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\begin{document}

\title{Haplotype transmission and yield association highlight the utility of an
exotic germplasm source in elite soybean cultivar development\\}



\author[1]{Ben Stewart-Brown}%
\author[1]{Justin}%
\affil[1]{University of Georgia}%


\vspace{-1em}



  
  \date{May 25, 2024}


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\selectlanguage{english}
\begin{abstract}
{}

{}

{}

Benjamin B. Stewart-Brown, Justin N. Vaughn, Thomas E. Carter Jr.,
Zenglu Li*\\

B. B. Stewart-Brown, Center for Applied Genetic Technologies \& Dep. of
Crop and Soil Sci., University of Georgia, Athens, GA 30602; J.N.
Vaughn, Center for Applied Genetic Technologies \& Dep. of Crop and Soil
Sci., University of Georgia, Athens, GA 30602; T. E. Carter, Jr.,
USDA-ARS, Raleigh, NC 27695; Z. Li,~~Center for Applied Genetic
Technologies \& Dep. of Crop and Soil Sci., University of Georgia,
Athens, GA 30602. *Corresponding author (zli@uga.edu).\\%
\end{abstract}%



\sloppy


\textbf{Abbreviations:}\\

\section*{\texorpdfstring{\textbf{ABSTRACT}}{ABSTRACT}}\label{auto-label-section-345103}

This Authorea document template can be used to prepare documents
according to a desired citation style and authoring guidelines.
Abstracts are not always required, but most academic papers have one and
writers should know how to produce a useful abstract. An abstract should
be a very short, clear and concise summation of the entire paper. An
abstract should provide enough of a preview that a typical reader will
know whether or not they wish to read the paper. It should reveal both
the purpose and conclusions of the paper.\\

The genetic diversity present in cultivated plant varieties generally
only represents a small fraction of the total diversity present in the
species from which the cultivar was derived (\hyperref[csl:1]{(Kovach and McCouch, 2008)}).~ This
reduction in diversity is dramatically exemplified by soybean, wherein
\textasciitilde{}85\% of North American breeding germplasm is derived
from 18 landraces (\hyperref[csl:2]{(Cornelious and Sneller, 2002)}).~ Because of lack of variation in
cultivars, the global diversity of a crop is commonly mined for
beneficial alleles, such as novel alleles for pest-resistance.~ These
alleles can then be introgressed into cultivars via marker assisted
selection.~ Historically, the introgression of traits from wild or
semi-wild germplasm has generally been limited to simple, Mendelian
traits.~ Such traits are easier to identify with confidence, they are
less dependent on genetic background, and they are simpler to track
during introgression.~ Though wild yield alleles have been successfully
identified using near isogenic lines, these methods are highly resource
intensive and often miss relevant alleles().~\\

~~~~~~~~~~~ PI416937 is a Japanese landrace found in the pedigree of
many major cultivars in the Southeastern US, most notably Woodruff
(\hyperref[csl:3]{(Boerma, 2012)}). ~Woodruff is 25\% PI416937 by pedigree and was
found to yield \% of elite checks in Southern USDA Regional trials in
year. PI416937 has many traits that distinguish it from more common
elite soybean varities, including slow-wilting (\hyperref[csl:4]{(Fletcher et al., 2007)};
\hyperref[csl:5]{(King et al., 2009)};~\hyperref[csl:6]{(Abdel-Haleem et al., 2012)}), expansive fibrous roots
(\hyperref[csl:7]{(Hudak and Patterson, 1996)};~\hyperref[csl:8]{(Pantalone et al., 1996)};~\hyperref[csl:9]{(Busscher et al., 2000)};
\hyperref[csl:10]{(Purcell et al., 2007)}; \hyperref[csl:11]{(Abdel-Haleem et al., 2010)}), aluminum tolerance
\selectlanguage{english}(\hyperref[csl:12]{(Goldman et al., 1989)}; \selectlanguage{english}\hyperref[csl:13]{(Bianchi-Hall et al., 2000)}), large leaf surface area
and overall drought stress tolerance in general (\hyperref[csl:12]{(Goldman et al., 1989)};
\hyperref[csl:14]{(Sloane et al., 1990)}). Many other lines derived from PI416937 exhibited
substantial vigor relative to checks and had a significant yield
advantage ().~Thus, unlike the more common case in which exotic
germplasm is used as a donor of a specific, Mendelian trait, PI416937
and its derived lines are a model for the effective use of exotic
germplasm in producing immediate yield increases and providing diversity
for long-term genetic gain.\\

~~~~~~~~~~~ In this study, we aimed to use genome-wide marker data and
the known pedigree information related to PI416937 in order to track
exotic regions that were selected for and against over the course of the
last 30 years.~ The idea of exploiting breeding pedigrees to detect
selected loci has been use previously in attempts to detect
agronomically important loci in soybean (\hyperref[csl:15]{(Shoemaker et al., 1992)};
\hyperref[csl:16]{(Lorenzen et al., 1995)}; \hyperref[csl:17]{(Sebastian et al., 1995)}; \hyperref[csl:18]{(Grainger and Rajcan, 2013)}). The
approach is analogous to transmission disequilibrium tests pioneered in
animal genetics.~ Released varieties or breeds are assumed to be the
product of selection and thus alleles conferring superior fitness are
expected to deviate from random (50\%) transmission
(\hyperref[csl:19]{(BINK et al., 2000)}).~ While original versions of the test depend on a
heterozygous parent, the test is easily adapted to selfing crops in
which the entire F1 population of an inbred cross is assumed to be
heterozygous for all segregating alleles (\hyperref[csl:20]{(Jannink et al., 2001)}). Though
the approach is theoretically very powerful, previous studies employing
it suffered from low marker density
(\hyperref[csl:15]{(Shoemaker et al., 1992)};~\hyperref[csl:16]{(Lorenzen et al., 1995)}) or gapped pedigrees that made
rigorous statistical inference problematic (\hyperref[csl:18]{(Grainger and Rajcan, 2013)}).~
Higher marker density allows for the confident inference of shared
haplotypes and thus the ability to accurately define and count the
number of crosses that truly test a locus for the influence of
selection.\\

~~~~~~~~~~~ Haplotype consolidation based on identity-by-descent (IBD)
should theoretically benefit any genotyping strategy that does not
result in complete knowledge of the allelic states of all polymorphisms
in the population under study (\hyperref[csl:21]{(Jordan et al., 2005)}).~ This conclusion
stems from the fact that regions that are IBD will share not only the
identical typed alleles but intervening untyped alleles as well
(excluding novel mutations that have occurred since divergence from the
last common ancestor).~ Since there is generally a greater chance of
these untyped alleles being the causal mutations underlying a phenotype,
IBD more effectively reflects the phenotypic impact of possessing that
genomic region.\\

~~~~~~~~~~~ In this study we used a two-step process that infers which
genomic regions are derived from the two parents and then which regions
in the parents are derived from PI416937.~ For all markers in the study,
any cross which contained a PI416937 allele in one parent and a
non-PI416937 allele in the other parent was considered a single test of
that locus.~ If the PI416937 allele was inherited in such tests more or
less frequently than a binomial model would predict, then we considered
this evidence for selection.~ Identified alleles were independently
tested across a range of environments and recombinant-inbred populations
for which at least one parent had PI416937 in its pedigree.~~\\

As potential validation for regions that appeared to be under positive
selection from PI416937, we examined previous literature for
quantitative trait loci (QTL) that had been previously mapped in studies
involving PI46937. We also investigated these peak regions under
selection for potential candidate genes that may be conferring a yield
advantage. Regions we found under negative selection were investigated
for QTL conferring traits considered agronomically undesirable for
soybean production. We examined five breeding populations composed of F5
derived recombinant inbred lines (RILs) with PI416937 in their pedigrees
to examine any relationship between regions under selection within the\\

RIL populations and our pedigree based analysis. These RIL populations
had undergone phenotypic selection based upon visual agronomic traits
(i.e., lodging, height, and visual estimation of yield). Selection
occurred on individual F5 plants as well as F5 derived plant rows from
selected plants. We also wanted to look at PI46937 derived lines from
our analysis that were considered the highest yielding to see if their
was a relationship between their phenotypic superiority and presence of
regions under positive selection from PI416937 while also having an
absence of regions under negative selection from PI416937.~\\

After identifying these regions from PI416937 under breeding selection,
we wanted to examine the potential to introgress these exotic alleles
into genomic regions that are low in diversity in North American
germplasm. There are regions throughout the soybean genome which have
been identified that have either lost diversity due to selection or have
historically, over the last several decades of North American breeding,
never had diversity to test against (). We examined if regions we found
under selection from PI416937 overlapped with these regions of low
diversity. The potential implications of this would be to have breeders
explore targeted introgression of beneficial alleles from PI416937 into
regions of low diversity, especially regions which historically have
experienced little to no known diversity.\\

\section*{MATERIALS AND METHODS}\label{materials-and-methods}

\section*{Plant material}\label{plant-material}

\subsection*{PI416937 pedigree
analysis}\label{pi416937-pedigree-analysis}

For this study we looked to obtain high yielding lines that were derived
from PI416937 as well as there parental lines. This combination of
PI416937 derived lines and parental lines formed what we will refer to
as a trio. Each entire trio was genotyped for the pedigree analysis. One
of the parental lines must obviously have been derived from PI416937. We
used 52 trios in our pedigree analysis. For seven of the trios, both
parents were derived from PI416937. By pedigree, percentage PI416937
ranged from 12.5\% to 50\% in the high yielding progeny line of the
trios. There were several lines that were derived from the same parent
combinations. We became concerned of biasing our results for certain
regions under selection if they were inherited to multiple progeny from
the same parental combination, but these progeny were derived from the
same F2 plant or an even later generation. This would indicate that
these multiple progeny were no longer multiple independent occurrences
where a breeder selected a high yielding line with a particular genomic
region from PI416937 so they should not be treated as such. In order to
identify lines we believed to be derived from the same F2 generation or
later, we looked for sister lines which contained what we deemed to be
an excess of shared recombination events. As long as we could determine
within a reasonable doubt that these lines were derived from different
F2 plants, we kept them in the analysis. The high yielding progeny lines
were chosen based on inclusion in the USDA Southern States Uniform Tests
(UT). This was deemed an acceptable criteria by us because the primary
breeding objective for soybean breeders is to develop high yielding
breeding lines. Breeders in the southeastern U.S. will take their
highest yielding breeding lines developed each year and test them
against the material of other breeders in the UT for at least one and
possibly multiple years to determine which lines are most elite to
possibly release as germplasm or a cultivar. At the very least, these
high yielding lines would most likely be recycled as parents in future
crosses by interested breeders. Lines chosen for the analysis were
present in the UT as early as 1994 and as recently as 2015, covering a
roughly twenty year timespan. N93-110-6 was not present in any of the
UT, but was still chosen to include because of it's elite pedigree and
the fact that this line was bred for superior yield, albeit under
specifically drought conditions (Devi 2014 reference). Forty-four of the
lines included in the analysis were bred at USDA Raleigh while the
remaining eight were bred at the University of Georgia (UGA). Seed for
the genotyping of these lines was obtained from their respective source
institutions. Not all of the lines in these trios needed to be procured
for growing out plants for genotyping because genotypic data was already
available from Soybase.~\\

\subsection*{RIL pedigree~and yield
analysis}\label{ril-pedigreeand-yield-analysis}

The UGA soybean breeding program developed five F5 derived RIL
populations with the original intention of developing lines for
germplasm release or eventual cultivar release. These populations
underwent a traditional breeding scheme which began with the initial
crosses, single pod descent to the F5 generation, single-plant
selections, and then single-plant-derived-row selections. Four of the
RIL populations (RIL-1, RIL-2, RIL-3, RIL-4) placed into yield testing
were comprised of 84 lines each. The fifth RIL population (RIL-5) was
derived from a more diverse cross and this population was composed of
150 lines. RIL-1 will refer to the population derived from a cross of
AUO2-3104 x G00-3213RR2Y. RIL-2 will refer to the population derived
from a cross of~G93-2225 x G09PR-54329RR2Y. RIL-3 will refer to the
population derived from a cross of G10PR-56248RR2Y x G10PR-56389RR2Y.
RIL-4 will refer to the population derived from a cross of G10PR-10 x
G10PR-56389RR2Y. RIL-5 will refer to the population derived from a cross
of G00-3213 x LG04-6000. The term trio can be used here as well to refer
to an individual RIL and both of the parental lines.~\\

\emph{Include pedigree information on these RIL populations}\\

\subsubsection*{\texorpdfstring{\textbf{RIL-1}\\
\textbf{RIL-2}\\
\textbf{RIL-3}\\
\textbf{RIL-4}\\
\textbf{RIL-5}}{RIL-1 RIL-2 RIL-3 RIL-4 RIL-5}}\label{ril-1-ril-2-ril-3-ril-4-ril-5}

\subsection*{NIL yield analysis}\label{nil-yield-analysis}

\subsubsection*{NIL-1}\label{nil-1}

The UGA soybean breeding program also developed two near isogenic
populations composed of near isogenic lines (NILs) segregating for the
YLD1 region for the purpose of yield testing. The first NIL population
(NIL-1) originating from a single breeding line from the USDA Raleigh
breeding program. This line was N01-11828 and was found to be
heterogeneous for the YLD1 locus. N01-11828 was derived from a cross of
`Graham' (PI594922) x N96-7031. `Graham'~is a MG V elite line developed
at USDA Raleigh that was released in 1997 (\hyperref[csl:22]{(Carter et al., 1997)}). `Graham'
was developed from a cross of N77-114 x `Pixie' (PI543856). N96-7031 is
an elite MG VIII line developed at USDA Raleigh. N96-7031 was developed
from a cross between `N7001' (PI615694) x N90-7241. `N7001' was
developed from a cross of N77-114 x PI416937. N90-7241 was derived from
a cross of `GaSoy17' (PI553046) x PI416937. Thus, by pedigree, N96-7031
was 50\% PI416937. F6 generation seed from N01-11828 were screened with
SSR markers linked to the YLD1 locus and found several individual~seed
which were heterozygous for the target QTL. Satt333 was an SSR marker we
used for genotyping of the YLD1 region. Satt333 was located from
39,911,032 to 39,911,097 bp on chromosome 8. This is 1.9 Mb away from
the SNP alleles we associated with the YLD1 region and thus we
considered this SSR in close linkage with the YLD1 region of interest in
the latter yield analysis of the YLD1 region. NILs were grown out from
these heterozygous seed and genotyped again at the YLD1 locus to
identify which lines inherited the PI416937 genomic region and which had
not. Twenty NILs were used for yield testing. The distribution for the
YLD1 locus was 15 lines contained the PI416937 allele and five lines
contained the `Graham' allele. We could have had an equal distribution
of the genotypes but there was a second allele, located on chromosome 6,
we were testing that needed to be relatively balanced as well.\\

\subsubsection*{NIL-2}\label{nil-2}

We developed a population of 150 near isogenic lines (NILs) in order to
test the region we referred to as YLD1 which was indicated as our
threshold region for significance in our pedigree analysis. We cross
`Boggs' (PI602597)~by `Woodruff' in order to create this NIL population.
`Boggs'~is a MG VI elite line developed at UGA that was~released in 1997
(\hyperref[csl:23]{(Boerma et al., 2000)}). `Boggs' was developed from a cross of G81-152 and
~'Coker 237' (PI556536). `Woodruff,' also referred to as G00-3209, is a
MG VII elite line developed at UGA that was patented in 2010
(\textbf{Boerma 2010}). `Woodruff' was developed from a cross of
~'N7001' (\hyperref[csl:24]{(Carter et al., 2003)}) and `Boggs.' `N7001' was developed form a
cross of N77-114 x PI416937. Thus, by pedigree, Woodruff was 25\%
PI416937. Satt333 was an SSR marker we used for genotyping of the YLD1
region. `Woodruff' and PI416937 had also been genotyped using the
SoySNP50k chip to ensure that this region of Woodruff containing YLD1
traced back to PI416937 and not `Boggs.' The development of the NIL
population was initiated from the crossing of `Boggs' x `Woodruff' in
the summer of 2010 at the University of Georgia Plant Sciences Farm near
Athens, GA. We obtained pollen from the 10 F1's that were true crosses
and backcrossed to `Boggs' in the UGA greenhouse during the winter of
2010-2011. We obtained three true BC1F1's from this first backcross of
F1's to `Boggs.' Satt333 was used to ensure all BC1F1's were
heterozygous at the YLD1 locus. In summer 2011, at the UGA Plant
Sciences Farm, we performed a second backcross to `Boggs' using pollen
from our BC1F1's. We ran satt333 and selected seven BC2F1's which were
heterozygous at the the YLD1 locus. These BC2F1's were selfed in the UGA
greenhouse facility during the 2011-2012 winter. During the summer of
2012, The BC2F2's were grown out at the UGA Plant Sciences Farm. We
obtained 441 BC2F2 plants and single plant threshed each plant to create
BC2F2:3's. During the 2012-2013 winter, these BC2F2:3's were sent to the
USDA winter nursery in Puerto Rico to be grown in individual BC2F2:3
plant rows. Two hundred and eighty rows were randomly selected for
genotyping. Leaf tissue from these rows was bulked by row and we ran
markers to see the distribution of the different YLD1 genotypes. We
selected 150 lines for yield testing that had a relatively equal
distribution of all three possible YLD1 genotypes. The distribution
broke down into 59 lines with the `Boggs' allele, 40 heterozygous, and
51 with the PI416937 allele based upon genotyping with Satt333. Similar
to the previous NIL population, we could have had an equal distribution
of the genotypes but there was a second allele, located on chromosome 6,
we were testing that needed to be relatively balanced as well.\\

\section*{Genotypic data}\label{genotypic-data}

\subsection*{PI416937 pedigree analysis and RIL pedigree
analysis}\label{pi416937-pedigree-analysis-and-ril-pedigree-analysis}

The USDA Soybean Germplasm Collection was genotyped using the SoySNP50K
iSelect BeadChips (). Genotypic data utilized in this manuscript was
obtained from Soybase on. Genotypic data for lines that were not present
in Soybase was procured courtesy of USDA Beltsivlle as well as Michigan
State University (MSU). These lines were also genotyped using the
SoySNP50K iSelect BeadChips. Due to discrepancies in SNPs between USDA
Beltsville and MSU, a final set of BLANK SNPs was established based upon
USDA Beltsivlle which had the fewer number of SNPs between the two
genotyping facilities. The five RIL populations were genotyped using the
SoySNP6K iSelect BeadChips courtesly of USDA Beltsville. The physical
distances of SNPs were originally based on genome assembly version
Glyma.Wm82.a1 (Gmax1.01) (Schmutz, 2010) but were converted to version
Glyma.Wm82.a2(Gmax2.0) for the analysis. SNPs which were mapped in
Gmax1.01 but not Gmax2.0, were excluded from the analysis.\\

DNA was isolated from RILs and lines included in pedigree based analysis
for which there was no previous genotypic data available. For each line,
20 seeds were planted in cups in a University of Georgia greenhouse
facility. Once seedling were three weeks old, tissue was bulk harvested
in 50ml falcon tubes for each line. Leaf tissue was lyophilized and
ground into fine powder. DNA was extracted using the 1:0.1 CTAB (cetyl
trim ethyl ammonium bromide): 2ME (betamercaptoethanol) method according
to (Keim et al. (1988)), with some modifications. Modifications were
made to improve purity of DNA which is required for successful
genotyping using the SoySNP50K and SoySNP6K iSelect BeadChips. Some key
modifications include adding ~Edwards Extraction Buffer, NaCl,
polyvinypyrrolidone, and proteinase k to the CTAB 2ME buffer. Another
modification was to add a second 24:1 chloroform:isoamyl alcohol step to
further remove proteins and polysaccharides. Isopropanol precipitatiton
was performed at negative 20 degrees Celsius to improve concentrations.
An additional 75\% ethanol wash was performed to remove excess salts
that fell out of solution during the isopropanol precipitation.~\\

\subsection*{\texorpdfstring{\textbf{PCA and phylogenetic tree}\\
\textbf{NIL marker
work}}{PCA and phylogenetic tree NIL marker work}}\label{pca-and-phylogenetic-tree-nil-marker-work}

\section*{Pedigree analysis to identify PI416937 regions under selection
in high yielding PI416937 derived lines and RIL
populations}\label{pedigree-analysis-to-identify-pi416937-regions-under-selection-in-high-yielding-pi416937-derived-lines-and-ril-populations}

This process was used for the pedigree analysis performed on the high
yielding PI416937 derived lines as well as the five RIL populations. In
this section, I will be referring to the high yielding PI416937 derived
lines and the RILs as progeny. The first step was to identify for each
progeny line, which alleles were inherited from which parent. To do
this, we took each line and began matching alleles to each parent. It
should be noted that for all genotypic data, SNPs were in order based
upon their physical map positions from Gmax2.0. No genetic maps were
made for the RIL populations so later we could have congruency with the
original analysis of the high yielding PI416937 derived lines. Our goal
was to calculate a matching score and whichever parent held a higher
score for a genomic region was called the parental source for that
region. Each matched allele was worth a point. If a locus in either the
parent or progeny line was heterozygous, this was called as half of a
point. Missing values were worth zero points. This matching continued
until the matching was broken by an opposite allele being present in the
parent. Then for a given matched segment, a score was calculated based
upon the quality of the match and the match with the higher score was
called the parent of origin. The parental match had to be at least two
markers longer in one parent to be considered definitively one parent
versus the other. In theory, a genomic region called for one parent
could be from a shorter consecutive~match because the longer match to
the other parent had an excess of heterozygous loci or missing data
points. We performed simulations to ensure this was an appropriate
methodology for parental genomic origin calling in the progeny. If a
given region had the same score in the two parents or the match was not
two markers longer in one parent, we were unable to definitively call
this region for either parent and it was called as ambiguous. Once the
parental regions were identified, we looked to identify the origin of
these regions with relation to PI416937 as well as to the predominant
ancestor lines of North American southern elite material according to
Vaughn et al. (\hyperref[csl:25]{(Vaughn and Li, 2016)}). We chose North American southern
ancestors versus all North American ancestors because the genotypes used
in our analysis were comprised predominantly of southern germplasm and
this also~lead to less ambiguity in identifying ancestor sources for the
genomic regions. We were now able to trace back the genomic origins of
our progeny lines to their parental sources and then to their original
ancestor sources. The regions we then became interested in were regions
in which one parent contained an allele from PI416937 at a a particular
locus and the other parent did not. This then became a test of this
PI416937 allele to determine if it had been transmitted to the progeny
during the breeding selection process. In some cases the ancestor source
of an allele in a parental line was identified as ambiguous but the most
likely sources, though similar to each other, were not similar to
PI416937 so this remained a test if the other parent contained an allele
at this locus definitively IBD with an allele from PI416937. To obtain
p-values for alleles under selection, we performed a two sided binomial
test in R (R Core Team, 2015). The number of successes was entered as
the number of times the PI416937 allele was inherited. The number of
trials was entered as the number of trios in which the PI416937 allele
was tested in the parents. The hypothesized probability of success was
entered as 0.5 because we expected that by chance, with no selection
pressure, the allele from PI416937 would be inherited 50\% of the time
it was tested in the parents. We set our significance threshold at 0.05
and performed a multiple test correction utilizing the Li and Ji
methodology to account for collinearity among linked
markers~(\hyperref[csl:26]{(Li and Ji, 2005)}). For the analysis utilizing the high
yielding PI416937 derived lines, we were unable to discover regions that
were statistically significant after the multiple testing correction so
we decided to focus on regions which were at least as significant as a
previously discovered genomic~region associated with yield from PI416937
referred to as Yld1 (David publication). For a majority of the RIL
populations we were able to find regions that were statistically
significant using the multiple test corrected threshold. Regions under
selection were defined as any run of consecutive markers surpassing our
chosen statistical significance threshold. There were several situations
in which markers showed varying levels of significance within a run of
consecutive markers. The markers with the highest levels of significance
within these runs of consecutive markers have the most evidence of
selection in a positive or negative direction. We also did not include
markers in our results that were not tested in at least 10 of our
trios.\\

\section*{Plant material
characterization}\label{plant-material-characterization}

\subsection*{\texorpdfstring{\textbf{PI416937 pedigree analysis}\\
\textbf{Pedigree tree (Helium)}\\
\textbf{Phylogenetic tree}\\
\textbf{GAPIT
PCA}}{PI416937 pedigree analysis Pedigree tree (Helium) Phylogenetic tree GAPIT PCA}}\label{pi416937-pedigree-analysis-pedigree-tree-helium-phylogenetic-tree-gapit-pca}

We traced the pedigrees of the high yielding PI416937 derived lines as
far back as was possible utilizing germplasm/cultivar releases, USDA
Southern States UT publications, and USDA GRIN. We used Helium to
display this information (\hyperref[csl:27]{(Shaw et al., 2014)}). ~We conducted PCA of the
trios used in the analysis along with North American ancestor lines and
a sample of relatively modern public and private soybean materials using
the GAPIT R package (\hyperref[csl:28]{(Lipka et al., 2012)}). The PCA was plotted using
TIBCO Spotfire (reference). We also constructed a distance matrix using
TASSEL v5.2.29 (\hyperref[csl:29]{(Bradbury et al., 2007)}). We used the neighbor joining
clustering method and then we created a circular phylogenetic tree using
FigTree v1.4.3 (reference).~\\

\section*{QTL identification}\label{qtl-identification}

\subsection*{PI416937 pedigree analysis and RIL pedigree
analysis}\label{pi416937-pedigree-analysis-and-ril-pedigree-analysis-1}

Once we identified genomic regions from PI416937 under favorable
selection, we examined the literature to see if any reported QTL from
mapping studies involving PI416937 had mapped QTL to the regions we
found in our analysis. There are several studies that have been
performed in the past involving PI416937 looking to map the following
traits: WUE (\hyperref[csl:30]{(Mian et al., 1996)}), aluminum tolerance
(\hyperref[csl:13]{(Bianchi-Hall et al., 2000)}), root morphology (\hyperref[csl:11]{(Abdel-Haleem et al., 2010)}), drought
tolerance (\hyperref[csl:31]{(Carpentieri-Pipolo et al., 2011)}), and canopy wilt (\hyperref[csl:6]{(Abdel-Haleem et al., 2012)}).
Using soybase, we identified the physical positions of the nearest
markers to the QTL of interest to estimate the QTL physical positions.
If the nearest marker was an SSR, we used the physical positions of the
primer sequences for SSR markers located 3' or 5' of the QTL in order to
estimate the physical positions of the QTL. If the nearest marker was a
SNP marker, the exact SNP position for the SNP located 3' or 5' of the
QTL was indicated. We then looked to see if any portion of these QTL
were located within regions we found to be under positive selection. We
determined it was not relevant to look for QTL mapped using PI416937
that were overlapping with PI416937 regions we found under negative
selection. These QTL mapping studies were designed to find favorable
genomic regions of PI416937, not the alternative parents. This being
said, several of these mapping studies found genomic regions from the
alternative parent to be favorable for a couple of these traits of
interest. For these cases, we looked to see if these QTL were in regions
under negative selection from PI416937, indicating that there is
something detrimental from PI416937 which was identified as unfavorable
in both a bi-parental QTL mapping study and our complex pedigree
analysis.~\\

\section*{Yield analysis}\label{yield-analysis}

\subsection*{\texorpdfstring{\textbf{RIL yield analysis}\\
\textbf{NIL yield
analysis}}{RIL yield analysis NIL yield analysis}}\label{ril-yield-analysis-nil-yield-analysis}

\subsubsection*{NIL-1}\label{nil-1-1}

The 20 NILs were yield tested across several different locations in GA
and NC over a period of~five years (2007-2011). The Georgia yield tests
were grown at UGA Plant Science Farm locations while the North Carolina
yield test were grown at North Carolina Agricultural Research Service
locations. During the summer of 2007, the NILs were yield tested in
Athens, GA; Plains, GA; and Caswell, NC. In 2008-2010, yield testing
occurred in the same three location with the addition of Plymouth, NC.
In 2011, yield testing occurred only in Athens, GA and Plains, GA. In
addition to the 20 NILs, elite checks were grown in the yield trials.
Three blocks of each line were grown in a randomized complete block
design in 2007 while four blocks were grown in 2008-2011. The elite
checks varied somewhat from year to year. In 2007, two replications of
`Boggs' and two replications of `Benning' were grown per block. An
additional elite check, `Vance,' was grown per block~in Caswell, NC. In
2008 and 2011, two replications of both `Boggs' and `Benning' were grown
at each location in each block. In 2009-2010, two replications of
`Boggs' and `Benning' were grown in each block. Yield tests from
2009-2010 in Caswell, NC and Plymouth, NC were tested with elite check,
`N7103' as well. Yield trials at the Georgia locations were grown in~two
row plots which were 4.9 m long and later end trimmed to 3.7 m near the
end of the vegetative stage. Rows were planted 76 cm apart and were
harvested for yield evaluation. For North Carolina locations, lines were
planted in three row plots which were 6.1 m long and row spacing was 97
cm between rows. These plots were end trimmed to 4.6 m near the
completion of the vegetative growth stage. Only the middle row was
harvested for yield evaluation. Various traits we measured were seed
yield, plant height, lodging score, seed size, seed protein content,
seed oil content, and maturity date.\\

Elite checks which were in the experimental design for the yield test,
were not genotyped for the yield analysis because we only wanted to
examine the yield differences among NILs for YLD1. We initially ran a
mixed model with YLD1, location, year, YLD1*location, and YLD1*year as
fixed effects and blocks within location*year as a random effect to
determine if there was significant GxE.\\

\subsubsection*{NIL-2}\label{nil-2-1}

~The BC2F2:4 plant rows of these 150 lines were yield tested at the UGA
Plant Sciences Farms in Athens, GA and Plains, GA in the summer of 2013.
These 150 lines were broken up into three equal sets of fifty based upon
maturity date. Each set also contained two replications of `Boggs' and
~two replications of `Woodruff.' There were two blocks of the experiment
per location set up in a randomized complete block design. These yield
trials were grown in two row plots which were 4.9 m long and later end
trimmed to 3.7 m near the end of the vegetative stage. Rows were planted
76 cm apart and were harvested for yield evaluation. Various traits we
measured were seed yield, plant height, lodging score, seed size, seed
protein content, seed oil content, and maturity date. Seed yield, plant
height, lodging score, seed size, seed protein content, and seed oil
content was measured for all blocks at each location. Maturity dates
were measured only at the Athens location.\\

Parental lines which were in in the experimental design for the yield
test, were not genotyped for the yield analysis because we only wanted
to examine the yield differences among NILs for YLD1. We initially ran a
mixed model with YLD1, location, and YLD1*location as fixed effects and
blocks nested within location as a random effect to determine if there
was GxE. ~Since there was significant evidence of GxE we ran the
location separately to see what the effect of YLD1 was within each
location. The models for the individual locations had YLD1 as a fixed
effect, while block was a~random effect. For each location, we then ran
a Tukey HSD multiple means comparison test to identify which groups
statistically were higher yielding within each location. For the two
individual locations, we performed the same analysis for plant height,
lodging score, seed size, seed protein content, seed oil content, and
maturity date.~\\

\section*{Comparison of PI416937 regions under selection with regions of
low diversity in North American elite breeding
lines}\label{comparison-of-pi416937-regions-under-selection-with-regions-of-low-diversity-in-north-american-elite-breeding-lines}\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Pedigree-ancestor-table/Pedigree-ancestor-table}
\caption{{\hypertarget{auto-label-caption-941479}{}
Table of PI416937 derived trios used in the pedigree analysis. Red
indicates line is derived from PI416937. ~ ~ ~%
}}
\end{center}
\end{figure}

\section*{RESULTS}\label{results}

\section*{Regions appear to be under both positive and negative selection
based upon pedigree analysis of high yielding PI416937 derived
lines}\label{auto-label-section-593128}

Using the 52 trios we assembled we identified several regions under both
positive and negative selection that can be traced back to PI416937. No
regions were found to be statistically significantly under selection
after performing a multiple testing correction (\hyperref[csl:26]{(Li and Ji, 2005)}).
Eickholt et al. (unpublished) identified a region on chromosome 8
significantly associated with a positive yield gain in a segregating RIL
population after performing replicated yield testing. This region was
referred to as YLD1 in Eickholt et al. (unpublished) and will be
referred to likewise in this publication. YLD1 was identified as our
eighth most significant region in terms of positive selection as it was
tested in 41 trios and inherited to 28 high yielding progeny (p-value:
0.028). Any markers that met this level of statistical significance or
greater, were determined by us to be regions with evidence of selection
for or against. In total, we identified 9 genomic regions across 3
chromosomes under positive selection and 17 genomic regions across 7
chromosomes under negative selection across from PI416937 (Figure for
all chromosomes).\\

In terms of the regions under positive selection, these ranged from a
single marker to 76 markers. Regions were found on chromosomes 8 (2
regions), 13 (3 regions), and 17 (4 regions). The physical distance of
the largest region under positive selection was 1,450,940 bp on
chromosome 17. This regions was located between 796,471 bp and 1,309,414
bp. There were two separate genomic regions on chromosome 17 with the
greatest evidence of positive selection (p-value: 0.002). The first
region was~ a 9,223 bp region located within a larger significant region
which was 206,570 bp. This peak region within this larger region was
located between 2,409,261 bp and 2,418,484 bp. The second genomic region
on chromosome 17 with the greatest evidence of positive selection was
985,307 bp long and located between 2,510,699 bp and 3,496,006 bp. There
was a genomic region with equal evidence of positive selection (p-value:
0.002) located on chromosome 13. This was a 99,528 bp region located
between 26,986,028 bp and 27,085,556 bp.~\\

In terms of regions under negative selection, these regions ranged from
a single marker to 137 markers. Regions were found on chromosomes 5 (2
regions), 8 (2 regions), 9 (2 regions), 12 (2 regions), 13 (6 regions),
16 (1 region), 17 (1 region), and 19 (1 region). The physical distance
of the largest region under negative selection was 4,011,395 bp and
located on chromosome 12. This region was located between 17,662,053 bp
and 21,673,448 bp. There was a peak region with higher statistical
significance within this larger region. This peak region was 2,748,460
bp long and located between 17,662,053 bp and 20,410,513 bp. The genomic
region with the greatest evidence of negative selection (p-value: 0.002)
was 82,263 bp long and~located on chromosome 13 between 30,683,322 bp
and 30,765,585 bp.\\

\section*{\texorpdfstring{\\
\textbf{Overlap between QTL mapping studies involving PI416937 and our
pedigree
analysis}}{ Overlap between QTL mapping studies involving PI416937 and our pedigree analysis}}\label{auto-label-section-522659}

We looked for QTL that had been discovered in mapping studies involving
PI416937 to see if there was overlap with favorable alleles from these
mapping studies and regions we discovered under favorable selection. One
region where there was overlap was with a QTL referred to as canopy wilt
2-6 which is located on chromosome 17 (\hyperref[csl:6]{(Abdel-Haleem et al., 2012)}). Abdel Haleem
et al. (2012) conducted the original QTL mapping study from RILs derived
from a cross between PI416937 and Benning in order to map genetic loci
associated with the canopy-wilting trait. PI416937 has been shown in
previous literature to exhibit slow wilting when undergoing drought
stress (\hyperref[csl:14]{(Sloane et al., 1990)}). This is thought to be a beneficial trait
for surviving prolonged droughts through limited transpiration at high
vapor pressure deficit (VPD), allowing for conservation of soil moisture
(\hyperref[csl:32]{(Tanaka et al., 2010)}).~Canopy wilt 2-6 was mapped between 2,201,427 bp
and 5,892,120 bp. Our pedigree analysis revealed three favorable genomic
regions from PI416937 that overlapped with this QTL. The first region is
a single marker located at 2,202,411 bp. The second region is located
between 2,246,668 bp and 2,453,238 bp. The third region is located
between 2,510,699 bp and 3,496,006 bp. The peak region with highest
significance was located between 2,409,261 bp and 2,418,484 bp. It is
important to note that for canopy wilt 2-6, Abdel Haleem found the
favorable allele for slow wilting to be inherited from Benning, not
PI416937. This may indicate that there is favorable genetic material
from PI416937 in this genomic region, but these favorable alleles are
not conferring slow wilting under drought stress. Canopy wilt 2-6 was
larger than the region we discovered under positive selection so it may
be the case that the unfavorable slow wilting allele from PI416937 is
located within the portion of the QTL mapped region which was not under
significant positive selection in our pedigree analysis. Though PI416937
was found to be the unfavorable allele relative to Benning for canopy
wilt 2-6, this may not be the case for PI416937 compared to other
parental lines in our pedigree analysis, hence we obtained evidence for
positive selection of this region in our analysis.\\

Advanced yield trials conducted by public breeders tend to be managed
more intensively to reduce stressors such as drought. Many of the QTL
mapped from PI416937 have been conducted to examine tolerance to drought
related conditions so it may not be a surprise that QTL mapped from
crosses involving PI416937 do not heavily overlap with regions we found
associated with yield. Also, we are looking to identify regions that are
under selection across diverse environments in predominantly North
Carolina and Georgia as well as across diverse genetic backgrounds from
these two breeding programs. These mapping studies may be identifying
QTL which are more environment or population specific.~\\

\section*{Pedigree analysis of chromosome 8 and subsequent yield analysis
of NILs for YLD1}\label{auto-label-section-828390}

For the mixed model of the combined environments, there was a
statistically significant difference in YLD1 (p-value: 0.0418) for yield
but there also appeared to be significant GxE (p-value: 0.0027). Due to
this observation, the analysis was broken up by location, to see the
effect of YLD1 for each environment. For Athens, we saw a statistically
significant difference for YLD1 in terms of yield. The NILs containing
the PI416937 allele had a least squares means estimate of 4361 kg/ha
compared to NILs containing the Boggs allele which had a least squares
mean estimate of 4057 kg/ha. This difference in yield was statistically
significantly different at an alpha of 0.05 . The NILs heterozygous at
the YLD1 loci had a least squares means estimate of 4372 kg/ha. This was
statistically significantly greater than the NILs containing the Boggs
allele as well, but there was no significant difference from the NILs
containing the PI416937 allele. NILs homozygous for the PI416937 allele
matured four days later than lines homozygous for the `Boggs' allele
which was statistically significant. The heterozygous lines matured
three day later than the lines homozygous for the PI416937 allele which
was also statistically significant. This difference in maturity date may
be explaining some of the differences in yield as there has been shown
in the literature to be a relationship between maturity and seed yield
for soybean (\hyperref[csl:33]{(Curtis et al., 2000)}). We saw no overlap between mapped
maturity related genes (\emph{E1-E4, E7}) and the YLD1 locus
(\hyperref[csl:34]{(Molnar et al., 2003)}). We also scanned Soybase () for maturity QTL that
have been mapped in this region in other studies. We found three
maturity related QTL mapped to chromosome 8. These QTL were pod maturity
13-1 (\textbf{Specht 2001}), pod maturity beginning 1-3 (\textbf{Tasma
2001}), and pod maturity 22-1 (\textbf{Reinprecht 2006}). These three
QTL were not found to be overlapping with the YLD1 locus but pod
maturity 13-1 and pod maturity beginning 1-3 were approximately 5.6 Mb
and 4.3 Mb downstream according to nearest sequence-based genetic
markers associated with these QTL. When analyzing the data from Plains,
we no longer saw a statistical difference in yield when comparing the
three genotypic classes for YLD1. No maturity data was taken in Plains
so we could not do a maturity date comparison. This indicates a possible
genotype by environment interaction where the PI416937 is exhibited a
yield benefit in Athens that is not being seen in Plains. There was no
significant G x E for plant height so we combined locations in this
analysis, NILs homozygous for the PI416937 allele were 4 cm taller than
lines homozygous for the `Boggs' allele and 1 cm shorter than lines that
were heterozygous. These differences in height were not statistically
significant nor seemed biologically relevant.\\

The YLD1 region was such a finely defined region (3.7 kb) in our
pedigree analysis, that we decided to see what gene models were present
using soybase. There was a single gene model present for Glyma.08g299800
which is a paralog to ATG24090.1, a chitinase A found in arabidopsis.
Glyma.08g299800 is located from 41,795,912 bp to 41,796,546 bp which
partially overlaps with the YLD1 region located from 41,792,467 bp to
41,796,167 bp. Chitinases are commonly associated with plant defense
against fungal pathogens or insects as chitin is a common component of
fungal cell walls and insect exoskeletons (\hyperref[csl:35]{(Sharma et al., 2011)}). There
are several QTL for various different traits that have been mapped to
this region, but one interesting one related to fungal resistance was
sclero 9-2 which is located from 39,910,959 bp to 44,689,972 bp
according to nearest sequence-based genetic markers in soybase
(\hyperref[csl:36]{(Guo et al., 2008)}). Sclero 9-2 was mapped from a cross of PI 391589B
by IA2053. The goal of this cross was to map resistance
of~\emph{Sclerotinia sclerotiorum}. The favorable allele for this QTL
was inherited from IA2053 which was the moderately susceptible parent in
the cross. It would be useful in the future to compare the YLD1
haplotypes of PI416937 and IA2053 for homology, indicating potential for
shared fungal resistance due to this genomic region. We have thus far
been unsuccessful in obtaining genomic data on IA2053 to perform said
analysis.\\

Due to the climate conditions of the southeastern U.S., it is~reasonable
to assume that fungal pressure is a constant concern. In Georgia, from
2005-2013, an average of \$2.2 million was spent annually on plant
disease control from biotic stressors (). ~If the PI416937 allele at
YLD1 is providing moderate resistance to fungal pathogens, it makes
sense why it would show an association with yield in our pedigree
analysis of southern U.S. breeding lines. Due to an unusually cool and
wet growing season, 2013 was a year in which farmers spent \$4.35
million on plant disease control. This was~nearly double the average
from 2005-2013. Due to the expectation of high disease pressure, the NIL
yield trial had two fungicide applications applied in both Athens and
Plains. Domark 230 ME (active ingredient: tetracomozole) was applied at
5 fl. oz. per acre the week of of July 29th, 2013 and September 2nd,
2013. The original purpose of yield testing the NILs was to observe if
there were yield differences under ideal conditions so allowing these
trials to experience disease pressure was not a consideration. That
being said, we saw a significant difference in terms of yield in Athens
so it's possible disease pressure was high enough due to an especially
ideal year for disease pressure to have an impact, even with two
fungicide applications. We saw this significant difference in yield in
Athens but not Plains. This could be a product of differing levels of
disease pressure in the two locations. The association of YLD1 with
fungal pathogen resistance needs to be verified in further
experiments.~\\

\section*{Comparison of PI416937 regions under selection with regions of
low diversity in North American elite breeding
lines}\label{auto-label-section-873619}

Regions of the soybean genome have suffered reduced diversity~\\

\section*{DISCUSSION}\label{auto-label-section-438080}

The conclusion should reinforce the major claims or interpretation in a
way that is not mere summary. The writer should try to indicate the
significance of the major claim/interpretation beyond the scope of the
paper but within the parameters of the field. The writer might also
present complications the study illustrates or suggest further research
the study indicates is necessary.

\selectlanguage{english}
\FloatBarrier
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