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Interval and Composite Interval Mapping of Somatic Cell Score, Yield, and Components of Milk in Dairy Cattle

Department of Animal Sciences University of Illinois, Urbana 61801

Corresponding author:
Sandra Rodriguez-Zas; e-mail:
rodrgzzs{at}uiuc.edu.

	ABSTRACT

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS ACKNOWLEDGEMENTS REFERENCES

 
Single-marker, interval-mapping (IM) and composite interval mapping (CIM) were used to detect quantitative trait loci (QTL) controlling milk, fat and protein yields, and somatic cell score (SCS). A granddaughter design was used to combine molecular genetic information with predicted transmitting abilities (PTA) and estimated daughter yield deviations (DYD) from eight Dairy Bull DNA Repository Holstein families. Models that included and excluded weights accounting for the uncertainty of the response variable were evaluated in each trait, family and phenotype (DYD and PTA) combination. The genotypic information consisted of 174 microsatellite markers along 29 Bos taurus autosomes. The average number of informative markers per autosome was three and the number of informative sons per family and marker varied between 21 and 173. Within-family results from the least squares single-marker analyses were used in expectation-maximization likelihood IM and CIM implemented with QTL Cartographer. Different CIM model specifications, offering complementary control on the background QTL outside the interval under study, were evaluated. Permutation techniques were used to calculate the genome-wide threshold test statistic values based on 1000 samples. Results from the DYD and PTA analyses were highly consistent across traits and families. The minor differences in the estimates from the models that accounted for or ignored the uncertainty of the DYD (variance) and PTA (inverse of reliability) may be associated to the elevated and consistent precision of the DYD and PTA among sons. The CIM model best supported by the data had 10 markers controlling for background effects. On autosome (BTA) three, a QTL at 32 cM influencing protein yield was located in family five and a QTL at 74 cM for fat yield was located in family eight. Two map positions associated with SCS were detected on BTA 21, one at 33 cM in family one and the other at 84 cM in family three. A QTL for protein yield was detected between 26 and 36 cM on BTA six, family six, and a QTL for milk yield was detected at 116 cM on BTA seven in family three. The IM and CIM approaches detected a QTL at 3 cM on BTA 14 influencing fat yield in family four. Two map positions on BTA 29 were associated with significant variation of milk (0 cM) and fat yield (14 cM) in family seven. These results suggest the presence of one QTL with pleiotropic effects on multiple traits or multiple QTL within the marker interval. Findings from this study could be used in subsequent fine-mapping work and applied to marker-assisted selection of dairy production and health traits.

Key Words: composite interval mapping • outbred population • quantitative trait loci • weighted analysis

Abbreviation key: BTA = Bos Taurus autosome, CMI05, CMI10, CMI20 = Composite interval mapping with five, ten and 20 background markers, DYD = Daughter yield deviation, IM = Interval mapping, PTA = Predicted transmitting ability, QTL = Quantitative trait loci

	INTRODUCTION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS ACKNOWLEDGEMENTS REFERENCES

 
Many studies have identified quantitative trait loci (QTL) associated with the genetic variation of complex dairy traits (Georges et al., 1995; Spelman et al., 1996; Ashwell et al., 1998b; Zhang et al., 1998; Heyen et al., 1999). These studies have used different phenotypic and genetic data and a variety of statistical methods, from single marker to interval mapping and variance components within and across families.

The interval mapping (IM) approach uses information from consecutive informative markers simultaneously to estimate the QTL location and effect (Knott et al., 1996). A disadvantage of IM is that QTL outside the interval under consideration could lead to false positive or negative results. The composite interval mapping (CIM) approach can address these limitations, integrating interval mapping with information from multiple markers outside the interval (Jansen, 1993; Zeng, 1993). The additional markers account for phenotypic variation due to linked QTL outside the interval, hence reducing the residual variance and augmenting the precision of estimates. The CIM approach is not commonly used in outbred populations (i.e. dairy cattle) due to the variable information content along the genome and inconsistent linkage phase among families (Hoeschele et al., 1997). Furthermore, different specifications of the CIM model can lead to substantial changes in the number of false positive results and power of the analysis.

In addition to the molecular genetic information, the phenotypic indicators studied may influence the ability to detect QTL. In dairy cattle populations, two commonly used phenotypic indicators are the daughter yield deviation (DYD; Georges et al., 1995; Spelman et al., 1996; Ashwell et al., 1998b) and the predicted transmitting ability (PTA; Ashwell et al., 1998a; Zhang et al., 1998). The PTA includes the deviation of the daughter performance from the population mean adjusted for the genetic merit of the mate (i.e. DYD) and the genetic merit of the grandsire and granddam (VanRaden and Wiggans, 1991). The ability to detect QTL can be influenced by the indicator used and the associated uncertainty. Likewise, the genome-wide significance level and the consequent comparison-wide level used affect the number of QTL identified. The more stringent the significance probability threshold, the lower the probability to claim false positive results but the lower the probability to identify true positive (power) associations. The main goal of this study was to implement interval mapping and composite interval mapping in an outbred population. The supporting objectives were, 1) to evaluate the feasibility and adequacy of using readily available software for line crosses in outbred populations, 2) to assess the impact of using different phenotypic indicators in QTL mapping and, 3) to evaluate the influence of accounting for the uncertainty of the phenotypic indicators in QTL mapping. Single-marker, IM and CIM approaches were implemented using milk and composite production and somatic cell score indicators from a US Holstein population.

	MATERIALS AND METHODS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS ACKNOWLEDGEMENTS REFERENCES

 
Data
Eight Holstein grandsires from the Dairy Bull DNA Repository (Heyen et al., 1999) with phenotypic and molecular genetic data following a granddaughter design (Geldermann, 1975; Weller et al., 1990) were studied. The genetic marker information on grandsires and their sons was combined with the phenotypic indicators of the sons. The dams were assumed to have one progeny, to be a random sample from the population and unrelated to each other and to the males. Two of the grandsires were half-sibs but were assumed to be unrelated. The marker information is the same used by Heyen et al. (1999), and consisted of 174 microsatellite markers on 29 Bos Taurus autosomes (BTA) in 1065 sons. The number of sons with traceable allele inheritance varied with marker and ranged between 21 and 173. The average interval between markers, based on Kosambi’s relative distances computed using CRIMAP 2.4 (Heyen et al., 1999) was 20 cM long and the number of markers per autosome varied between two to 11. The average length of the informative interval across the genome was 70 cM, and ranged from 52 to 81 cM. The phenotypic indicators (based on their daughter records and parental information) were the DYD and PTA (Animal Improvement Programs Laboratory, 2000) estimates for milk, fat and protein yields, somatic cell score (SCS = log₂ (cells/100) + 3), and associated number of daughters and reliability. The numbers of DYD and PTA records were 1016 and 1065, respectively, with missing DYD due to insufficient number of daughters to compute these estimates and corresponding PTA solely based on pedigree information. In the current study, 2000 genetic-evaluation based phenotypes were used. These records differ from the 1997 genetic-evaluation phenotypes studied by Heyen et al. (1999). The number of informative sons per marker and family ranged between 21 and 173. Statistical tests failed to reject the null hypothesis of normality for all the traits. The mean and standard deviation of DYD and PTA by trait and the number of informative sons are summarized in Table 1.

The general expression of the interval and composite interval mapping models is:

[1]

where y_ij is the DYD or PTA of a trait recorded on the daughters of son j, ß₀ is the intercept, ß₁ is the regressor associated with the sons’ probability of inheriting a marker (M_j) allele or QTL allele conditional on the genotype of the flanking markers (IM and CIM), ß_is (i = 2 to I) are the CIM regressors associated to the sons’ probability of inheriting a background marker allele (m_ij, indexed from 2 to I) at positions other than that represented by M_j, and e_ij is the random residual assumed to be independent, identical, and normally distributed.

The interval and composite interval mapping analyses were implemented using QTL Cartographer (Basten et al., 2000) and the QTL allele probability was computed every two cM. Three sets of analyses were conducted to assess the impact of the number and position relative to the interval (window width) of the additional (background) markers controlling for the QTL background effects: 1) five background markers with a window width of ten cM (CIM05), 2) ten background markers with a window width of ten cM (CIM10), and 3) 20 background markers with a window width of 20 cM (CIM20). All (5, 10 or 20, respectively) background markers outside the window width (10 or 20 cM, respectively) around the interval studied were considered. These included markers on the autosome where the interval was located as well as any of the other 28 autosomes. The selected window width allowed the effect of QTL in the proximity to be accounted for, while minimizing the feasibility of the model to detect a QTL in the interval. Based on preliminary analyses and on the number and spacing of the informative markers in this study, single marker positions significant at P < 0.2 were considered to be potential background markers. Fewer background markers were fitted in some families and traits due to the high probability values (P > 0.2) of the remainder markers.

The estimated DYD and PTA have associated uncertainties. The analyses of these indicators should include these uncertainties so that less weight is given to the less precise estimates, compared with the more precise ones found in sons with large numbers of daughters with records. In its current implementation, QTL Cartographer cannot account for varying uncertainty. In order to incorporate the uncertainty, the DYD and PTA were transformed by analyzing (L')^–1y, where y is the vector of DYD or PTA and L' is the transpose of the Cholesky factorization of associated weight matrix such that Var(y)=L'L²_e. Since Var[(L')^–1y] = (L')^–1Var(y) L^–1 = (L')^–1 L'L²_e L^–1 = I ²_e (where I is the identity matrix), the transformation renders a variable with constant variance that is compatible with the QTL Cartographer implementation. In the present study, L is a diagonal matrix with entries equal to the uncertainty of y. Matrices with off-diagonal entries, accounting for the correlation among estimates can be used. The measures of uncertainty used in this study were the variance of the DYD (Spelman et al., 1996) for the DYD model, and the inverse of the reliability for the PTA model (Georges et al., 1995).

Genome-wide Bonferroni and empirical (Churchill and Doerge, 1994) significance thresholds were computed within family and trait to account for the multiple and dependent testing along the genome, while providing complementary information. Results from preliminary interval mapping analyses using 1000 and 10,000 permutations of all markers across all families and traits provided a similar threshold. Hence, empirical thresholds corresponding to P < 0.1, 0.05, and 0.01 genome-wide significance levels were computed for interval and composite interval mapping likelihood ratio tests based on 1000 permutations. Thresholds for the analyses of adjusted and unadjusted PTA and DYD were consistent, and thus the most stringent set of thresholds were used in this study. Since the results from the analysis of PTA- and DYD-within-trait were highly correlated, PTA and DYD were assumed to represent the same trait, and testing was not adjusted within trait.

	RESULTS AND DISCUSSION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS ACKNOWLEDGEMENTS REFERENCES

 
The genome-wide permutation thresholds for DYD increased as the number of background markers included in the model increased (Table 2). This was most evident in families seven and eight, which had fewer sons than the rest of the families. Since the same background markers are included in the full (with QTL) and reduced (without QTL) model likelihood functions, the threshold increase could be explained by the number of informative sons contributing to the likelihood calculations in certain marker haplotypes. A low number of background markers in the model resulted in the analysis of more sons with genotypic information for all markers. For the data analyzed, 10 to 20 background markers were sufficient to reliably account for background QTL. In a few family-trait cases, twenty markers tended to give spurious likelihood ratio test peaks. This may be due to the limited number of sons informative for the background markers. In most cases, likelihood ratio peaks corresponding to the IM and CIM analyses suggested a QTL at the same or close positions although some analyses may have not surpassed the genome-wide threshold. Figure 1 depicts this situation for the analysis of protein DYD on BTA 27, in family five. In general, five background markers or no background marker (IM) resulted in higher residual variance and fewer results surpassing the genome-wide significance thresholds than using ten or twenty background markers.

View larger version (19K): [in this window] [in a new window]   Figure 1. Likelihood ratio test values from the interval and composite interval mapping analyses of transformed protein DYD and associated 0.1, 0.05 and 0.01 permutation within-trait genome-wide P values for BTA 27 and family five. IM = interval mapping, CIM05, CIM10 and CIM20 = composite interval mapping with five, ten and 20 background markers. Informative marker positions are denoted by marks on the threshold lines.

The results from the analysis of PTA and DYD records were consistent for most families and traits. Within family and trait, the untransformed DYD and PTA likelihood ratio test statistics from the IM analysis were highly (0.95 to 0.99) correlated. A few CIM DYD and PTA results differed on selected genome segments and traits. Evaluation of these cases showed that different background markers were included in the model. The DYD and PTA results were highly correlated when the same background markers were included. Due to the similarity between PTA and DYD results, only DYD results will be presented.

The results from the analysis of untransformed and transformed traits were typically very similar. The similarity of the results may be due to the large number of daughters available across sons, leading to similar transformation weights. This behavior is depicted in Figure 2, where the CIM10 likelihood ratio test estimates from the analysis of protein yield DYD and PTA likelihood ratio tests along BTA six in family three are plotted. Some variability across the different phenotypic indicators was expected, since the length of the first autosome interval was 64 cM. This suggests that in data sets with similar uncertainty level across observations, both phenotypic indicators can be used in QTL detection studies without weighting for the uncertainty. For SCS, the results from the untransformed DYD, transformed DYD, and untransformed PTA differed only slightly from untransformed PTA. This could be due to our use of the inverse of the reliability of the son as an approximation of the variance of the PTA, the lower heritability of SCS (compared to yield traits), and to analysis of PTA that are solely based on pedigree information.

View larger version (20K): [in this window] [in a new window]   Figure 2. Likelihood ratio test values from the composite interval analysis using ten background markers (CIM10) of (un)transformed protein daughter yield deviation (DYD) and predicted transmitted ability (PTA) and associated 0.1, 0.05 and 0.01 permutation genome-wide P values for BTA six and family three. Informative marker positions are denoted by marks on the threshold lines.

Table 3 summarizes the 22 results that surpassed the within trait genome-wide P < 0.1 level for fat yield DYD. Thirty-two percent of these results surpassed the across-trait, genome-wide significance threshold (P < 0.05). All three CIM models supported the presence of three QTL on different families and autosomes. One or two QTL for fat DYD at position 60 cM on BTA three were detected in families two and eight. This location was also reported for families one and two by Heyen et al. (1999) by means of an IM approach. Ron et al. (1998), Zhang et al. (1998), and Heyen et al. (1999) indicated that a marker at 41 cM (TGLA 263) on BTA three was associated with variations on fat yield DYD and PTA. Ron et al. (2001) identified a QTL that influences fat percentage near position 55 cM on BTA six in two Israeli Holstein families. In our study, a QTL influencing fat DYD was located at 68 cM on BTA 12 in family four. This result is consistent with Heyen et al. (1999), who reported an association between the trait and marker BM4028 (68 cM) in the same family. The QTL detected with CIM10 at 0 cM on BTA 14 had an estimated effect of –6.8 kg on fat yield DYD in family four. This finding is consistent with Ron et al. (1998), Zhang et al. (1998), and Heyen et al. (1999), who reported QTL for the same trait and location in some of the same families considered in this study. Heyen et al. (1999) reported that marker ILSTS39, at the start of the linkage group on BTA 14, was associated with variations on fat DYD, and Ron et al. (1998) detected an association between marker CSSM66 (13 cM) and fat yield PTA. The CIM20 approach indicated the presence of a QTL 17 cM apart from that detected by CIM10 on the same family and trait with an opposite effect (10.9 kg). Examination of the likelihood ratio test values along the autosome indicated that the peak between 10 and 20 cM was only significant at a genome-wide level for CIM20 (across trait genome-wide P < 0.05). The available marker data do not permit further elucidation of this map position. On BTA 22, a QTL located at 18 cM had a significant (across trait genome-wide P < 0.05) effect on fat yield DYD in family two.

Several studies have reported multiple QTL on the same autosome (Spelman et al., 1996; Heyen et al., 1999; Ron et al., 2001). While composite interval mapping can account for linked QTL in this study, none of the families showed evidence of multiple QTL on the same chromosome influencing the same trait. Although in family eight the scan on BTA three indicated multiple likelihood ratio test peaks, these did not reach the significance threshold for different CIM models (Figure 3). Two QTL locations (26 to 36 cM and 80 cM) on BTA six were identified in two families (families 3 and 2, respectively) associated with protein yield DYD (Table 4). This suggests that some of the families studied could have been homozygous for at least one QTL and that insufficient marker information was available to account for multiple QTL. The incorporation of more marker information in the promising intervals and in intervals over 20 cM long will permit verification of some of the findings and reduce the uncertainty in other regions.

View larger version (23K): [in this window] [in a new window]   Figure 3. Likelihood ratio test values from the composite interval analysis using ten background markers (CIM10) for fat yield daughter yield deviation (DYD) in families two and eight and protein yield DYD in family five on BTA three and associated 0.1, 0.05 and 0.01 permutation genome-wide P values. Informative marker positions are denoted by marks on the threshold lines.

View larger version (22K): [in this window] [in a new window]   Figure 4. Likelihood ratio test values from the composite interval analysis using ten background markers (CIM10) for milk yield daughter yield deviation (DYD) in families seven, protein yield and SCS DYD in family three on BTA 21 and associated 0.1, 0.05 and 0.01 permutation genome-wide P values. Informative marker positions are denoted by marks on the threshold lines.

	CONCLUSIONS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS ACKNOWLEDGEMENTS REFERENCES

 
Interval mapping and composite interval mapping approaches developed for line crosses were extended to detect QTL in an outbred dairy cattle population. A granddaughter design was modeled as a backcross design and the linkage phases were based on the most frequent haplotypes found in the sons. The approach was successful in identifying QTL influencing production and health traits. Some estimated QTL positions were consistent with previous studies and new locations were uncovered. Few reported locations were not corroborated in this study. These could be due to the inability of the model to detect some QTL signals due to background markers effects or false positive results reported in previous studies. Virtually identical location estimates were obtained from the analysis of DYD and PTA records regardless of the presence or absence of adjustment for the uncertainty of the estimator. This was attributed to similar weights between sons due to a large number of daughters. In general, the information from the sparse map was insufficient to detect significant (across trait genome-wide P < 0.05) interval mapping positions but the inclusion of background markers in the composite interval mapping approach enhanced the ability to detect QTL. Information from more informative markers, more families, and more sons per family will further enhance the power of the study to detect QTL. A few composite interval mapping results may be spurious since few sons were genotyped for all the markers included in the model. A number of map locations were associated with more than one trait. These positions may stem from QTL with pleiotropic effects or multiple QTL in the region. The utilization of multivariate composite interval mapping models may enhance the power to detect QTL, the precision of the estimates and permit to test pleiotropic versus multiple QTL models. The estimates of QTL location and effect can be used to further fine mapping and positional candidate gene studies and ultimately applied in marker-assisted selection programs to enhance dairy cattle production and health traits.

	ACKNOWLEDGEMENTS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS ACKNOWLEDGEMENTS REFERENCES

 
We thank the collaborators of regional project NC209 and the contributing AI organizations for initiating, maintaining, and contributing to the Dairy Bull DNA Repository. Continuous support from CSREES, project number ILLU-35-0350 is greatly appreciated. This work was partly funded by grants from the National Center for Supercomputing Application numbers MCB990004N and MCB990029N.

Received for publication March 27, 2002. Accepted for publication June 3, 2002.

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TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS ACKNOWLEDGEMENTS REFERENCES

 


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