PRIORITIZATION AND VALIDATION OF CANDIDATE GENES FROM DATABASES OF QTLs GOVERNING ECONOMICALLY IMPORTANT TRAITS IN RICE (Oryza sativa L.)
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Date
2024-05-01
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Acharya N G Ranga Agricultural University
Abstract
Rice (Oryza sativa L.) yield is a complex trait and is controlled by several minor
genes with small effects. Elucidation of the genetic architecture of the complexly
inherited yield and its associated traits is essential for progressive rice improvement. To
this end, it is a prelude to pinpointing the genes and their intrinsic regulatory networks.
For the past two decades, several genomic regions or QTLs have been uncovered for
important agronomic traits. However, due to their large confidence intervals,
pinpointing their candidate genes becomes difficult and prevents them from deploying
straight away into rice breeding. The present investigation aimed to prioritize the
candidate genes underlying important QTLs governing various yield traits based on
publicly available diverse multi-omics databases.
In order to prioritize candidate genes, a pipeline has been developed. As per the
pipeline, a total of 99 QTLs consisting of six QTLs for heading date, five for tiller
number, three for panicle number, five for panicle length, nine for plant height, 25 for
grain number, five for spikelet fertility, 18 for grain length, 12 for grain weight and 11
for yield were targeted for gene prioritization. Among the selected QTLs, the range of
PVE is 10-43% while LOD is 3-53.71. In addition, the QTL, qSNP-4a (12.53Mb) has
the longest confidence interval while the QTL qDTY1.1 has the shortest interval of
0.08Mb. The QTL qGRL7.1 (398) has more annotated genes while qGL-3a (8) has the
least annotated genes within the QTL region. These targeted QTLs have been
distributed on all chromosomes except chromosome 11. More QTLs i.e., 23 have been
found to be located on chromosomes 1 and 3 while fewer are on chromosome 3 with 8
QTLs.
In total, 206 candidate genes have been predicted for 99 QTLs governing 10
economically important yield traits. To be specific, for heading date 15 candidate genes
from six QTLs, for plant height 15 candidate genes from nine QTLs, for tiller number
11 candidate genes from five QTLs, for panicle number eight candidate genes from
three QTLs, for panicle length nine candidate genes from five QTLs, for spikelet
fertility nine candidate genes from five QTLs, for grain weight 28 candidate genes from
12 QTLs, for grain length 31 candidate genes from 18 QTLs, for grain number 59
candidate genes from 25 QTLs and for yield 21 candidate genes from 11 QTLs were
prioritized. Among the candidate genes, some of the important transcription factors
were also identified such as MADS-box transcription factor, growth-regulating factor,
WRKY34, helix-loop-helix DNA-binding domain-containing protein, TCP family
transcription factor, MYB family transcription factors, GRAS family transcription
factor containing protein, auxin response factor, and nuclear transcription factor Y
subunit. The role of the prioritized candidate genes is also predicted in already-known
pathways of the targeted traits.
To select the contrasting genotypes for the targeted traits, 102 diverse rice
genotypes have been evaluated under field conditions and recorded data of the 10
economically important agronomic traits. Analysis of the variance of rice genotypes for
yield traits revealed that there is a significant difference among all the genotypes
suggesting considerable variability for the selection of contrasting rice genotypes. The
majority of the traits have shown normal distribution except spikelet fertility and chaffy
grains indicating that these traits are typical quantitative traits controlled by several
genes with small effects.
For validation of sequence variants from prioritized candidate genes, 22 primers
have been designed for large frameshift mutations of 22 prioritized genes underlying 21
QTLs governing eight traits. Of them, 11 markers showed polymorphism, and 8 showed
monomorphism while five markers were not amplified or produced inconsistent results
in agarose gel electrophoresis. In general, none of the markers showed clear
polymorphism between the contrasting rice genotype groups for the selected traits.
However, few markers showed polymorphism between selected genotypes of the
contrasting characters. The polymorphism witnessed between a few contrasting
genotypes can be assumed as genotype-specific and therefore, these markers can be
used for the marker-assisted improvement of the specific genotypes.
Sequencing of the selected prioritized genes such as (LOC_Os04g22120) for the
plant height QTL qPHT4-2, (LOC_Os03g28270) for the grain length QTL GL1 and
(LOC_Os02g57290) for the panicle length QTL, pl2.1 revealed several sequence
variations such as SNPs, multiple SNPs, insertions, and deletions.
The gene expression analysis revealed significant fold changes in three predicted
genes viz., LOC_Os03g28270 (Leucine Rich Repeat family protein) for the grain length
QTL, GL1 LOC_Os06g16400 (helix-loop-helix DNA-binding domain-containing
protein) for the grain weight QTL, gw-6 and LOC_Os02g57290 (cytochrome P450) for
the panicle length QTL, pl2.1. Interestingly, the genes GL1 (LOC_Os03g28270) and
pl2.1 (LOC_Os02g57290) exhibited clear variations in both gene sequences and gene
expression.
Through the present investigation, it was obvious that it is possible to narrow
down a large number of annotated genes in a QTL to very few numbers of the most
probable candidates using the pipeline developed in the study. Based on the findings of
the prioritization of candidate genes for QTLs based on multi-omics databases,
validation of sequence, and gene expression, it is very obvious that the candidate genes
are very specific to genotypes. In order to find the function of each prioritized candidate
gene, their evolution and domestication have to be elucidated besides functional
characterization through the development of mutants or overexpression lines or gene
editing by CRISPR and marker-assisted breeding before being exploited in rice
breeding. Employing the pipeline developed in the study, other crops as well as animal
species can be targeted to dissect the causal genes from QTL regions