Supplemental Text: Literature search and Analyses

Transcription

1 A supplement to the Annual Reviews of Ecology, Evolution and Systematics article on Genotype-byenvironment interaction and plasticity: exploring genomic responses of plants to the abiotic environment by David L. Des Marais, Kyle M. Hernandez and Thomas E. Juenger. Supplemental Text: Literature search and Analyses Literature search - We used the following search keywords in Web of Science (WOS): QTL x environment, QTL by environment, genotype by environment with a subsearch of QTL, and gene by environment with a subsearch of QTL. All searches were reduced to the plant sciences option. In addition, we included familiar literature from our own research that was not discovered in the WOS search (n=6) and was primarily associated with the model plant, Arabidopsis thaliana. Database - All environment-environment comparisons were nested within study, species, traits, and the lowest level of experimental factors. For example, if an experiment was performed in multiple locations and implemented a well-watered and a drought treatment at each location, the comparisons were only made between the water availability treatments within each location. We also converted estimates of additive effects to a if the authors presented 2a (i.e., divided by 2), and if the authors were not explicit, we assumed the authors presented a (Supplemental Table 2). Statistical analyses - We used the R v statistical software (R Core Team 2012) to fit distributions (bbmle v1.05.2; Bolker & R Development Core Team 2012), implement random regression/mixed models (lme4; Bates et al. 2012), and create figures (ggplot2; Wickham 2009). To control for dependency and variance issues associated with the different manuscripts in our database, we fit each study as a random effect. Since we are interested in general patterns, we collapsed traits, treatments, and species into related classes (Supplemental Table 3); however, due to available degrees of freedom, we were limited in the number of interactions we could test among these classes. All tests of significance were based on likelihood ratio tests that compare reduced and full models. To examine the number of loci associated with a quantitative trait, we modeled counts of significant QTL as a Poisson distributed dependent variable. Our next set of analyses focused on standardized additive effects. We first modeled the distribution of standardized effects across all significant QTL (i.e., excluding censored QTL). Next, we modeled QTL effect plasticity as the absolute difference in additive effects across environment pairs (data points are already scaled), including censored data, and tested for the significance of the factors in our database. To model phenotypic plasticity, we performed similar analyses; however, plasticity was measured as the absolute difference in trait values across environment pairs/across environment trait means. Finally, we used a random regression model with phenotypic plasticity as a predictor of QTL effect plasticity. Here, we fit random slopes and intercepts for each study, and (given enough degrees of freedom) tested for differences in slopes across the factors in our database. Literature Cited Bates, D., Maechler, M., and Bolker, B lme4: Linear mixed-effects models using S4 classes. R package version Bolker, B. and R Development Core Team bbmle: Tools for general maximum likelihood estimation. R package version Web of Science Thomson Reuters. Wickham, H ggplot2: Elegant graphics for data analysis. Springer, New York.

2 Supplemental Figure Density Number of QTLs in an Environment Supplemental Figure 1 The distribution of the number of QTL for a trait in an environment.

3 Supplemental Figure Density Standardized additive effect Supplemental Figure 2 The distribution of the absolute standardized additive effects across all factors.

4 Supplemental Figure 3 Crop Natural 1.5 Phenotypic plasticity Antagonistic pleiotropy Differential sensitivity Environment specific Supplemental Figure 3 The interaction between genetic architecture and species classes in phenotypic plasticity.

5 Supplemental Table 1 References used for exploring QTL additive effects and phenotypic plasticity I.D. a Type b Search c d Reference WOS: Natural Added Alonso-Blanco, C et al (1998) Genetics 149:749 WOS: Natural Web of Science Borevitz, JO et al (2002) Plant Biology 160:683 WOS: Natural Web of Science Loudet, O et al (2003) Plant Physiology 131:345 WOS: Natural Added Loudet, O et al (2003) Genetics 163:711 WOS: Natural Added Li, Y et al (2006) Plos One WOS: Crop Web of Science Lanceras, JC et al (2004) Plant Physiology 135:384 WOS: Natural Added Hausmann, NJ et al (2005) Evolution 59:81 WOS: Crop Web of Science Zhang, ZH et al (2005) Annals of Botany 95:423 WOS: Natural Added Juenger, TE et al (2005) Genetica 123:87 WOS: Crop Web of Science Zhu, JM et al (2005) Plant and Soil 270:299 WOS: Crop Web of Science Zhu, JM et al (2005) Theoretical and Applied Genetics 111:688 WOS: Crop Web of Science Yue, B et al (2005) Theoretical and Applied Genetics 111:1127 WOS: Natural Added Alonso-Blanco, C et al (2005) Plant Physiology 139:1304 WOS: Crop Web of Science Argyris, J et al (2005) Theoretical and Applied Genetics 111:1365 WOS: Natural Web of Science Calenge, F et al (2006) Plant Physiology 141:1630 WOS: Natural Web of Science Botto, JF et al (2007) Plant Cell and Environment 30:1465 WOS: Crop Web of Science Uptmoor, R et al (2008) Molecular Breeding 21:205 WOS: Crop Web of Science Kato, Y et al (2008) Euphytica 160:423 WOS: Natural Web of Science Kiani, SP et al (2008) Plant Science 175:565 WOS: Crop Web of Science Xue, DW et al (2009) Euphytica 169:187 WOS: Crop Web of Science Baga, M et al (2009) Journal of Experimental Botany 60:3575 WOS: Crop Web of Science Abdel-Haleem, H et al (2010) Euphytica 172:405 WOS: Crop Web of Science Abdel-Haleem, H et al (2010) Euphytica 172:419 WOS: Natural Web of Science Frerot, H et al (2010) New Phytologist 187:355 WOS: Crop Web of Science Wang, Z et al (2010) Euphytica 174:447 WOS: Natural Web of Science Vallejo, AJ et al (2010) Annals of Botany 106:833 WOS: Natural Web of Science Coluccio, MP et al (2011) Journal of Experimental Botany 62:167 WOS: Crop Web of Science Li, W et al (2011) Euphytica 178:427 WOS: Crop Web of Science Naruoka, Y et al (2011) Theoretical and Applied Genetics 123:1043 WOS: Crop Web of Science Gu, JF et al (2012) Journal of Experimental Botany 63:455 WOS: Crop Web of Science Blair, MW et al (2012) Molecular Breeding 29:71 WOS: Crop Web of Science Borras-Gelonch, G et al (2012) Euphytica 183:303 WOS: Crop Web of Science Sabadin, PK et al (2012) Theoretical and Applied Genetics 124:1389 WOS: Crop Web of Science Liu, GF et al (2012) Theoretical and Applied Genetics 125:143 WOS: Natural Web of Science Ungerer, MC et al (2003) Genetics 165:353 WOS: Crop Web of Science Wang, ZF et al (2012) Theoretical and Applied Genetics 125:807 WOS: Natural Web of Science Sanchez-Bermejo, E et al (2012) Plant Cell and Environment 35:1672 a Refers to the unique reference number in Web of Science. b The classification of species in the respective study. c Denotes whether the study was listed in our Web of Science queries or was intentionally added. d Reference format: first author, year, journal, volume, starting page.

6 Supplemental Table 3 The distribution of QTL information across the various classes in our database Factor a Levels b # QTL Factor a Levels b # QTL WOS: Germination 31 WOS: Growth Architecture 100 WOS: Growth/Size 246 WOS: Metabolites/Molecules 64 Trait Class WOS: Nutrient Accumulation 50 WOS: Phenology 141 WOS: Physiology 98 WOS: Yield/Fitness 180 WOS: Damage 11 WOS: Density 158 WOS: Light Availability 188 WOS: Treatment Nutrient Availability 102 WOS: Class Osmotic 50 WOS: Temperature 38 WOS: Vernalization 50 WOS: Water Availability 313 WOS: Backcross 31 WOS: Double Haploid 140 Study WOS: F2 5 Cross Class WOS: Recombinant Inbred Line 601 WOS: Single Seed Descent 14 WOS: Single Segment Substitution 119 WOS: WOS: WOS: Arabidopsis halleri 5 WOS: Arabidopsis thaliana 358 WOS: Brassica oleracea 11 WOS: Helianthus annuus 48 WOS: Hordeum vulgare 94 WOS: Species Lactuca sativa 8 WOS: Oryza sativa 252 WOS: Phaseolus vulgaris 67 WOS: Sorghum bicolor 18 WOS: Triticum aestivum 35 WOS: Zea mays 14 WOS: Crop 499 Species Type WOS: Natural 411 a Various classification schemes used in our database b The possible levels of each classification factor (Supplemental Table 4)

7 Supplemental Table 4 Classification scheme of species, traits, and treatments in our database Factor a Level Contains b Species Type Crop Natural Oryza sativa, Zea mays, Lactuca sativa, Brassica oleracea, Hordeum vulgare, Triticum aestivum, Phaseolus vulgaris, Sorghum bicolor Arabidopsis thaliana, Helianthus annuus, Arabidopsis halleri Trait Class Treatment Class Germination Growth Architecture Growth/Size Metabolites/Molecules Nutrient Accumulation Phenology Physiology Yield/Fitness Damage Density Light Availability Nutrient Availability Osmotic Temperature Vernalization GR, Germination, germination_percentage, germination_energy, germination_rate no_branches, leaf_n_bolting, dfln, LS, SE, SE/LS, bolting_time, elong_axils, reproductive_phase, sec_mer, second_inf_meristems TLN, hypocotyl_growth, PH, root_biomass, rosette_area, shoot_biomass, RL, SL, RHL, LRL, RGR, TN, FLN, height, hypocotyl_length, tiller_number, tillers, plant_height, rosette_diameter, rosette_leaf_bolting, LN AA, fructose, glucose, starch, sucrose, acid_detergent_fiber, protein_content, starch_content, ISDMD, particle_size NP, CL, HU, percent_n, K, NA, NAK, zinc_accumulation FT, flowering_time, DFAIG, DFT, time_to_floral_induction, dant, days_flower, days_maturity, FL-HD, HD, J-FL, early_flowers, time_maturity d13c, CT, LDS, LRS, specific_water_use, 1-qP, NPQ, psip, psipsii, A, Fv_Fm, gs, psii, qp, Tr DM, BY, GY, HI, PN, PSS, TSN, fitness, fruit_length, no_fruit, DW, RSF, RY, freezing_tolerance, DWP, GNP, SPL, SPP, seed_weight, yield, yield_per_day, grain_yield, aux_fruits, inflorescence_fruits, total_fruits, DRW, DSW damaged, undamaged, clipped, unclipped high_d, low_d, c1, c2, c3 Blue, BRZ, Dark, Far-Red, GA, Red, White, spain1, spain2, sweden1, sweden2, long_day, short_day, dark, EOD, WL, WL_EOD, extended, natural, LD, SD N-, N+, 3, 10, High_P, Low_P, high_n, low_n C, S, 50_nacl, water, PEG, control, NACL_5, NACL_7 15C, 20C, 25C, high_t, low_t, 12C, 18C, 6C, dormancy, non-dormancy LD_unvern, LD_vern, SD_unvern, non-vernalized, vernalized Water Availability a The factors from our database with arbitrary assignments. b See Supplemental Table 2 and primary source for more information. W0, W1, W2, W3, W4, dry, wet, paddy_soil, sandy_soil, stressed, well_watered, water-stressed, well-watered, combined, irrigated, rain_fed, high_pollution, low_pollution, rainfed, water_stressed, drought