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USDA, ARS, US Meat Animal Research Center, Clay Center, NE 68933-0166
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Abstract |
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 Top Abstract INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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Key Words: association analysis • backfat depth • single nucleotide polymorphism • swine
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INTRODUCTION |
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TopAbstractINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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; Óvilo et al., 2006; Vidal et al., 2006). In this approach, researchers identify a polymorphism in a gene with a known function and test the association of these polymorphisms with changes in the recorded phenotypes (Tabor et al., 2002). Although attractive relative to statistical power and determining physiological causation, this approach relies on a priori knowledge of gene function. Detected associations may be due to linkage rather than causation. Unclassified genes and nontranslated genomic regions would likely be ignored.
Alternatively, genomic regions identified through QTL mapping, rather than genes with identified functions, can be considered as positional candidates for association analyses. By developing a moderately dense map of markers (e.g., SNP) within these QTL regions, one can narrow the position of the mutations responsible for the phenotypic differences, ultimately leading to fine mapping. Markers highly associated with phenotypic differences may be in linkage disequilibrium with a causative mutation and can potentially be used for marker-assisted selection after validation in other populations (Dekkers, 2004).
Backfat depth is economically relevant in swine production because of its implications for carcass composition and growth efficiency. A Meishan-derived population at the US Meat Animal Research Center has been scanned for QTL related to backfat depth (Rohrer and Keele, 1998; Rohrer, 2000). Both studies identified QTL regions for backfat on SSC 1 and 7, based on microsatellite markers. This population has since been managed as a segregating, randomly mated population, making it useful for association analysis of markers within the QTL regions identified.
The objective of this study was to detect SNP and genomic regions associated with ultrasonic backfat in this population.
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MATERIALS AND METHODS |
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TopAbstractINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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).
Population and Phenotypes
The population examined was descended from the QTL resource population described in detail by Rohrer et al. (1999). Briefly, the original resource population was formed by reciprocally mating Meishan and a White Composite (
Chester White, British Landrace, Yorkshire, British Large White) line to form an F1 generation, followed by the formation of both backcross lines. Backcross lines were mated together to form an F3 population (50% of each breed), which was mated inter se for an additional generation.
After data collection for QTL scans (Rohrer and Keele, 1998; Rohrer et al., 1999; Rohrer, 2000), this resource population was mated to a different, lean White Composite (
Yorkshire, American Landrace). Thereafter, the line (
White Composite, Meishan) was mated inter se for 5 additional generations. Line development is summarized in Figure 1. Phenotypic records for this study were collected from the F8 and F10 generations born in 2001 and 2003, respectively. The number of sires and dams used to produce these generations is presented in Table 1.
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Marker Data
Previous studies with this population had identified QTL for backfat on SSC 1 and 7 (Rohrer and Keele, 1998; Rohrer, 2000). The QTL regions identified in these studies were targeted for SNP discovery. Candidate SNP were identified using methods described by Fahrenkrug et al. (2002). Sequenced genes were selected using the random targeting approach (Fahrenkrug et al., 2002) or from comparative mapping of SSC1 (Nonneman et al., 2005b) and SSC7. In total, 6 SNP from SSC1 and 13 SNP from SSC7 were selected.
In addition to being measured for backfat depth, each gilt’s age at puberty, ovulation rate, and nipple number were recorded. Based on QTL regions for these traits (Rohrer et al., 1999; Rohrer, 2000), genes were also selected from 3 regions on SSC3 (Mousel et al., 2006), from 2 regions on SSC8 (Campbell et al., 2003), and from SSC10 (Nonneman and Rohrer, 2004; Nonneman et al., 2006). An additional 16, 8, and 31 SNP were selected on SSC 3, 8, and 10, respectively, bringing the total number of SNP to 74. Although no significant associations with backfat were expected from these additional SNP, associations of these regions with backfat depth were analyzed to reveal possible antagonistic effects of SNP that may be significant for reproductive traits.
Assays for SNP genotyping were designed using a SpectroTyper (Sequenom, San Diego, CA). Assays used hME chemistry on a MassArray system (Sequenom). Briefly, 10-µL PCR reactions contained 5 ng of genomic DNA, 0.25 U of AmpliTaq Gold Taq (Applied Biosystems, Foster City, CA), 1x of supplied buffer, 1.5 mM MgCl2, 200 µM dNTP, and 0.4 µM forward and reverse-tailed primers. The primer extension reaction used 0.6 µM of probe primer according to the manufacturer’s protocol.
Gilts were genotyped for the 74 SNP (see Supplemental Table, available online at http://jas.fass.org) and also for a polymorphism in the thyroxine-binding globulin gene (TBG) on the porcine X chromosome (Nonneman et al., 2005a). This polymorphism had shown an association with backfat depth in this population in previous work (Rohrer et al., 2004). The TBG gene lies within a backfat QTL region identified by Rohrer and Keele (1998) and Rohrer (2000). The allelic frequency of the TBG polymorphism in this population was 0.258 C and 0.742 A.
Statistical Analysis
A multilocus version of GenoProb (Thallman, 2002) was used to check the genotyping data for errors, to calculate genotypic probabilities for gilts that were unsuccessfully genotyped (for the number of animals successfully genotyped per SNP, see the Supplemental Table, available online at http://jas.fass.org), and to determine parental origin of alleles for heterozygous markers. Only 64 SNP had robust genotypic calls, as defined by GenoProb; these SNP were analyzed for associations. After investigation of the marker data, nonrobust calls were attributed to poor assay performance (a low number of animals successfully genotyped or null alleles detected).
The resulting GenoProb probabilities for each animal were used to derive regressors for regressions of backfat on additive, dominance, and parent-of-origin effects of each SNP. For animal j, the regressors (row vector xj) were calculated as
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where paa is the probability that j is homozygous for allele a, pAA is the probability that j is homozygous for allele A, and paA and pAa are the probabilities that j is heterozygous and inherited allele a from its dam or from its sire, respectively. Values xA, xD, and xP are then the additive, dominance, and parent-of-origin (via contrast of heterozygotic phase) regressors for animal j. Probabilities of heterozygous phases (paA and pAa) were generally quite high; the average phase probability was over 0.95 for all but 6 SNP. No parent-of-origin vectors (xP) were predicted for these 6 SNP. This high average probability can be attributed to 2 factors: the pedigree structure of the phenotyped animals (8 to 10 offspring per sire, approximately 3 offspring per dam) and GenoProb accounting for multilocus inheritance.
Initially, each SNP was fitted individually using the model
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with
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where y is a vector of backfat data; b is a vector of fixed effects, including contemporary group (farrowing group and scan date), TBG genotype, and BW as a covariate; a is a vector of random polygenic breeding values; bA, bD, and bP are the regression coefficients for the additive, dominance, and parent-of-origin effects of the SNP, respectively; and e is a vector of random residuals. Incidence matrices X and Z relate phenotypes to combinations of fixed or random effects. In the variance structure of a and e, A is the numerator relationship matrix, 
2a is the remaining additive variance (after accounting for TBG and the SNP fitted), and 2e is the residual variance. Pedigree records (3,897 animals) dating to the initial foundation of the line were included in the calculation of A–1. All analyses were performed using MTDFREML (Boldman et al., 1995). In cases where the additive and dominance regressions were partially confounded due to low or no observations of the minor allele homozygous class, only the additive and parent-of-origin regressions (if parent-of-origin vectors were calculated) were fitted. Each SNP was also analyzed in a model in which the parent-of-origin regression was not fitted to add power for detection of additive and dominance effects. The power of the genotypic regressions was dependent on the genotypic frequencies for each SNP; SNP effects had to be large to detect significance when the frequency of one allele was low. Furthermore, the inclusion of the polygenic effect in the evaluation model reduces the effect of family structure for these SNP with low frequency alleles.
Variances were estimated from a model with no SNP fitted except TBG by allowing MTDFREML to iterate until a convergence criterion of 10–10 was obtained. These variance component estimates were consistent with other literature values for composite populations (e.g., Cassady et al., 2002; Klindt et al., 2006) and, because of erratic behavior of variance components in this small data set, were fixed for all subsequent SNP evaluation models. Effects of SNP were tested for significance using an F-test with 3 numerator df when all 3 SNP regressions were fitted and 2 numerator df when only the additive and dominance regressions were fitted. Individual regressions were also tested for significance using an individual t-test. No adjustments were made for multiple comparisons or multiple tests.
All SNP with significant F-tests or significant components were fitted simultaneously in a single model. To determine a final model, the least significant SNP was removed one at a time in a step-down manner until all remaining SNP were significant at a level of P = 0.10 or less. This backward selection procedure was performed with an initial model that included parent-of-origin (3DF) and a model in which parent-of-origin was not fitted for any SNP. In the 3DF case, parent-of-origin effects were only included for SNP markers when the individual parent-of-origin regression approached significance (P < 0.10).
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RESULTS |
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TopAbstractINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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Results from fitting individual markers with P < 0.10 are shown in Table 2 (parent-of-origin effects included) and in Table 3 (no parent-of-origin effect fitted). Associations were detected for 12 SNP markers when parent-of-origin was fitted in the model. In these models, additive effects (bA; generally of AA value) ranged from 0.6 to 1.0 mm of backfat except for SNP 17465.1h, which had an additive effect of 2.22 mm. Parent-of-origin effects were detected for several markers based on the heterozygous phase contrast. When only additive and dominance effects were fitted for individual SNP, only 5 associations were detected. Additive effects were of similar magnitudes to the 3DF analyses.
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). With no parent-of-origin effects, only 2 SNP remained in the model (Table 5).
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DISCUSSION |
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TopAbstractINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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). However, results from research studies used to detect these associations are specific to the inference population. One must consider the source of linkage disequilibrium when extrapolating results to other populations. In this study, phenotypes were measured on a Meishan Composite population. Linkage disequilibrium from the initial cross of the Meishan founders to the White Composite lines likely still remains in this population, given the diverse backgrounds of these breeds. Resulting associations therefore may represent regions of interest rather than individual SNP that are in general linkage disequilibrium across diverse swine populations. Unfortunately, tissue samples were not available on all foundation lines to estimate SNP frequency differences in this population, which may have given rise to this source of linkage disequilibrium. The regions identified in this study should be scanned at a higher density in outbred populations to discover markers with higher levels of associations. Significant individual SNP should be validated in commercial populations to determine their effects before broad use.
Identification of SNP beneath QTL peaks described in Rohrer and Keele (1998) and Rohrer (2000) in the ancestors of this population facilitated the discovery of polymorphisms with significant associations with ultrasonic backfat depth (Tables 2 and 3) on SSC7. Associated SNP were located between 50 and 63 cM. This region has been identified as having a QTL for backfat depth in several studies involving Meishan germplasm (Wang et al., 1998; Rattink et al., 2000; Wada et al., 2000; Bidanel et al., 2001; Milan et al., 2002; Geldermann et al., 2003; Sato et al., 2003). There is general agreement that the QTL on SSC7 of Meishan origin results in lower backfat with an additive effect (bA) of approximately 1 to 2 mm. The current population was a segregating 25% Meishan Composite. Three SSC7 SNP (13438.1h, 11807.1h, and 17281.1h) showed an additive effect of similar, though smaller, magnitude (1.75 to 2.12 mm difference between homozygous classes; bA = 0.87 to 1.06 mm) with the minor allele having less depth of fat. When all of the SNP were fitted simultaneously (Tables 4 and 5), marker 17281.1h remained in the final, backward-selected model regardless of whether markers showing evidence of parent-of-origin effects were included. This marker is the most favorable candidate for the QTL: its effect is of similar magnitude to previously reported QTL, the sample minor allele frequency (0.324) is not far removed from the 25% Meishan germplasm in this population, and the minor allele is favorable for reduced fat depth. Marker 17281.1h is in intron 7 of the zinc finger protein 76 (ZNF76) gene, located at 35 Mb on human chromosome 6. This gene is very close to peroxisome proliferative activated receptor delta (PPARD) at 35 Mb, which is a positional and physiological candidate for affecting backfat thickness. Markers 45796.161h and 45796.385h in PPARD were associated with backfat when fitted as a single marker (Table 2), but dropped out in subsequent analyses. If the causative mutation does lie within PPARD, 17281.1h is certainly close enough to be in linkage disequilibrium, yet other genes in the region should not be overlooked as candidates. The impact of the associated SNP on SSC7 and particularly the causative mutation of the previously reported QTL may be minute given the limited penetration of Meishan germplasm in the US swine industry. However, other studies (Nagamine et al., 2003; Vidal et al., 2005) have reported QTL segregating for backfat in this region of SSC7 in commercial swine populations, suggesting that this marker or marker area may be useful for selecting for leanness even without Meishan influence. At a minimum, identification of this SNP may allow introgression of this Meishan mutation for reduced backfat depth. If this marker is segregating in nonMeishan populations, it may be incorporated into marker assisted selection programs. Further testing of SSC7 should be conducted, especially in other populations, in order to validate the effects of this marker or to find other markers in LD with the causative mutation that are robust across different populations.
Marker SNP from SSC 3, 8, and 10 had been identified to test for mutations in association with QTL for age at puberty, ovulation rate, and number of nipples (Rohrer et al., 1999; Rohrer 2000). These same gilts had been genotyped for these SNP, and therefore, the SNP were analyzed for an association with average backfat. Resulting associations on 2 regions of SSC3 and on SSC10 (Tables 2 and 3) were not expected. Few studies have reported putative QTL for backfat measures in either of these chromosomal linkage groups. Knott et al. (1998) reported suggestive QTL for slaughter backfat on SSC3 at 113 cM (males) and 169 cM (females) in a segregating F2 Wild Boar, Large White population. In an F2 Pietrain x Wild Boar cross, Beeckmann et al. (2003) and Geldermann et al. (2003) discovered QTL for backfat at about 70 cM on SSC3. Putative QTL from both of these studies are likely too distal from the associated SNP in this study to be in linkage disequilibrium. Similarly, suggestive QTL for backfat on SSC10 have only been identified in a few studies: at 27 cM in females (Quintanilla et al., 2002), at 67 cM (Rohrer et al., 2005), and at 86 cM (Rohrer and Keele, 1998; first rib backfat only). In this case, the QTL peaks from Rohrer et al. (2005) and from Rohrer and Keele (1998) are close to the suggestive associations of SNP on SSC10 observed in this study at 79.5 cM (Table 2).
Ancestors of this population had been scanned for QTL for backfat at slaughter (Rohrer and Keele, 1998) and for ultrasonic backfat (Rohrer, 2000) with QTL (suggestive or significant at the whole genome level) identified on SSC 1, 7, and X. None of the SNP on SSC1 were associated with backfat in the present research, implying further SNP testing on SSC 1 may be warranted. Lack of map density may have contributed to the lack of significant associations on SSC1. The SNP associations on 2 regions of SSC 3 and on 10 in the present research may be more questionable because no QTL had been identified on these chromosomes; the SNP on SSC10 are especially questionable because none of them remained in the model after backward selection. However, there are several possible reasons for significant SNP without QTL being discovered in previous studies in this population or in other QTL scans. The SNP on SSC3 were significant primarily because of parent-of-origin effects rather than traditional additive or dominance effects; with some exceptions (e.g., Rattink et al., 2000; Rohrer et al., 2005) models with parent-of-origin effects were not used to detect QTL. Most QTL methodology relies on lines being nearly fixed for opposite alleles. In this case, if the SNP were already segregating in both the Meishan and early White Composite, or it originated from the new White Composite, its effect may not have been detected in the QTL scan. The association model used to analyze these data would have detected a QTL if either of these scenarios existed.
Some caution is still justified with respect to 17465.1h on SSC3. Although its additive effect was large and significant, the allelic frequency is quite low. Virtually no individuals homozygous for the minor allele were detected or predicted from GenoProb. Therefore, the additive estimate is based on the difference between a low frequency heterozygous class (mostly from related animals) and the major allele homozygous class. Fitting polygenic effects in the model should have reduced the effect of genetic relationships creating spurious associations, but this marker should still be validated in a separate population to see whether this association is confirmed.
Parent-of-origin effects have not, as yet, been extensively studied for production traits in swine though some associations have been detected for candidate genes (e.g., DLK1 and IGF2; Nezer et al., 1999 and Kim et al., 2004), and some QTL scans for backfat have included parent-of-origin effects (e.g., Rattink et al., 2000; Milan et al., 2002; Thomsen et al., 2004). When examining parent-of-origin trends from the single SNP analyses in this study (Table 2), patterns seem to follow a paternal origin trend on SSC10 and a maternal origin tendency on SSC7. Thomsen et al. (2004) suggested paternal and maternal inheritance of carcass measures in this same region on SSC10, and maternally expressed QTL were identified for muscle depth at 56 cM (Rattink et al., 2000) and belly weight at 63 cM (Milan et al., 2002) on SSC7. Both studies imply that parent-of-origin effects may be present in these regions. However, neither of these genomic regions shows parent-of-origin effects in the final model suggesting these effects are either spurious or too small to be detected in conjunction with a Mendelian model of inheritance. Parent-of-origin patterns on SSC3 are less clear with the genotypes expressing more of a polar overdominance pattern (especially in the final model; Table 4). In the murine genome, imprinted genes have been identified in the corresponding regions to where markers 6559.2h, 16963.2h, and 27514.1h are located on SSC3 (Nikaido et al., 2003), suggesting the possibility of imprinting in this region. However, the genes that contain these markers have not been implicated directly. On SSC7, a QTL for backfat with possible parent-of-origin effects has been detected (Rattink et al., 2000) at approximately 57 cM—close to 45796.161h and 45796.385h at 63.0 cM on SSC7 (Table 2). These SNP did not remain in the final, backward-selected model, suggesting that the additive effects from 17281.1h were more indicative of variation in backfat on SSC7.
The final backfat model contained a total of 5 markers when parent-of-origin effects were allowed in the model (Table 4) and 3 markers when parent-of-origin effects were not allowed (Table 5). Selecting markers using backward selection after identifying SNP with individual effects is useful to identify markers with the most potential for use in breeding schemes or for pinpointing areas of the genome to continue sequencing for polymorphisms in linkage disequilibrium with QTL. No multiple comparison procedures were used to account for false discovery rates when testing SNP individually; with the number of tests conducted on 64 robust SNP, 8 to 13 associations were expected at the P < 0.10 level. The backward selection process resulted in a more conservative estimate of markers with actual associations. Also, the backward selection process avoids overstating the number of significantly associated SNP resulting from high correlations between regressions of closely linked markers; in this case, the number of associated markers was reduced from 12 to 4 (5 including TBG) when parent-of-origin effects were allowed and from 5 to 2 (3 with TBG) without parent-of-origin effects. When simultaneously fitting multiple linked markers in the same model, it is probable that these markers will explain similar variation in the trait of interest, resulting in multicollinearity. Systematic removal of SNP resulted in a more concise and likely more meaningful model and leads to isolating regions to concentrate resources in finding new SNP. As theory and software evolve, and as increased numbers of SNP are identified, a model fitting haplotypes (Meuwissen and Goddard, 2000) and their interactions may be preferable. At any rate, targeting areas under QTL peaks for SNP markers appears to be an effective strategy for developing usable industry markers. Using the results from this study, the areas where associated SNP were discovered can be further saturated with SNP from sequence data to identify markers with stronger associations with back-fat and further detail the gene action in areas exhibiting parent-of-origin effects. This step will bring us closer to fine mapping these regions and to developing more robust genetic markers.
Results in this study were derived using a model with polygenic effects. As such, phenotypic resemblance between related animals due to factors beyond SNP marker genotypes was accounted for through the numerator relationship matrix. Related animals are often the main source of some of the genotypes, especially when alleles have low frequencies (i.e., less than 5%). Therefore, fitting a polygenic model reduces the likelihood of significant but spurious associations for some of the SNP arising due to animal relationships rather than the marker being examined.
In the final model, 3 markers were retained with additive effects: 17465.1h on SSC3, 17281.1h on SSC7, and TBG (also expressed dominance) on SSC X. Adding markers 6559.2h and 27514.1h, which showed effects of parent-of-origin, seems to be of little consequence relative to the effects of these additive markers. However, it is unlikely that any of them except possibly TBG represent a causative mutation for backfat deposition. They should be validated in other populations before extensive use. Since the TBG marker is located on SSC X, an examination of its effect on male backfat deposition is also warranted. The QTL region of SSC1 where no associated SNP were discovered should also be examined more closely to detect markers in linkage disequilibrium with a causative mutation or to determine whether the QTL detected there actually exists (i.e., the detected QTL was false).
In conclusion, single nucleotide polymorphisms that are associated with backfat deposition and, thereby, carcass leanness have been identified on swine chromosomes 3, 7, and X. Their effects should be validated in other populations and fine mapping in these regions should continue to develop robust genetic markers. These markers were identified by targeting areas of the genome where quantitative trait loci had been detected in previous studies. Targeting areas detected by QTL scans for SNP discovery is the next logical step in developing markers that will be useful for selection and should be continued in advanced generations of other populations where QTL have been identified. Backward selection procedures helped to identify markers with the strongest associations with backfat; potential problems with over-selection of correlated markers were avoided as a result.
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Footnotes |
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2 The authors thank the USMARC swine operations employees for data collection, S. Hauver, K. Simmerman, and B. Quigley for technical assistance, and J. Watts for secretarial support.
3 Corresponding author: Kreg.Leymaster{at}ars.usda.gov
Received for publication October 26, 2006. Accepted for publication January 16, 2007.
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LITERATURE CITED |
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TopAbstractINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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