Biology Reference
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nucleotide polymorphism (SNPs), has become
very inexpensive compared to other marker sys-
tems. Utilizing an Illumina NGS platform, Ed
Buckler's lab at Cornell University has devel-
oped a technically very simple and highly mul-
tiplexed (96-plex/384-plex) method for rapidly
and inexpensively sequencing large numbers of
DNA samples, and subsequently analysing the
sequencer output with an associated bioinfor-
matics pipeline for genotyping germplasm of any
species. This protocol is referred as genotyping-
by-sequencing (GbS) (Elshire et al. 2011). For
this procedure, genome complexity is reduced
by digestion of each DNA sample with restric-
tion enzymes, and the resulting restricted frag-
ments are then ligated with sample-specific “bar-
codes,” called “restriction site-associated DNA
tags” (RAD tags), and the restricted, barcoded
DNA samples are then multiplexed (at 48-, 96-
or 384-plex) and subjected to “skim” sequenc-
ing to a depth of 0.1X. The resulting 66-base
pair sequence reads (after sorting by barcode)
are aligned to the reference genome sequence
of BTx623 (Paterson et al. 2009) to identify
SNPs with the help of customized bioinformatics
pipelines. This analytical pipeline can readily be
adapted for species lacking a reference-aligned
genome sequence.
By employing GbS,
the individual genes and/or regulatory elements
associated with variation in stay-green pheno-
type. Once genomic regions associated with
underlying mechanisms of the stay-green trait
are identified, tagged SNPs (reduced representa-
tions of SNPs based on their linkage) can be iden-
tified and converted to a customized SNP assay
using the BeadXpress platform (currently avail-
able in ICRISAT's Genomics Service Laboratory
at Patancheru) or CAPS (cleaved amplified poly-
morphic sequences) markers. Such SNP mark-
ers can be used individually or in small multi-
plexes at a much lower cost than that required for
genome-wide genotyping with the GbS platform
and will be appropriate for use in foreground
genotyping and identification of recombination
events occurring in QTL-flanking regions dur-
ing the transfer of this trait to desired sorghum
recurrent-parent backgrounds. This will greatly
improve the efficiency of introgression of the
stay-green trait and its components, by reduc-
ing the number of breeding cycles required (for
recurrent-parent background genotype recovery)
and facilitating stacking of complementary stay-
green alleles at various loci as may be needed for
improved variety development.
Application of NGS tools such as GbS for
dissecting complex traits such as stay-green at
the DNA-sequence level will capture most of
the functional factors of the genome related to
trait expression. However, another application
of NGS tools in RNA-sequencing (commonly
referred as RNA-seq) will help to capture the
regulatory elements (Ozsolak and Milos 2011).
For a complex development trait such as stay-
green, many plant growth and development path-
ways are involved, probably throughout the life
cycle of the plant, for trait expression. Applica-
tion of RNA-seq can help us understand the role
of regulatory and transcription factors (includ-
ing small RNA, micro RNA) and their inter-
actions with other pathways. We hope to uti-
lize recent advances in RNA-seq technologies
with the recombinants identified from the ongo-
ing fine-mapping exercise to move toward a
better understanding of stay-green expression
265,000 SNPs have
been identified for stay-green donor parents E
36-1 and B35 by aligning their skim sequence
reads against the sorghum reference-genome
sequence. The primary challenges involved in
handling these large data sets are the need
for substantial computational power. Analysis
of combined field, pot, and lysimeter pheno-
type data sets for QTL introgression line sets
and RIL populations is underway at present,
and in the near future we expect to be able
to identify genomic regions (major and minor
effect QTLs) associated with putative compo-
nents of the stay-green phenotype, with or with-
out terminal-drought tolerance, in sorghum. The
high marker-density and genome-wide coverage
that is possible with this GbS-SNP platform will
help us to identify SNPs closest to or inside
∼
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