Detection of multiple quantitative trait loci and their pleiotropic effects in outbred pig populations

Background

Simultaneous detection of multiple QTLs (quantitative trait loci) may allow more accurate estimation of genetic effects. We have analyzed outbred commercial pig populations with different single and multiple models to clarify their genetic properties and in addition, we have investigated pleiotropy among growth and obesity traits based on allelic correlation within a gamete.

Methods

Three closed populations, (A) 427 individuals from a Yorkshire and Large White synthetic breed, (B) 547 Large White individuals and (C) 531 Large White individuals, were analyzed using a variance component method with one-QTL and two-QTL models. Six markers on chromosome 4 and five to seven markers on chromosome 7 were used.

Results

Population A displayed a high test statistic for the fat trait when applying the two-QTL model with two positions on two chromosomes. The estimated heritabilities for polygenic effects and for the first and second QTL were 19%, 17% and 21%, respectively. The high correlation of the estimated allelic effect on the same gamete and QTL test statistics suggested that the two separate QTL which were detected on different chromosomes both have pleiotropic effects on the two fat traits. Analysis of population B using the one-QTL model for three fat traits found a similar peak position on chromosome 7. Allelic effects of three fat traits from the same gamete were highly correlated suggesting the presence of a pleiotropic QTL. In population C, three growth traits also displayed similar peak positions on chromosome 7 and allelic effects from the same gamete were correlated.

Conclusion

Detection of the second QTL in a model reduced the polygenic heritability and should improve accuracy of estimated heritabilities for both QTLs.     

Analysis of chromosomal localization of loci controlling milk production traits in cattle

Analysis of the pattern of the chromosomal localization of quantitative trait loci (QTLs) is necessary for comprehensively understanding their functions. The chromosomal localization of QTLs controlling milk production traits has been studied in cattle chromosomes. The distribution of QTLs between chromosomes has proved to be binomial. Their distribution along each chromosome was, in general, uniform, except for the QTLs controlling the somatic cell score (SCS), which tended towards telomeric location. However, there are chromosomes either enriched with or particularly poor in QTLs. The QTL distribution patters are the most similar for the milk yield (M) and milk protein yield (P) and for milk fat yield (F) and milk fat content (%F). The pattern of the SCS QTLs stands out among those of other QTLs. The distance between the QTLs of contrasting traits is the shortest for M and P QTLs, longer for M and milk protein content (%P) QTLs, and still longer for M and %F QTLs, which may be explained by QTL pleiotropy, a common phenomenon in cattle.     

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.     

Detection of QTL for milk production on Chromosomes 1 and 6 of Holstein cattle

Seventy to 75 sons of each of six Holstein sires were assayed for genotypes at a number of microsatellite loci spanning Chromosomes (Chrs) 1 and 6. The number of informative loci varied from three to eight on each chromosome in different sire families. Linkage order and map distance for microsatellite loci were estimated using CRI-MAP. Estimates of QTL effect and location were made by using a least squares interval mapping approach based on daughter yield deviations of sons for 305-d milk, fat, protein yield, and fat and protein percentage. Thresholds for statistical significance of QTL effects were determined from interval mapping of 10,000 random permutations of the data across the bull sire families and within each sire family separately. Across-sire analyses indicated a significant QTL for fat and protein yield, and fat percentage on Chr 1, and QTL effects on milk yield and protein percentage that might represent one or two QTL on Chr 6. Analyses within each sire family indicated significant QTL effects in five sire families, with one sire possibly being heterozygous for two QTLs. Statistically significant estimates of QTL effects on breeding value ranged from 340 to 640 kg of milk, from 15.6 to 28.4 kg of fat, and 14.4 to 17.6 kg of protein. Received: 19 November 1999 / Accepted: 31 August 2000     

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.     

Quantitative trait loci for carcass traits on pig chromosomes 4, 6, 7, 8 and 13

For 22 carcass traits, we identified 16 QTLs (based on data for pig resource population no. 214, including 180 F2 hybrids of 3 Yorkshire boars and 8 Meishan sows) and mapped them with the use of 39 microsatellite marker loci on chromosomes 4, 6, 7, 8 and 13. Five QTLs were highly significant (P < or = 0.01 at chromosome level): for skin weight (on chromosome 7 at SW1856 and on chromosome 13 at SW1495), skin percentage (on chromosome 7 between SW2155 and SW1856 and on chromosome 13 between SW1495 and SW520), and ratio of leg and butt to carcass (on chromosome 4 at SW1996). The remaining 11 QTLs were significant (P < or = 0.05 at chromosome level): for backfat thickness at shoulder, loin eye width, loin eye height, fat meat weight, lean meat weight, skin weight, bone weight, skin percentage, fat meat percentage, and ratio of lean meat to fat meat. The proportion of phenotypic variance explained by these QTLs ranged from 0.06% (QTL for loin eye width on chromosome 8 between SW1037 and SW1953) to 18.04% (QTL for ratio of lean meat to fat meat on chromosome 7 between SW252 and SW581). Seven of the QTLs reported here are novel.     

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.     

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).     

中国荷斯坦牛IL8基因遗传多态性与泌乳性状以及体细胞评分的关联

以上海某奶牛场30个公牛家系的610头中国荷斯坦牛为试验材料,采用聚合酶链式反应-单链构象多态性(PCR-SSCP)技术对Interleukin-8(IL8)基因的遗传多态性进行了分析,采用混合动物模型分析了IL8基因突变位点与测定日产奶量、测定日乳脂率、测定日乳蛋白率、305d校正产奶量、305d乳脂量、305d乳蛋白量及测定日体细胞评分7个性状的相关性,寻找可用于生产实际的分子标记。共检测到KK、KA和AA3种基因型,频率分别为0.187、0.451和0.362,等位基因K和A的频率分别为0.412和0.588。该位点突变对测定日产奶量、305d乳蛋白量、305d校正产奶量和305d乳脂量以及体细胞评分影响达到极显著水平(P0.01),对测定日乳蛋白率的影响达到显著水平(P0.05),对测定日乳脂率影响不显著(P0.05)。多重比较表明:KK基因型对测定日产奶量、305d校正产奶量、305d乳蛋白量和305d乳脂量极显著高于AA和KA基因型(P0.01)。KK基因型的体细胞评分(SCS)最小二乘均值极显著低于KA、AA基因型(P0.01)。对于测定日的乳蛋白率AA基因型显著低于KA、KK型(P0.05)。IL8基因遗传突变对中国荷斯坦牛泌乳性状和乳房炎抗性有较大的遗传效应,可用于中国荷斯坦牛的分子标记辅助选择。     

Effect of leptin gene polymorphism on the breeding value of milk production traits in Iranian Holstein

New molecular techniques focused on genome analysis, open new possibilities for more accurate evaluation of economiclly important traits in farm animals. Milk production traits are typical quantitative characteristics controlled by a number of genes. Mutations in their sequences may alter animal performance as well as their breeding values. In this study, we investigated the effect of Kpn2I restriction fragment length polymorphisms in the leptin gene, on bull breeding values for milk yield, fat, and protein yield, and their percentage. In order to test for an association between the leptin single-nucleotide polymorphism in exon 2 and milk productivity, we genotyped 134 Iranian Holstein bulls. Breeding values for milk-related traits (milk yield, fat, and protein yield and percentage) were estimated using the BLUP based on an animal model. The effect of the genotypes of Kpn2I polymorphism on the breeding values for milk-related traits was examined using least square methods. The T allele frequency was 0.425. Genotypes were distributed according to the Hardy-Weinberg equilibrium. Bulls with TT genotype had higher milk, fat and protein yield compared with TC and CC bulls (P < 0.05). Bulls with CC genotype had higher protein percentage compared with TT and TC bulls (P < 0.05). The association between leptin polymorphism with milk production traits suggests that this marker may be useful for selection based on molecular information.     

A candidate gene association study for nine economically important traits in Italian Holstein cattle

We genotyped 58 single nucleotide polymorphisms (SNPs) in 25 candidate genes in about 800 Italian Holstein sires. Fifty‐six (minor allele frequency >0.02) were used to evaluate their association with single traits: milk yield (MY), milk fat yield (FY), milk protein yield (PY), milk fat percentage (FP), milk protein percentage (PP), milk somatic cell count (MSCC); and complex indexes: longevity, fertility and productivity–functionality type (PFT), using deregressed proofs, after adjustment for familial relatedness. Thirty‐two SNPs were significantly associated (proportion of false positives <0.05) with different traits: 16 with MSCC, 15 with PY, 14 with MY, 12 with PFT, eight with longevity, eight with FY, eight with PP, five with FP and two with fertility. In particular, a SNP in the promoter region of the PRLR gene was associated with eight of nine traits. DGAT1 polymorphisms were highly associated with FP and FY. Casein gene markers were associated with several traits, confirming the role of the casein gene cluster in affecting milk yield, milk quality and health traits. Other SNPs in genes located on chromosome 6 were associated with PY, PP, PFT, MY (PPARGC1A) and MSCC (KIT). This latter association may suggest a biological link between the degree of piebaldism in Holstein and immunological functions affecting somatic cell count and mastitis resistance. Other significant SNPs were in the ACACA, CRH, CXCR1, FASN, GH1, LEP, LGB (also known as PAEP), MFGE8, SRC, TG, THRSP and TPH1 genes. These results provide information that can complement QTL mapping and genome‐wide association studies in Holstein.     

Mapping and Analysis of Dairy Cattle Quantitative Trait Loci by Maximum Likelihood Methodology Using Milk Protein Genes as Genetic Markers

Maximum likelihood methodology was used to estimate effects of both a marker gene and a linked quantitative trait locus (QTL) on quantitative traits in a segregating population. Two alleles were assumed for the QTL. In addition to the effects of genotypes at both loci on the mean of the quantitative trait, recombination frequency between the loci, frequency of the QTL alleles and the residual standard deviation were also estimated. Thus six parameters were estimated in addition to the marker genotype means. The statistical model was tested on simulated data, and used to estimate direct and linked effects of the milk protein genes, β-lactoglobulin, κcasein, and β-casein, on milk, fat, and protein production and fat and protein percent in the Dutch dairy cattle population. β-Lactoglobulin had significant direct effects on milk yield and fat percent. κ-Casein had significant direct effects on milk yield, protein percent and fat yield. β-Casein had significant direct effects on milk yield, fat and protein percent and fat and protein yield. Linked QTL with significant effects on fat percent were found for κ-casein and β-casein. Since the β-casein and κ-casein genes are closely linked, it is likely that the same QTL was detected for those two markers. Further, a QTL with a significant effect on fat yield was found to be linked to κ-casein and a QTL with a significant effect on protein yield was linked to β-lactoglobulin.     

A quantitative trait locus for live weight maps to bovine Chromosome 23

A multiple-marker mapping approach was used to search for quantitative trait loci (QTLs) affecting production, health, and fertility traits in Finnish Ayrshire dairy cattle. As part of a whole-genome scan, altogether 469 bulls were genotyped for six microsatellite loci in 12 families on Chromosome (Chr) 23. Both multiple-marker interval mapping with regression and maximum-likelihood methods were applied with a granddaughter design. Eighteen traits, belonging to 11 trait groups, were included in the analysis. One QTL exceeded experiment level and one QTL genome level significance thresholds. Across-families analysis provided strong evidence (P_experiment= 0.0314) for a QTL affecting live weight. The QTL for live weight maps between markers BM1258 and BoLA DRBP1. A QTL significant at genome level (P_genome= 0.0087) was mapped for veterinary treatment, and the putative QTL probably affects susceptibility to milk fever or ketosis. In addition, three traits exceeded the chromosome 5% significance threshold: protein percentage of milk, calf mortality (sire), and milking speed. In within-family analyses, protein percentage was associated with markers in one family (LOD score = 4.5). Received: 14 December 1998 / Accepted: 28 March 1998     

The identification of QTL that affect the fatty acid composition of milk on sheep chromosome 11

In this work, we analysed 11 genetic markers localized on OAR11 in a commercial population of Spanish Churra sheep to detect QTL that underlie milk fatty acid (FA) composition traits. Following a daughter design, we analysed 799 ewes distributed in 15 half‐sib families. Eight microsatellite markers and three novel SNPs identified in two genes related to fatty acid metabolism, acetyl‐CoA carboxylase α (ACACA) and fatty acid synthase (FASN), were genotyped in the whole population under study. The phenotypic traits considered in the study included 22 measurements related to the FA composition of the milk and three other milk production traits (milk protein percentage, milk fat percentage and milk yield). Across‐family regression analysis revealed four significant QTL at the 5% chromosome‐wise level influencing contents of capric acid (C10:0), lauric acid (C12:0), linoleic conjugated acid (CLA) and polyunsaturated fatty acids (PUFA) respectively. The peaks of the QTL affecting C10:0 and PUFA contents in milk map close to the FASN gene, which has been evaluated as a putative positional candidate for these QTL. The QTL influencing C12:0 content reaches its maximum significance at 58 cM, close to the gene coding for the glucose‐dependent insulinotropic polypeptide. We were not able to find any candidate genes related to fat metabolism at the QTL influencing CLA content, which is located at the proximal end of the chromosome. Further research efforts will be needed to confirm and refine the QTL locations reported here.     

Casein haplotypes and their association with milk production traits in the Finnish Ayrshire cattle

Polymorphism of casein genes was studied in half-sib families of artificial insemination bulls of the Finnish Ayrshire dairy breed. Ten grandsires and 300 of their sons were genotyped for the following polymorphisms: α_S1-casein (B, C), β-casein (A¹, A²), the microsatellite within the K-casein gene (ms⁵, ms⁴) and K-casein (A, B, E). Nine different combinations of these alleles, casein haplotypes, were found. Associations between casein haplotypes and milk production traits (milk and protein yield, fat and protein percentage and milking speed) were studied with ordinary least-squares analysis to find a direct effect of the haplotypes or an association within individual grandsire families using the granddaughter design. Estimated breeding values of sons were obtained from cow evaluation by animal model. No direct effect of the casein haplotypes on the traits was found. Within grandsire families, in one out of four families the chromosomal segment characterized by haplotype 3 (B-A²-ms⁴-A) was associated with an increase in milk yield ( P <0.01) and a decrease in fat percentage ( P < 0.01) when contrasted with haplotype 8 (B-A¹-ms⁴-E). The results provide evidence that in the Finnish Ayrshire breed at least one quantitative trait locus affecting the genetic variation in yields traits is segregating linked to either haplotype 3 (B-A²-ms⁴-A) or 8 (B-A¹-ms⁴-E).     

Combined analysis of data from two granddaughter designs: A simple strategy for QTL confirmation and increasing experimental power in dairy cattle

A joint analysis of five paternal half-sib Holstein families that were part of two different granddaughter designs (ADR- or Inra-design) was carried out for five milk production traits and somatic cell score in order to conduct a QTL confirmation study and to increase the experimental power. Data were exchanged in a coded and standardised form. The combined data set (JOINT-design) consisted of on average 231 sires per grandsire. Genetic maps were calculated for 133 markers distributed over nine chromosomes. QTL analyses were performed separately for each design and each trait. The results revealed QTL for milk production on chromosome 14, for milk yield on chromosome 5, and for fat content on chromosome 19 in both the ADR- and the Inra-design (confirmed within this study). Some QTL could only be mapped in either the ADR- or in the Inra-design (not confirmed within this study). Additional QTL previously undetected in the single designs were mapped in the JOINT-design for fat yield (chromosome 19 and 26), protein yield (chromosome 26), protein content (chromosome 5), and somatic cell score (chromosome 2 and 19) with genomewide significance. This study demonstrated the potential benefits of a combined analysis of data from different granddaughter designs.     

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.     

The role of the bovine growth hormone receptor and prolactin receptor genes in milk, fat and protein production in Finnish Ayrshire dairy cattle

We herein report new evidence that the QTL effect on chromosome 20 in Finnish Ayrshire can be explained by variation in two distinct genes, growth hormone receptor (GHR) and prolactin receptor (PRLR). In a previous study in Holstein-Friesian dairy cattle an F279Y polymorphism in the transmembrane domain of GHR was found to be associated with an effect on milk yield and composition. The result of our multimarker regression analysis suggests that in Finnish Ayrshire two QTL segregate on the chromosomal region including GHR and PRLR. By sequencing the coding sequences of GHR and PRLR and the sequence of three GHR promoters from the pooled samples of individuals of known QTL genotype, we identified two substitutions that were associated with milk production traits: the previously reported F-to-Y substitution in the transmembrane domain of GHR and an S-to-N substitution in the signal peptide of PRLR. The results provide strong evidence that the effect of PRLR S18N polymorphism is distinct from the GHR F279Y effect. In particular, the GHR F279Y has the highest influence on protein percentage and fat percentage while PRLR S18N markedly influences protein and fat yield. Furthermore, an interaction between the two loci is suggested.     

QTLs for grain dry milling properties, composition and vitreousness in maize recombinant inbred lines

Vitreousness and kernel hardness are important properties for maize processing and end-product quality. In order to examine the genetic basis of these traits, a recombinant inbred line population resulting from a cross between a flint line (F-2) and a semident line (Io) was used to search for vitreousness and kernel composition QTLs. Vitreousness was measured by image processing from a kernel section, while NIR spectroscopy was used to estimate starch, protein, cellulose, lipid and semolina yield. In addition, thousand-grain weight and grain weight per ear were measured. The MQTL method was used to map the QTLs for the different traits. An additional program allowed for the detection of interaction QTLs between markers. The total number of main-effect and interaction QTLs was similar. The QTLs were not evenly distributed but tended to cluster. Such clusters, mixing main-effect and interaction QTLs, were observed at six positions : on chromosomes 1, 2, 3, 6, 8 and 9. Two of them, on chromosomes 6 and 9, concerned both QTLs for kernel-weight traits and QTLs for kernel-composition traits (protein and cellulose). Technological-trait QTLs (vitreousness or semolina yield) were located less than 16 cM from a protein-content QTL on chromosome 2, and were co-located with lipid- and starch-content QTLs on chromosome 8. The co-location of a vitreousness and a semolina-yield QTL at the telomeric end of the chromosome 2 (Bin 2.02) is likely to be meaningful since measurement of these related traits, made by completely different methods (NIRS vs image processing), yielded very close QTLs. A similar location was previously reported independently for a kernel-friability QTL. Comparing the map location of the numerous loci for known-function genes it was shown that three zein loci were closely linked to QTLs for vitreousness on chromosome 3, for semolina yield and starch on chromosome 4, and for protein, cellulose and grain weight on chromosome 9. Some other candidate genes linked to starch precursor metabolism were also suggested on chromosomes 6 and 8. Received: 27 April 2000 / Accepted: 3 July 2000     

Modelling QTL effect on BTA06 using random regression test day models

In statistical models, a quantitative trait locus (QTL) effect has been incorporated either as a fixed or as a random term, but, up to now, it has been mainly considered as a time-independent variable. However, for traits recorded repeatedly, it is very interesting to investigate the variation of QTL over time. The major goal of this study was to estimate the position and effect of QTL for milk, fat, protein yields and for somatic cell score based on test day records, while testing whether the effects are constant or variable throughout lactation. The analysed data consisted of 23 paternal half-sib families (716 daughters of 23 sires) of Chinese Holstein-Friesian cattle genotyped at 14 microsatellites located in the area of the casein loci on BTA6. A sequence of three models was used: (i) a lactation model, (ii) a random regression model with a QTL constant in time and (iii) a random regression model with a QTL variable in time. The results showed that, for each production trait, at least one significant QTL exists. For milk and protein yields, the QTL effect was variable in time, while for fat yield, each of the three models resulted in a significant QTL effect. When a QTL is incorporated into a model as a constant over time, its effect is averaged over lactation stages and may, thereby, be difficult or even impossible to be detected. Our results showed that, in such a situation, only a longitudinal model is able to identify loci significantly influencing trait variation.