Supplementary Materials for...Jun 30, 2015 · plant root depth or profile depth. Depth to water table was modeled by (68) in 30 arc-second grid squares using a numerical model. We

www.advances.sciencemag.org/cgi/content/full/1/6/e1400218/DC1

Supplementary Materials for

Genome-environment associations in sorghum landraces predict

adaptive traits

Jesse R. Lasky, Hari D. Upadhyaya, Punna Ramu, Santosh Deshpande, C. Tom Hash, Jason Bonnette,

Thomas E. Juenger, Katie Hyma, Charlotte Acharya, Sharon E. Mitchell, Edward S. Buckler, Zachary

Brenton, Stephen Kresovich, Geoffrey P. Morris

Published 3 July 2015, Sci. Adv. 1, e1400218 (2015)

DOI: 10.1126/sciadv.1400218

The PDF file includes:

Fig. S1. Landrace accessions included in the study classified into botanical races

based on morphological classification (five new world accessions not shown).

Fig. S2. Rainout shelter where plants were grown in Austin.

Fig. S3. Seedlings planted in the Austin experiment.

Fig. S4. Plants growing in Austin.

Fig. S5. A representative accession (IS 25836) under irrigated (left) and imposed

terminal drought (right) conditions at experimental plot in India.

Fig. S6. Proportion of total SNP variation among accessions with known

collection locations (excluding spatial outlier landraces from the Americas, China,

and Southeast Asia/Oceania) explained by spatial structure or environmental

variables.

Fig. S7. Predictions of phenotypes averaged across well-watered and drought

conditions from drought treatment across growing season in Austin, United

States.

Fig. S8. Predictions of phenotype change between well-watered and drought

conditions from drought treatment across growing season in Austin, United

States.

Fig. S9. Predictions of phenotypes averaged across well-watered and drought

conditions from drought treatment late in growing season in Hyderabad, India.

Fig. S10. Predictions of phenotype change between well-watered and drought

conditions from drought treatment late in growing season in Hyderabad, India.

Fig. S11. GWAS for harvest index plasticity (harvest index in wet/dry) in U.S.

experiment, using SNP associations with precipitation in the warmest quarter as a

prior.

Fig. S12. GWAS for panicle weight plasticity (panicle weight in wet − dry) in

India experiment, using SNP associations with growing season length as a prior.

Fig. S13. GWAS for root growth plasticity (growth in control/Al toxic) in

published aluminum toxicity experiment (44), using SNP associations with topsoil

pH as a prior.

References (78–81)

Other Supplementary Material for this manuscript includes the following:

(available at www.advances.sciencemag.org/cgi/content/full/1/6/e1400218/DC1)

Table S1 (Microsoft Excel format). Landraces studied and environment of origin

data.

Table S2 (Microsoft Excel format). Accession phenotypes from experiment in

Austin, United States.

Table S3 (Microsoft Excel format). Mean phenotypes across the 2 years of the

experiment in Hyderabad, India.

Table S4 (Microsoft Excel format). Spearman’s rank correlation test results for

two SNPs that tag known candidate genes potentially involved in local adaptation.

Table S5 (Microsoft Excel format). EMMA t test results for two SNPs that tag

known candidate genes potentially involved in local adaptation.

Table S6 (Microsoft Excel format). Predictions for each accession in the Austin

experiment based on SNP-environment associations in the landrace panel.

Table S7 (Microsoft Excel format). Predictions for each accession in the

Hyderabad experiment based on SNP-environment associations in the landrace

panel.

Table S8 (Microsoft Excel format). Predictions for each accession in the Caniato

et al. (44) experiment based on SNP-environment associations in the landrace

panel.

Table S9 (Microsoft Excel format). Predicted environments for accession in the

three experiments based on kinship associations with environment of landraces

(gBLUP).

Table S10 (Microsoft Excel format). Pearson’s correlations between predictions

based on environment-genome associations and phenotypes in Austin.


based on environment-genome associations and phenotypes in Hyderabad.


based on environment-genome associations and phenotypes in the Caniato et al.

(44) Al toxicity experiment.

Table S13 (Microsoft Excel format). Pearson’s correlations between phenotypes

and environment of origin (where known) for landraces in the Austin experiment.

Table S14 (Microsoft Excel format). Pearson’s correlations between phenotypes

and environment of origin (where known) for landraces in the Hyderabad

experiment.

Table S15 (Microsoft Excel format). Pearson’s correlations between relative net

root growth and environment of origin (where known) for landraces in the

Caniato et al. (44) experiment.

Table S16 (Microsoft Excel format). Pearson’s correlation coefficients between

predicted phenotypes in the Austin experiment and observed, where predictions

based on genome associations with phenotypes in fivefold cross-validation.


predicted phenotypes in the Hyderabad experiment and observed, where

predictions based on genome associations with phenotypes in fivefold cross-

validation.


predicted phenotypes in the Caniato et al. (44) experiment and observed, where

predictions based on genome associations with phenotypes in fivefold cross-

validation.

Table S19 (Microsoft Excel format). Top 1000 SNPs associated with harvest

index plasticity in Austin using SNP associations with precipitation of the

warmest quarter as priors.

Table S20 (Microsoft Excel format). Top 1000 SNPs associated with relative net

root growth (comparing control treatment with Al toxic treatment) in Caniato et

al. (44) experiment, using SNP associations with topsoil pH as priors.

Table S21 (Microsoft Excel format). Top 1000 SNPs associated with panicle

weight plasticity in Hyderabad, using SNP associations with growing season

length as priors.

Supplementary Materials

1. Data

1.1 Genotype data

In order to maximize geographic coverage of landraces, we started with the 469 landraces

in (23) and sequentially added landraces from the NPGS-GRIN (accessions with ‘PI' in

the prefix of their name) or ICRISAT gene banks (accessions with ‘IS' in the prefix of

their name). At each step, we selected the landrace with the greatest distance to the

nearest neighboring landrace that was already in the panel.

After imputation, an average of 15.6% of SNP calls remained missing (median =

10.7%, SD = 15.6% of accessions missing for each SNP; median = 12.5%, SD = 10.8%

of SNPs missing for each accession).

Figure S1. Landrace accessions included in the study classified into botanical races based on

morphological classification (five new world accessions not shown). Classification was obtained

from germplasm passport data obtained from Genesys or GRIN.

1.2 Environmental data

1.2.1 Climate data

VPD is the difference between water vapor partial pressure and maximum

potential pressure at a given air temperature, reflecting the evaporative demand

experienced by plants. We extracted mean monthly relative humidity and temperature

from CRU data (65) and calculated VPD at mean conditions (78).

Reanalysis data (66), which we used to calculate inter-annual precipitation

variability, were generated on a T62 grid (resolution ~ 210 km) for the years 1948-2009

(data provided by NOAA/OAR/ESRL PSD, http://www.esrl.noaa.gov/psd/).

1.2.2 Edaphic data

Estimated water capacity in (67) was based on soil texture, organic matter content, and

plant root depth or profile depth. Depth to water table was modeled by (68) in 30 arc-

second grid squares using a numerical model. We used the harmonized world soil

database (v. 1.21) to extract topsoil pH for the most common soil type in each 30 arc-

second grid cell (70).

1.2.3 Predicted growing seasons

The FAO criteria for growing season determination for dryland crops

(http://www.fao.org/nr/climpag/cropfor/lgp_en.asp;

http://www.fao.org/geonetwork/srv/en/metadata.show?id=73) requires monthly

precipitation to equal at least 0.4 * monthly PET for growing season months.

Additionally, monthly precipitation in excess of monthly PET can accumulate as stored

http://www.fao.org/nr/climpag/cropfor/lgp_en.asp

http://www.fao.org/geonetwork/srv/en/metadata.show?id=73

http://www.fao.org/geonetwork/srv/en/metadata.show?id=73

soil moisture up to the maximum soil moisture capacity (smc) for the site (see data

above). Stored soil moisture (ssm) carried over to the following month was calculated as

𝑠𝑠𝑚𝑡+1 = max{0,min{𝑠𝑚𝑐, 𝑠𝑠𝑚𝑡 + 𝑟𝑎𝑖𝑛𝑓𝑎𝑙𝑙𝑡 − 𝑃𝐸𝑇𝑡}}

Thus the growing season can be extended to months where rainfall + stored soil moisture

is at least 0.4 * monthly PET, i.e. when

𝑠𝑠𝑚𝑡 + 𝑟𝑎𝑖𝑛𝑓𝑎𝑙𝑙𝑡 ≥ 0.4𝑃𝐸𝑇𝑡.

1.3 Phenotype data

1.3.1 Drought experiment in USA

The study was conducted at a site located within the 82 acre Brackenridge Field

Laboratory property of the University of Texas at Austin located in Austin, TX, adjacent

to the Colorado River. Mean maximum temperature (July-August) is ~35.0 °C, and the

mean minimum temperature (December) is ~3.0 °C. Soils are Yazoo sandy loam and are

greater than 1.25 m deep.

The rainout shelter was covered by a clear 240 μm polyethylene roof, which

transmits 90% of photosynthetically active radiation (PAR). The shelter consists of two

laterally connected arched structures with 2.1 m high open sidewalls and 4.2 m high open

ends. The height of the shelter arches maximizes airflow and heat dissipation to maintain

conditions underneath the shelter near ambient.

The planting site contains 34 planting beds each measuring 2.15 x 15.25 m and

oriented perpendicular to the long edge of the shelter. Each planting bed was isolated on

the long edge from neighboring beds by an 8 mm thick corrugated HDPE plastic barrier

that extends from the soil surface down to a depth of 1.25 m. Irrigation was supplied to

each planting bed via three rows of drip irrigation tape with a 10.15 cm emitter spacing

(Chapin Twin Wall BTF Drip Tape, Jain Irrigation Systems Ltd., Jalgaon, India). The

irrigation system is closed and pressure regulated at 10 psi with 2 independently

controllable zones of 17 beds each.

Figure S2. Rainout shelter where plants were grown in Austin.

Genetic Material and Experimental Design: We randomly placed 4 plants of each

accession into 4 of the 17 beds for each treatment, such that no accession was represented

by more than one plant per bed. We then randomly placed plants into beds, with the

conditions that A) no accession had more than one plant located along the short ends of

beds and B) accessions had a total of 1-2 plants per treatment located in the center

columns of their respective beds (remember accessions never have more than one plant in

a bed).

Planting and Irrigation Treatments: Plants were sown over two days (April 18-19, 2013)

in a temperature regulated greenhouse at 24°C into 3.8cm x 3.8 cm x 6 cm pots. Pots

were filled with a rice hull based manufactured soil (Ranch Rose, GeoGrowers, Austin,

TX). Two seeds per pot were sown and emergence was generally complete by April 27.

Pots were thinned to one seedling during May 1-9. Seedlings were watered as needed.

Planting beds at the field site were prepared with a rotary tine tiller to a depth of

20 cm. Woven ground cover (Sunbelt Brand, The DeWitt Company, Sikeston, MO) was

applied to each planting bed for weed control. The ground cover measured 1.85 m wide

and extended the entire length of each planting bed. The outermost 0.15 m of each

planting bed adjacent to the plastic irrigation barrier was left uncovered to prevent any

surface runoff between neighboring beds.

Prior to planting, 25 x 25 cm holes were cut in the groundcover for each planting

location. Each bed contained 3 rows of 32 plants at a 45 cm plant-to-plant distance, with

the center row centered in the bed, resulting in ~29k plants ha-1. Irrigation by broadcast

sprinklers was applied on May 14, 20, and 27 in order to well establish the transplants.

All subsequent irrigation was conducted with drip irrigation. Each row of plants within

each bed was irrigated by a line of drip tape placed adjacent to the base of the plant.

Irrigation was applied at a rate of 5.11 liters per minute per bed and began on June 9 2013

and ended on October 25 2013.

To further characterize our treatment, we sampled soil moisture of the top 20 cm

by gravimetric methods using a 2 cm diameter soil core. Cores were randomly taken from

both well-watered and drought rows over four July sampling dates (n = 120). Cores were

pooled in sets of three, dried, and gravimetric water content was calculated as (mass of

water/bulk mass). We analyzed soil water content by fitting a linear mixed model using

SAS Proc Mixed with irrigation treatment, sampling date, and their interaction as fixed

effects and row as a random effect. This model detected a highly significant treatment ×

Figure S3. Seedlings planted in the Austin experiment.

sampling date interaction (p-value < 0.001), indicating an increasing degree of soil

moisture deficit over the growing season. On average, the irrigation manipulation

resulted in a ~50% reduction in gravimetric soil moisture content of the top 20cM in the

drought relative to the well-watered beds (mean ± SE; well-watered=0.10 ±,

drought=0.045 ± 0.003).

Figure S4. Plants growing in Austin.

Phenotype measurements: Measurements for the approximation of leaf chlorophyll

content were taken with a SPAD 502 meter (Konica Minolta, Tokyo, Japan).

Measurements were taken during mid-day, July 23-25, on the first fully emerged leaf that

was not the flag leaf, on the adaxial side of the leaf midway between the midrib and leaf

margin, halfway down the length of the leaf. Three readings, taken 1 cm apart, were

averaged for the final value. Since the output of the SPAD 502 meter does not reflect a

linear relationship to chlorophyll content over the range of its detection, we transformed

the output values using the monocot consensus equation (CHL[est.] =

(82.2*SPAD)/(135-SPAD)) (79). Tiller number was assessed during the course of the

SPAD measurements.

Prior to harvest, mesh seed bags (Midco Enterprises, Kirkwood, Missouri) were

applied successively to seed heads in the field during the hard dough stage prior to

harvest to reduce grain loss from shattering and pests. Harvest began on September 9 and

continued through November 30. For all accessions with seed set to the hard dough stage

by November 25, seed heads were harvested separately from whole plants. Accessions

that had no seed set to the hard dough stage, or had not yet flowered by November 25

were harvested whole. Plants were harvested at the soil surface and panicles were

harvested with enough stem to allow threshing. AGB was determined by harvesting and

drying plant material in Kraft paper bags (whole plants and panicles separately) at 50°C

for 10 days and then weighing.

We noted strong positional effects on phenotypes within plots, primarily with

respect to plants found at the edge of rainout shelters. In order to correct for these effects,

we built a linear mixed-model where accession phenotypes and phenotypic responses to

drought were fixed effects, plant presence in a row or column at edges of each of the two

shelters were fixed effects (four fixed effects for each long edge of the two shelters, one

fixed effected for short edge of whole plot; data were thrown out from the other short

edge of plot), while block effects were random effects (blocks were three plants, i.e. one

bed width, by four plants). We used the R package ‘lme4’ (80) to fit mixed effects

models. We estimated a fixed effect parameter for each accession's phenotype in each

condition (well-watered or drought). We tested for positive autocorrelation in the

residuals of the model and found no significant autocorrelation (Moran's-I, one-sided test,

all p > 0.25).

1.3.2 Drought experiment in India

The planting was on vertisol Kasireddipally series-isohypothermic Typic Pellustert. The

minimum monthly average temperature during postrainy seasons at Patancheru ranged

from 10.9 to 22.8oC and maximum monthly average temperature from 27.3 to 37.5oC.

The cumulative rainfall was 23.9 mm in 2010-2011 and 41.0 mm during 2011-2012. The

average day length across the experiment was 11.64 hours each year (11.08 hours from

October-December and 12.75 hours from January-April). Plot size consisted of one row

of 4 meter, with an inter- and intra-row spacing of 75 cm and 10 cm, respectively,

resulting in ~133,000 plants ha-1. Ammonium phosphate was applied at the rate of 150 kg

ha-1 as a basal dose, while urea at the rate of 100 kg ha-1 was applied as top dressing three

weeks after planting. Days to 50% flowering was recorded on a plot basis, while

observations on plant height (cm) on five plants were measured at maturity. Panicle

weight (g plant-1) and grain yield (g plant-1) were recorded on 5 representative plants.

2. SNP-environment associations

2.1 Partitioning Sorghum SNP variation

We found that on average 30.5% of SNP variation among African and Eurasian

accessions could be explained by environmental variables (SD = 1.7% across 10,000

resamples) while 28.4% could be explained by spatial variables (SD = 1.7%, Figure S6).

The portion of SNP variation explained by collinear environmental and spatial variables

was large, 23.4% (SD = 1.7%) leaving only 7.1% to be explained by environment

independent of spatial variables (SD = 0.6%) and 5.0% to be explained by spatial

Figure S5. A representative accession (IS 25836) under irrigated (left) and imposed terminal drought (right) conditions at experimental plot in India.

variables independent of environment (SD = 0.5%). Compared to Arabidopsis thaliana

populations in their native Eurasian range, sorghum shows greater environmental (31%

vs. 16%) and spatial (28% vs. 17%) structure in SNP variation (37). The stronger

geographic structure in sorghum versus Arabidopsis may reflect the deliberate movement

of sorghum genotypes to similar climatic zones through human migration and trade

versus the undirected dispersal of Arabidopsis following glacial retreat (81).

Figure S6. Proportion of total SNP variation among accessions with known collection locations (excluding spatial outlier landraces from the Americas, China, and Southeast Asia/Oceania) explained by spatial structure or environmental variables. The box represents all SNP variation among accessions, with white space inside the box showing residuals unexplained by either environmental or spatial variables. Due to rounding, annotated proportions do not sum to 1.

2.2 Enrichment of SNP variation explained by climate in non-synonymous SNPs

We found that environmental variables explained a higher proportion of variation in non-

synonymous SNPs than genome-wide controls (permutation test, z = 6.33, p < 0.002),

even after removing the effects of geographic spatial structure among accessions (z =

7.74, p < 0.002), suggesting that some environmentally structured variants contribute to

adaptation and are not merely neutral (Figure 3C). By contrast, the environmental

structure in intergenic SNPs was not different from genome-wide controls, before (z =

0.34, p = 0.744) and after removing spatial structure (z = -1.60, p = 0.122). Environment

explained less variation than expected in synonymous SNPs (syn.: z = -4.72, p < 0.002)

but after accounting for spatial structure, environment explained a higher than expected

proportion of synonymous SNP variation (z = 8.19, p < 0.002). The enrichment of

synonymous SNPs for environmental structure after accounting for space may be due to

linkage with nearby adaptive non-synonymous SNPs given that LD extends over many

kb (e.g. r2 decays to half the maximum by 3kb on average in our data set).

3. Predicting phenotypes based on SNP association models

3.1 Predictions based on environmental associations

The observations that accessions carrying aridity-associated alleles had higher yield

components (Figures S7 and S9), that landraces from arid environments had higher yield

components (Tables S1 -S1 ), and that there was a positive relationship between

accession biomass and yield in wet versus drought treatments (Tables S2-S3), implies

that there is no trade-off between plant-level yield components and drought adaptation (at

least within the diverse germplasm we studied). The higher biomass and grain weight

associated with alleles from arid environments may reflect selection associated with

lower planting density in arid environments. In arid locations, plants are typically grown

at low density and face low competition from other plants, while in moist environments

plants are grown at high densities likely leading to greater competition among plants. As

a result of negative competition effects, the highest yielding individual genotypes in

3 4

moist environments may not be the highest yielding when grown in monoculture, leading

to farmers to select for reduced competitive effect in high density environments.

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Figure S7. Predictions of phenotypes averaged across well-watered and drought conditions from

drought treatment across growing season in Austin, United States. Predicted (x-axes in left

column of panels) versus observed average phenotypes across treatments (y-axes in same

panels) for breeding lines and landraces (circles in left column of panels), with predictions based

on SNP associations with environmental gradients. Comparison (right column of panels) of

predictions using different numbers of predictor SNPs, including a kinship matrix based on all

SNPs (right column, r = Pearson's correlation coefficient). Note that phenotype data were not

used in predictions. The best prediction is shown for each trait in panels in left column.

Predictions were standardized to z-scores.

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Figure S8. Predictions of phenotype change between well-watered and drought conditions from

drought treatment across growing season in Austin, United States. Predicted (x-axes in left

column of panels) versus observed change in phenotypes across treatments (y-axes in same

panels) for breeding lines and landraces (circles in left column of panels), with predictions based

on SNP associations with environmental gradients. Comparison (right column of panels) of

predictions using different numbers of predictor SNPs, including a kinship matrix based on all

SNPs (right column, r = Pearson's correlation coefficient). Note that phenotype data were not

used in predictions. The best prediction is shown for each trait in panels in left column.

Predictions were standardized to z-scores.

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Temp. seasonality score, 250 SNPs + kin.

Flo

we

rin

g tim

e a

vg

., w

et

& d

ry (

days)

r = −0.41

●

●

● ●

●

●

●

●

●

●

●

●

●

−0.4

−0

.3−

0.2

−0

.1

r

10

0 S

NP

s

25

0 S

NP

s

50

0 S

NP

s

10

00 S

NP

s

50

00 S

NP

s

10

00

0 S

NP

s

Kin

sh

ip

−4 −3 −2 −1 0 1 2

35

40

45

50

55

Grow. seas. aridity score, 100 SNPs + kin.

Sp

ad

avg

., w

et

& d

ry

r = 0.30●

●

●

●

●

●

●

●

●

●

●

●

●

0.1

50

.20

0.2

50

.30

r

10

0 S

NP

s

25

0 S

NP

s

50

0 S

NP

s

10

00 S

NP

s

50

00 S

NP

s

10

00

0 S

NP

s

Kin

sh

ip

−5 −4 −3 −2 −1 0 1

010

20

30

40


Gra

in a

vg.,

wet

& d

ry (

g)

r = 0.31

●

●

●

●

●

●

●

●

●●

●

●

●

0.1

00.1

50.2

00

.25

0.3

0

r

10

0 S

NP

s

25

0 S

NP

s

50

0 S

NP

s

10

00 S

NP

s

50

00 S

NP

s

10

00

0 S

NP

s

Kin

sh

ip

−5 −4 −3 −2 −1 0 1

01

020

30

40

50

60


Pan

icle

avg.,

wet

& d

ry (

g)

r = 0.28

●

●

●

●

●

●

●

●

●

●

●

●

●

0.1

00

.15

0.2

00

.25

r

10

0 S

NP

s

25

0 S

NP

s

50

0 S

NP

s

10

00 S

NP

s

50

00 S

NP

s

10

00

0 S

NP

s

Kin

sh

ip

Figure S9. Predictions of phenotypes averaged across well-watered and drought conditions from

drought treatment late in growing season in Hyderabad, India. Predicted (x-axes in left column of

panels) versus observed average phenotypes across treatments (y-axes in same panels) for

breeding lines and landraces (circles in left column of panels), with predictions based on SNP

associations with environmental gradients. Comparison (right column of panels) of predictions

using different numbers of predictor SNPs, including a kinship matrix based on all SNPs (right

column, r = Pearson's correlation coefficient). Note that phenotype data were not used in

predictions. The best prediction is shown for each trait in panels in left column. Predictions were

standardized to z-scores. Note that relationships between predicted and observed were even

stronger after removing the outliers for low predicted growing season aridity (bottom two left

panels).

−4 −3 −2 −1 0 1 2

020

40

60

Grow. seas. aridity score, 500 SNPs

He

ight

(we

t −

dry

, cm

)r = −0.12

●

●●

●

●

●

●

●●

●● ●

●

−0.1

00.0

00

.05

0.1

0

r

10

0 S

NP

s

25

0 S

NP

s

50

0 S

NP

s

10

00 S

NP

s

50

00 S

NP

s

10

00

0 S

NP

s

Kin

sh

ip

●

●

●

Env.−associated markersMarkers & kinship combinedEnv.−kinship associations

−4 −3 −2 −1 0 1 2

−5

05

Temp. seasonality score, kin.

Flo

we

rin

g tim

e (

we

t −

dry

, d

ays) r = 0.14

●

●

●

●

●

●

●

●

●

●

● ●

●

−0

.05

0.0

00

.05

0.1

0

r

10

0 S

NP

s

25

0 S

NP

s

50

0 S

NP

s

10

00 S

NP

s

50

00 S

NP

s

10

00

0 S

NP

s

Kin

sh

ip

−6 −4 −2 0 2

−5

05

10

Aridity score, 1000 SNPs + kin.

Sp

ad

(w

et

− d

ry)

r = 0.16●

●

●

●

●●

●

●

●

●

●

●

●

0.0

00

.05

0.1

00

.15

r

10

0 S

NP

s

25

0 S

NP

s

50

0 S

NP

s

10

00

SN

Ps

50

00

SN

Ps

10

00

0 S

NP

s

Kin

sh

ip

−6 −4 −2 0 2

−5

05

10

15

20

Prec. warmest q. score, 250 SNPs + kin.

Gra

in (

wet

− d

ry, g

)

r = 0.17

●

●

●

●

●

●

●

●

●

●

●

●

●

0.0

80

.10

0.1

20

.14

0.1

6

r

10

0 S

NP

s

25

0 S

NP

s

50

0 S

NP

s

10

00 S

NP

s

50

00 S

NP

s

10

00

0 S

NP

s

Kin

sh

ip

Figure S10. Predictions of phenotype change between well-watered and drought conditions from

drought treatment late in growing season in Hyderabad, India. Predicted (x-axes in left column of

panels) versus observed change in phenotypes across treatments (y-axes in same panels) for

breeding lines and landraces (circles in left column of panels), with predictions based on SNP

associations with environmental gradients. Comparison (right column of panels) of predictions

using different numbers of predictor SNPs, including a kinship matrix based on all SNPs (right

column, r = Pearson's correlation coefficient). Note that phenotype data were not used in

predictions. The best prediction is shown for each trait in panels in left column. Predictions were

standardized to z-scores.

3.2 Comparison with predictions based on phenotype associations

For comparison with predictions based on SNP-environment associations, we also

developed predictions based on SNP-phenotype associations, i.e. the traditional

implementation of MAS and GS. We tested this approach on biomass, grain weight,

panicle weight, harvest index, and relative net root growth (i.e. phenotypes discussed in

the main text). These models were implemented using the same methods as described

above (section 3.1) for predictions based on SNP-environment associations, with two

exceptions. First, association models (EMMA and gBLUP) were run with phenotypes as

responses. Second, because these phenotypic predictions were for the same accessions on

which the model was fit, we conducted 5-fold cross validation, leaving out one fifth of

accessions (chosen randomly) for each fold. Phenotypic predictions for each accession

were thus based on associations for different accessions.

In the Austin drought experiment phenotype associations with kinship (the best

model) were reasonable predictors of grain weight (r = 0.40), harvest index averaged

across treatments (r = 0.36; Table S1 ). Biomass association with kinship (the best

model) was a very good predictor of biomass (r = 0.80). However, changes in grain and

harvest index were predicted very poorly by both kinship and SNPs (best grain model:

top 5,000 SNPs, r = -0.02; best harvest index model: top 5,000 SNPs, r = 0.07). Change

in biomass was best predicted by combined predictions of kinship and the top 1,000

SNPs (r = 0.20).

In the Hyderabad drought experiment, phenotype associations with kinship (the

best model) were good predictors of grain weight (r = 0.65) and panicle weight (0.64)

averaged across treatments (Table S1 ). By contrast, the changes in grain and panicle

weight across treatments were not predicted as well (best grain model: top 500 SNPs +

kinship, r = 0.22; best panicle model: top 1,000 SNPs + kinship, r = 0.10).

The best predictor of root growth response to aluminum soil toxicity was the

kinship association with this trait (r = 0.60, Table S1 ).

3. 3 Tests for enrichment of environment–associated SNPs in candidate loci under

selection

We tested for enrichment of regions identified by Mace et al. (46) with SNPs in

the 0.05 lower tail of p-values for all SNPs genome-wide. We focused on associations

with the three environment variables that predicted genotype by environment interactions

(Figure 4). Our observed test statistic was the 0.05 quantile for EMMA (mixed-model)

association p-values of SNPs found within the candidate regions of (46). We generated a

6

7

8

null expectation by circularly permuting SNP classifications as within or outside of their

candidate regions, and then calculating the 0.05 quantile for p-values of SNPs within the

permuted regions. We conducted 10,000 permutations of these classifications and used

the observed tail density * 2 as an empirical two-tailed p-value.

We also tested whether these regions were enriched in multivariate environmental

structure. In these candidate regions we found significant enrichment for environmental

structure independent of spatial structure among landraces (p < 0.02). Surprisingly, these

candidate regions had significantly less spatially-structured climate variation than

expected (p < 0.02).

Figure S11. GWAS for harvest index plasticity (harvest index in wet/dry) in U.S. experiment,

using SNP associations with precipitation in the warmest quarter as a prior. The top panel shows

results from traditional mixed-model association (EMMA) (31) while the bottom panel shows the

approximate posterior probability of association (APPA), which uses an informative prior for each

SNP based on associations with environment in the landrace panel.

Figure S12. GWAS for panicle weight plasticity (panicle weight in wet – dry) in India experiment,

using SNP associations with growing season length as a prior. The top panel shows results from

traditional mixed-model association (EMMA) (31) while the bottom panel shows the approximate

posterior probability of association (APPA), which uses an informative prior for each SNP based

on associations with environment in the landrace panel.

Figure S13. GWAS for root growth plasticity (growth in control/Al toxic) in published aluminum

toxicity experiment (44), using SNP associations with topsoil pH as a prior. The top panel shows

results from traditional mixed-model association (EMMA) (31) while the bottom panel shows the

approximate posterior probability of association (APPA), which uses an informative prior for each

SNP based on associations with environment in the landrace panel.

Supplementary Materials for...Jun 30, 2015 · plant root depth or profile depth. Depth to water table was modeled by (68) in 30 arc-second grid squares using a numerical model. We

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