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* Laboratory of Genetics, University of Wisconsin, Madison, Wisconsin 53706 and DuPont Crop Genetics Research, Dupont Experimental Station, Wilmington, Delaware 19880
4 Corresponding author: Laboratory of Genetics, University of Wisconsin, 425 Henry Mall, Madison, WI 53706.
E-mail: jdoebley{at}wisc.edu.
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ABSTRACT |
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 TOP ABSTRACT MATERIALS AND METHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSLITERATURE CITED |
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To identify other maize genes that were targets of selection during domestication, approaches based on molecular population genetics have been employed (WRIGHT and GAUT 2005). Evidence of selection can be detected either by standard tests of the neutral equilibrium model or by a coalescence-simulation (CS)-based test. The coalescence-simulation-based test assays whether the relative loss of nucleotide diversity in maize as compared to teosinte is too large to be accounted for by a domestication bottleneck alone such that selection can be inferred. The Hudson–Kreitman–Aguadé (HKA) test, a standard neutrality test, assays whether the amount of nucleotide diversity in the gene of interest is significantly lower than the amount of nucleotide diversity in neutral genes in maize. Application of these tests provided evidence that the domestication genes tb1 and tga1 were both targets of selection during domestication (WANG et al. 1999, 2005; CLARK et al. 2004).
Recently, large-scale genomic screens using molecular population genetics methods have identified a long list of genes that were possible targets of selection during maize domestication (VIGOUROUX et al. 2002; WRIGHT et al. 2005; YAMASAKI et al. 2005). An initial study found evidence for selection during domestication at 10 loci by screening simple sequence repeats located in 501 maize genes (VIGOUROUX et al. 2002). A subsequent study using single nucleotide polymorphism (SNP) markers and a sample of 774 genes found 30 putative domestication or crop improvement genes (WRIGHT et al. 2005). Eight genes with strong evidence of selection during domestication were identified by a third study, which examined genes with zero diversity in 14 maize inbred lines (YAMASAKI et al. 2005). In total, these studies have identified 48 loci that may have been targets of selection during maize domestication and subsequent improvement.
In this study, we have taken an approach similar to that used by VIGOUROUX et al. (2002) and YAMASAKI et al. (2005) to investigate candidate genes for signatures of selection associated with maize domestication. Similar to these studies, our candidate gene pool consists of genes with very low genetic diversity in maize inbred lines. Unlike these previous studies, we filtered our candidate gene sample to include only those with sequence homology to known regulatory genes. These include DNA-binding transcription factors, receptor kinases, regulators of RNA metabolism, and components in signal transduction pathways. Our candidate gene sample consisted of 72 expressed sequence tags (ESTs) identified as putative regulatory genes.
Here we report that 17 of our 72 candidate genes (23.6%) exhibit evidence that they were targets of selection during domestication. An additional 21 of our 72 candidate genes (29.2%) were identified as potential targets of selection in teosinte. By comparing our results with those from another study, we conclude that there is minimal evidence that regulatory genes are overrepresented among genes that show evidence of selection. When the genetic map positions of our candidate domestication genes were tested for association with previously mapped QTL responsible for maize domestication traits, we found no evidence that our candidate domestication genes are clustered near domestication QTL. However, map positions from a subset of the 17 candidate domestication genes, those with sequence homology to known kinases and phosphatases, significantly colocalize with known domestication QTL. Finally, by examining expression profiles of genes in distinct maize plant tissues, we observed that candidate domestication genes tend to be more highly expressed relative to neutral genes in kernel and other reproductive tissues as opposed to vegetative tissues where no significant difference was observed.
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MATERIALS AND METHODS |
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TOPABSTRACTMATERIALS AND METHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSLITERATURE CITED |
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For diversity analysis, we collected DNA sequences from 16 maize landraces and 16 teosinte (Zea mays ssp. parviglumis) individuals for each of these 119 ESTs (supplemental Table 1). As previously described (TENAILLON et al. 2001), this geographically diverse sample of maize landraces represents the genetic diversity present in the maize population before modern breeding efforts. The 16 different teosintes were chosen on the basis of geographic criteria and cover the entire natural distribution of Z. mays ssp. parviglumis. Three accessions of Tripsacum dactyloides, which belong to the sister genus of Zea, were used as outgroup individuals for some analyses (supplemental Table 1). T. dactyloides alleles were successfully isolated for 43 of the 72 candidate genes and 22 of the 47 control genes.
Two pairs of PCR primers, including a nested pair, were designed to amplify each EST. Often the targeted segment of the gene included the 3'-untranslated region and a portion of the open reading frame. To facilitate PCR product sequencing, the internal forward and reverse primers were equipped with T3 (5'-aattaaccctcactaaaggg-3') and T7 (5'-gtaatacgactcactatagggc-3') 5'-tails. For each locus, an initial PCR reaction was performed using the outer primer set under the following conditions: 95° for 5 min, followed by 24 cycles of 94° for 20 sec, 55° for 30 sec, 72° for 2 min, and a final step of 72° for 10 min. The reaction products were diluted 10-fold with TE buffer and used for a subsequent round of PCR with the nested primer set under the same PCR conditions as described above. The products from this round of PCR were then used for DNA sequencing in both directions, using T3 and T7 primers and a standard protocol (Applied Biosystems, Foster City, CA) on an ABI 3700 DNA sequencer.
The forward and reverse DNA sequences from each individual were assembled using Sequencher software (Gene Codes). Individual sequences from the maize landraces, teosinte, and outgroup individuals were then manually aligned using BioEdit software (HALL 1999). Since our teosinte individuals are partial inbreds, sites where base calls were ambiguous due to potential heterozygosity were coded as "N." Unique single-base-pair variants (singletons) were double checked by manually inspecting their raw chromatogram peaks.
Tests for neutrality:
Molecular population genetics statistics were estimated separately for maize landraces and teosinte individuals using DnaSP (ROZAS et al. 2003). Nucleotide polymorphism (
) (WATTERSON 1975) and nucleotide diversity (
) (TAJIMA 1983) were calculated on the basis of all sites. Estimates of the population recombination rate (4Nc, where N is the effective population size and c is the recombination rate per base pair per generation) (HUDSON 1987) were also calculated using all sites. The HKA test (HUDSON et al. 1987) for neutrality was also performed. For the HKA test, T. dactyloides was used as the outgroup. Eleven neutral loci (adh1, an1, asg75, bz2, csu1138, csu1171, csu381, csu1132, fus6, glb1, and umc128) (EYRE-WALKER et al. 1998; HILTON and GAUT 1998; TENAILLON et al. 2001) were used for HKA tests involving maize landraces, while a smaller set of neutral loci (adh1, glb1, bz2, csu1132, and csu1171) (TENAILLON et al. 2004) were used in HKA tests involving teosinte. For each HKA test, an overall 
2 value was obtained by summing the individual 2 values calculated using each neutral locus. This overall 2 value was then used to obtain an overall P-value.
Coalescence-simulation analysis for selection:
For each candidate and control gene, a coalescence-simulation-based test was used to determine if the gene was a potential target of selection during domestication. We used a modified version of the standard coalescence procedure (HUDSON et al. 1987) that incorporated the domestication bottleneck as previously described (EYRE-WALKER et al. 1998). All parameters in the model were assigned to previously established values (EYRE-WALKER et al. 1998; TENAILLON et al. 2004). The severity of the bottleneck (k) was defined as a function of the population size during the bottleneck (Nb) and the duration of the bottleneck (d) such that k = Nb/d. Using sequence data from 44 neutral genes, the best multilocus estimate of k was found to be 2.0 using methods previously described (TENAILLON et al. 2004). To estimate k, we used the number of segregating sites (S) as the summary statistic and d was set equal to 1000. Finally, k values ranging from 0.5 to 5 (in increments of 0.1) were explored.
We used the coalescence model described above to test for selection in 68 candidate genes. Four of the 72 candidate genes were excluded from analysis because no polymorphism in teosinte was observed in which case the test cannot be performed. For each of the 68 candidate genes, 10,000 simulations were conducted. A gene was considered to be a potential target of selection during domestication if Smaize was <97.5% of the Ssimul values.
Expression analysis:
We obtained the expression pattern of candidate and control genes using information from a Massively Parallel Signature Sequencing (MPSS) database (BRENNER et al. 2000). This database includes 109 tissue libraries generated using the maize inbred line B73 (supplemental Table 2). The MPSS method utilizes a 17-mer sequence tag that is generated using the 3'-most DpnII site in a given cDNA. The abundance of the sequence tag is then measured and used to infer the relative abundance of the corresponding gene transcript (BRENNER et al. 2000). The accuracy of the expression profiles obtained using the MPSS method was confirmed by comparing these results with previously reported expression patterns for several genes (supplemental Materials and Methods; supplemental Figure 1; supplemental Figure 2). Transcript abundance was recorded in parts per million. Signals were considered as background noise if lower than an arbitrary cutoff of 5 ppm. Using the methods described above, we were able to obtain expression profiles for a total of 66 genes (27 control and 39 candidate genes).
Several analyses including principal component analysis (PCA), permutation t-tests, and analysis of variance (ANOVA) were used to characterize any overall pattern in the expression profiles. Using the statistical computing package R, PCA was conducted with transcript abundance transformed to a logarithmic scale. Permutation t-tests were used to identify pairwise differences in expression levels between three tissue types (vegetative, kernel, and nonkernel reproductive tissues; supplemental Table 2) or between gene classes (neutral genes vs. genes selected during domestication). The effect of interaction between tissue type and gene class on the abundance of expressed transcripts was assessed using ANOVA. For both permutation t-tests and ANOVA, signals were transformed to logarithmic scale as in PCA analysis. For the ANOVA, we fit a linear mixed model using an R module (BATES 2007). In our model, gene class and tissue type were considered fixed effects while individual genes and libraries were considered random effects. The significance of the interaction term between tissue type and gene class was determined by comparing the fit of two models, one with the interaction term (model 1) and another model without the interaction term (model 2).
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RESULTS |
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TOPABSTRACTMATERIALS AND METHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSLITERATURE CITED |
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(WATTERSON 1975) for the control genes and observed that maize landraces retained 70.7% (r = maize/teosinte) of the genetic diversity found in teosinte (Table 1). This value is similar to that found in previous studies that reported that maize retained 57–80% of the genetic diversity found in teosinte (TENAILLON et al. 2004; WRIGHT et al. 2005). Thus, our set of neutral or control genes is consistent with such genes in prior studies.
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35% as measured by . This calculation excludes four genes with no polymorphism in teosinte (PZC07071, PZC08281, PZC11007, and PZC15464). The greater loss of diversity in maize as compared to teosinte for candidate genes was statistically significant (MW test: P < 0.001). This suggests that, overall, the candidate genes have lost more diversity than a random sample of genes from the maize genome.
Statistical tests for neutrality:
The HKA test was conducted on a subset of 43 candidate and 22 control genes for which outgroup sequences were obtained. For each gene, the HKA test was performed separately on maize landraces and teosinte. The HKA test is based on the theoretical prediction that under neutrality the ratio of diversity within a species to divergence between this species and an outgroup should be equivalent for all genes in the genome. If this ratio was significantly smaller for a candidate gene than for the neutral genes, then selection was inferred. We considered a gene as a potential target of selection during domestication if the HKA test was significant in maize landraces but not in teosinte. If the HKA test was significant in teosinte, we considered the gene as a target of selection in teosinte. Using the criteria above, we identified three control genes and 14 candidate genes as putative domestication genes (Tables 2 and 3). We also identified four control genes and 21 candidate genes as putative targets of selection in teosinte (Tables 2 and 3).
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Comparison of candidate gene results with previous studies:
To assess whether regulatory genes were a major target of selection, we compared the proportion of putative selected genes in our candidate gene pool with that found in a previous study. YAMASAKI et al. (2005) examined 35 candidate genes that had no diversity in maize inbred lines using a strategy similar to ours. However, their study did not filter candidate genes in regard to function, while we specifically chose candidate genes with sequence homology to putative regulatory genes. To assess the role of regulatory genes in selection, we compared the results from these two sets of candidate genes.
To determine whether any differences between the two sets of candidates could be attributed to a sampling bias, we compared the sequence statistics of the two groups (Table 4). On average, similar numbers of maize landraces and teosintes were amplified for genes in both studies. Although our regulatory gene alignments are almost twice as long as their unfiltered candidate genes, the number of haplotypes (h) and average nucleotide diversity () are similar between the two studies for the maize landraces (MW test, P = 0.59 for h and P = 0.53 for ) as well as for teosinte (MW test, P = 0.73 for h and P = 0.15 for ).
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We used a similar procedure to assess whether regulatory genes were a major target of selection in teosinte. The HKA test identified statistically similar proportions of teosinte-selected genes in both our regulatory candidate gene pool (48.8%) and the unfiltered candidate genes (31.4%) (Fisher's exact test, P = 0.16). From this analysis, we found no convincing evidence supporting regulatory genes as a more frequent target of natural selection in teosinte.
Finally, given that we have relatively few selected genes and thus low power to detect any differences in these proportions, we pooled genes identified by the HKA test as selected in either maize or teosinte into one group. We then compared the proportion of pooled selected genes among the regulatory candidates (81.4%) to that among the unfiltered candidates (48.6%) and observed a significant (Fisher's exact test, P = 0.0035) excess of selected genes among the regulatory candidate genes. This result provides some weak evidence that regulatory genes are overrepresented among selected genes and suggests that the negative results described above are due to low power. We consider the evidence weak because our study may have had more power than YAMASAKI et al. (2005) to detect selected genes due to the fact that our alignments are twice as long as those analyzed in their study.
Association of selected genes with maize domestication QTL:
To assess if the domestication genes identified in our analysis were potentially causative for previously identified domestication QTL controlling morphological traits, we tested for association between the map positions of the two groups. Genetic map positions were obtained for 43 control and 60 candidate genes, which included 14 genes identified as targets of selection during domestication by the HKA test and/or the CS test (Figure 1). We used a permutation test to assess if the 14 putative domestication genes as a whole were more closely located to domestication QTL than a random sample of genes (WRIGHT et al. 2005). The P-value of the permutation test (P = 0.15) was calculated as the proportion of the 100,000 random samples whose mean distances were as small or smaller than that observed for the domestication genes. This test provided no evidence for a significant clustering of putative domestication genes near known domestication QTL controlling morphological traits.
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Expression analysis:
Expression profiles of 66 genes in 17 distinct maize tissues were obtained from a MPSS database (see MATERIALS AND METHODS). This set of 66 genes consisted of 16 putative candidate domestication genes, 23 neutral candidate genes, and 27 neutral control genes. As an initial form of comparison, we used PCA to detect any patterns in the expression profiles of domestication and neutral genes. The two leading principal components collectively explained 63.4% of the overall variation in expression. When the two leading principal components were plotted, domestication genes were distributed with neutral genes in a common cluster (Figure 2). By plotting the two leading principal components, three outliers, two neutral genes, and one putative domestication gene were identified. The two neutral genes (PZC03717 and PZC01861) are expressed at high levels in pollen as opposed to other tissues while the domestication gene (PZC04046) is expressed at high levels in all tissues except pollen. These results suggest that domestication genes do not have an expression profile across different tissues that is distinct from neutral genes due to the fact that they have experienced different selection histories during domestication.
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DISCUSSION |
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TOPABSTRACTMATERIALS AND METHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSLITERATURE CITED |
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Search for targets of selection:
We used both the HKA test and the CS test to identify domestication genes within our candidate pool. Fourteen putative domestication genes were identified by the HKA test and 10 putative domestication genes were identified by the CS test. Seven of these genes were identified by both tests, providing more evidence that they underwent selection during domestication. In total, these two tests identified 17 putative domestication genes. The HKA test was also used to identify 21 genes that underwent selection in teosinte. Similar to that observed in a previous study (YAMASAKI et al. 2005), our strategy of examining genes with low diversity in maize inbred lines resulted in a much larger proportion of identified putative domestication genes (23.6%) than that reported by previous studies (2.0%, VIGOUROUX et al. 2002; 2–4%, WRIGHT et al. 2005) that examined genes at random.
Although our strategy of identifying putative selected genes among candidate genes with low diversity in maize was successful, our results should be considered with caution. The tests that we used to identify putative selected genes are tests of neutrality; thus a significant result simply implies deviation from neutrality, which is not necessarily due to selection. Population structure and history can also lead to a deviation from neutrality. The CS test takes one aspect of the history of the population (the domestication bottleneck) into account; however, the results rely on modeling this aspect accurately and ignore other aspects of population history. The HKA test also compensates for the domestication bottleneck since the control genes have experienced the same history as the test gene. Another issue is that our selected genes may be hitchhiked regions of selective sweeps on neighboring genes rather than direct targets of selection, and our approach does not have power to distinguish between the two possibilities. Thus, we emphasize that the selected genes identified through these analyses are candidates and that further analyses are needed to validate that these genes were targets of selection.
The role of regulatory genes in evolution:
One goal of this study was to test the hypothesis that regulatory genes are overrepresented among selected genes. Our results provide limited support for this hypothesis. A direct comparison found that our study identified a significantly larger proportion of selected genes as compared to a previous study using a similar strategy with candidate genes that were unfiltered in regard to function (YAMASAKI et al. 2005); however, this difference could be due to a difference in the power to detect selected genes between the two studies. An overrepresentation of regulatory genes among selected genes is not surprising, given the number of selected regulatory genes that control domestication traits (DOEBLEY et al. 1997; FRARY et al. 2000; WANG et al. 2005; LI et al. 2006; SIMONS et al. 2006). Regulatory genes are obvious candidates for controlling traits that have undergone selection due to the fact that changes in function or expression of regulatory genes can potentially change the expression of downstream structural or regulatory genes. Further validation of the putative regulatory selected genes in this study as well as the identification of more putative selected genes in maize and other organisms will be necessary to determine if regulatory genes are a more frequent target of selection in general.
Domestication genes and maize–teosinte QTL:
Some of our 17 candidate domestication genes colocalize with QTL controlling maize–teosinte morphological divergence (Figure 1). Although we observed that the average distance to the closest domestication QTL is smaller for domestication genes than for neutral genes, this difference is not statistically significant. We consider several reasons that may explain the weak statistical evidence. First, the method we used for comparison may not be powerful enough, given that we simply consider the distance to the nearest QTL without taking into account clustered QTL and the size of the QTL effect. Second, QTL used here explain only the variation of visible morphological traits (DOEBLEY and STEC 1991, 1993) and are not expected to be associated with domestication genes selected for other traits. Third, some domestication genes might be QTL with minor effects or QTL not expressed stably across environment. These types of QTL are not easily detected with certainty and may not have been identified in earlier studies (DOEBLEY and STEC 1991, 1993).
We did observe a significant association between the map positions of selected kinase/phosphatase genes with those of known domestication QTL. We hypothesize that kinase/phosphatase genes may have been targets of selection for morphological change during domestication due to the importance of such genes in regulating plant development through signal transduction pathways. Although a kinase has been found to be responsible for flowering-time differences among varieties in rice (TAKAHASHI et al. 2001), to date, no crop domestication QTL has been fine mapped to a kinase or phosphatase. This class of genes represents a good candidate class that should be considered in future candidate gene analyses.
Gene expression patterns:
One of the intriguing questions in evolutionary biology is how variation in gene expression patterns contributes to evolution. During the evolution of maize, artificial selection acted on some genes, which regulate divergent traits between maize and teosinte, while other genes evolved under neutrality without contributing to domestication. Genes under selection are expected to be expressed in tissues that differ in morphology between maize and teosinte, while neutral genes could be expressed in any tissue type. This hypothesis is also supported by a recent study that examined expression levels of 48 selected genes and 658 neutral genes and concluded that selected genes were more highly expressed in the ear, a tissue that is strikingly different in maize and teosinte (HUFFORD et al. 2007). In our study, we observed a similar phenomenon: substantially stronger expression for selected genes as opposed to neutral genes in the kernel and other reproductive tissues, but not in vegetative tissues.
The results from this study have four important implications that should be considered in the search for selected genes. First, the comparison of several studies have indicated that the strategy of choosing candidate genes with low diversity in maize inbreds has resulted in a much higher proportion of identified putative selected genes. Second, we observed that regulatory genes are overrepresented among selected genes. Our results also suggest that a subset of regulatory genes, kinases and phosphatases, may have been targets of selection for morphological change during domestication. Third, our analysis of the expression profiles of domestication and neutral genes within reproductive tissues supports the inference that selected genes are expressed at a higher level in these tissues as compared to neutral genes. This result suggests that a substantial portion of the domestication genes identified by our study are real as opposed to false positives. Finally, although at present the number of putative domestication genes is small, the approaches used in this study and those described elsewhere, as well as advancements in sequencing technology, will make the identification of domestication genes easier in the near future. As the list of identified domestication genes grows, so will our understanding of the underlying process of domestication.
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ACKNOWLEDGEMENTS |
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TOPABSTRACTMATERIALS AND METHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSLITERATURE CITED |
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FOOTNOTES |
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2 Present address: Department of Biostatistics, University of Washington, Seattle, WA 98195.
3 Present address: Centre IRD de Montepellier, 911 av. Agropolis, 34394 Montpellier, Cedex 5, France.
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LITERATURE CITED |
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