Download genetic mapping and dna sequencing of the loblolly pine genome

GENETIC MAPPING AND DNA SEQUENCING OF THE LOBLOLLY PINE
GENOME
David B. Neale, Claire S. Kinlaw & Mitchell M. Sewell
Institute of Forest Genetics, Pacific Southwest Research Station, USDA Forest Service, Albany and Placerville,
California, USA
Received April 1, 1995; accepted Augustll, 1995
ABSTRACT
We are constructing genetic maps and sequencing genes in loblolly pine (Pinus taeda L.) to gain a deeper
understanding of the organization and evolution of pine genomes. Two genetic maps were constructed using
restriction fragment length polymorphism (RFLP) markers using complementary DNA (cDNA) probes from
loblolly pine. Three generation outbred pedigrees were used for segregation and linkage analysis. One map was
used to identify five major quantitative trait loci controlling wood specific gravity. A large number of RFLP
markers were positioned on both maps, thus it was possible to merge the two independent maps to form a
consensus map. All (200+) cDNA probes used for mapping were partially sequenced. The DNA sequences were
compared to databases which resulted in the gene identification of a large (>30%) proportion of the mapped
RFLP markers. The most interesting result from our mapping and sequencing activities is the extraordinarily
large gene families that were found in loblolly pine.Gene families that exists in just few copies in angiosperms
were found to have 10, 50, or even 100 copies in loblolly pine. The map positions of members of gene families
suggests that gene amplification more likely occurred by a mechanism such as retrotransposition than by
polyploidization. Our current focus is to use cDNA mapping. QTL mapping, and cDNA sequencing to identify
the genes which determine wood properties in loblolly pine.
Key words: genetic mapping, gene sequencing, gene families, loblolly pine
INTRODUCTION
Genetic mapping and D N A sequencing are the central
experimental approaches to all genome research
programs. A genetic map is the fundamental organizational tool for genome researc. Maps show the
chromosomal location of genes as well as provide a
numerical accounting of genes. Once a map is constructed it serves as the basis for isolating and
identifying new genes henceforth. At a higher resolution
view of the genome, D N A sequencing of genes
provides the basic informtion about gene structure and
Sunction. Genetic maps and gene sequencies contribute
much mor knowledge about genomes than serving
solely as an organizational tool for basic information.
They provide insight into chromosome and gene
evolution and can also contribute towards understanding how genes are expressed. Genetic mapping
and gene sequencing are utilized throughout human and
agricultural genome research. Mapping and sequencing
has led to the discovery of scores of genes from
humans, many of which code for the worst human
diseases (Science 1992, vol. 258, pp. 1-188). In
agriculture, mapping and sequencing have identified
numerous genes of agronomic importance. such as those
coding for resistancc of pathogens (STASKAWICZet d.
0 A R B O R A PUBLISHERS
1995) and those influencing crop yield (TANKSLEY
1993).
Genetic mapping and gene sequencing have more
recently been applied to forest trees. Genetic maps have
been constructed for many species (Table l ) , with a
variety of goals in mind. The most important applications of tree genetic maps are: (1) basic knowledge of
genome organization and evolution, (2) localization of
simply inherited and polygenic traits for marker-aided
breeding, and (3) studies of genetic diversity. Gene
sequencing has been applied in forestry to a much lesser
extent. The Genbank DNA sequence database lists only
134 accessions from conifers and just 2 0 from loblolly
pine (Table 2j, whereas there are 2 16,150 from humans,
18.479 from Arabidopsis tlzaliana, 12,05 1 from rice
and 1,892 from corn. The main reason for the lack of
D N A sequence information from trees is the small
number of researchers in this field, but also is a function
of the difficulties associated with working with the large
genomes of trees.
Loblolly pine (Pinus taeda L.) is a member of the
very large (> 100 species) and important genus Pinus.
Loblolly pine is a native to the southeastern United
States and is the most commercially important forest
tree in this region. Because of its commercial value,
loblolly pine is the primary species of several large
D. B. NEALEETAL. :GENETICMAPPING AND DNA SEQUENCING OF THE LOBLOLLY PINE GENOME
Table 1 Genetic maps of forest trees
Genus
Species
Marker types
References
Pinus
brutia
elliottii
lambertiana
palustris
pinaster
taeda
RAPD
RAPD
RAPD
RAPD
RAPD
RFLP, isozyme
RFLP, isozyme
KAYAet al. 1995
NELSONet al. 1993
DEVEYet al. 1995
NELSONe t a / . 1994
et al. 1995a, 1995b
PLOMION
DEVEYet al. 1994
GROOVER
et a/. 1994
Picea
abies
glauca
RAPD
RAPD
Tax~ls
Cryptomeria
japonica
Eucalyptus
RAPD
GOCMEN
et al. 1995
RFLP, RAPD, isozyme
MUKAIet al. 1995
RAPD
& SEDEROFF
1994
GRATTAPAGLIA
et al. 1995
GRATTAPAGLIA
& SEDEROFF
1994
GRATTAPAGLIA
et al. 1995
GRATTAPAGLIA
BYRNEi. 1995
RAPD
RFLP, RAPD, isozyme
Pop~lLls
trichocarpa x deltoides
RFLP, RAPD, STS
RFLP, isozyme
et al. 1994,
BRADSHAW
& STETTLER
1995
BRAD~HAW
1993
LIU& FURNIER
Table 2 Numbers of DNA sequences reported in the GenBank and dbEST databases for a few representative species
Species
Homo sapiens (human)
Arabidopsis thaliana
Olyza sativa (rice)
Zea mays (corn)
Conifers
P i w s taeda (loblolly pine)
GenBank
dbEST
216,150
18,479
12,051
1,892
134
20
218,295
18,484
10,990
1,145
0
0
breeding programs and is also the subject of much basic
genetic research. Loblolly pine is also one of the very
few pines for which multigeneration pedigrees have
been developed. For these reasons, loblolly pine is the
most appropriate pine for a large genetic mapping and
gene sequencing project. There are, however, some
technical challenges with mapping and sequencing in
pine; the most significant problem being the size of the
genome. WAKAMIYAet al. (1993) estimated the C-value
for loblolly pine to be 21-22 pg. This makes the
loblolly pine genome at least three times larger than
corn and 100 times larger than Arabidopsis. Restriction
fragment length polymorphism (RFLP) analysis and
gene iso-lation are significantly more difficult with
large genomes. Pines are diploid ( 2 n = 2 4 ) and the
estimated total number of map units in the genome is
2,000-3,000 cM, thus linkagc mapping is no more
difficult than in most crops.
The goal of this paper is to review the research on
genetic mapping and gene sequencing on loblolly pine
conducted at the Institute of Forest Genetics during the
last five years. The review is not meant to be comprehensive for loblolly pine; much has been published by
S E D E R Ocoworkers
~ ~ ~ at North Carolina State University, NEWTONand coworkers at Texas A&M University, and DAVIS and coworkers at the University of
Florida to name just a few. The underlying goal of our
genome research in loblolly pine is to better understand
the organization and evolution of pine genomes through
genetic mapping and gene sequencing. We will summarize: (1) the construction of two RFLP maps, ( 2 ) the
mapping of quantitative trait loci (QTL), (3) the
construction of a consensus map, (4) the DNA
sequencing of mapped complementary DNA (cDNA)
clones, and (5) the organization of the gene families
within the loblolly pine genome.
GENETIC MAPPING
The first genetic maps for loblolly pine were
constructed using isozyme genetic markers (ADAMS&
JOLY 1980; CONKLE 1981). These early efforts
established the linkage relationships among the small
number of isozyme markers available for assay, but
wcre too few in number to yield a genetic map to
represent the twelve pair of chromosomes in loblolly
pine. It was not until the advent of DNA-based markers
such as RFLPs and RAPDs (random amplified
polymorphic DNA) that genetic maps representative of
the entire genome could be constructed. We began our
efforts to construct a detailed genetic map for loblolly
pine in the late 1980s using RFLP markers (NEALE&
WLLLIAMS1991; DEVEYet al. 1991). At this time
RFLPs were the "state-of-art" marker. PCR-based
markers had not yet been developed. RFLP probes
derived from cDNA and genomic DNA were evaluated
for use in loblolly pine. It was determined that as high,
or higher, levels of polymorphism could be detected
with cDNA probes than with genomic DNA probes
(DEVEYet nl. 1991). Fur-thermore, cDNAs are the
product of expressed genes which can be sequenced and
potentially identified, thus we determined that mapping
cDNAs would best meet our reasons for constructing
maps.
The second important consideration was the type of
mapping population and pedigree structure to employ.
The isozyme linkage studies in loblolly pine and most
other conifers were based on segregating haploid
megagametophytes from open-pollinated seed trees
(ADAMS& JOLY 1980; CONKLE1981). This type of
mapping population is highly informative for linkage
analysis and easy to obtain. Unfortunately, megagametophytes could not be used with RFLP technology
because sufficient quantitities of DNA could not be
isolated from single megagametophytes. In addition,
pedigree structures involving inbreeding, such as F,s or
backcrosses, were generally not available. Thus, it was
decided that the best pedigree structure for mapping in
loblolly pine would be multigeneration outbred
pedigrees, just as are used in human genetic mapping.
The important difference with trees was, of course, that
large full-sib families could easily be obtained thus
enabling linkage analysis from a single family and
avoiding the statistical problems of combining data over
multiple families as is done in human genetics. A simple two-generation pedigree (single-pair mating) would
have been sufficient for linkage mapping, but because
O ARBORA PUBLISHERS
of the added information on linkage phase from the
grandparent generation, we elected to use three
generation pedigrees. The RFLP phenotype of the
grandparents has also been valuable for making the
correct genetic interpretations from the complex RFLP
patterns seen in loblolly pine (DEVEYet al. 1991).
Our first RFLP linkage map for loblolly pine was
completed in 1993 (DEVEYet al. 1994). The map was
based on the segregation data from 95 progeny of a
single, three generation outbred pedigree. Seventy-three
RFLP loci and two isozyme loci were positioned onto
20 linkage groups. The map was constructed with the
linkage program GMENDEL 2.0 (LIU& KNAPP1990).
This program estimates linkages from phase-uknown
data, although the segregation data must be reduced to
two alleles per locus. The map we constructed was
based on the joint segregation of alleles from both
parents; no attempt was made to construct maternal- and
paternal-specific maps. This map gave us our first look
at the distribution of gene families in a pine genome.
We had observed carly on that loblolly pine gene
families appeared inordinately large based on the RFLP
patterns (DEVEY et al. 1991). It is not possible,
however, to tell from RFLP patterns how many
members of these large gene families are in fact
functional gene copies. Some duplicated gene families
were tightly-linked whereas others were dispersed
throughout the genome. No single or simple mechanism(~)for this high level of gene amplification was
apparent at this time.
QUANTITATIVE TRAIT LOCUS MAPPING
Our second RFLP map for loblolly pine was also based
on a three-generation pedigree (GROOVER
et al. 1994).
This pedigree was selected to be used for mapping
&
QTLs for wood specific gravity (WSG) (WILLIAMS
NEALE1992). The strategy was to select grandparentpairs with high and low WSG values such that WSG
QTLs would be segregating in the progeny population.
Statistically independent maps were constructed for
both the maternal and paternal parent of the cross using
the linkage program JoinMap 1.4 (STAM1993). This
program was initially designed to be used for integrating genetic maps but can also be used for creating
single maps. Like GMENDEL 2.0, Join Map 1.4
estimates linkages from phase-unknown data but differs
in the algorithm used to order loci on linkage groups.
The maternal map included 87 loci positioned onto 17
linkage groups and the paternal map had 75 loci on 23
linkage groups. There were 26 loci that were mapped to
both the maternal and paternal map. It will be discussed
later how these markers are used to integrate the
independent maps into a consensus map. Once the maps
were constructed an ANOVA was performed to detect
D. B. NEALEETAL.: GENETICMAPPING AND DNA SEQUENCING OF THE LOBLOLLY PINE GENOME
the presence of WSG QTLs. Five major QTLs on five
different linkage groups were identified from this
analysis. Because some of the RFLP loci linked to
QTLs were segregating for three or four alleles (i.e,. a
fully informative marker), it could be shown that some
of the QTLs were also segregating for more than two
alleles among the parents. In addition, two of the QTLs
showed a genotype x environment interaction but there
was no evidence found for digenic epistasis among
QTLs.
The identification of QTLs in experimental populations is the first step towards applying marker-aided
breeding. In most tree breeding programs, however,
select trees in breeding populations are highly heterozygous. Thus it becomes necessary to have genetic markers which are tightly-linked in coupling to favorable
alleles at quantitative trait loci. Marker selection will
also be enhanced if pairs of tightly-linked markers flanking the QTL can be identified. For these reasons, we
have developed a strategy to identify additional tightlylinked markers flanking the pre-viously identified WSG
QTLs (KIEHNE et al. 1995). Our approach is an
et al. (1991) to
extension of the idea of GIOVANNONI
find additional markers linked to a previously mapped
RFLP marker. We constructed four pooled DNA
samples based on the four non-recombinant genotypic
classes in the progeny from a mating of two parents
with a fully informative RFLP marker linked to the
QTL. The pooled DNA samples were screened with
large numbers of RAPD primers. It was shown that the
profile of presence or absence of RAPD bands among
the four pooled samples is predictive of the zygosity
and linkage phase of the RAPD locus linked to the
RFLP locus. KIEHNEet al. (1995) used this approach to
quickly identify nine new RAPD loci linked to one of
et al. (1994).
the WSG QTLs reported by GROOVER
CONSENSUS MAP CONSTRUCTION
Genetic maps are constructed from segregation data
from individual mapping populations and are thus
specific to that population. However, different populations segregate at different marker loci and also at
different QTLs. An understanding of the organization of
all genes within the genome can only be obtained if all
mapping information can be synthesized into a single
consensus map for a species. Consensus maps are
artificial constructions and the position of genes reflects
a species average, but are nevertheless useful for
summarizing information and providing greater insight
into genome organization and evolution. For these
reasons we have recently constructed a consensus map
for loblolly pine from our two existing RFLP maps
(SEWELL& NEALE 1995). Four independent maps, one
for the maternal parent and one for the paternal parent
from both pedigrees, were merged using JoinMap 1.4.
An example of the merging of a single linkage group
from the four maps is shown in Fig. 1. There are markers that mapped on both the maternal and paternal
linkage group of one but not both pedigrees. These are
designated as fully informative markers and are used to
join maternal and paternal linkage groups; examples
included 2536a (QTL map) and 2564c (Base map).
Other markers map to all four linkage groups and serve
to integrate maternal and paternal maps and also maps
from the two pedigrees; examples include 2885b and
2538b. There are other markers such as 1626a and
2986a that serve to link together smaller linkage groups
that had previously been unlinked in one pedigree. This
example demonstrates the utility of fully informative
markers for integrating genetic maps and also shows the
value of using common genetic markers in constructing
maps from unrelated pedigrees. Without such markers
map integration would not be possible and the synthesis
of all genetic mapping information could not be achieved.
GENE SEQUENCING
Forest molecular geneticists have been using traditional
approaches to cloning and sequencing of genes from
trees, including loblolly pine (Table 2). Approaches
have included: (1) screening DNA libraries from trees
with DNA or antibody probes from genes of other
plants, (2) purifying proteins from trees and constructing DNA probes after determining the amino acid
sequence of a portion of the protein, or (3) cloning
DNA fragments from trees using PCR primers of
conserved domains of genes sequenced from other
plants. The approaches have succesfully led to the
cloning and sequencing of genes of special interest but
are very time consuming and laborious. The Human Genome Project has led to an alternative approach to
identifying genes based on automated sequencing of
large numbers of anonymous cDNAs. This approach
was first employed by ADAMSet al. (1991, 1992) to
sequence nearly 3000 cDNAs from human brain. Highthroughput anonymous cDNA sequencing has more
recently been applied to a few plant species, including
Arabidopsis ( H o r n et al. 1993; NEWMANet al. 1994)
et al. 1992), corn (KEITHet al. 1993),
rice (UCHIMIYA
and Brassica (PARKet al. 1993). The number of DNA
sequences reported in the dbEST (database Expressed
Sequence Tags) database is growing rapidly (Table 2).
As of July 1995, however, there were no sequences
from trees reported in dbEST.
We initiated a cDNA sequencing project at the
Institute of Forest Genetics in 1993. We began by
sequencing all of the cDNA probes (approximately 200)
D. B. NEALEETAL.: GENETICMAPPING AND DNA SEQUENCING OF THE LOBLOLLY PINE GENOME
that had been used to construct our two RFLP maps.
The cDNAs were isolated from a library prepared from
mRNA isolated from roots and shoots of 12-day-old
loblolly pine seedlings (DEVEY et a/. 1991). The
cDNAs were sequenced manually in the laboratory of
Dr. Chris Baysdorfer at California State University at
Hayward. The cDNA sequences were compared to
sequences in NCBI (National Center for Biotechnology
Information) databases. Approximately 30% of the
loblolly pine cDNAs were assigned tentative identities
based on these comparisons. Most of the genes
identified are involved with some aspect of cellular
metabolism as would be expected from the tissue from
which the library was constructed. These cDNA sequences will soon be submitted to dbEST.
More recently we have expanded the scope of our
cDNA sequencing efforts. cDNA libraries have been
constructed from more highly differentiated tissues,
including several stages of developing xylem, phloem,
seedling roots, expanding vegetative buds, pollen
strobili, and seed-cone strobili. We will obtain sequences of several hundred to a thousand cDNAs from each
of these libraries. It is expected that the sequence data
obtained from these cDNAs will provide preliminary
insight into the types of genes which are expressed in
these tissues and some estimate of the levels of their
expression. We intend to map as many of these
sequenced cDNAs to our existing maps as possible.
GENOME ORGANIZATION IN PINE
In the absence of genetic maps for pines it has been
difficult to learn much about the organization of pine
genomes. The RFLP maps we have constructed for
loblolly pine are based primarily on mapping of
cDNAs, hence these maps do provide insight into the
organization of structural gene loci in the pine genome.
Even before constructing maps, it was clear from the
large numbers of bands revealed from Southern blots
that many gene families in pine are significantly larger
than their counterparts in angiosperms. The lipid
transfer protein gene family, for example, has at least 14
members in loblolly pine in contrast to the simplicity
reported for this gene family in other plants (KINLAWet
al. 1994). We have since identified many other large
gene families in loblolly pine (Fig 2). All of these gene
families exist in 10 or many more copies in loblolly
pine. Large gene families are not unique to loblolly
pine; AHUJAet al. (1994) found that other pines as well
as other conifers also had many large gene families.
However, the sizes of families varied considerably
among species suggesting that gene amplification is
ongoing in conifers.
Two compelling questions arise from the discovery
of these large gene families in pine: (1) What is the
mechanism(s) which created the large gene families ?
and (2) How many members of these gene families are
functional? Two possible mechanisms for gene
amplification are (1) polyploidization and (2) retrotransposition. All conifers are diploid, with the
exception of redwood (Sequoia senzpewirens (D. Don)
Endl.) which is hexaploid. There is no cytogenetic
evidence that polyploidization has occurred in pines but
it can not be ruled out that it once occurred during the
evolution of pines and that all extant species are ancient
polyploids. Amplification due to retrotransposition
seems more plausible. Earlier, we discovered a retrotransposable element in pine called IFG (KOSSACK
1989). This element exists in at least 100,000 copies in
the pine genome. It is possible that this element has also
been responsible for amplification of structural genes by
reverse transcription of mRNA templates and integration into new regions of the genome. KVARNHEDEN
et a / . (1995) recently showed that pseudogenes of the
cdc2 gene family in spruce exist and that the structure
of these pseudogenes suggests an origin by retrotransposition.
We have been able to map multiple members of
many gene families. Our ability to map gene family
members is strictly a function of whether or not a
genetic locus segregates in one or both of our mapping
populations. Therefore, we have observed many large
gene families where we have mapped just one or two
members because the loci coding for the other members
do not segregate. Nevertheless, we have mapped two or
more loci from 39 of 84 (4870) of our cDNA mapping
probes. A few gene families are linked on a single
linkage group, whereas most gene families are
dispersed, such as PtIFG2899 (Fig. 3). We have
recently developed a computer tool to display the
relationships between members of gene families (B.
SHERMAN,
pers. comm.). Some rather striking patterns
emerge from these analyses. Loblolly pine linkage
groups 1, 2 , 4 , 6, and 9 share many gene families. This
pattern of gene amplification is much more suggestive
of retrotransposition than of polyploidization. A
different set of linkage groups (3, 5, 7, 8, and lo),
however, have no gene families in common. It appears
that the mechanism(s) leading to gene amplification do
not operate on all regions of the genome equally.
We are unable to determine how many and which
members of gene families code for structural genes
based on our existing mapping data. To date, we have
mapped gene families using cDNA probes from a
random-primed and non-directionally cloned cDNA
library that was constructed several years ago. These
cDNA probes likely hybridize to all related
genesequences. As such, they have been useful for
determining sizes of gene families and mapping of the
pine. W e plan t o sequence 1000 or more cDNAs from
developing xylem to identify many of the genes which
are expressed during wood formation. These cDNAs
will b e mapped using either conventional RFLP
analysis or by converting to STS (sequenced tagged
site) mar-kers prior to mapping. We will also map
QTLs for the component traits of wood specific gravity
such as earlywood and latewood specific gravity and
microfibril angle, and QTLs for the chemical (lignin
and cellulose) and physical (tracheid length) properties
of wood. B y combining these various approaches w e
hope to identify the individual genes most responsible
for determining complex traits such as wood properties. W e hope that this knowledge will ultimately lead
to the development of trees with superior wood properties as well as further our understanding of the genetics
of,this basic biological process.
ACKNOWLEDGMENTS
This research was supported through funding from the USDA
Forest Service, Weyerhaeuser Company, and the USDNNRI
Plant Genome Competitive Grants 91-37300-6363,
92-37300-7589.and93-37300-8759.
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