Species from productive and unproductive habitats differ inherently in their relative growth rate (RGR) and a wide range of correlated quantitative traits. We investigated the genetic basis of this trait complex, and specifically assessed whether it is under the control of just one or a few genes that can act as ‘master switches‘ by simultaneously affecting a range of traits in the complex. To address this problem, we crossed two Hordeum spontaneum lines originating from two habitats that differ in productivity. The F3 offspring, in which parental alleles are present in different combinations due to recombination and segregation, was analysed for RGR and its underlying components (leaf area ratio, unit leaf rate, photosynthesis, respiration), as well as a number of other physiological and morphological parameters. For this intra-specific comparison, we found a complex of positively and negatively correlated traits, which was quite similar to what is generally observed across species. A quantitative trait loci (QTL) analysis showed three major and one minor QTL for RGR. Most other variables of the growth-trait complex showed fewer QTLs that were typically scattered over various locations on the genome. Thus, at least in H. spontaneum, we found no evidence for regulation of the trait complex by one or two master switches.
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We thank Arjen Biere, Basten Snoek, Brian Forster, Jos van Damme, Margreet ter Steege, Rens Voesenek and Ton Peeters for valuable suggestions and comments during research and the writing of the manuscript. The Earth and Life Science Foundation (ALW), which is subsidised by the Netherlands Organisation for Scientific Research (NWO), supported this study financially.
Electronic Supplementary Material
Appendix: the H. spontaneum map
Appendix: the H. spontaneum map
AFLP and SSR markers
Seventeen primer combinations were selected: E33M54, E33M61, E35M48, E35M54, E35M61, E38M55, E38M58, E42M51 and E45M55 (Qi and Lindhout 1997), E37M32, E37M33, E40M38, E41M32, E41M40, E42M32, E42M40 (Becker et al. 1995) and E31M55. DNA was isolated from 2-week-old leaves of the 233 F2 plants, using the CTAB method (Ausubel et al. 1999). The AFLP protocol was essentially as described in Vos et al. (1995). The DNA was double digested with the restriction enzymes EcoRI and MseI. The EcoRI-specific primers were labeled with either 700 or 800 nm infra-red dye (IRD700, IRD800) for detection with an automated laser sequencer (Li-Cor). In addition, markers generated by the primer combinations E32 M61, E33 M55, E39 M61, E42 M48 and E38 M54 (Qi and Lindhout 1997) were genotyped co-dominantly by Keygene.
Separately, a set of 13 SSR markers was used (Ramsay et al. 2000). The primers were labelled with either IRD700 or IRD800 for the Li-Cor sequencer. Approximately 20 ng of template DNA was used in the PCR reaction mixture, which further consisted of 1× PCR buffer, 0.5 U Ampli Taq polymerase (Perkin Elmer Inc., Wellesley, Mass., USA), 200 μM dNTPs and 1 pmol forward and reverse primer. The reaction volume was 10 μl. Four different PCR programs were used for amplification as described by Ramsay et al. (2000).
The AFLP markers from 17 primer combinations were scored dominantly as the absence or presence of an amplification product. The scoring was done by eye with the help of the Cross Checker program (Buntjer 1999). The markers from five additional primer combinations were generated and scored co-dominantly by Keygene using their QuantarPro software, which enables a distinction between the homozygous and heterozygous state based on band intensity. The AFLP marker names were designated from the primer combination and size of the amplification product. SSRs were scored co-dominantly.
During the crossing process we kept track of the structure of the mapping population. Inspection of the data revealed that some of the F2 sub-families did not segregate for a number of markers, due to partial homozygosity of the parental F1 plant. This resulted from heterozygosity at such a marker for one of the parents of our cross. Neglecting this would affect the mapping results, i.e. by segregation distortion of these markers and an overestimation of recombination frequency between ‘affected’ and ‘non-affected’ markers (P. Stam, unpublished results). Therefore, for each marker we checked its segregation in each F2 sub-family and removed data from non-segregating sub-families.
With the corrected data set, a linkage map was constructed using the JoinMap 3.0 software package (Van Ooijen and Voorrips 2000). Linkage groups were assigned using an LOD threshold of 5.0. Kosambi’s mapping function was used to calculate map distances. To assign the linkage groups to known barley chromosomes, SSR loci (Ramsay et al. 2000) as well as AFLP markers in common with earlier maps from several cultivated barley populations L94 × Vada (Qi et al. 1998), L94 × 115-6 (P. Lindhout, personal communication), Apex × Prisma (Yin et al. 1999) and Proctor × Nudinka (Becker et al. 1995) were used.
The marker data revealed that the Ashqelon parent must have been heterozygous: 59% of the Ashqelon-specific markers did not segregate in at least one F2 sub-family. Heterozygosity was less prominent in the Mehola parent; with 7% of the Mehola-specific markers not segregating in at least one F2 sub-family. As a consequence, 45% of the markers contained no useful linkage information. After adjustment, 202 markers (196 AFLP and six SSR) could be mapped without problems.
The resulting linkage map is shown in Fig. 6. The markers are distributed over 11 linkage groups. Except for group U1, each linkage group contains both dominant and co-dominant markers. These co-dominant markers provided sufficient anchors to enable integration of the parental maps. The total map length equals 445 cM. This makes an average of 18 markers per linkage group, the range being from two to 36. The average distance between two markers was 2.2 cM. No gaps between two adjacent markers were larger than 20 cM.
The assignment of linkage groups to barley chromosomes is based on AFLPs and SSRs that are in common with other linkage maps of cultivated barley (Table 6). Seven of the 11 linkage groups could unambiguously be assigned to known barley chromosomes. Three groups (2A, U1 and U2) did not contain any common markers, and one group (6) contained markers that mapped to different chromosomes in other mapping populations. Groups 2A and 6 were tentatively assigned to chromosomes based on weak linkage of some markers that were not mapped in our population, but have been mapped in at least one of the other populations. Linkage groups U1 and U2 remained unassigned, due to lack of markers shared with other maps.
The χ2 values for goodness-of-fit ranged from 0.83 to 1.60 for the 11 linkage groups, indicating a good overall fit. Therefore, even though the current map had to be assembled by removing a substantial number of markers, the remaining data still resulted in a reliable map that can serve as a basis for further linkage and QTL mapping.
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Poorter, H., van Rijn, C.P.E., Vanhala, T.K. et al. A genetic analysis of relative growth rate and underlying components in Hordeum spontaneum. Oecologia 142, 360–377 (2005). https://doi.org/10.1007/s00442-004-1705-1
- Specific leaf area
- Unit leaf rate
- Quantitative trait loci
- Trait complex