Article

Estimation of Quantitative Trait Loci Effects in Dairy Cattle Populations

Authors:
To read the full-text of this research, you can request a copy directly from the authors.

Abstract

Standard animal model programs can be modified to include the effect of a quantitative gene, even if only a fraction of the population is genotyped. Five methods to estimate the effect of a diallelic quantitative gene affecting a quantitative trait were compared to a standard animal model (model I) on simulated populations, based on mean squared errors and bias. In models II, III, and IV complete linkage between a single genetic marker and the quantitative trait gene was assumed. In models II and III the elements of the incidence matrix for the gene effect were 0 or 1 for genotyped individuals, and the probabilities of the possible candidate gene genotypes for individuals that were not genotyped. In model III segregation analysis was used to compute these probabilities. If only some of the cows were genotyped, the model III estimates were nearly unbiased, while model II underestimated the simulated effects. When only sires were genotyped, model II overestimated the simulated effect. In models V and VI two markers bracketing the quantitative gene with recombination frequencies of 0.1 and 0.2 with the quantitative gene were simulated, and the algorithm of Whittaker et al. (1996) was used to derive estimates of gene effect and location. In model V marker allele effects were included in the animal model analysis. In model VI, the model I genetic evaluations were analyzed. Model V estimates for both effect and location of the quantitative gene were unbiased, while model VI estimates were only 0.25 of the simulated effect.

No full-text available

Request Full-text Paper PDF

To read the full-text of this research,
you can request a copy directly from the authors.

... QTL effects via a modified animal model, even though only a small fraction of the population was genotyped, provided that QTL genotype probabilities can be derived for all animals. Kerr and Kinghorn (1996) derived an algorithm to estimate genotype probabilities for all animals in a population, based on a sample of individuals with known genotypes. Israel and Weller (2002) applied this method on simulated data to obtain unbiased estimates of QTL effects via a modified animal model analysis. Meuwissen and Goddard (2002) proposed that population-wide linkage disequilibrium (LD) could be used to fine map QTL. Looft et al. (2001) presented evidence for population-wide LD in the German dairy cattle population ...
... The estimated allelic frequencies as a function of birth year were computed for the entire population of bulls and cows. Modified animal model evaluations were computed for the entire population for milk, fat, and protein production including the QTL as a fixed effect as described by Israel and Weller (2002). The QTL effect was assumed to be additive. ...
Article
Full-text available
A population-wide linkage disequilibrium on bovine chromosome 14 between microsatellite ILSTS039 and DGAT1, a putative quantitative trait locus affecting milk production traits, was found in the Israeli Holstein population. A total of 394 bulls were genotyped for both DGAT1 and ILSTS039, and 1747 cows were genotyped for ILSTS039. The ILSTS039 allele termed "225," and the DGAT1 K allele (substitution of a lysine residue with alanine), were associated with decreased milk production, and increased fat production and fat and protein percent. The number of 225 ILSTS039 and K DGAT1 alleles per individual were the same for 80% of the bulls genotyped. From the effects associated with cows homozygous for the 225 allele, the effect of the quantitative trait locus appears to be approximately codominant. The substitution effect was 0.16% fat. Genotype probabilities for the quantitative gene were determined for the entire Israeli Holstein milk-recorded population, including 507,725 cows and 1442 bulls, using segregation analysis. Overall frequency of the allele that increased fat percent was 8.9% in cows and 15.5% in bulls. The frequency of this allele decreased from 1981 until 1990, from 15 to 5%, and since has increased to 10%. The effects estimated on the population-wide analyses of both cows and bulls were similar to the effect associated with DGAT1 in the daughters of genotyped bulls. Modified animal model evaluations were computed for the entire population with the effect of this gene included in the model. The correlations between the modified and standard animal model evaluations for all traits were > 0.99.
... Dans la pratique, les index dérégresssés ne sont utilisés que dans les cas où les DYD ne sont pas disponibles. En revanche quelques études ont comparé l'utilisation des DYD par rapport aux valeurs génétiques estimées (EBV) comme phénotypes, principalement pour la détection de QTL, donnant tantôt l'avantage aux DYD (VanRaden and Wiggans, 1991;Hoeschele and VanRaden, 1993;Israel and Weller, 1998), tantôt l'avantage aux EBV (Israel and Weller, 2002). Sur données simulées, les précisions des évaluations génomiques obtenues avec les EVB sont légèrement plus élevées (entre 0,3 et 3,6%) que celles obtenues avec les DYD. ...
Thesis
La sélection génomique, qui a révolutionné la sélection génétique des bovins laitiers notamment, est désormais envisagée dans d’autres espèces comme l’espèce caprine. La clé du succès de la sélection génomique réside dans la précision des évaluations génomiques. Chez les caprins laitiers français, le gain de précision attendu avec la sélection génomique était un des questionnements de la filière en raison de la petite taille de la population de référence disponible (825 mâles et 1945 femelles génotypés sur une puce SNP 50K). Le but de cette étude est d’évaluer comment augmenter la précision des évaluations génomiques dans l’espèce caprine. Une étude de la structure génétique de la population de référence caprine constituée d’animaux de races Saanen et Alpine, a permis de montrer que la population de référence choisie est représentative de la population élevée sur le territoire français. En revanche, les faibles niveaux de déséquilibre de liaison (0,17 entre deux SNP consécutifs) de consanguinité et de parenté au sein de la population, similaires à ceux trouvés en ovins Lacaune, ne sont pas idéaux pour obtenir une bonne précision des évaluations génomiques. De plus, malgré l’origine commune des races Alpine et Saanen, leurs structures génétiques suggèrent qu’elles se distinguent clairement d’un point de vue génétique. Les méthodes génomiques (GBLUP ou Bayésiennes) « two-step », basées sur des performances pré-corrigées (DYD, EBV dérégressées) n’ont pas permis une amélioration significative des précisions des évaluations génomiques pour les caractères évalués en routine (caractères de production, de morphologie et de comptages de cellules somatiques) chez les caprins laitiers. La prise en compte des phénotypes des mâles non génotypés permet d’augmenter les précisions des évaluations de 3 à 47% selon le caractère. L’ajout des génotypes de femelles issues d’un dispositif de détection de QTL améliore également les précisions (de 2 à 14%) que ce soit pour les évaluations two steps ou les évaluations basées sur les performances propres des femelles (single step). Les précisions sont augmentées de 10 à 74% avec les évaluations single step comparées aux évaluations two steps, ce qui permet d’atteindre des précisions supérieures à celles obtenues sur ascendance. Les précisions obtenues avec les évaluations génomiques multiraciales, bicaractères et uniraciales sont similaires même si la précision des valeurs génomiques estimées des candidats à la sélection est plus élevée avec les évaluations multiraciales. La sélection génomique est donc envisageable chez les caprins laitiers français à l’aide d’un modèle génomique multiracial single step. Les précisions peuvent être légèrement augmentées par l’inclusion de gènes majeurs tels que celui de la caséine αs1 notamment à l’aide d’un modèle « gene content » pour prédire le génotype des animaux non génotypés.
... These probabilities can be readily computed for the entire population using the segregation analysis method of Kerr and Kinghorn (1996). Israel and Weller (2002) extended this method to a situation of a QTL bracketed by two genetic markers, based on the regression analysis method of Whittaker et al. (1996). ...
Article
Full-text available
The method of Israel and Weller (Estimation of candidate gene effects in dairy cattle populations. Journal of Dairy Science 1998, 81, 1653-1662) to estimate quantitative trait locus (QTL) effects when only a small fraction of the population was genotyped was investigated by simulation. The QTL effect was underestimated in all cases, but bias was greater for extreme allelic frequencies, and increased with the number of generations included in the simulations. Apparently, as the fraction of animals with inferred genotypes increases, the genotype probabilities tend to 'mimic' the effect of relationships. Unbiased estimates of QTL effects were derived by a modified 'cow model' without the inclusion of the relationship matrix on simulated data, even though only a small fraction of the population was genotyped. This method yielded empirically unbiased estimates for the effects of the genes DGAT1 and ABCG2 on milk production traits in the Israeli Holstein population. Based on these results, an efficient algorithm for marker-assisted selection in dairy cattle was proposed. Quantitative trait loci effects are estimated and subtracted from the cows' records. Genetic evaluations are then computed for the adjusted records. Animals are then selected based on the sum of their polygenic genetic evaluations and QTL effects. This scheme differs from a traditional dairy cattle breeding scheme in that all bull calves were considered candidates for selection. At year 10, total genetic gain was 20% greater by the proposed algorithm as compared to the selection based on a standard animal model for a locus with a substitution effect of 0.5 phenotypic standard deviations. The proposed method is easy to apply, and all required software are 'on the shelf.' It is only necessary to genotype breeding males, which are a very small fraction of the entire population. The method is flexible with respect to the model used for routine genetic evaluation. Any number of genetic markers can be easily incorporated into the algorithm, and the reduction in genetic gain due to incorrect QTL determination is minimal.
... Data from LD or direct markers on the other hand, can be incorporated in existing genetic evaluation procedures as fixed genotype or haplotype effects (Van Arendonk et al., 1999). If not all animals are genotyped, which will be the case in practice, marker data must be supplemented with genotype probabilities, which can be derived using pedigree and marker data (e.g., Israel and Weller, 2002). Nevertheless, computational requirements for incorporating LD or direct markers in genetic evaluation are much less than for LE markers. ...
Article
Full-text available
During the past few decades, advances in molecular genetics have led to the identification of multiple genes or genetic markers associated with genes that affect traits of interest in livestock, including genes for single-gene traits and QTL or genomic regions that affect quantitative traits. This has provided opportunities to enhance response to selection, in particular for traits that are difficult to improve by conventional selection (low heritability or traits for which measurement of phenotype is difficult, expensive, only possible late in life, or not possible on selection candidates). Examples of genetic tests that are available to or used in industry programs are documented and classified into causative mutations (direct markers), linked markers in population-wide linkage disequilibrium with the QTL (LD markers), and linked markers in population-wide equilibrium with the QTL (LE markers). In general, although molecular genetic information has been used in industry programs for several decades and is growing, the extent of use has not lived up to initial expectations. Most applications to date have been integrated in existing programs on an ad hoc basis. Direct markers are preferred for effective implementation of marker-assisted selection, followed by LD and LE markers, the latter requiring within-family analysis and selection. Ease of application and potential for extra-genetic gain is greatest for direct markers, followed by LD markers, but is antagonistic to ease of detection, which is greatest for LE markers. Although the success of these applications is difficult to assess, several have been hampered by logistical requirements, which are substantial, in particular for LE markers. Opportunities for the use of molecular information exist, but their successful implementation requires a comprehensive integrated strategy that is closely aligned with business goals. The current attitude toward marker-assisted selection is therefore one of cautious optimism.
... In general, using DYD for QTL estimation has been recommended, because of the preference of a phenotypic over a regressed measure. Several studies supported this recommendation, while others showed that analysis of EBV lead to accurate estimates of QTL positions (e.g., Israel and Weller, 2002, cited the method by Whittaker et al., 1996). ...
Article
Full-text available
The primary aim of this study was to investigate whether previous findings of similar quantitative trait loci (QTL) positions for correlated yield traits are due to a pleiotropic QTL. We applied a multitrait variance component based QTL mapping method to a dataset involving five granddaughter families from the German Holstein dairy cattle population. The marker map contained 16 microsatellite markers, distributed across chromosome BTA6. A chromosomewise significance threshold was used, because BTA6 is known to harbor QTL for several milk traits. To evaluate the results from the multivariate, across-family analysis, we also conducted single-family analyses using the least squares method of QTL estimation. The results provided two significant QTL findings at 49 and 64 cM for milk yield in different families and putative QTL at 68 cM for fat yield and at 71 cM for protein yield in another family. The results for fat and protein yield were confirmed by a univariate, across-family variance components analysis. The multivariate analysis of three bivariate trait combinations resulted in a significant pleiotropic QTL finding at 68 cM for fat yield and protein yield, bracketed by markers TGLA37 and FBN13. The estimates of variance contribution due to this QTL were 23% and 25%, respectively.
... The properties of EBV in association studies with DNA markers have been evaluated in several reports, mainly dealing with QTL mapping and association studies in dairy cattle, suggesting a smaller, or at best equivalent, power in using EBV as compared with phenotypic measurements (Thomsen et al., 2001;Israel and Weller, 2002;Viitala et al., 2003;Daetwyler et al., 2008). The use of EBV is convenient in commercial pig breeding stocks because they are routinely calculated by the company or the herdbook. ...
Article
Full-text available
The objective of this study was to evaluate the effects of mutations in 2 genes [IGF2 and cathepsin D (CTSD)] that map on the telomeric end of the p arm of SSC2. In this region, an imprinted QTL affecting muscle mass and fat deposition was reported, and the IGF2 intron3-g.3072G>A substitution was identified as the causative mutation. In the same chromosome region, we assigned, by linkage mapping, the CTSD gene, a lysosomal proteinase, for which we previously identified an SNP in the 3'-untranslated region (AM933484, g.70G>A). We have already shown strong effects of this CTSD mutation on several production traits in Italian Large White pigs, suggesting a possible independent role of this marker in fatness and meat deposition in pigs. To evaluate this hypothesis, after having refined the map position of the CTSD gene by radiation hybrid mapping, we analyzed the IGF2 and the CTSD polymorphisms in 270 Italian Large White and 311 Italian Duroc pigs, for which EBV and random residuals from fixed models were calculated for several traits. Different association analyses were carried out to distinguish the effects of the 2 close markers. In the Italian Large White pigs, the results for IGF2 were highly significant for all traits when using either EBV or random residuals (e.g., using EBV: lean cuts, P = 2.2 x 10(-18); ADG, P = 2.6 x 10(-16); backfat thickness, P = 2.2 x 10(-9); feed:gain ratio, P = 2.3 x 10(-9); ham weight, P = 1.5 x 10(-6)). No effect was observed for meat quality traits. The IGF2 intron3-g.3072G>A mutation did not show any association in the Italian Duroc pigs, probably because of the small variability at this polymorphic site for this breed. However, a significant association was evident for the CTSD marker (P < 0.001) with EBV of all carcass and production traits in Italian Duroc pigs (lean content, ADG, backfat thickness, feed:gain ratio) after excluding possible confounding effects of the IGF2 mutation. The effects of the CTSD g.70G>A mutation were also confirmed in a subset of Italian Large White animals carrying the homozygous genotype IGF2 intron3-g.3072GG, and by haplotype analysis between the markers of the 2 considered genes in the complete data set. Overall, these results indicate that the IGF2 intron3-g.3072G>A mutation is not the only polymorphism affecting fatness and muscle deposition on SSC2p. Therefore, the CTSD g.70G>A polymorphism could be used to increase selection efficiency in marker-assisted selection programs that already use the IGF2 mutation. However, for practical applications, because the CTSD gene should not be imprinted (we obtained this information from expression analysis in adult skeletal muscle), the different modes of inheritance of the 2 genes have to be considered.
... Many previous studies have been conducted on the efficiency of EBV and DYD as response variables for QTL mapping and estimation of QTL effects. Several studies supported the DYD approach (e.g., Van Raden & Wiggans 1991; Hoeschele & VanRaden 1993; Israel & Weller 1998), whereas others showed EBV analysis lead to accurate estimates of QTL positions (e.g., Israel & Weller 2002 ). In addition, Rodri- Zas et al. (2002) showed that results from DYD and predicted transmitting ability analyses were highly consistent in interval and composite interval mapping of somatic cell scores, yield and milk components in dairy cattle. ...
Article
This study compared genomic predictions using conventional estimated breeding values (EBV) and daughter yield deviations (DYD) as response variables based on simulated data. Eight scenarios were simulated in regard to heritability (0.05 and 0.30), number of daughters per sire (30, 100, and unequal numbers with an average of 100 per sire) and numbers of genotyped sires (all or half of sires were genotyped). The simulated genome had a length of 1200 cM with 15,000 equally spaced Single-nucleotide polymorphism (SNP) markers and 500 randomly distributed Quantitative trait locus (QTL). In the simulated scenarios, the EBV approach was as effective as or slightly better than the DYD approach at predicting breeding value, dependent on simulated scenarios and statistical models. Applying a Bayesian common prior model (the same prior distribution of marker effect variance) and a linear mixed model (GBLUP), the EBV and DYD approaches provided similar genomic estimated breeding value (GEBV) reliabilities, except for scenarios with unequal numbers of daughters and half of sires without genotype, for which the EBV approach was superior to the DYD approach (by 1.2 and 2.4%). Using a Bayesian mixture prior model (mixture prior distribution of marker effect variance), the EBV approach resulted in slightly higher reliabilities of GEBV than the DYD approach (by 0.3-3.6% with an average of 1.9%), and more obvious in scenarios with low heritability, small or unequal numbers of daughters, and half of sires without genotype. Moreover, the results showed that the correlation between GEBV and conventional parent average (PA) was lower (corresponding to a relatively larger gain by including PA) when using the DYD approach than when using the EBV approach. Consequently, the two approaches led to similar reliability of an index combining GEBV and PA in most scenarios. These results indicate that EBV can be used as an alternative response variable for genomic prediction.
... Several studies, mainly in dairy cattle, have evaluated the properties of EBV in association studies, suggesting a lower, or at best equivalent, power in using EBV as compared with raw or adjusted phenotypic measurements (e.g. Thomsen et al., 2001;Israel and Weller, 2002). Other simulation studies have reported that using EBVs could produce a higher level of type I errors compared with other approaches (Ekine et al., 2010), even if evaluation for the level of type II errors is not reported. ...
Article
Full-text available
The proprotein convertase subtilisin/kexin type 1 (PCSK1) gene encodes the prohormone convertase 1/3 enzyme that processes prohormones into functional hormones that, in turn, regulate central and peripheral energy metabolism. Mutations in the human PCSK1 gene cause severe monogenic obesity or confer risk of obesity. We herein investigated the porcine PCSK1 gene with the aim of identifying polymorphisms associated with fat deposition and production traits in Italian heavy pigs. By re-sequencing about 5.1 kb of this gene in 21 pigs of different breeds, we discovered 14 polymorphisms that were organized in nine haplotypes, clearly distributed in two clades of putative European and Asian origin. Then we re-mapped this gene on porcine chromosome 2 and analysed its expression in several tissues including gastric oxyntic mucosa of weanling pigs in which PCSK1 processes the pre-pro-ghrelin into ghrelin, which in turn is involved in the control of feed intake and energy metabolism. Association analyses between PCSK1 single-nucleotide polymorphisms (SNPs) and production, carcass and several other traits were conducted on five groups of pigs from three different experimental designs, for a total of 1221 animals. Results indicated that the analysed SNPs were associated (P < 0.01 or P < 0.05) with several traits including backfat thickness and visible intermuscular fat in Italian Duroc (ID) and growth performances in Italian Large White (ILW) and in ILW × Italian Landrace pigs. However, the effects estimated in the ILW were opposite to the effects reported in the ID pigs. Suggestive association (P < 0.10) was observed with muscle cathepsin B activity, opening, if confirmed, potential applications to reduce the excessive softness defect of the green hams that is of particular concern for the processing industry. The results obtained supported the need to further investigate the PCSK1 gene to fully exploit the value of its variability and apply this information in pig breeding programmes.
... This last approach was chosen to maximize the possibilities to identify true associations and overcome potential biases derived by the different ways in which the traits are recorded and/or calculated, considering their low heritability. Properties of EBV in association studies have been mainly evaluated in dairy cattle for which a lower or equivalent power has been reported as compared to raw or adjusted phenotypic parameters (Israel and Wellert, 2002). However, in a simulation study based on a commercial pig population, Ekine et al. (2010) indicated that using EBVs in association studies could result in a higher level of false positives for traits with low heritability. ...
... Dans la pratique, les index dérégresssés ne sont utilisés que dans les cas où les DYD ne sont pas disponibles. En revanche quelques études ont comparé l'utilisation des DYD par rapport aux valeurs génétiques estimées (EBV) comme phénotypes, principalement pour la détection de QTL, donnant tantôt l'avantage aux DYD (VanRaden and Wiggans, 1991;Hoeschele and VanRaden, 1993;Israel and Weller, 1998), tantôt l'avantage aux EBV (Israel and Weller, 2002). Sur données simulées, les précisions des évaluations génomiques obtenues avec les EVB sont légèrement plus élevées (entre 0,3 et 3,6%) que celles obtenues avec les DYD. ...
Thesis
Full-text available
Genomic selection which is revolutionizing genetic selection in dairy cattle has been tested in several species like dairy goat. Key point in genomic selection is accuracy of genomic evaluation. In French dairy goats, gain in accuracy using genomic selection was questioning due to the small size of the reference population (825 males and 1 945 females genotyped). The aim of this study was to investigate how to reach adequate genomic evaluation accuracy in French dairy goat population. The study of reference population structure (Alpine and Saanen breeds) showed that reference population is similar to the whole population of French dairy goats. But the weak level of linkage disequilibrium (0.17 between two consecutive SNP), inbreeding and relationship between reference and candidate population were not ideal to maximize genomic evaluation accuracy. Despite their common origin, genetic structure of Alpine and Saanen breeds suggested that they were genetically distant. Two steps genomic evaluation (GBLUP, Bayesian) based on performances corrected for fixed effect (DYD, deregressed EBV) did not improve genetic evaluation accuracy compared to classical evaluations for milk production traits, udder type traits and somatic cells score classically selected in French dairy goat. Taking into account phenotypes of ungenotyped sires increased genomic evaluation from 3 to 47% depending on the trait considered. Adding female genotypes also improved genomic evaluation accuracies from 2 to 4% depending on the method (two steps or single step) and on the trait. When using gemomic evaluation directly based on female performances (single step), accuracy of genomic evaluation reach the level obtained from ascendance in classic evaluation which was not the case using two steps evaluations. Genomic evaluation accuracies were similar when using multiple-trait model, multi-breed or single breed evaluation. But accuracies derived from prediction error variances were better when using multi-breed genomic evaluations. Genomic selection is feasible in French dairy goats using single step multi-breed genomic evaluations. Accuracies could be slightly improved integrating major gene as αs1 casein especially when using « gene content » approach to predict genotypes of ungenotyped animals.
Article
Full-text available
The leptin gene has been known as a candidate gene and a key regulator of processes that is related to economic traits. In order to study the polymorphism of Leptin gene, total blood samples were collected randomly from 200 of native and crossbreed cows of Guilan province. DNA was extracted using salting out procedure and polymerase chain reactions (PCR) were carried out using a specific primer pairs for amplification a 522 bp fragment of leptin gene. Amplified fragment was digested by BsaAI restriction enzyme and then samples were genotyped. For the studied locus Two patterns of A and B were identified with the frequency of 0.455 and 0.545 in native cows and 0.365 and 0.635 crossbreed cows, respectively. Three genotypes of AA, AB and BB with the frequency of 0.19, 0.53 and 0.28 in native cows and 0.14, 0.45 and 0.41 in crossbreed cows were detected, respectively. The chi-square test results showed that the studied populations was in Hardy-Weinberg equilibrium. Nei and Shanon indexes, observed and expected heterozygosities were calculated as 0.496 and 0.689, 0.53 and 0.498 in native cows and 0.464 and 0.656, 0.45 and 0.466 in crossbreed cows, respectively. The results revealed that the marker site of Leptin gene is polymorph in studied cows and the PCR-RFLP technique has a great potential for detection of such polymorphism.
Article
Full-text available
O objetivo deste trabalho foi avaliar a eficiência do método de regressão em detectar QTL com base na utilização de dados da estrutura de família (irmãos completos e meios-irmãos), como aqueles gerados em um núcleo MOET. Foram simulados dados fenotípicos e genotípicos em uma estrutura de núcleo MOET fechado de seleção. Três arquivos foram analisados, contendo: a) informações conjuntas de irmãos completos e meios-irmãos; b) apenas informações de irmãos completos e c) apenas informações de meios-irmãos. Verificou-se que o método da regressão, para dados discretos ou contínuos, foi capaz de detectar associações entre marcador e QTL em níveis bastante expressivos de significância (P
Article
Two markers bracketing a quantitative gene with a substitution effect of 0.5 or 0.3 phenotypic standard deviations with recombination frequencies of 0.1 and 0.2 with the quantitative gene were simulated. Ten simulated populations with 20 sires heterozygous for both markers with varying numbers of daughters were analyzed for each combination of gene effect and allele frequency. Sire quantitative gene genotype was determined by the regression of the daughter genetic evaluations on their paternal markers. Sires were determined to be heterozygous by an F-test of the model to residual sum of squares. Three values for probability of type I error were simulated: 0.05, 0.1 and 0.20. Marker allele effects were then included in an animal model analysis of the simulated populations. The algorithm of Whittaker et al. [Heredity 77 (1996) 23] was used to estimate gene effects and location. Estimates for both effect and location of the quantitative gene were nearly unbiased. Cow genetic evaluations were always more accurate by the model proposed than by a standard animal model. The increase of genetic gain due to marker-assisted selection of young sires, as compared to dam selection was between 2% and 15%. Type I error rate did not appreciably affect selection decisions.
Article
Full-text available
The objective of this study was to evaluate the efficiency of the regression method to detect QTL using data from full and half-sib families, like those generated in a MOET nucleus. For this study, genotypic and phenotypic data were simulated in a structure of a closed selection MOET nucleus. Three files were analyzed containing: a) the joint information of full and half sibs; b) only full sibs data; and c) only half sibs data. The method of regression, for continuous or discrete data, was able to detect associations of marker and QTL in very expressive levels of significance (P<0.001 P<0.0001), when the file containing the joint information of full and half sisters was used. The results indicated the possibility of using this methodology for studies of QTL detection / validation in MOET nucleus or herds under selection.
Article
An efficient algorithm is described for marker-assisted selection appropriate for large populations, even though only a small fraction of the population is genotyped. Genotype probabilities for specific loci are computed for all animals without genotypes. Effects of the quantitative trait loci (QTL) are then estimated by a "cow model" and the appropriate effects are subtracted from the cows' records. Selection is based on genetic evaluations computed from the adjusted records after addition of each animal's QTL genotype effect. The proposed scheme was applied to 10 simulated populations of 37,000 cows generated over 30 yr and compared with a selection scheme based on a standard animal model. Two diallelic QTL with substitution effects of 0.5 and 0.32 phenotypic standard deviations were simulated with initial frequencies of 0.5 for both alleles. Means and standard errors of estimates of the QTL effects at yr 30 were 0.498 +/- 0.011 and 0.347 +/- 0.008. Thus, estimation of the larger QTL was nearly exact, whereas the smaller QTL was slightly overestimated. At yr 9 through 12 after the beginning of the breeding program, genetic gain in the marker-assisted selection scheme was 0.17 standard deviations greater than the standard scheme. This corresponds to nearly 2 yr of genetic progress relative to the standard scheme, or more than 40% of the total genetic gain obtained by the standard scheme at yr 9. Although genetic gain of the 2 schemes was nearly equal by yr 30, the Gibson effect of eventual greater progress by trait-based selection was not observed. Extension of the methods proposed in the current study could be applied to rank sires accurately including both marker and pedigree information for the large number of segregating QTL that will be detected by whole-genome single nucleotide polymorphism scans.
Article
Full-text available
To estimate and to use the effects of single genes on quantitative traits, genotypes need to be known. However, in large animal populations, the majority of animals are not genotyped. These missing genotypes have to be estimated. However, currently used methods are impractical for large pedigrees. An alternative method to estimate missing gene content, defined as the number of copies of a particular allele, was recently developed. In this study, the proposed method was tested by assessing its accuracy in estimation and use of gene content in large animal populations. This was done for the bovine transmembrane growth hormone receptor and its effects on first-lactation milk, fat, and protein test-day yields and somatic cell score in Holstein cows. Estimated gene substitution effects of replacing a copy of the phenylalanine-coding allele with a copy of the tyrosine-coding allele were 295 g/d for milk, -8.14 g/d for fat, -1.83 g/d for protein, and -0.022/d for somatic cell score. However, only the gene substitution effect for milk was found to be significant. The accuracy of the estimated effects was evaluated by simulations and permutations. To validate the use of predicted gene content in a mixed inheritance model, a cross-validation study was done. The model with an additional regression of milk, fat, and protein yields and SCS on predicted gene content showed a better capacity to predict breeding values for milk, fat, and protein. Given these results, the estimation and use of allelic effects using this method proved functional and accurate.
Article
Full-text available
Abstract A strategy of multi-step minimal conditional regression analysis has been developed to determine the existence of statistical testing and parameter estimation for a quantitative trait locus (QTL) that are unaffected by linked QTLs. The estimation of marker-QTL recombination frequency needs to consider only three cases: 1) the chromosome has only one QTL, 2) one side of the target QTL has one or more QTLs, and 3) either side of the target QTL has one or more QTLs. Analytical formula was derived to estimate marker-QTL recombination frequency for each of the three cases. The formula involves two flanking markers for case 1), two flanking markers plus a conditional marker for case 2), and two flanking markers plus two conditional markers for case 3). Each QTL variance and effect, and the total QTL variance were also estimated using analytical formulae. Simulation data show that the formulae for estimating marker-QTL recombination frequency could be a useful statistical tool for fine QTL mapping. With 1 000 observations, a QTL could be mapped to a narrow chromosome region of 1.5 cM if no linked QTL is present, and to a 2.8 cM chromosome region if either side of the target QTL has at least one linked QTL.
Article
Full-text available
The use of information from flanking markers to estimate the position and size of the effect of a quantitative trait locus (QTL) lying between two markers is shown to be affected by QTLs lying in neighbouring regions of the chromosome. In some situations the effects of two QTLs lying outside the flanked region are reinforced in such a way that a 'ghost' QTL may be mistakenly identified as a real QTL. These problems are discussed in the framework of a backcross using a regression model as the analytical tool to present the theoretical results. Regression models that use information obtained from three or more nearby markers are shown to be useful in separating the effects of QTLs in neighbouring regions. A simulated data set exemplifies the problem and is analysed by the interval mapping method as well as by the regression model.
Article
Full-text available
We consider the properties of the regression of phenotype on marker-type in F2 and backcross populations. We show that this regression provides exactly the same information about the location and effect of QTL as conventional regression mapping. For certain QTL configurations this information is insufficient to map the QTL. Where the QTL can be mapped, the position and effect of QTL can be estimated directly from the coefficients of the regression of phenotype on marker-type. This requires much less computational effort than conventional regression mapping. Examples are given to illustrate the development of the theory.
Article
Full-text available
When using molecular markers to estimate the locations and sizes of effects of QTLs in plant populations a common problem is the loss of marker genotypes for many individuals. Here we present a method that uses the information from individuals with missing marker genotypes when fitting regression mapping models using two or three neighbouring markers. The approach uses other nearby markers to recover information from the individuals with missing markers. The method is presented in detail for the two markers regression mapping technique applied to backcross or double haploid populations. The method is exemplified with a simulated data set and with data on a quantitative character in double haploid lines of barley. Generalizations of the method to three markers regression mapping models and to F2 populations are outlined.
Article
Full-text available
There is considerable interest in bovine DNA-level polymorphic marker loci as a means of mapping quantitative trait loci (QTL) of economic importance in cattle. Progeny of a sire heterozygous for both a marker locus and a linked QTL, which inherit different alleles for the marker, will have different trait means. Based on this, power to detect QTL, as a function of QTL effect, heritability of the trait, and number of animals tested was determined for 1) daughter design, marker genotype and quantitative trait values assessed on daughters of sires heterozygous for the markers; and 2) granddaughter design, a newly devised alternative design in which marker genotype is determined on sons of heterozygous sires and quantitative trait value measured on daughters of the sons. For equal numbers of assays, power increased with the number of daughters per sire (design 1) and sons per grandsire (design 2). For equal power and heritability less than or equal to .2, design 2 required half as many marker assays as design 1, e.g., with heritability of .2, QTL effect of .2 SD units, and type 1 error of .01, power was .70 if 400 daughters of each of 10 sires were assayed for the markers and .95 if markers were assayed on 100 sons of each of 20 sires with 50 granddaughters per son.
Article
Full-text available
We have exploited "progeny testing" to map quantitative trait loci (QTL) underlying the genetic variation of milk production in a selected dairy cattle population. A total of 1,518 sires, with progeny tests based on the milking performances of > 150,000 daughters jointly, was genotyped for 159 autosomal microsatellites bracketing 1645 centimorgan or approximately two thirds of the bovine genome. Using a maximum likelihood multilocus linkage analysis accounting for variance heterogeneity of the phenotypes, we identified five chromosomes giving very strong evidence (LOD score > or = 3) for the presence of a QTL controlling milk production: chromosomes 1, 6, 9, 10 and 20. These findings demonstrate that loci with considerable effects on milk production are still segregating in highly selected populations and pave the way toward marker-assisted selection in dairy cattle breeding.
Article
Full-text available
Standard programs for animal models can be modified to include the effect of a candidate gene, even if only a fraction of the population is genotyped. The elements of the incidence matrix for this effect is 0 or 1 for genotyped individuals and for the probabilities of the candidate gene genotypes for individuals that were not genotyped. The effects of a diallelic candidate gene that were estimated by this method were compared on simulated populations with three alternative estimation methods: analysis of genetic evaluations, yield deviations, and daughter yield deviations, all of which were derived from a standard animal model. The bases of comparison were mean squared error and bias. Four types of experimental designs were considered: genotyping sires only, genotyping cows only, genotyping half of the cows but no sires, and genotyping half of the sires and half of the cows. The estimates that were derived from the three alternative methods all underestimated the simulated effects. The genetic evaluations were more biased than were the yield deviations and the daughter yield deviations. The proposed method was significantly biased only for the design in which half of the daughters, but no sires, were genotyped. Bias and mean squared errors were always lowest by the proposed method.
Article
Full-text available
Saturated genetic marker maps are being used to map individual genes affecting quantitative traits. Controlling the "experimentwise" type-I error severely lowers power to detect segregating loci. For preliminary genome scans, we propose controlling the "false discovery rate," that is, the expected proportion of true null hypotheses within the class of rejected null hypotheses. Examples are given based on a granddaughter design analysis of dairy cattle and simulated backcross populations. By controlling the false discovery rate, power to detect true effects is not dependent on the number of tests performed. If no detectable genes are segregating, controlling the false discovery rate is equivalent to controlling the experimentwise error rate. If quantitative loci are segregating in the population, statistical power is increased as compared to control of the experimentwise type-I error. The difference between the two criteria increases with the increase in the number of false null hypotheses. The false discovery rate can be controlled at the same level whether the complete genome or only part of it has been analyzed. Additional levels of contrasts, such as multiple traits or pedigrees, can be handled without the necessity of a proportional decrease in the critical test probability.
Article
Full-text available
A genome scan was conducted in the North American Holstein-Friesian population for quantitative trait loci (QTL) affecting production and health traits using the granddaughter design. Resource families consisted of 1,068 sons of eight elite sires. Genome coverage was estimated to be 2,551 cM (85%) for 174 genotyped markers. Each marker was tested for effects on milk yield, fat yield, protein yield, fat percentage, protein percentage, somatic cell score, and productive herd life using analysis of variance. Joint analysis of all families identified marker effects on 11 chromosomes that exceeded the genomewide, suggestive, or nominal significance threshold for QTL effects. Large marker effects on fat percentage were found on chromosomes 3 and 14, and multimarker regression analysis was used to refine the position of these QTL. Half-sibling families from Israeli Holstein dairy herds were used in a daughter design to confirm the presence of the QTL for fat percentage on chromosome 14. The QTL identified in this study may be useful for marker-assisted selection and for selection of a refined set of candidate genes affecting these traits.
Article
Full-text available
A strategy of multi-step minimal conditional regression analysis has been developed to determine the existence of statistical testing and parameter estimation for a quantitative trait locus (QTL) that are unaffected by linked QTLs. The estimation of marker-QTL recombination frequency needs to consider only three cases: 1) the chromosome has only one QTL, 2) one side of the target QTL has one or more QTLs, and 3) either side of the target QTL has one or more QTLs. Analytical formula was derived to estimate marker-QTL recombination frequency for each of the three cases. The formula involves two flanking markers for case 1), two flanking markers plus a conditional marker for case 2), and two flanking markers plus two conditional markers for case 3). Each QTL variance and effect, and the total QTL variance were also estimated using analytical formulae. Simulation data show that the formulae for estimating marker-QTL recombination frequency could be a useful statistical tool for fine QTL mapping. With 1,000 observations, a QTL could be mapped to a narrow chromosome region of 1.5 cM if no linked QTL is present, and to a 2.8 cM chromosome region if either side of the target QTL has at least one linked QTL.
Article
Twenty Dutch Holstein-Friesian families, with a total of 715 sires, were evaluated in a granddaughter experiment design for marker-QTL associations. Five traits—milk, fat and protein yield and fat and protein percent—were analyzed. Across-family analysis was undertaken using multimarker regression principles. One and two QTL models were fitted. Critical values for the test statistic were calculated empirically by permuting the data. Individual trait distributions of permuted test statistics differed and, thus distributions, had to be calculated for each trait. Experimentwise critical values, which account for evaluating marker-QTL associations on all 29 autosomal bovine chromosomes and for five traits, were calculated. A QTL for protein percent was identified in one and two QTL models and was significant at the 1 and 2% level, respectively. Extending the multimarker regression approach to an analysis including two QTL was limited by families not being informative at all markers, which resulted in singularity. Below average heterozygosity for the first and last marker lowered information content for the first and last marker bracket. Highly informative markers at the ends of the mapped chromosome would overcome the decrease in information content in the first and last marker bracket and singularity for the two QTL model.
Article
A Bayesian method was developed for identifying genetic markers linked to quantitative trait loci (QTL) by analyzing data from daughter or granddaughter designs and single markers or marker pairs. Traditional methods may yield unrealistic results because linkage tests depend on number of markers and QTL gene effects associated with selected markers are overestimated. The Bayesian or posterior probability of linkage combines information from a daughter or granddaughter design with the prior probability of linkage between a marker locus and a QTL. If the posterior probability exceeds a certain quantity, linkage is declared. Upon linkage acceptance, Bayesian estimates of marker-QTL recombination rate and QTL gene effects and frequencies are obtained. The Bayesian estimates of QTL gene effects account for different amounts of information by shrinking information from data toward the mean or mode of a prior exponential distribution of gene effects. Computation of the Bayesian analysis is feasible. Exact results are given for biallelic QTL, and extensions to multiallelic QTL are suggested.
Article
In this paper we consider the detection of individual loci controlling quantitative traits of interest (quantitative trait loci or QTLs) in the large half-sib family structure found in some species. Two simple approaches using multiple markers are proposed, one using least squares and the other maximum likelihood. These methods are intended to provide a relatively fast screening of the entire genome to pinpoint regions of interest for further investigation. They are compared with a more traditional single-marker least-squares approach. The use of multiple markers is shown to increase power and has the advantage of providing an estimate for the location of the QTL. The maximum-likelihood and the least-squares approaches using multiple markers give similar power and estimates for the QTL location, although the likelihood approach also provides estimates of the QTL effect and sire heterozygote frequency. A number of assumptions have been made in order to make the likelihood calculations feasible, however, and computationally it is still more demanding than the least-squares approach. The least-squares approach using multiple markers provides a fast method that can easily be extended to include additional effects.
Article
This study investigates the value of a `bottom-up' approach to marker-assisted selection in a conventional progeny-testing dairy-breeding programme. By marker genotyping the daughters in the progeny test for markers known to be closely linked to a quantitative trait locus (QTL), it can be decided whether their sire is heterozygous for the QTL. If the sire is heterozygous with allelic contrast greater than some threshold, c, then only those bull-sons which inherited the favourable QTL allele are retained for subsequent progeny testing. In this way, posterior information on a sire's genotype from his daughters is used to preselect his sons and thereby increase the selection differential in the new generation of bulls. Simulations were used to predict the genetic gains and costs of using the bottom-up approach in a national dairy breeding scheme in which 500 young bulls were progeny-tested each generation. It was found that rates of genetic gains could be increased by 8%, 14% and 23% compared with conventional progeny testing if selection was based on 1, 2 and 5 QTL, respectively, and that this would cost less than US$100,000 per locus. A `top-down' approach selecting QTL alleles inherited from the grandsires was also evaluated and shown to be highly profitable, though less so than for the bottom-up scheme.
Article
Three methods of analysis: 1) multiparity analysis with the first three parities analyzed as correlated traits, 2) multiparity analysis with all lactations analyzed as a single trait, and 3) first parity only analysis were compared on simulated data. A selection index was used to compute multiparity evaluations from the separate parity evaluations of the multitrait analyses. Twenty simulated populations, each of 8,500 cows, were generated by an algorithm that approximated the multitrait model. Populations were simulated with both random and yield-based culling of cows after first and second parity. Populations simulated with yield-based culling were analyzed both with the first parity records of all cows included and with an arbitrary one-third of first parity records deleted. First parity records of all cows were included in the analyses of the randomly culled populations. Accuracy of evaluation, estimated by correlations between true effects and evaluations and prediction error variances, was highest by the multitrait analysis and lowest by the first parity only analysis. Evaluations obtained by the two multilactation methods were nearly identical with random culling. Regression of effect on evaluation was close to unity for the multitrait evaluation; was only .94 and .90 for the all lactation single trait evaluation with random and yield culling, respectively; and was .80 for the index of sire effects on the first parity only analysis. Single-trait multilactation method may be preferred, as it is nearly as accurate as the multitrait method and easier computationally.
Article
New terms and definitions were developed to explain national USDA genetic evaluations computed by an animal model. An animal's PTA combines information from its own records and records of all its relatives through a weighted average of 1) average of parents' evaluations, 2) half of its yield deviation, and 3) average across progeny of twice progeny evaluation minus mate's evaluation. Yield deviation is a weighted average of a cow's lactation yields minus solutions for management group, herd-sire, and permanent environmental effects. Bulls do not have yield deviations; however, a weighted adjusted for mates' merit can provide a useful, unregressed measure of daughter performance. Reliability is the squared correlation of predicted and true transmitting ability. An animal's parents, own records, and progeny each contribute amounts of information measured in daughter equivalents. Reliability of USDA evaluations then is computed as (total daughter equivalents)/(total daughter equivalents + 14).
Article
Linkage between the amylase-1 (Am-1) locus and a quantitative trait locus influencing fat content in milk was studied in offspring from heterozygous sires of the Swedish Red and White dairy breed. The effect on bull breeding values for fat content was estimated as interactions between sire and paternal Am-1 allele using a model eliminating the direct effects of sire and Am-1 allele. There were strong indications of linkage, confirming results of previous studies. The interaction was caused by strong associations in 7 out of 14 site families. A test for within-family variance heterogeneity performed on the whole population of breeding bulls also supported the presence of a major gene for fat content in milk. The results indicate that there is genetic linkage between the Am-1 locus and a locus with large effect on milk fat content.
Article
Individual loci affecting economically important traits can be located using genetic linkage between quantitative trait loci and genetic markers. In the 'granddaughter' experimental design, heterozygous grandsires and their sons are genotyped for the genetic marker, while the quantitative trait records of the granddaughters are used for statistical analysis. Ten DNA microsatellite markers were used to look for associations with quantitative trait loci affecting milk production traits in seven Israeli Holstein grandsire families. At least 60% more grandsires were heterozygous, and 40% fewer individuals were discarded because of unknown paternal allele origin, as compared with diallelic markers. The effects of paternal alleles for locus D21S4 on kg milk and protein were significant (P < 0.025). The allele substitution effects for sire 783 were 283 kg milk and 5.7 kg protein. For both traits, progeny of sire 783 that inherited allele '18' had higher evaluations than progeny that inherited allele '21'. These results were verified by genotyping 151 of his daughters. Thus, the rate of genetic gain for protein production can be increased by selecting progeny of sire 783 carrying allele '18' at this locus.
Article
The effect of a segregating economic trait locus (ETL) can be detected with the aid of a linked genetic marker, if specific alleles of each locus are in association among the individuals genotyped for the genetic marker. For dairy cattle this can be achieved by application of the 'granddaughter design'. If only the sires and their sons are genotyped for the genetic markers, then the allele origin of sons having the same genotypes as their sires cannot be determined. Seven sires and 101 sons were genotyped for five microsatellites. The mean frequency of heterozygous sires was 77%. The mean number of alleles per locus was 8.2. Frequency of informative sons per locus ranged from 60% to 80% with a mean of 72%. With highly polymorphic microsatellites, at least 60% more grandsire families can be included in the analysis, and the number of sons assayed can be reduced by 40%, as compared to diallelic markers.
Article
Quantitative trait loci affecting milk yield, health, and conformation traits were studied for eight large US Holstein grandsire families by using the granddaughter design. A total of 105 microsatellite markers, located throughout the bovine genome, were selected for the scan. The data analyzed include genotypes for 35 markers in eight families not previously reported and genotypes for 70 markers reported previously in seven of those families. Analyses of markers previously reported were updated. Effects of marker alleles were analyzed for 38 traits, including traits for milk production, somatic cell score, productive life, conformation, calving ease, and 16 canonical traits derived from conformation and production traits. Permutation tests were used to calculate empirical trait-wise error rates. A trait-wise critical value of P = 0.1 was used to determine significance. Eight putative quantitative trait loci associated with 7 of the 35 new markers were identified within specific families. Two of these markers were associated with differences in strength and rump angle on chromosomes 4 and 9, respectively. Different markers were associated with protein percentage, milk yield, and somatic cell score on chromosomes 6, 7, and 10 in different families. Differences in the canonically transformed traits were associated with markers on chromosomes 5, 6, and 9. Additional marker-trait combinations were identified in the across-family tests, including effects on chromosomes 3, 4, and 9 for protein percentage, body depth, and canonical conformation traits, respectively. Additional markers are being added to allow interval analysis for putative quantitative trait loci that have been identified and to increase marker density.
A genome scan for QTL influencing milk production and health traits in dairy cattle
  • Heyen
On the mapping of QTL by regression of phenotype on marker-type
  • Whittaker