Detection of QTL for forage yield, lodging resistanceand spring vigor traits in alfalfa (Medicago sativa L.)

Per McCord • Vanessa Gordon • Gopesh Saha •

Jacqueline Hellinga • George Vandemark •

Richard Larsen • Mark Smith • David Miller

Received: 27 January 2014 / Accepted: 21 May 2014

� Springer Science+Business Media Dordrecht (outside the USA) 2014

Abstract Alfalfa (Medicago sativa L.) is an inter-

nationally significant forage crop. Forage yield,

lodging resistance and spring vigor are important

agronomic traits conditioned by quantitative genetic

and environmental effects. The objective of this study

was to identify quantitative trait loci (QTL) and

molecular markers associated with increased forage

yield, resistance to lodging, and spring vigor. A

backcross population composed of 128 progeny was

developed by crossing the breeding parents

DW000577 (lodging susceptible) and NL002724

(lodging-resistant) and back-crossing an individual

F1 plant to the maternal parent (i.e. DW000577). A

linkage map of NL002724 was developed based upon

the segregation of 236 AFLP, SRAP, and SSR markers

among the backcross progeny. The markers were

distributed among 14 linkage groups, covering an

estimated recombination distance of 1497.6 centiMor-

gans (cM). Replicated clones of both parents and

backcross progeny were evaluated in the field for

estimated forage yield, lodging, and spring vigor in

Washington and Wisconsin during 2007 and 2008.

Significant QTL were found for all three traits. In

particular, two QTL for lodging resistance were

identified that explained C14 % of trait variation,

and were significant in all years and locations. Major

QTL explaining over 25 % of trait variation for forage

yield were detected in multiple environments at two

separate locations on chromosome III. Several QTL

for spring vigor were located in the same or similar

positions as QTL for forage yield, possibly explaining

the significant correlation between these traits. Molec-

ular markers associated with the aforementioned QTL

were also identified.Electronic supplementary material The online version ofthis article (doi:10.1007/s10681-014-1160-y) contains supple-mentary material, which is available to authorized users.

P. McCord (&) � V. Gordon

USDA-ARS Sugarcane Field Station, 12990 Highway

441, Canal Point, FL 33438, USA

e-mail: per.mccord@ars.usda.gov

G. Saha

Department of Crop and Soil Science, Washington State

University, PO Box 646420, Pullman, WA 99164-6420,

USA

J. Hellinga

Department of Microbiology, University of Manitoba,

418 Buller Building, Winnipeg MB R3T 2N2, Canada

G. Vandemark

USDA-ARS Grain Legume Genetics & Physiology

Research Unit, 303 Johnson Hall, Pullman,

WA 99164-6434, USA

R. Larsen

Irrigated Agriculture Research and Extension Center,

Washington State University, 24106 N. Bunn Road,

Prosser, WA 99350, US

M. Smith � D. Miller

DuPont Pioneer, W8131 St. Highway 60, Arlington,

WI 53911, USA

123

Euphytica

DOI 10.1007/s10681-014-1160-y

Keywords AFLP � Alfalfa � Autopolyploid �Linkage mapping � Lodging resistance �QTL detection � SRAP � SSR

Introduction

Alfalfa (Medicago sativa L.) is an internationally

significant perennial forage crop species. It is most

often harvested as hay, but can also be ensiled,

processed into meal, cubes, or pellets, fed as green-

chop, or grazed. In addition to its primary use as feed,

alfalfa is an important rotation crop for its ability to fix

atmospheric nitrogen and is currently being

researched as a source of cellulosic ethanol (Samac

et al. 2006). It is widely adapted to various climatic

conditions.

High forage yield, lodging resistance, and spring

vigor are important agronomic traits in alfalfa. Forage

yield is the most significant factor in the marketability

of a cultivar. Lodging resistance is important in crop

plants for reducing yield losses when harvesting, and

can reduce disease severity (Banniza et al. 2005;

Miklas et al. 2004). Spring vigor is the ability of plants

to produce strong growth once favorable growing

conditions return in the spring. Enhanced spring vigor

can be an indicator of winter hardiness (D. Miller,

personal communication), reduce weed pressure, and

also allows for an earlier first cutting (Pennsylvania

State University 2011). These traits are conditioned by

both quantitative genetic and environmental effects,

making improvement through traditional breeding

more challenging. Because it is an autopolyploid

(2n = 4x = 32), alfalfa displays tetrasomic inheri-

tance, resulting in complex segregation ratios that

require large population sizes to study effectively.

Unlike other polyploid, polysomic crops (such as

potato and strawberry), alfalfa is sexually propagated

for commercial production, and so ideal genotypes

cannot be fixed and released en masse as a new

cultivar. Furthermore, alfalfa is generally self-infertile

(Wilsie 1950) and displays inbreeding depression

(Wilsie 1950; Busbice and Wilsie 1966), making it

unwise to attempt to fix desirable traits through selfing.

The identification of QTL and molecular markers for

important traits in alfalfa would benefit alfalfa breed-

ing in two ways. First, QTL mapping can be used to

guide breeding strategies by determining the number

and effect of loci involved in a trait. Second, initial

selection via markers can make the breeding process

more efficient by eliminating undesirable genotypes

before they are tested in the field. The objective of this

work was to develop a linkage map and detect

significant QTL for forage yield, lodging resistance

and spring vigor, and identify markers linked to those

traits that could be utilized for marker assisted

breeding of this challenging crop.

Materials and methods

Population development

A mapping population, BC1.3, was developed by first

crossing the parents DW000577 (lodging susceptible,

good spring vigor, good combining ability for forage

yield) and NL002724 (lodging resistant, good spring

vigor, good combining ability for forage yield). One

healthy F1 plant from this cross was tested via SRAP

markers to confirm it was indeed a hybrid. This plant

was then backcrossed to DW000577. A total of 128

individuals from this backcross were used for pheno-

typing, linkage map construction, and QTL analysis.

Trait evaluations

The original parents (i.e., DW000577 and NL002724),

the selected F1 individual, and the individuals of

BC1.3 were evaluated in field trials planted at both

Arlington, WI (soil type: Plano silt loam) and Connell,

WA (soil type: Warden very fine sandy loam). The

trials were established in June 2006, and rated in 2007

and 2008. Experimental plots at each location were

arranged in a randomized complete block design with

three replications. Two rooted cuttings of each geno-

type were planted per plot at 0.38 m intervals, with

0.76 m between each plot and between each row. The

trial in Connell was irrigated as needed for healthy

growth using a linear overhead sprinkler system; the

trial in Arlington received only natural rainfall. On

either the day of cutting, or the day prior to cutting,

plots were scored for lodging tolerance using the

standard rating of Standability Expression (Johnson

et al. 2006). The scale ranges from 0 (susceptible) to 9

(resistant), and is based on the percentage of erect

stems per plot. Plots were rated for lodging three times

in 2007 and two times in 2008. Spring vigor and

estimated forage yield were rated on a scale from 1

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(poor) to 9 (excellent). Spring vigor was based on new

growth, canopy height, and color, while estimated

forage yield was based on plant vigor and visual

estimates of forage height, canopy width, and canopy

density. Visual estimates of forage yield have previ-

ously been shown to be well-correlated with measured

dry weight yield (Campbell and Arnold 1973; Ud-Din

et al. 1993; O’Donovan et al. 2002). For the trials

located at Connell, yield estimates were made four

times in 2007, and two times in 2008, while at

Arlington, estimates were made three times each

season.

Statistical analysis

Field data were analyzed using SAS version 9.2 (SAS,

Cary, NC). Phenotypic correlations for all traits were

calculated using the CORR procedure. Histograms of

trait distributions were generated using PROC UNI-

VARIATE. PROC GLM was used conduct ANOVA

of location, replicate (within location), genotype, and

genotype 9 location effects.

Genotyping

Amplified fragment length polymorphism (AFLP)

fingerprints were generated essentially according to

the methods described in Vos et al. (1995), with the

only significant modification being the substitution

of radiolabeled EcoRI primer with a fluorescent

labeled primer. Fingerprints were resolved and

detected on a LI-COR 4300 DNA Analyzer (Li-

Cor, Lincoln, NE).

For sequence related amplified polymorphism

(SRAP) markers (Li and Quiros 2001) reactions were

set up in 20 ll volumes containing 50 ng DNA,

1.5 mM MgCl2, 1 mM dNTPs, 1X Promega PCR

buffer (Promega, Madison, WI), 37.5 ng (each)

forward and reverse primer, and 2.5 units of Taq

DNA polymerase. The initial denaturation tempera-

ture was 95 �C for 60 s, followed by 10 cycles of 94,

35 and 72 �C, respectively, for 60 s each. This was

followed by 30 cycles of 94 �C for 60 s, 50 �C for

60 s, and 72 �C for 60 s, and a final extension for

7 min at 72 �C. Amplicons were resolved using 2 %

agarose gels in Tris–borate EDTA buffer, stained with

ethidium bromide, and detected under an ultraviolet

light. Parents were initially screened to detect poly-

morphisms and primer pairs that produced reliable

polymorphisms were used to screen the mapping

population.

Simple sequence repeat (SSR) markers were

selected from a panel of markers developed by Sledge

et al. (2005). Markers selected for mapping were

polymorphic between the parents, and dispersed along

the length of each chromosome according to the map

developed by Sledge et al. The M13 ‘tail’ procedure

(Schuelke 2000; Rampling et al. 2001), with slight

modifications, was used to fluorescently label the PCR

products. PCR reactions (10 uL) consisted of 1X PCR

buffer (10 mM Tris–HCl pH 8.5, 50 mM KCl,

1.5 mM MgCl2, 0.1 % Triton-X 100), 800 lM (total)

dNTPs, 0.1 pmol forward primer with a 50M13 tail,

0.5 pmol reverse primer, 0.5 pmol M13 primer

(50CACGACGTTGTAAAACGAC30) labeled with

DY682 or DY782 near-infrared dye (Dyomics GmbH,

Jena, Germany), 0.6 units Taq polymerase, and 2 ll

genomic DNA. Cycling conditions were as follows:

initial denaturation at 94 �C for 2 min 30 s, followed

by 15 cycles of 94 �C for 15 s, 65 �C for 30 s,

decreasing 1 �C for each subsequent cycle, and 72 �C

for 1 min, and subsequently 25 cycles of 94 �C for

15 s, 50 �C for 30 s, and 72 �C for 1 min, followed by

a final incubation at: 72 �C for 7 min to complete

extension. Amplicons were resolved and detected on a

LI-COR 4,300 DNA Analyzer as for the AFLP

markers. Gel images from the DNA Analyzer were

scored using CrossChecker (Buntjer 2000).

Linkage mapping and QTL analysis

Linkage mapping and QTL analyses were performed

using TetraploidMap for Windows (Hackett et al.

2007). TetraploidMap is designed to handle the

segregation ratios present in autotetraploid popula-

tions, can utilize dominant and co-dominant markers,

and automatically identifies homologous linkage

groups.

Briefly, TetraploidMap develops linkage maps by

first identifying the most probable dosage of each

marker based on segregation ratios. Then, a cluster

analysis is performed to identify linkage groups. As

part of the cluster analysis, the software identifies

repulsion phase linkages between markers and uses

these to identify homologous linkage groups. A two-

point analysis is then used to determine the recombi-

nation fraction and associated logarithm of odds

(LOD) score for all pairs of markers in a cluster. For

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determination of the final (optimal) marker order, the

‘ripple’ option was used.

For the AFLP and SRAP markers, only markers

present in NL002724 were used to create the linkage

map. In addition, AFLP and SRAP markers segregat-

ing at a ratio C2.12:1 were excluded due to extreme

segregation distortion (from the 1:1 ratio expected of

single dose markers). A LOD cutoff of 3.0 was used to

declare significant linkage between markers within a

linkage group and between markers of homologous

groups/chromosomes.

For QTL mapping, TetraploidMap incorporates

both single marker and interval mapping approaches.

Single marker analysis is performed via ANOVA of

marker class means. Normally, this is only done in

TetraploidMap for dominant markers, but alleles of

co-dominant markers can be analyzed by re-coding the

marker as a set of individual ‘dominant’ alleles. In this

study, the interval mapping approach was used for all

but unlinked markers. All putative QTL were sub-

jected to a permutation test of C100 iterations, with

stable QTL (present in at least two environments)

tested further with C1,000 iterations. It was only after

this testing that a QTL was considered significant

(C90th percentile of all permutations). Only stable

QTL are reported. The map figures including QTL

locations were generated using MapChart (Voorrips

2002).

Fig. 1 Trait distributions

for forage yield, spring

vigor, and lodging tolerance

for BC 1.3. Only data from

2007 (both locations) is

shown

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Results

Phenotypic data

As can be seen in the histograms in Fig. 1, the

distributions for the traits in all environments were

essentially normal. Some skewing was observed;

generally, it was towards the superior phenotype.

Forage yield and spring vigor were significantly

positively correlated (p value\0.0001) in all environ-

ments. The strength of this correlation ranged from

0.66 in Washington in 2007, to 0.90 in Wisconsin in

2008. The correlation was higher in both years in

Wisconsin (data not shown). Lodging tolerance was

significantly negatively correlated with both forage

yield and spring vigor in Washington in 2007 and 2008

(p value \0.05), and in Wisconsin in 2008 (p value

\0.0001). Though statistically significant, the corre-

lations between lodging tolerance and forage yield/

spring vigor were strong only in Wisconsin in 2008

(-0.47 and -0.46, respectively). For all traits in both

years, there was a significant genotype 9 environment

effect (p value\0.0001 for all tests). For this reason,

the QTL analysis was performed on each environment

(year 9 location) separately.

Linkage map

The linkage map for NL002724 incorporated 58

SRAP, 142 AFLP, and 36 SSR markers, for a total

of 236. The parental genotype of some multi-allelic

SSRs could not be resolved by TetraploidMap. In

these cases, and in cases where some SSR alleles could

not be reliably scored, each allele was scored

separately as a dominant marker. The markers were

distributed in 14 linkage groups (Fig. 2). Eight of

these groups were anchored to alfalfa chromosomes

(hereafter abbreviated ‘C’) by the use of previously

mapped SSRs (Sledge et al. 2005), while the remain-

ing six groups remained unanchored (hereafter abbre-

viated ‘UG’). An additional 52 markers included in the

dataset were either unlinked or were members of

linkage groups with three or fewer markers. The total

map length was 1497.6 cM.

QTL analysis

Stable QTL (i.e., present at the same chromosomal

location in more than one environment) were detected

for all three traits (Table 1 and Fig. 2). These include

several major QTL (explaining more than 10 % of trait

variation). For lodging resistance, three QTL were

detected. The first was detected at the same position on

C III in all four environments; it explained an average of

15.4 % of observed variation for lodging. The AFLP

marker ACAXCTC_109 was closely linked (2 cM) to

the QTL position. Though derived from the lodging

resistant parent, NL002724, this marker was associated

with a lower lodging score (increased lodging). A

second QTL for lodging was detected in all four

environments on C VI that accounted for an average of

14 % of the variation. The SRAP marker F9XR7b was

linked (1 cM) to the QTL and associated with increased

lodging (in Washington only), while the AFLP marker

AGCXCTC_193 (19 cM from the average location)

was associated with reduced lodging (in Wisconsin in

2007 only). TetraploidMap does not perform single-

marker ANOVA on SSR markers. However, by

recoding SSR markers as a series of dominant alleles,

we found an allele of SSR marker BF69 (13 cM from

the average QTL position), that was strongly associated

with increased lodging. The third QTL for lodging was

also detected on C IV, in two environments (Wisconsin

2007, Washington 2008); it explained an average of

15.3 % of the variation. The AFLP marker ACA-

XCTC_251 was linked to this QTL at a distance of

16 cM, and associated with increased lodging. For

forage yield, two major QTL were detected on C III.

The LOD peak was centered at 40 cM in 2007 (both

locations), and at approximately 80 cM in 2008 (both

locations). Secondary LOD peaks (data not shown)

suggest that there are two different QTL for yield on C

III, with environmental effects dictating which QTL

had the highest LOD score in a given year. QTL on C III

explained an average of 27.9 % (2007) and 27.3 %

(2008) of the trait variation. By recoding the marker as a

series of dominant alleles, we determined that an allele

of the SSR marker MtBA12D03F, which was tightly

linked (2 cM) to the 2007 QTL, was associated with

higher forage yield. In addition, the AFLP marker

ACCXCAT_64 was linked (13 cM) to the 2007 QTL,

and also associated with higher forage yield. The AFLP

marker ACAXCTC_205 was linked to the 2008 QTL at

an average distance of 9 cM, and was again associated

with higher forage yield. Five additional QTL for

forage yield were detected, on C IV (two environments,

10.1 % of variation), C V (two environments, 9.4 % of

variation), C VII (two environments, 12.3 % of

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variation), C VIII (three environments, 12 % of vari-

ation), and UG 6 (two environments, 10.4 % of

variation). On C IV, the SSR marker BI93_4 was

closely linked to the QTL (4 cM on average), and was

associated with higher yield. On C V, a linked marker

(14 cM from the QTL position) was significant in only

one environment (Washington 2007); this was the

AFLP marker ACGXCAA_325, which was associated

with lower yield. The SSR marker BI64_1 was closely

linked to the QTL on C VII (average 5 cM), and was

associated with higher yield. On C VIII, the AFLP

marker AGCXCAG_493 co-localized with the QTL,

and was associated with higher forage yield. On UG 6,

the AFLP markers AGCXCAG_212 and AGCX-

CAG_174 flanked the QTL, but were only significant

in one environment (Washington 2007); they were both

associated with lower forage yield. For spring vigor,

four QTL were detected, on C III (three environments,

23.3 %), C IV (two environments, 15.7 %), C V (two

environments, 10.6 %), and C VII (two environments,

11.3 %). On C III, AFLP markers ACAXCTC_205 and

ACAXCTC_109 were associated with increased spring

vigor in Wisconsin and Washington, respectively; both

were closely linked to the QTL. On C IV, an allele of

SSR marker BI93 (BI93_2) was linked to the QTL at an

average distance of 24 cM, and was strongly associated

with increased spring vigor. On C V, single-marker

ANOVA only detected a significant marker (AFLP

ACGXCAA_325) in one environment; it was linked to

the QTL at a distance of 2 cM, and was associated with

increased spring vigor. An allele of the SSR marker

BI64 (BI64_3) was linked to the QTL on C VII (9 cM

from the QTL position), and also associated with

enhanced spring vigor.

Discussion

In comparison with a number of other agronomic

crops, the application of molecular breeding technol-

ogy in alfalfa has lagged. Due to its autopolyploidy

and allogamy, genetic improvement of alfalfa is

difficult. Even today, virtually all alfalfa breeding is

traditional (not marker-assisted), and relies heavily on

recurrent phenotypic selection. This approach is

reasonably effective with simply inherited or highly

heritable traits, but less so with traits that are

quantitative, especially if heritability is low. Linkage

mapping and QTL analysis of important agronomic

traits can provide an understanding of the number of

loci involved in a trait, as well as their effects, which

can guide breeding methodologies. In addition, it can

facilitate the development of molecular markers that

can be used to make the breeding process more

efficient by eliminating the costly field testing of

plants that are not truly of interest. This study has

provided insight into the genetic architecture of

lodging resistance, estimated forage yield, and spring

vigor in alfalfa. Multiple QTL were found for each of

these traits, reinforcing the phenotypic evidence of

their quantitative nature. In this study, QTL for

lodging were detected on Cs III and VI. In field pea

(Pisum sativum L.), Tar’an et al. (2003) detected two

major QTL for lodging that explained a total of 58 %

of the trait variation. These QTL were located on pea

linkage groups III and VI, which share some synteny

(especially group III) with alfalfa Cs III and VI,

respectively (Zhu et al. 2005). It is significant to note

that despite the fact that the donor parent, NL002724,

is resistant to lodging, all but one of the markers linked

to QTL for this trait were associated with increased

lodging. We do note that an unlinked SRAP marker,

ME3XEM6a, was associated with reduced lodging in

three environments (data not shown). We detected

stable QTL for estimated forage yield on Cs III, IV, V,

VII, and VIII. These results compare well with

Fig. 2 Linkage maps including QTL of BC1.3. Groups are

represented as a composite of all homologs. AFLP markers each

begin with the letter ‘A’, while SRAP markers begin with the

letters ‘F’ or ‘M’. For AFLP markers, the three-letter codes

flanking the X indicate the three-base extension of the EcoRI

and MseI selective primers, respectively; the number after the

underscore indicates the size of the amplicon. For SRAP

markers, the two or three-character codes flanking the X indicate

the forward and reverse primers, respectively; a lower case letter

following the reverse primer code indicates that more than one

DNA fragment was scored for the primer combination.

Sequences of the SRAP primers are listed in Supplementary

Table 1. Markers in bold are SSRs and were used to anchor

linkage groups to known chromosomes; SSR markers with an

underscore and number following the primer name indicate that

alleles were scored separately (as dominant markers). Chromo-

somes are titled with Roman numerals; unanchored linkage

groups are titled with Arabic numberals (note that unanchored

groups without QTL are not included). Linkage groups with the

suffix ‘b’ were anchored to the particular chromosome, but not

reliably linked to the rest of the markers in that chromosome.

QTL positions are denoted by black bars covering a 1-LOD

interval on either side of the LOD peak. The first three letters of

the QTL name refer to the trait (see Table 1), the two digits refer

to the year (2007 or 2008), and the last two letters denote the

location (Washington or Wisconsin)

c

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previous studies in alfalfa. Maureira-Butler et al.

(2007) used single marker ANOVA to identify

markers for forage yield on Cs IV, VII, and VIII.

Robins et al. (2007) also used single marker ANOVA,

and detected markers for yield on C VII. More

recently, Li et al. (2011) used association mapping

incorporating population structure information to

detect marker associated with yield in multiple

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Fig. 2 continued

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environments on Cs I, III, IV, V, VII, and VIII.

Because Cs IV, VII, and VIII have been identified as

containing QTL for yield by both this and two prior

studies (all of which utilized different germplasm),

there is a strong indication that these chromosomes

contain stable, effective loci for increasing forage

yield. As mentioned previously, the correlation

between estimated forage yield and spring vigor was

quite high, especially in Wisconsin. Based on this

correlation, we would expect to see a number of QTL

for each of these traits that map to the same or similar

positions. Indeed, QTL for each trait were found at

similar locations on C I in Wisconsin, C III at

Washington in 2007, and 2008 at both locations, C

V in 2007 at Washington and in 2008 at Wisconsin, C

VII in 2008 at Washington, and UG 6 in 2008 at

Washington. Not all the co-located QTL were stable,

but they were significant according to a permutation

test in the environment they were detected, and do

contribute to the correlation between forage yield and

spring vigor in a given environment. The significance

of genotype by environment interactions can be partly

explained by the presence of year-specific QTL, such

as those detected for forage yield on C III, or location-

specific QTL, such as those for spring vigor detected in

only Wisconsin on Cs IV and VII. QTL which were

present in only one environment (data not shown)

could also be contributing to the interaction.

In addition to exploring the genetic architecture

of traits, this research detected DNA markers for all

three traits. Except for the SSRs, these markers need

to be converted to be sequence specific (both AFLP

and SRAP technologies are based on the use of

random ‘universal’ primers), but they could be

quickly deployed to test their robustness in other

germplasm. The sequences of these markers, if they

correspond to actual genes, could also lead to a

more focused candidate gene approach to under-

standing the trait(s) and developing additional, gene-

specific markers. Recent next-generation RNA

sequencing and single nucleotide polymorphism

(SNP) analysis projects by Han et al. (2011) and

Yang et al. (2011) have generated thousands of

potential new SNP-based markers. Markers such as

these that are located near QTL could be screened

singly through techniques such as high-resolution

DNA melting (Han et al. 2011) or in a massively

parallel fashion via single-base extension arrays.

Fig. 2 continued

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New QTL and markers could also potentially be

identified using these new SNPs. In addition, an

open-architecture technique such as genotyping by

sequencing (Elshire et al. 2011) could be used to

search for QTL and markers in regions not detected

in this study.

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Table 1 List of QTL which passed a permutation test of at least 1,000 iterations at the 90th percentile, and which were present in at

least two environments

Trait Chromosome Position Year Location LOD Percent

FY III 40 2007 WA 5.5 25.1

FY III 76 2008 WA 5.1 20.0

FY III 84 2008 WI 7.6 34.6

FY III 40 2007 WI 6.8 30.6

SPR III 86 2008 WA 3.1 18.1

SPR III 82 2007 WI 3.8 15.8

SPR III 84 2008 WI 9.5 36.1

NLC III 90 2007 WA 2.9 12.9

NLC III 92 2008 WA 2.7 11.2

NLC III 92 2007 WI 4.1 13.7

NLC III 86 2008 WI 5.0 23.8

FY IV 50 2007 WA 2.7 10.1

FY IV 50 2008 WI 3.3 12.0

SPR IV 64 2007 WI 2.9 11.5

SPR IV 64 2008 WI 2.8 11.2

NLC IV 64 2007 WI 4.9 16.0

NLC IV 64 2008 WA 4.2 14.6

FY V 16 2007 WA 2.4a 10.0

FY V 2 2008 WI 2.3a 8.7

SPR V 2 2007 WA 2.7 11.7

SPR V 2 2008 WI 2.5a 9.4

NLC VI 24 2007 WA 3.9 10.0

NLC VI 24 2008 WA 3.3a 8.9

NLC VI 24 2008 WI 3.2a 8.9

NLC VI 12 2007 WI 5.8 28.2

SPR VII 8 2007 WI 2.7 10.9

SPR VII 6 2008 WI 3.0 11.7

FY VII 6 2007 WA 3.7 15.4

FY VII 4 2008 WA 2.8 9.1

FY VIII 100 2008 WI 4 15.6

FY VIII 100 2007 WA 3.5 12.4

FY VIII 100 2007 WI 3.8 13.5

FY 6 50 2007 WA 2.9 11.8

FY 6 50 2008 WA 2.3a 8.9

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