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The Plant Genome Accepted paper, posted 04/15/2015. doi:10.3835/plantgenome2014.12.0090
Genomic prediction of biomass yield in two selection cycles of a tetraploid alfalfa breeding
population
Xuehui Li, Yanling Wei, Ananta Acharya, Julie L. Hansen, Jamie L. Crawford, Donald R.
Viands, Réal Michaud, Annie Claessens, and E. Charles Brummer*
X. Li, Department of Plant Sciences, North Dakota State University, Fargo, ND 58108
A. Acharya, Department of Crop and Soil Sciences, University of Georgia, Athens, GA 30602
J.L. Hansen, J.L. Crawford, and D.R. Viands, School of Integrative Plant Science, Plant
Breeding and Genetics Section, Cornell University, Ithaca, NY 14850
R. Michaud and A. Claessens, Agriculture and Agri-Food Canada, Quebec, QC G1V 2J3,
Canada
Y. Wei and E.C. Brummer, Plant Breeding Center and Department of Plant Sciences, The
University of California, Davis, CA 95616
Received ___________. *Corresponding author ([email protected]).
Abbreviations:
MAS, marker-assisted selection; GS, genomic selection; LD, linkage disequilibrium; GBS,
genotyping-by-sequencing.
Abstract
Alfalfa is a widely planted perennial forage legume grown throughout temperate and dry
subtropical regions in the world. Long-breeding cycles limit genetic improvement of alfalfa,
particularly for complex traits such as biomass yield. Genomic selection (GS), based on
predicted breeding values obtained using genomewide molecular markers could enhance
breeding efficiency in terms of gain per unit time and cost. In this study, we genotyped tetraploid
alfalfa plants that had previously been evaluated for yield during two cycles of phenotypic
selection using genotyping-by-sequencing (GBS). We then developed prediction equations using
yield data from three locations. Approximately 10,000 SNP markers were useful for GS
modeling. The genomic prediction accuracy of total biomass yield ranged from 0.34 to 0.51 for
the Cycle 0 population and from 0.21 to 0.66 for the Cycle 1 population, depending on the
location. The GS model developed using Cycle 0 as the training population in predicting total
biomass yield in Cycle 1 resulted in accuracies up to 0.40. Both genotype by environment
interaction and the number of harvests and years used to generate yield phenotypes had effects
on prediction accuracy across generations and locations., Based on our results, the selection
efficiency per unit time for GS is higher than phenotypic selection, although accuracies will
likely decline across multiple selection cycles. This study provided evidence that GS can
accelerate genetic gain in alfalfa for biomass yield.
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Alfalfa is one of the most important perennial forage legumes in temperate and
subtropical regions in the world. Most alfalfa cultivars are synthetic populations of genetically
heterogeneous plants that are heterozygous at many loci throughout the genome. Breeding is
conducted as recurrent selection with or without progeny testing. The focus of most breeding
programs is on improvements in disease and insect resistance, which have been striking over the
past 60 years, on long-term persistence, and on other specific traits, such as multifoliolate leaflets
and recently, transgenes, such as Roundup Ready. Increased yield is often not explicitly selected
and in fact, yield improvement has stagnated in recent years (Brummer and Casler 2014). Even
with intentional selection for biomass yield, progress is likely to be slow due to long selection
cycles required to adequately assess multi-year persistence.
One way to improve breeding selection efficiency is to select molecular markers
associated with desirable target trait alleles (Lande and Thompson, 1990). Genomic selection
(GS) using markers across the genome to predict breeding values could accelerate genetic gain in
both animals and plants. To conduct GS, phenotypic and genotypic data are collected in a
training population so that effects of all markers can be simultaneously estimated to develop a
prediction equation (Meuwissen et al., 2001). Based on the GS prediction model, genomic
estimated breeding values (GEBVs) are computed on individual plants and serve as the basis for
selection in subsequent cycles. Similar to marker-assisted selection (MAS), GS could accelerate
breeding by selecting candidate individuals at an early stage and/or in an off-season nursery or
green house. Instead of only focusing on previously identified major quantitative trait loci
(QTLs) in MAS, small- to medium-effect QTLs could be also captured using markers across the
entire genome, thereby contributing to prediction accuracy in GS (Meuwissen et al., 2001).
Numerous simulation and empirical studies have shown that GS is superior to MAS on
prediction accuracy (Bernardo and Yu, 2007; Heffner et al., 2011; Lorenzana and Bernardo,
2009; Zhong et al., 2009). Genomic selection has been reported to result in greater response in
actual selection experiments and breeding programs (Asoro et al., 2013; Massman et al., 2013).
Alfalfa and other perennial crops could greatly benefit from GS by reducing the time to
complete each breeding cycle (Hayes et al., 2013; Li and Brummer, 2012; Resende et al., 2014).
Transcriptome sequencing has identified millions of single nucleotide polymorphisms (SNP) in
alfalfa (Han et al., 2011; Li et al., 2012; Yang et al., 2011), some of which have been used to
develop an Illumina Infinium SNP array containing about 10,000 SNP markers (Li et al., 2014a).
In addition, we have identified thousands of SNP using Genotyping-by-Sequencing (GBS)
(Elshire et al., 2011), and have genetically mapped them in a tetraploid alfalfa biparental
population (Li et al., 2014b). These advances in genotyping make exploration of GS in an alfalfa
breeding population possible.
For a previous experiment, we conducted two cycles of recurrent selection for high yield
in the autotetraploid alfalfa breeding population NY0358. In this experiment, we genotyped the
same plants that were evaluated in each of those two selection cycles using GBS and explored
genome prediction accuracy of biomass yield across locations and breeding cycles.
MATERIALS AND METHODS
Plant material
The tetraploid breeding population (NY0358) used in this experiment was described in Li et al.
(2011b). Briefly, NY0358 was a strain cross between three commercial cultivars, 5454, Oneida
VR (Viands et al., 1990), and AC Viva. The fall dormancy ratings (Teuber et al., 1998) were 4
for 5454 and 3 for Oneida VR and AC Viva. Approximately 100 plants of each cultivar were
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intercrossed in the greenhouse. Seeds from those crosses were germinated in the greenhouse, and
approximately 100 randomly chosen progeny were subsequently intercrossed to form the Cycle 0
(C0) base population.
Phenotypic selection and data collection
Evaluations were conducted using clonal ramets in each of two cycles of selection. Details of the
first cycle of selection were given in Li et al. (2011b). Briefly, cuttings of 190 randomly selected
genotypes from the C0 population were made in the greenhouse. The rooted clones were then
planted into field experiments at the Agronomy and Agricultural Engineering Research Farm
west of Ames, IA, in April 2004; at the Snyder 5 east field adjacent to the Game Farm Road
Weather Station in Ithaca, NY, in June 2004; and at the Harlaka experimental site in Levis, QC,
in May 2005. At each location, each genotype was clonally replicated nine times in the
experiment, with three clones of each genotype representing an experimental unit in each of
three replications of a randomized complete block design. Clones were spaced 15 cm apart
within plot and plots were spaced 75 cm apart with rows. Rows were 75 cm apart. Nurseries in
IA and NY were oversown with the low-growing turfgrass creeping red fescue (Festuca rubra
L.) to minimize weed encroachment and eliminate the need for cultivation. No yield data were
collected in the year of establishment. Yield data were collected in 2005 at Iowa and New York
and at all locations in 2006 by hand clipping forage approximately 5 cm above the soil surface.
In NY and IA, grass was removed prior to weighing each plot. Between two and four harvests
were taken at each location in each year (Table 1). For each harvest in a given year and location,
random samples were taken of harvested biomass, weighed wet, dried for 5 d at 60oC in a forcedair dryer, and then weighed dry. The average dry matter content of the samples was used to
compute dry matter biomass yield of each plant.
The 20 highest yielding genotypes based on the mean biomass yield across the three
locations were selected from Cycle 0 and randomly mated to generate the Cycle 1 (C1)
population. In spring 2009, 185 C1 genotypes were clonally propagated and planted at NY and
QC as described above; Iowa was not included in the second cycle. Biomass yield was measured
at NY in 2010 (NY10) and 2011 (NY11) and at QC in 2010 (QC10) (Table 1). Two or three
harvests were taken each year at each location (Table 1). The dry matter biomass yield was
collected for each harvest as described in Cycle 0 above, with the exception of the first harvest at
NY in 2011. For that harvest, grass was mowed in between rows prior to harvest, but grass
within the row was included in the plot weight.
Phenotypic data analysis
Yield data were collected for 14 harvests across three locations for Cycle 0 and eight
harvests across two locations for Cycle 1. The yield of individual harvests at a location was
summed to obtain total biomass yield at each location. Total biomass yield across locations was
then analyzed using a generalized linear model as follows:
yijk = µ +lk + ri(k) + gj + gljk + εijk
(1)
in which yijk is the yield for the jth genotype in the ith replication of the kth location, µ is the
grand mean, lk is the effect of the kth location, ri(k) is the effect of ith replication nested in the kth
location, gj is the genetic effect of the jth genotype, glij is the interaction effect of jth genotype
and kth location, and eijk is the residual. All factors were considered as random, and the ANOVA
was performed using PROC GLM (SAS Institute, 2010). Significant genotype × location
interaction was found in both cycles (data not shown), and therefore, we subsequently analyzed
individual locations separately.
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For each individual harvest in a given environment, best linear unbiased predictors
(BLUPs) were estimated using a random effects model. The model was
yij = µ + ri + g j + εij
(2)
in which yij was the yield value for the jth genotype in the ith replication, µ was the grand mean
yield of the specific harvest in the specific environment being evaluated, ri was the effect of the
ith replication, gj was the genetic effect of the jth genotype, and εik was the residual. The grand
mean is the only fixed effect and others are considered as random effects. The BLUPs of total
biomass yield at each location were estimated with the same mixed model (2), substituting total
biomass yield for yield of a specific harvest. The estimated BLUPs, denoted as y, were
considered as the observed phenotypic values for GS prediction model evaluation.
To estimate the BLUP of total biomass yield across locations, the estimated BLUPs at
each location were fit to a mixed model as follows:
y jk = µ + lk + g j + ε jk
(3)
in which yjk is the estimated BLUPs for the jth genotype in the kth location, µ is the grand mean,
lk is the effect of the kth location, gj is the genetic effect of the jth genotype. The grand mean and
location are considered as fixed effects and the genotypes were treated as random effects.
In all cases, the BLUPs estimation was performed using the R package lme4 (R
Development Core Team, 2008). The estimated BLUPs were considered as the observed
phenotypic values of total biomass yield for further GS prediction model evaluation.
The genotype (σ2g) and residual (σ2) variance components were estimated using the
mixed model (2) and used to compute broad-sense heritability as:
H2 =
σ g2
(σ 2 / r + σ g2 )
(4)
DNA isolation, GBS library construction, and sequencing
DNA was isolated from C0 plants using the traditional CTAB method (Doyle and Doyle, 1990)
in a 96 well format; for C1, DNA was isolated with the Wizard® Genomic DNA Purification Kit
(Promega, A1125) per the manufacturer’s instructions. DNA was quantified with a Quant-iT
PicoGreen dsDNA assay kit (Life Technologies, P7589). One library was constructed each for
C0 at 190-plex and C1 at 185-plex, using the protocol of Elshire et al. (2011) with minor
modifications. Briefly, 100 ng of each DNA was digested with ApeKI (NEB, R0643L), and then
ligated to a unique barcoded adapter and a common adapter. An equal volume of the ligated
product from each genotype was pooled and the mixture was purified using the QIAquick PCR
purification kit (QIAGEN, 28104) prior to PCR amplification. In the PCR, 50 ng of template
DNA was mixed with NEB 2X Taq Master Mix and two primers (5 nmoles each) in a 50 ul total
volume and amplified on a thermocycler for 18 cycles with 10 sec of denaturation at 98 ºC,
followed by 30 sec of annealing at 65 ºC, and finally 30 sec extension at 72 ºC. Each library was
sequenced in two lanes on Illumina HiSeq 2000 at the Genomic Sequencing and Analysis
Facility at the University of Texas at Austin, Texas, USA. All sequences were submitted to the
National Center for Biotechnology Information (NCBI) Short Read Archive (study
#SRX685967).
Genotype calling
The UNEAK pipeline (Lu et al., 2013) was used for SNP discovery and genotype calling.
Briefly, the raw reads (100 bp, single end read) obtained from the sequencer were first quality
filtered and de-multiplexed. All reads that began with one of the expected barcodes immediately
followed by the expected cut site remnant (CAGC or CTGC for ApeKI) were trimmed to 64 bp
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(including the cut site remnant but removing the barcode). Reads with identical sequences were
grouped into one tag. The tags with 10 or more reads across all individuals were retained for
pairwise alignment. Pairwise alignment was performed to find pairs of tags that differed by only
1 bp. For each SNP marker, the read distribution of the paired tags in each individual was used
for SNP genotype calling as described by Li et al. (2014b). Briefly, for a given SNP (e.g., A/T),
if only a single allele was observed for a given individual, then a minimum of eleven reads was
required to call a homozygote (e.g., AAAA). If fewer than eleven reads were present, we
assigned a missing genotype to avoid misclassifying a triplex heterozygote (AAAT) as
homozygous. The probability of miscalling a triplex heterozygote as a homozygote (AAAA) is
less than 0.05 if eleven or more reads are present and allele amplification is unbiased. When both
alleles were observed in a given individual, we required a minimum of two reads per allele and a
minimum minor allele frequency for that locus in a given individual greater than 0.10 to call a
heterozygote; otherwise a missing genotype call (NA) was assigned. Requiring two reads of the
minor allele limits the likelihood that an allele resulted from a sequencing error. However, if a
large number of sequencing reads are available for a given locus, multiple sequencing errors
might be likely. Therefore, we included the minor allele frequency limit to avoid calling
homozygotes as heterozygotes that were obtained solely due to sequence errors. Reliably
discriminating among the three heterozygote genotypes in an autotetraploid would require a read
depth of at least 60 (Uitdewilligen et al., 2013). Because only a small percentage of our GBS
SNP markers met this criterion, we did not attempt to distinguish among heterozygote genotypes.
The Basic Local Alignment Search Tool (BLAST) was used to query the consensus sequence of
each tag pair containing a SNP against the M. truncatula reference genome Version 4.1. The best
hit with a cutoff of E-value < 1 × 10-5 was selected as the physical location of the GBS SNP
markers.
Population structure and linkage disequilibrium (LD) estimation
For each breeding cycle, the GBS SNP markers with fewer than 20% missing values were used
for model-based clustering using the R package HDclassif (Berge et al., 2012). The best model,
which had the optimal number of clusters, was established according to the Bayesian Information
Criterion (BIC).
For each cycle, the LD between pairs of GBS SNP markers was estimated as r2 using the
software program TASSEL (Bradbury et al. 2007), with heterozygous calls treated as missing
values. We estimated LD in four datasets representing GBS SNP markers with fewer than 30%,
50%, 70%, or 80% missing genotypic values and, in all cases, with a minor allele frequency
higher than 5%. The functional relationship between LD and physical distances on the M.
truncatula genome was evaluated by fitting a non-linear model. The expected r2 under driftrecombination equilibrium was E(r 2 ) = 1 / (1+ C) , and C = 4ad , where d is the physical distance
in bp and a is a regression coefficient estimated from our data (Sved 1971). With a low level of
mutation and finite sample size n, the expectation becomes (Hill and Weir 1988):

 (3+ C)(12 + 4C + C 2 ) 
10 + C
E(r 2 ) = 

1+
n(2 + C)(11+ C) 
 (2 + C)(11+ C) 
(4)
GS prediction model development and validation
The general GS prediction model used was:
y = µ + Zu + ε
(5)
in which y is the observed phenotypic value [estimated BLUP from equation (1)], µ is the
intercept, Z is the marker matrix, and u is a vector of estimated marker effects. Ridge regression
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method was used to estimate effects of markers using the R package rrBLUP (Endelman, 2011).
GBS SNP marker data sets with varying levels of missing values were used to model each trait at
each environment. Prior to fitting the models, the missing values were imputed using the random
forest method with the R package missForest (Stekhoven and Buhlmann, 2012).
We evaluated genomic prediction accuracy for single harvest biomass yield in each
location and total biomass yield at each location and across locations. The genomic prediction
model was validated by cross validation, in which 90% of individuals were randomly selected as
a training population to estimate marker effects and the remaining 10% of individuals were used
to validate the genomic prediction accuracy. The genomic prediction accuracy was estimated as
the Pearson correlation (r) between estimated GEBVs and estimated BLUPs of phenotypic
values. Random sampling training and validation sets were repeated 3,000 times and the mean of
correlations was defined as the genomic prediction accuracy.
For total biomass yield, we evaluated the accuracy of GS models developed using one
location as the training population to predict biomass yield in other locations. We also evaluated
the accuracy of GS models developed using Cycle 0 as the training population to predict biomass
yield in Cycle 1. For this analysis, all individuals from Cycle 0 were used to develop a GS
prediction model and all individuals from Cycle 1 were used for validation based on the SNP
markers in common with the Cycle 0 training set.
RESULTS
Phenotype data
The broad-sense heritability of biomass yield ranged from 0.70 to 0.93 across harvests for Cycle
0 and from 0.61 to 0.88 across harvests for Cycle 1 (Table 1). These results indicated that large
genetic variation for biomass yield existed in the population at all harvests and in all
environments in both Cycle 0 and Cycle 1.
GBS genotype data
After quality filtering and processing, we identified a total of 448,080,472 sequencing reads for
Cycle 0 and 369,843,942 for Cycle 1. The read number per genotype was 2,358,318 on average
for Cycle 0, ranging from 430,090 to 10,390,898. One genotype in Cycle 1 had only 2,776 reads
and was dropped from further analysis. The average read number per genotype in Cycle 1 was
2,010,006, ranging from 1,213,037 to 6,354,979.
The sequence reads from both breeding cycles were analyzed together using the UNEAK
pipeline. In total 177,205 GBS SNP markers were identified in this breeding population and
73,256 of them (41.3%) aligned to the eight chromosomes of M. truncatula pseudomolecules
v4.1. After read-depth filtering, we genotyped 18,525 SNPs in Cycle 0 and 14,913 in Cycle 1
that had up to 90% missing values (Table S1 and S2). We aligned 76% of Cycle 0 SNPs and
77% of Cycle 1 SNPs to the eight chromosomes of the M. truncatula reference genome (Tables
S1 and S2). Within these SNP datasets, 7,075 SNPs in Cycle 0 and 6,241 SNPs in Cycle 1 had
fewer than 50% missing values and about 85% aligned to the M. truncatula pseudomolecules
(Table S1 and S2). For the markers having fewer than 10% missing values, about 90% aligned to
the M. truncatula pseudomolecules (Table S1 and S2). In all cases, we imputed missing data
using random forest imputation prior to model development.
Population structure and LD
For each breeding cycle, we used model-based clustering analysis of GBS SNP markers with
fewer than 20% missing values to assess population substructure. Based on the Bayesian
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Information Criterion (BIC), the best model for either Cycle 0 or Cycle 1 was a single cluster
(data not shown), suggesting no subpopulation structure.
We measured LD as r2 between a pair of markers based on marker data sets having
missing values for fewer than 30%, 50%, 70%, or 80% of the genotypes. The estimated LD
decreased as the percentage of missing values increased to 70% in both cycles (Figure 1). Using
the dataset with SNP markers having fewer than 70% missing values, LD in Cycle 0 decayed to
0.42 at 200 Kbp between markers, but in Cycle 1, LD was more extensive, with r2 = 0.74 at 200
Kbp (Figure 1).
Genomic prediction accuracy
Marker number is one of major factors affecting genomic prediction accuracy (Heffner et al.,
2011; Heslot et al., 2013; Poland et al., 2012 and Zhong et al., 2009). For single harvest biomass
yield at each location and total biomass yield at each location and across locations, we developed
and validated GS models using a series of GBS SNP marker datasets that had a threshold for
missing values that ranged from 10 to 90% of the genotypes in the population. Marker sets that
accepted higher amounts of missing data had more markers. We found that the prediction
accuracies increased as missing values increased to between 60 and 70% in Cycle 0 (Table S3)
and to 80% in Cycle 1 (Table S4). The harvest NY11-1 in Cycle 1 had consistently low
accuracies and was unusual compared to all other harvests (Table S4). The broad-sense
heritability of this particular harvest was much lower than other harvests, implying poor quality
phenotypic data (Table 1). These results likely resulted because a flail-type harvester was used
for this harvest and the oversown companion grass was included in the plot weight. Based on
these results, we used SNP datasets represented by up to 65% missing data for Cycle 0 and up to
80% missing data for Cycle 1 to develop and validate GS models.
Validation within Cycle 0
Using the dataset of 9,906 markers that each had fewer than 65% missing genotypic values, the
prediction accuracy among the 14 harvests in Cycle 0 was 0.26 to 0.51 (Table S3). Accuracies
from one harvest from IA (IA05-1) and one harvest from NY (NY06-3) were lower than 0.30
(Table S3).
In Cycle 0, six harvests in total from two years were taken at IA and NY and two harvests
from one year at QC. We further evaluated genomic prediction accuracy of total biomass yield
within a location and between locations. The prediction accuracy of total biomass yield was 0.43
at IA, 0.49 at NY, 0.44 at QC and 0.51 across the three locations (Table 2). The prediction
accuracies validated between locations, especially between IA and QC, were much lower than
prediction accuracies validated within location (Table 2).
Based on NY data only, 16 of the top 20 highest yielding genotypes would have also
been selected using GEBVs (data not shown). Across all locations, 11 of 20 individuals selected
for maximum yield based on phenotypic data were also among the highest 20 individuals based
on GEBVs (data not shown).
Validation within Cycle 1
In Cycle 1, an accuracy of 0.40 to 0.67 was observed for the seven individual harvests (excluding
NY11-1) in NY and QC using the 11,282 markers with fewer than 80% missing values (Table
S4). The prediction accuracy of total biomass yield was 0.45 at NY based on all five harvests and
0.51 based on the four harvests excluding NY11-1 (Table 3). The prediction accuracy of total
biomass yield was 0.66 for QC and 0.59 across the two locations (Table 3). The prediction
accuracies validated between the two locations were 2-3 times lower than the prediction
accuracies validated within location (Table 3).
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Prediction across generations
We further evaluated prediction accuracy of total biomass yield across generations. We
identified 9,077 of the 9,906 markers that were part of the Cycle 0 data set that were also part of
the Cycle 1 dataset. We developed a genomic prediction model considering Cycle 0 as the
training population to then predict biomass yield in Cycle 1. The GS model developed from NY
C0 showed an accuracy of 0.40 when applied to data from NY C1, the best of all models, and
0.39 when applied to QC C1 (Table 4). Models developed from QC C0 had low prediction
accuracy for C1 at either location. IA C0 models also had low prediction to C1 from NY or QC
(Table 4). Models developed from total yield across the three locations at C0 showed higher
prediction accuracies compared to IA C0 and QC C0 models, but lower prediction accuracies
relative to the NY C0 model (Table 4). Removing Iowa and analyzing NY and QC together
resulted in an accuracy of 0.38 for predicting mean yield across NY and QC in the Cycle 1
population.
DISCUSSION
Genotyping-By-Sequencing
With GBS, we were able to identify over 175,000 SNP in a tetraploid alfalfa breeding program.
However, only about 10% of these SNP could be genotyped due to an insufficient read depth in
enough individuals in the population for the loci to be useful in mapping. The main issue we face
in alfalfa (as in other allogamous polysomic polyploids) is heterozygosity; in order to effectively
call a genotype as heterozygous or homozygous, multiple reads are required, and consequently,
only 18,525 GBS SNP markers were genotyped in our population. These markers were
distributed throughout the M. truncatula reference genome. About 90% of loci for which
genotype calls could be made in over 90% of individuals aligned to the M. truncatula reference
genome, but only 41.3% of all markers could be aligned. Genomic regions with highly conserved
sequences between M. truncatula and M. sativa – such as exons – would also be more conserved
among diverse alfalfa haplotypes and therefore, possibly more commonly sequenced.
Large numbers of genome-wide markers are needed to apply genomic selection to alfalfa,
or to conduct various other mapping and trait dissection experiments. Previously, we developed
an Illumina Infinium array with ~10,000 features (Li et al., 2014a). Arrays have particular
advantages, including very little missing data, known loci as markers (selected for specific
reasons), and facile comparison among experiments. However, they suffer ascertainment bias,
and the number of markers is fixed, so that experiments needing fewer markers or those
requiring many more cannot be accommodated, and the cost is quite high on an experiment
basis. In contrast, GBS-based SNP markers are typically cheaper, and assuming a relatively large
amount of missing data can be handled and accepted, they represent an alternative highthroughput genotyping platform. Compared to the SNP array, GBS has no ascertainment bias
and is tunable to generate more or fewer markers depending on the purpose of the experiment.
Using GBS, we have generated about 9,000 SNP markers polymorphic for a tetraploid alfalfa biparental F1 mapping population and mapped over 3,500 SNP markers on 32 linkage groups for
each of the two parents, four linkage groups per chromosome representing the four haplotypes
(Li et al., 2014b). For our purposes here, GBS appears to be the only way to get sufficient
markers within budget constraints to implement a GS program in alfalfa at the current time.
Population structure and LD
Population structure has effects on GS accuracy and needs to be integrated into a GS model if
present (Lorenz et al., 2011; Hayes et al., 2009; Toosi et al., 2010). Based on 71 SSR markers,
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we found no subpopulation structure in Cycle 0 of this breeding population (Li et al., 2011b).
Using the GBS SNP markers, we conducted model based analysis and found no subpopulations
in either breeding cycle. These results indicated that the two generations of random mating used
to develop Cycle 0 of this breeding population successfully intermixed the parental genomes.
The number of markers needed to perform GWAS or GS in this population can be
inferred from LD estimates. LD quickly decayed within a few Kbp in a wild diploid alfalfa
collection based on SSR markers (Sakiroglu et al., 2012). Using the Illumina Infinium alfalfa
SNP array, we evaluated genome-wide LD with a large number of markers and found that LD
quickly decayed to 26 Kbp at r2=0.2 over all germplasms tested, but varied by population (Li et
al., 2014a). In this study, we estimated LD of the two breeding cycles using GBS SNP markers
and found that the estimated LD decreased as the percentage of missing values increased to 70%.
Removing markers with more missing values results in fewer markers tested, which could bias
the LD estimation. In contrast, including the markers with more missing values results in a small
sample size for some markers, which could cause an overestimation of LD (Yan et al., 2009).
Using markers with fewer than 70% missing values, we found that the LD is about r2=0.42 in
Cycle 0 and r2=0.74 in Cycle 1 at a distance of 200 Kbp. One cycle of selection likely caused the
increased LD in Cycle 1. Based on the estimated LD, about 42% of the genetic variation in Cycle
0 could be captured by using 5,000 markers, assuming an alfalfa genome size of 1,000 Mbp and
equally distributed markers throughout the genome. However, our LD estimates were made
based on the physical distances between markers in the M. truncatula reference genome.
Although numerous studies have shown a high level of synteny between M. truncatula and
alfalfa (Choi et al., 2004; Li et al., 2011a), the estimated LD could be biased because we do not
have a full genome sequence of alfalfa for comparison. Nevertheless, while we did not
completely cover the genome with this experiment, we were able to make considerable
improvement relative to previous mapping experiments.
Genomic prediction accuracy and implication in alfalfa breeding
We imputed missing GBS SNP genotypes using the random forest method. Imputation errors are
expected to increase as the amount of missing marker data increases (Poland et al., 2012).
However, because the number of markers increases in the dataset as the stringency for missing
data is relaxed, the imputation errors may be outweighted by improved models with more
markers. In this study, we found that the average GS accuracies increased as marker number
increased until the allowable amount of missing data per marker exceeded 70-80% in spite of the
potential higher imputation error caused by more missing values. Our results are congruent with
an experiment in wheat where 35,000 GBS SNP markers with up to 80% missing data points
gave higher prediction accuracy than 2,000 DArT markers with 2% missing values (Poland et al.,
2012). The better genomic prediction accuracy observed using GBS was mainly due to the large
increase in marker numbers and not to ascertainment bias from the DArT markers (Heslot et al.,
2013). Thus, more markers, even with increased amounts of missing values, will generally result
in higher prediction accuracy until the missing data percentage per marker becomes very high
(>70-80%).
High genomic prediction accuracies were observed in numerous empirical studies from a
diverse set of plant species such as 0.39 for leaf length and 0.48 for leaf width in maize (Guo et
al., 2013), 0.72 for fusarium head blight resistance in barley (Lorenz et al., 2012), 0.70-0.90 for
various fruit quality traits in apple (Kumar et al., 2012), and 0.63-0.74 for height in loblolly pine
(Resende et al., 2012). As a perennial forage crop, alfalfa generally survives in the field at least
3-4 years and, depending on location, can be harvested from two to ten or more times each year.
Page 18 of 43
Page 19 of 43
If genomic prediction accuracies of total biomass yield can be identified that are reasonably
robust, then the gain from GS could be significant relative to phenotypic selection that occurs
after 3-4 years. In this experiment, we observed prediction accuracies of 0.43 to 0.66 for total
biomass yield when validated within a location, providing evidence that genomic prediction
accuracies in an autotetraploid species and in a synthetic alfalfa breeding population could
conceivably be high enough to make GS worthwhile for complex traits like biomass yield. We
also found that one harvest (NY11-1) caused lower prediction accuracy when it was included in
total biomass yield for that location, reinforcing the point that high quality phenotypic data from
as many harvests as possible is essential for alfalfa GS prediction models. Lower prediction
accuracies between locations compared to the accuracies validated within location suggest
presence of genotype × location interaction for biomass yield, as found in the analysis of
variance, particularly between IA and the two northeastern North American locations.
A scheme of GS in alfalfa could be developed in which one generation is used as a
training population to develop a model that is then applied to selection in later generations (Li
and Brummer, 2012). By making use of phenotypic and genotypic data from the two breeding
cycles, we estimated the GS prediction accuracy across generations to be 0.40 for populations
grown in NY. Even more interesting was the 0.38 prediction accuracy of Cycle 1 NY-QC yield
based on the NY-QC model from Cycle 0. The lower GS accuracy across generations compared
to that validating within a generation is expected and can be explained by less relatedness of
individuals across generations than within generations and by genotype × environment
interaction (Ly et al. 2013). In this experiment, the Cycle 1 genotypes are the products of
phenotypic selection; we did not conduct GS to generate the C1 population. The prediction
accuracy of the model developed in C0 could have been different if the C1 population had been
developed based on GS selection.
For the GS model developed at IA in Cycle 0, the accuracy in predicting biomass yield in
Cycle 1 was low for NY and very poor for QC. This implied that there was G×E interaction on
biomass yield between location IA and QC. However, for the GS model developed at QC in
Cycle 0, the accuracy in predicting biomass yield in Cycle 1 was very low even at the same
location. Only two harvests were taken from the first production year at QC in Cycle 0. No data
were collected in subsequent years because many plants were killed by severe winter. This could
result in a GS model that did not capture most biomass yield genes across ages or growth seasons
and therefore that had the poor prediction. Taken together, GS models for alfalfa yield prediction
need to be developed using multiple harvests from multiple years and likely need to be applied to
a certain target breeding region to avoid bias from G×E interaction. Nevertheless, the model
developed across all locations from Cycle 0 to predict Cycle 1 still had an accuracy of 0.33,
which would still be useful in a breeding program, and which shows that the presence of G×E
does not preclude across location prediction, at least in this population.
Compared to phenotypic selection, the higher selection efficiency from GS is mainly
derived from the cumulative response of more selection cycles per unit time (Jannink, 2010). In
alfalfa, recurrent selection is the common breeding method, and it generally takes 2-5 years to
conduct one cycle of selection. It could take only half a year to conduct one cycle of genomic
selection. Given the prediction accuracy of 0.40 and assuming a reduction of 75% per breeding
cycle, the selection efficiency per unit time of GS is estimated to be about 60% higher compared
to phenotypic selection. The prediction accuracy was estimated as the correlation between
GEBVs and observed phenotypes here. A higher correlation between GEBVs and true breeding
Page 20 of 43
Page 21 of 43
values could be expected, particularly if the observed phenotypic variation has a significant
component of dominance genetic variance. Our reasonably good accuracies between generations
suggest that, in this population, yield is under substantial additive genetic control. The prediction
accuracy observed here with no more than 200 individuals of training population can be higher
given a larger training population in alfalfa breeding industry. In addition, an improved statistical
model that could integrate allele dosage information might result in a better prediction for
polyploid alfalfa.
Alfalfa biomass yield has had a low genetic gain per year and have been stagnant since
the 1980’s (Brummer 1999; Lamb et al., 2006, Brummer and Casler, 2014). In addition to long
breeding cycles, less intense selection is another reason for the slower genetic gain. For alfalfa
and also other perennial forage crops, breeding populations of small size have been commonly
evaluated for selection in each cycle of phenotypic selection. More intense selection in larger
selection population could be carried out in GS, which could result in higher selection efficiency
compared to phenotypic selection (Heffner et al., 2009).
CONCLUSION
Previously, we used GBS to construct a high-density linkage map for a tetraploid alfalfa F1
mapping population. Here we showed that GBS could be used to generate a large number of
genome wide markers for GS studies in a tetraploid alfalfa breeding population. The high
genomic prediction accuracy of total biomass yield was observed even across generations in this
study and many ways could be implemented to potentially enhance the prediction accuracy in the
future studies or breeding applications. This study demonstrates that GS could accelerate
improvement of alfalfa biomass yield and other complex traits by shortening breeding cycles and
increasing selection intensity.
Acknowledgements: The population NY0358 was initially developed to evaluate the potential
value of clonal selection to improve biomass yield as part of the NE1010 Multistate Cooperative
Research Project. This experiment was funded in part by the NE1010 Multistate Cooperative
Research Project (DRV and ECB) and by a USDA-DOE Plant Feedstock Genomics for
Bioenergy grant to ECB, award # 2009-65504-05809. We thank Mark Smith for technical
assistance in Iowa.
Page 22 of 43
Page 23 of 43
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Figure captions
Figure 1. Linkage disequilibrium estimated in C0 and C1 using markers with fewer than 30%,
50%, 70%, and 80% missing values.
Page 30 of 43
Page 31 of 43
Table 1. Description of biomass yield at each harvest and their broad-sense heritability.
Generation
Location
Year
Harvest Abbreviation
Heritability
Cycle 0
IA
2005
2006
NY
2005
2006
QC
Cycle 1
NY
2006
2010
2011
QC
2010
1
IA05-1
0.78
2
IA05-2
0.77
3
IA05-3
0.83
4
IA05-4
0.81
1
IA06-1
0.73
2
IA06-2
0.78
1
NY05-1
0.90
2
NY05-2
0.92
3
NY05-3
0.91
1
NY06-1
0.93
2
NY06-2
0.83
3
NY06-3
0.84
1
QC06-1
0.76
2
QC06-2
0.70
1
NY10-1
0.88
2
NY10-2
0.88
1
NY11-1
0.61
2
NY11-2
0.78
3
NY11-3
0.79
1
QC10-1
0.82
2
QC10-2
0.77
3
QC10-3
0.85
Table 2. The genomic prediction accuracy of total biomass yield in Cycle 0 validated within and
across locations.
Validation
†
Estimation
IA
NY
QC
IA-NY-QC
IA
0.43
0.43
0.34
0.47
NY
0.37
0.49
0.39
0.48
QC
0.34
0.46
0.44
0.46
IA-NY-QC †
0.40
0.49
0.41
0.51
IA-NY-QC is the total biomass yield across the three locations of IA, NY, and QC.
Page 32 of 43
Page 33 of 43
Table 3. The genomic prediction accuracy of total biomass yield in Cycle 1 validated within and
across locations.
Validation
†
Estimation
NY-A
NY-B
QC
NY-QC
NY-A †
0.45
0.49
0.22
0.29
NY-B
0.47
0.51
0.21
0.28
QC
0.22
0.21
0.66
0.60
NY-QC
0.29
0.29
0.61
0.59
NY-A is the total biomass yield from all five harvests at NY; NY-B is the total biomass yield
from the four harvests by excluding the harvest NY11-1. NY-QC is the total biomass yield
across the two locations of NY and QC.
Page 34 of 43
Table 4. Genomic prediction accuracy of total biomass yield across generations.
Validation in Cycle 1
Estimation in
†
Cycle 0
NY-A ‡
NY-B
QC
NY-QC
IA
0.26
0.28
0.08
0.14
NY
0.40
0.40
0.39
0.42
QC
0.26
0.24
0.22
0.25
NY-QC †
0.36
0.36
0.35
0.38
IA-NY-QC
0.38
0.39
0.28
0.33
NY-QC is the total biomass yield across the two locations of NY and QC; IA-NY-QC is the
total biomass yield across the three locations of IA, NY, and QC in Cycle 0.
‡
NY-A is the total biomass yield from all five harvests in Cycle 1; NY-B is the total biomass
yield from the four harvests by excluding the harvest NY11-1, which had a low heritability; NYQC is the total biomass yield across the two locations of NY and QC in Cycle 1.
Page 35 of 43
Figure 1. Linkage disequilibrium estimated in C0 and C1 using markers with fewer than 30%, 50%, 70%,
and 80% missing values.
152x152mm (300 x 300 DPI)
Page 36 of 43
Table S1. Number of GBS SNP markers genotyped in Cycle 0 and markers' distribution along the eight chromosome
Chromosome
10
20
30
chr1
119
307
506
chr2
93
287
447
chr3
102
289
461
chr4
109
339
561
chr5
79
228
378
chr6
30
81
133
chr7
86
239
414
chr8
109
291
460
Total genotyped markers aligned to M. truncatula
727
2061
3360
Percentage of the markers aligned to M. truncatula
91.4
89.3
87.7
Total genotyped markers
795
2308
3832
Mean percentage missing genotype calls (%)
7.1
12.6
17.7
Page 37 of 43
along the eight chromosomes of theM. truncatula reference genome
Percentage of missing values
40
50
60
65
70
80
90
717
900
1113
1245
1368
1659
2106
619
770
930
1014
1104
1351
1669
652
837
1044
1163
1259
1565
2023
759
975
1184
1316
1435
1731
2176
532
691
851
945
1026
1273
1664
201
281
361
398
451
601
857
572
742
904
988
1099
1360
1744
636
794
966
1066
1183
1436
1854
4688
5990
7353
8135
8925
10976
14093
86.2
84.7
82.9
82.1
81.3
79.1
76.1
5441
7075
8872
9906
10982
13880
18525
22.9
28.1
33.6
36.6
39.7
47.2
56.9
Page 38 of 43
Table S2. Number of GBS SNP markers genotyped in Cycle 1 and markers' distribution along the eight chromosome
Chromosome
10
20
30
chr1
230
403
542
chr2
194
358
460
chr3
208
358
507
chr4
240
421
570
chr5
160
290
406
chr6
60
103
155
chr7
190
317
429
chr8
212
370
488
Total genotyped markers aligned to M. truncatula
1494
2620
3557
Percentage of the markers aligned to M. truncatula
90.2
88.3
86.5
Total genotyped markers
1656
2968
4114
5.8
9.7
13.9
Mean percentage missing genotype calls (%)
Page 39 of 43
along the eight chromosomes of theM. truncatula reference genome
40
50
60
65
70
80
90
663
797
937
1032
1133
1372
1774
563
673
786
851
925
1099
1363
625
756
900
999
1095
1307
1635
694
844
998
1088
1183
1422
1771
487
595
730
813
881
1066
1368
202
238
303
337
368
487
682
534
632
762
836
917
1101
1419
587
722
850
934
999
1198
1507
4355
5257
6266
6890
7501
9052
11519
85.1
84.2
83.5
82.8
82.1
80.2
77.2
5116
6241
7506
8322
9139
11282
14913
18.2
23.1
28.5
31.9
35.1
42.9
53.3
Page 40 of 43
Table S3. Genomic prediction accuracy of biomass yield at each single harvest and total biomass at each location an
using marker datasets with varied levels of missing values
Traits
10
20
30
40
50
60
IA05-1†
0.196
0.195
0.212
0.222
0.251
0.261
IA05-2
0.322
0.319
0.338
0.351
0.379
0.382
IA05-3
0.342
0.397
0.398
0.396
0.415
0.430
IA05-4
0.383
0.452
0.473
0.472
0.485
0.491
IA06-1
0.334
0.371
0.355
0.379
0.390
0.406
IA06-2
0.345
0.379
0.400
0.390
0.405
0.400
NY05-1
0.358
0.385
0.403
0.397
0.415
0.405
NY05-2
0.425
0.417
0.422
0.413
0.433
0.435
NY05-3
0.360
0.381
0.399
0.396
0.415
0.409
NY06-1
0.396
0.467
0.471
0.473
0.492
0.494
NY06-2
0.247
0.275
0.295
0.305
0.339
0.316
NY06-3
0.156
0.252
0.215
0.232
0.255
0.233
QC06-1
0.315
0.374
0.391
0.394
0.403
0.413
QC06-2
0.393
0.434
0.450
0.449
0.460
0.454
IA
0.341
0.369
0.372
0.371
0.400
0.425
NY
0.407
0.432
0.437
0.441
0.463
0.459
QC
0.343
0.402
0.420
0.415
0.422
0.440
IA-NY-QC
0.405
0.466
0.476
0.473
0.495
0.497
†IA is the total biomass yield at IA; NY is the total biomass yield at NY; QC is the total biomass yield at QC; IA-NY
across the three locations.
Page 41 of 43
otal biomass at each location and across locations in Cycle 0
issing values
65
70
80
90
0.260
0.261
0.241
0.188
0.398
0.384
0.372
0.344
0.428
0.422
0.413
0.390
0.486
0.457
0.439
0.433
0.413
0.409
0.387
0.348
0.420
0.415
0.399
0.358
0.425
0.429
0.406
0.384
0.462
0.457
0.437
0.432
0.446
0.441
0.432
0.417
0.513
0.513
0.500
0.469
0.356
0.362
0.348
0.281
0.278
0.279
0.270
0.240
0.410
0.399
0.402
0.372
0.474
0.458
0.444
0.422
0.433
0.414
0.389
0.356
0.485
0.482
0.467
0.448
0.443
0.429
0.424
0.397
0.515
0.514
0.487
0.466
otal biomass yield at QC; IA-NY-QC is the total biomass yield
Page 42 of 43
Table S4. Genomic prediction accuracy of biomass yield at each single harvest and total biomass yield at each locati
datasets with varied levels of missing genotype calls.
Traits
10
20
30
40
50
NY10-1†
0.336
0.343
0.350
0.340
0.350
NY10-2
0.445
0.461
0.486
0.479
0.485
NY11-1
0.074
0.041
0.065
0.061
0.082
NY11-2
0.391
0.405
0.398
0.399
0.407
NY11-3
0.359
0.353
0.361
0.356
0.369
QC11-1
0.550
0.566
0.590
0.611
0.626
QC11-2
0.521
0.538
0.552
0.574
0.585
QC11-3
0.570
0.593
0.605
0.607
0.621
NY-A
0.381
0.402
0.396
0.393
0.400
NY-B
0.426
0.448
0.447
0.439
0.447
QC
0.527
0.541
0.547
0.556
0.570
NY-QC
0.453
0.476
0.482
0.489
0.494
†NY-A is the total biomass yield from all five harvests at NY; NY-B is the total biomass yield from the four harvests
biomass yield at QC; NY-QC is the total biomass yield across the two locations.
Page 43 of 43
al biomass yield at each location and across locations in Cycle 1 using marker
60
65
70
80
90
0.351
0.372
0.382
0.399
0.360
0.493
0.512
0.526
0.536
0.507
0.082
0.094
0.096
0.084
0.113
0.418
0.428
0.437
0.458
0.428
0.372
0.382
0.398
0.433
0.386
0.649
0.658
0.654
0.669
0.650
0.612
0.621
0.622
0.635
0.606
0.636
0.644
0.646
0.651
0.642
0.405
0.415
0.430
0.457
0.420
0.454
0.465
0.478
0.511
0.462
0.573
0.578
0.579
0.665
0.635
0.502
0.506
0.515
0.588
0.568
ss yield from the four harvests by excluding the harvest NY11-1; QC is the total

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