects of a single QTL: in such cases, recombination should separate desirable QTLs from genes with undesirable effects. The prominence of such trait associations in wide crosses appears partly due to infrequent reciprocal recombination between heterozygous chromosomal segments flanked by homozygous regions. Substitution mapping is particularly applicable to gene introgression from wild to domestic species, and generally useful in narrowing the gap between linkage mapping and physical mapping of QTLs.     

Bayesian inference of genetic parameters for test-day milk yield, milk quality traits, and somatic cell score in Burlina cows

The aim of the study was to infer (co)variance components for daily milk yield, fat and protein contents, and somatic cell score (SCS) in Burlina cattle (a local breed in northeast Italy). Data consisted of 13576 monthly test-day records of 666 cows (parities 1 to 8) collected in 10 herds between 1999 and 2009. Repeatability animal models were implemented using Bayesian methods. Flat priors were assumed for systematic effects of herd test date, days in milk, and parity, as well as for permanent environmental, genetic, and residual effects. On average, Burlina cows produced 17.0 kg of milk per day, with 3.66 and 3.33% of fat and protein, respectively, and 358000 cells per mL of milk. Marginal posterior medians (highest posterior density of 95%) of heritability were 0.18 (0.09–0.28), 0.28 (0.21–0.36), 0.35 (0.25–0.49), and 0.05 (0.01–0.11) for milk yield, fat content, protein content, and SCS, respectively. Marginal posterior medians of genetic correlations between the traits were low and a 95% Bayesian confidence region included zero, with the exception of the genetic correlation between fat and protein contents. Despite the low number of animals in the population, results suggest that genetic variance for production and quality traits exists in Burlina cattle.