Evaluating haplotype associations for brown planthopper resistance in rice using the HAPLOANNOTATOR bioinformatics tool

doi:10.21203/rs.3.rs-2090719/v1

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Evaluating haplotype associations for brown planthopper resistance in rice using the HAPLOANNOTATOR bioinformatics tool

https://doi.org/10.21203/rs.3.rs-2090719/v1

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Arthropod pests are a major contributor to rice production losses at a global scale and are predicted to increase in severity due to anthropogenic activity. It is generally recognised that an urgent requirement is to understand the genomic basis of crop-insect resistance to develop successful selective crop breeding programs capable of sustainably supporting future generations of human civilization. We evaluated phenotypic assay of rice resistance against its predominant arthropod pest, Nilaparvata lugens (brown planthopper; BPH) in Thailand. Using four geographically distinct BPH populations against a 227 strong rice accession panel, we identified eight key functional gene haplotypes (FGHs) in rice that statistically associate with high BPH resistance. Three of these were shown to associate with high resistance against all populations of examined BPH which likely represents two BPH biotypes. Finally, correlation analyses failed to detect strong signals of FGH pyramiding indicating that identified FGHs may be capable of conferring resistance independently. Our work relies on a newly developed bioinformatics tool, haploAnnotator, that provides graphical and statistical annotation of FGHs and should prove valuable to breeding research among all crops (not only rice) that possess a published genome.

rice

functional gene haplotypes (FGHs)

insect pest resistance

brown planthopper

bioinformatics

haploAnnotator

Rice (Oryza sativa L.) is one of the world’s most important crops providing a staple food for nearly half of the global population (FAO 2004). Alongside adverse environmental conditions, rice arthropod pests are considered the major contributor to losses in rice production around the world (Pathak and Khan 1994). Total yield losses from insect pests may total 90% (Seni and Naik 2017a) although more often around 20% (Matteson 2000). Rice arthropod pests are highly diverse with nearly 300 species hailing from numerous insect orders, and 23 known species causing severe economic damage (Pasalu and Katti 2006). In Thailand, the whitebacked planthopper (WBPH) Sogatella furcifera (Horváth) (Homoptera: Delphacidae) (Ramesh et al 2014), brown plant hopper (BPH), Nilaparvata lugens (Stål) (Seni and Naik 2017b) and Asian rice gall midge, Orseolia oryzae (Wood-Mason) (Kumar et al 2011) are the major causes of economic crop losses. Devising breeding programs contingent on genomic determinants of insect resistance is a primary strategy recommended for the development of a sustainable global agriculture capable of feeding humanity’s burgeoning nutritional demands (Horgan 2017).

Among these insect pests, BPH is the most problematic in South-East Asia and has affected rice production with increasing frequency over recent years (Hu et al 2011). The primary control method of BPH is through the application of commercially available chemical pesticides (Wu et al 2020). However, the extensive use of chemical pesticides can have several detrimental consequences including selection for insect resistance in target species, injurious impact on target species’ natural enemies (Matteson 2000), increases in rice production costs, and negative human health and environmental hazards including pollution and residue persistence (Nagata 1984; Liu et al 2003; Jin et al 2008; Kontsedalov et al 2012). There is also evidence that increased pesticide use may increase BPH damage due to exogenous chemical disruption of host-plant transcriptomes involved in plant-insect defence mechanisms (Kenmore 1991; Cheng et al 2012; Ali et al 2014), while there is also evidence that synthetic pesticides kill natural enemies and may promote the emergence of new BPH biotypes (Tanaka et al 2000). There have been at least 29 cases of evolved insecticide resistance to major classes of insecticides including organophosphates, carbamates, pyrethroids, neonicotinoids, insect growth regulators, and phenylpyrazoles in the BPH alone (Wu et al 2018). Notably, different BPH biotypes are known to undermine specific resistance genotypes indicating genomic variation in BPH resistance breakdown (Jing et al 2017).

BPH cause critical damage to rice by attacking the xylem and phloem tissues, resulting in tissue damage caused by "hopper burn" with concomitant yield losses. Indirect damage from BPH involves transmission of rice grassy stunt virus and ragged stunt virus, that again strongly impact yields (Balachiranjeevi et al 2019). Despite its notable successes (Pingali 2012), the adoption of Green Revolution agricultural practices (Matteson 2000) appears at least partially responsible for ongoing rice crop insect pest outbreaks. Modern agriculture, including heavy fertiliser use, the use of a limited number of high yield varieties (i.e., with concomitant reduction in genetic diversity potentially imposing compromised defences), and year-round multi-cropping, have favoured the build-up of rice pest populations (Way and Heong 1994). BPH can adapt to commonly used rice varieties by evolving resistant genes (Bottrell and Schoenly 2012).

Recently, 40 candidate loci for BPH resistance genes have been identified largely through marker-assisted selection (MAS) or QTL mapping. There are four prominent gene clusters that contain many BPH resistance genes. Cluster A on chromosome 12 including BPH1, BPH2, BPH7, BPH9, BPH10, BPH18, BPH21, and BPH26. Cluster B on chromosome 4S, including BPH12, BPH15, BPH17, BPH17-ptb, BPH20, BPH22(t), BPH30. Five genes on chromosome 6 (cluster C), including BPH3, BPH4, BPH25, BPH29, and BPH32. Cluster D on chromosome 4L includes BPH6, BPH18(t) and BPH27, BPH27(t), and BPH34. Seven have been map using large scale populations. Four genes were identified by linkage mapping, and a further seven were cloned and characterized for BPH resistance (BPH9, BPH14, BPH17, BPH18, BPH26, BPH29, and BPH32) (Nguyen et al 2019).

In order to inform breeding programs via the identification of genomic determinants of insect resistance and understand the impact of key functional variants genome-wide association studies (GWAS) methodologies have become a commonplace tool (Korte and Farlow 2013), yet they suffer from some inherent drawbacks. These include the influence of rare variants associated with extreme phenotypes (Wray et al 2013) and low-informativeness of SNP markers (Collard et al 2005). Moreover, they do not account for epistatic interactions that may synergistically modify functional effectiveness (Clark 2004; Bardel et al 2005). Thus, haplotype mining tools offer potentially valuable technologies in genomics-assisted breeding (Bhat et al 2021). Haplotype identification may increase informativeness (Hamblin and Jannink 2011), while incorporating epistatic relationships within identified genomic regions.

Here we conduct bioinformatic analyses of BPH resistance across a large whole-genome sequenced rice crop panel held at the Rice Gene Discovery Unit in Thailand. Using phenotypic assay data evaluating four separate BPH populations against a single susceptible rice variety, our analyses identified functional gene haplotypes (FGHs) associating with BPH resistance. To do this we built on the previously published riceExplorer resource (Darwell et al 2022) to devise a novel bioinformatics pipeline, haploAnnotator, that incorporates both WGS and phenotypic data sources to produce annotated gene maps for 27 chromosomal regions (i.e., genes) of interest for which we identified definite chromosome number and base pair start-end positions. Additionally, we examined whether linked identified haplotypes associated with BPH genes are likely to occur across different genes in resistant rice varieties. Our new pipeline sheds light on the genomic architecture of BPH resistance, indicating functional SNP and indel haplotype arrangements, among Thai rice accessions and provides a novel bioinformatics tool that should aid rice (and other crop) breeding across the globe.

Insect materials

Four populations of BPH collected from four provinces of Thailand including Kamphaeng Phet (KPP), Sa Kaeo (SKO), Sing Buri (SBR) and Ayutthaya (AYT). The insects were reared on 7-day-old Taichung Native 1 (TN1) seedlings in a collection room at 28°C, 16(light)/8(dark) hours of daylight. The insects fed and reproduced on TN1 seedlings. Continuous culture of BPH had been maintained in the room until mass propagation was required for BPH phenotyping. The mass insect rearing was done in a 40-mesh greenhouse without supplemented lights.

Phenotyping

Seed germination was done in Petri dishes at room temperature for 48 hours. A row of six germinating seeds/accession were arranged in 10x20-well plastic trays filled with paddy field soil. TN1 seedlings, a standard BPH susceptibility check variety (Hargrove et al 1979) were grown in a greenhouse without fertilizer application. 25-day-old seedlings were transferred into BPH screening cages and infested by 2nd- or 3rd-instar nymphs at the rate of 20–25 insects/plant. This completely randomized experiment was carried with three replications. The standard evaluation system for rice (SES 2002) was used to score the BPH damage. Insect damage score across all study accessions was recorded from Day 1 when TN1 (susceptible check) died. The scores were recoded every other day until Day 9. BPH resistance was quantified by the area under the curve (AUC) and was calculated using trapezoid rule method (Litsinger 1991). The experiment was conducted in a greenhouse facility of Rice Gene Discovery Unit, Rice Science Center, Kasetsart University, Kampaengsaen Campus, Nakhon Pathom, Thailand.

The Thai Rice Resource

The Thai Rice Resource (TRR) consists of hundreds of rice germplasm accessions that have been whole-genome sequenced (WGS) which comprise elite cultivars, local landraces, internationally sourced varieties (e.g. through the International Rice Research Institute) and other varieties developed by the Thai Rice Department. We used 222, 227, 221 and 227 available sequenced accessions for populations AYT, KPP, SBR and SKO, respectively (Table S1). Whole-genome resequencing data were generated using Illumina HiSeq 2,500 System at Novogene (Beijing, China) under the whole-genome resequencing project at the Rice Gene Discovery, National Center for Genetic Engineering and Biotechnology, Thailand (unpublished data). The SNPs were called using the standard GATK pipeline called against the Nipponbare reference genome (Kawahara et al 2013) to produce SNP calls in the gvcf format.

Bioinformatic analyses

We built on the riceExplorer pipeline (Darwell et al 2022) to develop haploAnnotator (available for free download at: https://github.com/ctdarwell/haploAnnotator) to analyse our whole-genome sequencing (WGS) data in conjunction with our phenotypic data. In brief, the pipeline calls all variants, using the bcftools software suite (Danecek et al 2021), identified against the rice Nipponbare (japonica) reference genome (Kawahara et al 2013), across the TRR panel with 27 known BPH resistance genes gleaned from the scientific literature for which we identified definitive chromosome number and base pair start-end positions according to the Nipponbare genome (Table S2) before using snpEff software (Cingolani et al 2012) to filter out any variants considered to have no predicted functional impact. From the remaining variant calls, individual functional gene haplotypes (FGHs) are identified across all samples at each examined locus. Then, associated BPH resistance phenotypes (represented by total AUC scores covering the entire nine-day period) for all individual accessions containing each FGH are clustered and their mean values are calculated. Calculated means are then statistically evaluated to examine which percentile of the normal distribution of all total AUC scores they correspond to. Thus, mean values found at higher percentiles (e.g., the 95th would be considered significantly high) are considered to indicate an FGH associated with BPH resistance. Additionally, we employed a threshold of six accessions containing an FGH to denote whether we considered the FGH to be robustly associated with extreme phenotypes (rather than a rare variant that happens to have a resistant phenotype).

Further analyses

To attempt to evaluate whether any kind of pyramiding/gene networking phenomena are in operation (i.e., if any identified FGHs are required to be paired with specific FGHs from alternative genes to function optimally), correlations between pairwise combinations of 23 genes featuring FGHs associated with high resistance were calculated. In order to calculate this, we counted all accessions that possess high resistance FGHs from either of a pair of examined genes and then counted the number of accessions featuring both FGHs for both genes. These figures were then expressed as a percentage, i.e.:

= no. accessions with both FGHs / (no. of accessions with FGH1 + no. of accessions with FGH2) * 100

Figure 1 shows histograms of BPH resistance among the four populations (AYT, SKO, SBR, KPP). Most individuals are heavily infested after 5 days while numbers increase at 9 days (Table 3). BPH populations at AYT (Fig. 1a,b), KPP (Fig. 1c,d) and SKO (Fig. 1g,h) generally appear less aggressive than the SBR population (Fig. 1e,f). Infestation was calculated as area of damage up to 9cm² (NB AUC scores of resistances, derived from area of damage, also produce skewed data plots).

Our analyses identified 23 annotated gene regions out of 27 examined gene regions that contained functional gene haplotypes (FGHs) featuring a total of 875 distinct haplotypes (Table 1). We identified between two and 121 distinct functional haplotypes per gene, with mean and median values of 37.26 and 10.0, respectively (Fig. 2a); interestingly, the data is bi-modally distributed. Most functional haplotypes are found on a handful of accessions (Fig. 2b), although many genes feature a single dominant haplotype found on the majority of accessions (Fig. 2c).

Table 1

Numbers of distinct functional gene haplotypes (FGHs) among all examined accessions across 23 genes associated with brown planthopper resistance.
Gene	nHaplotypes
Bph14	121
Bph6	119
BPH26/BPH18	112
Os12t0559200-01	101
LOX11	101
LecRK2/OsLecRK2/Bph3	93
Os12t0559200-02	78
LecRK1/OsLecRK1/Bph3	32
LecRK3/OsLecRK3/Bph3	23
OM64	14
OsLOX1	14
BPH33	11
OsEBF1	10
OsbHLH96	7
OsGRF8	7
BPH29	6
OsBphi262	6
OsJMT1	5
OsF3H	4
OsMAPK20-5	4
OsPAL6	3
Os6PGDH1	2
OsHLH61	2
Total	875

Of these, eight distinct FGHs among seven annotated gene regions (and found on a minimum of six accessions) have mean AUC values among the upper 95th percentiles of the brown planthopper (BPH) resistance score data (Table 2). Of these, haplotypes 01, 02 and 04, on genes OM64, OsbHLH96 and BPH29, respectively, were associated with BPH resistance across all four BPH populations, while other haplotypes only conferred resistance more sporadically. Populations AYT, KPP, SBR and SKO record four, five, four and six distinct haplotypes, respectively. Each population recorded between 4–6 extreme FGHs. Additionally, we identified a further 77 distinct haplotypes among 16 annotated gene regions that are found on five or fewer accessions (i.e., rare haplotypes) and associate with high resistance phenotypic assay.

Table 2

Identified FGHs associated with extreme brown planthopper total AUC resistance scores (mean values above the 95th percentile confidence interval value).
Haplotype	No. accessions	µAUC	Population	Gene
Hap01	15	60.23	AYT	BPH29
Hap01	18	63.07	KPP	BPH29
Hap01	15	69.16	SBR	BPH29
Hap01	18	61.91	SKO	BPH29
Hap02	6	70.67	AYT	OM64
Hap02	9	70.96	KPP	OM64
Hap02	6	70.65	SBR	OM64
Hap02	9	68.89	SKO	OM64
Hap03	7	62.67	AYT	OsbHLH96
Hap03	8	64.25	KPP	OsbHLH96
Hap03	7	64.86	SBR	OsbHLH96
Hap03	8	63.33	SKO	OsbHLH96
Hap04	8	61.41	AYT	OsLOX1
Hap04	10	62	KPP	OsLOX1
Hap05	40	56.52	KPP	OsMAPK20-5
Hap05	40	57.15	SKO	OsMAPK20-5
Hap06	14	67.77	SBR	OsEBF1
Hap07	32	60.89	SKO	OsBphi262
Hap08	14	62.86	SKO	OsEBF1

An illustrative example is shown for gene BPH29, found on chromosome six (Fig. 3). Six distinct FGHs were found on this gene of which a single dominant haplotype was found on 202 accessions. These accessions have a mean AUC score of 49.72 which is not an extreme value and lies within the non-extreme ranges of the data (i.e., 25th percentile). However, haplotype 3 (Hap003) is found on 15 accessions and has an extreme AUC score (60.23) found in the 95th percentile of the data range. Notably, Hap003 features no alternative SNP or indel variants relative to the japonica (Nipponbare) reference genome. There are also a number of rare haplotypes found only across a few accessions (that also contain non japonica identified variants), which, for this gene, also associate with extreme AUC score values. Figure 3 also illustrates the standard output of our pipeline (generated for every evaluated gene region – in several formats – that features FGHs) and its graphical representation of FGH diversity across the panel. Three SNPs and two indels are found at four reference genome nucleotide positions on chromosome 6. SNPs are found at positions 484938, 484999 and 485018, while a pairs of distinct indels (of 12 and 23bp) are found at position 485049. See supplementary information for all graphical outputs for each evaluated gene from each BPH population (SI zip file).

Correlations between gene pairs suggest little evidence for essential pyramiding of FGHs (Table 3). In general, accessions featuring a high-resistance associated haplotype at a single marker did not have a further high-resistance associated haplotype at an examined paired gene. However, some examined gene pairs did display reasonable resistance associated haplotype correlations above 25%. For example, six (out of 13) accessions feature high resistance associated haplotypes at genes OsLOX1 and OM64.

Table 3

Correlations of accessions featuring high resistance associated haplotypes. Fraction scores indicate number of accessions featuring high-resistance associated haplotypes at both examined genes over the total number accessions featuring a single high-resistance associated haplotype at either marker.
	OM64	OsbHLH96	OsLOX1	OsMAPK20-5	OsEBF1	OsBphi262
BPH29	6/21	4/22	6/22	2/56	0/32	3/47
OM64	-	4/14	6/13	3/46	0/23	0/41
OsbHLH96	-	-	3/15	0/48	1/21	0/40
OsLOX1	-	-	-	3/47	0/24	3/39
OsMAPK20-5	-	-	-	-	1/53	3/69
OsEBF1	-	-	-	-	-	3/43

Arthropod pests are a major contributor to losses in rice production around the world. Moreover, over recent decades, the prevalence of such phenomena have increased seemingly in response to modern agricultural practices and increased demands on land use. As such, it is generally perceived that an urgent requirement is to understand the genomic basis of crop-insect (and other pests) resistance in order to devise successful selective crop breeding programs that may predicate a sustainable modern agriculture capable of supporting future generations of human civilization. Here we report a genomic evaluation of rice resistance against its predominant arthropod pest, Nilaparvata lugens (brown planthopper; BPH) in Thailand. Using four geographically distinct BPH populations against a 227 strong rice accession panel we identify key functional haplotypes and evaluate their effectiveness based on phenotypic resistance assay. Our works relies on a newly developed bioinformatics tool, haploAnnotator, that should prove valuable to breeding research among all crops (not only rice) that possess a published genome.

Preliminary phenotypic assay of BPH resistance against our rice accession panel indicates that there are most likely two distinct genetic demes among our four sampled BPH populations (Fig. 1) as distinct resistance signatures are observed; population SBR appears distinct in causing more aggressive pathology. At least four distinct biotypes have been identified among BPH (Jing et al 2017) and it is likely that phenotypic assay of our geographic sampling indicates our analyses feature two separate biotypes (featuring the SBR population as distinct from the other three). Applying our phenotypic resistance data to the haploAnnotator pipeline in conjunction with whole-genome sequenced rice accession panel data we were able to identify functional gene haplotypes (FGHs) among 23 of 27 (Kawahara et al 2013) examined gene regions for which we could unambiguously identify chromosomal and base pair start-end point locations (Table S2) within the genome. Of these, haplotypes 01, 02 and 04, on genes BPH29, OM64 and OsbHLH96 respectively, correlate with BPH resistance (according to our 95th percentile criterion) across all four BPH populations, while other haplotypes only conferred resistance in two or less populations.

Finally, we performed correlation analyses to test whether there is any evidence that identified FGHs from distinct gene regions that associate with high resistance phenotypes are likely to co-occur within accessions to support the hypothesis that gene pyramiding effects contribute to BPH resistance in rice (Table 3). In general, we found little support for this idea although there is some evidence that occasional pairs of resistance associated FGHs co-occur relative often. Further detailed experimentation would be required to ascertain whether any combinations of identified FGHs have synergistic effects on BPH resistance.

haploAnnotator performance

Underpinning our analyses we present a new bioinformatics tool, haploAnnotator, that we believe should be highly useful in breeding research in order to visualise and assess which gene regions may contribute to desirable phenotypic traits among agricultural crop accessions. Our motivation for this, in contrast to GWAS methods which do not assess whether linked variants act contingently to confer phenotypic outcomes (i.e., epistasis), was to develop a tool that allows visualisation of entire genomic regions juxtaposed with statistically relevant information. We evaluated genes regions known to influence BPH resistance (Table S2) with identifiable chromosomal locations (Kawahara et al 2013). Figure 3 (and SI zip file) outlines the utility of our software. For the gene region coding as resistance marker BPH29, our software identified six FGHs, i.e., unique combinations of variants that were evaluated by our pipeline to have likely functional impact of gene regulatory mechanisms (i.e., non-synonymous or indel variants within coding regions). haploAnnotator generates graphical output to visualise such variation against a number of useful associated data points. First, we give each identified FGH a unique identifier code which can be referenced among other haploAnnotator output files. Second, we annotate how many accessions among the examined panel it was found among, indicating an FGH’s rarity. Third, we assign a mean trait value associated with the FGH. And finally, percentile range of the mean trait value is annotated so the user may visualise whether the haplotype is likely to contribute to extreme phenotypic values and whether it should be the target of future breeding programs. These assessments are outputted in multiple formats (e.g., PDF, CSV, XLSX) highlighting salient features at both the level of haplotype and individual accessions to facilitate both user visualisation and subsequent downstream processing. Additionally, it should be pointed out that any length of chromosomal region, not only specified known gene regions, can be evaluated as stipulated by the user.

Overall, our results indicate that there is much scope for future guided selective rice breeding among Thai accessions following our analyses. Our results indicate FGHs among 23 genes that were previously unidentified despite a long history of rice research in Thailand. Only future work can determine whether these identified functional haplotypes are indeed of value to breeding programs. However, without our software such evidence would have remained unrealised. haploAnnotator can be used both in conjunction and to give greater context to GWAS to evaluate potentially useful variants. A known drawback from GWAS is that variant-phenotype associations are evaluated in isolation and synergistic process among variants (epistasis) are unknown. Our software goes some way in highlighting which constellations of variants may be involved in delivering useful phenotypes. Additionally, the software can be used against any panel of sequenced agricultural resources (not just rice crops) for which a reference genome has been published.

Understanding the genomic basis of crop insect resistance is challenging. While commonly employed genome-wide association studies offer a powerful methodological tool, they suffer from some drawbacks most notable that genomic variants may epistatically operate in concert to impart synergistic gene function outcomes. Here we present a tool that aims to contextually evaluate functional genomic variants by annotating predicted functionally impactful SNPs within genomic regions and evaluating their statistical associations with phenotypic data. We believe our newly developed tool, haploAnnotator, will provide a useful accompaniment to other available methodologies and assist selective crop breeding programs by providing additional context when determining likely functional drivers of desirable crop traits.

FGH – functional gene haplotype

BPH - brown planthopper (Nilaparvata lugens)

GWAS - genome-wide association studies

SNP – single nucleotide polymorphism

SKO - Sa Kaeo (SKO),

SBR - Sing Buri

AYT - Ayutthaya

KPP – Kamphaeng Phet

TRR - Thai Rice Resource

AUC – Area under curve

Ethics approval and consent to participate: not applicable

Consent for publication: not applicable

Availability of data and materials: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request

Competing interests: The authors declare that they have no competing interests

Funding: The National Research Council of Thailand (NRCT) grants: P-20-52126 and NRCT-RTA/812/2563

Author contributions: NS – analyses, data curation, wrote primary draft; WJ – methodology, resources; SA – conceptualization, writing; SW – conceptualization, resources, writing; TT - supervision, project administration, funding acquisition; CTD – bioinformatics, programming, statistical analyses, conceptualization, writing.

Software:

Project name: haploAnnotator
Project home page: https://github.com/ctdarwell/haploAnnotator
Operating system(s): e.g. Linux
Programming language: e.g. Bash, Python3 (≥3.7)

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Evaluating haplotype associations for brown planthopper resistance in rice using the HAPLOANNOTATOR bioinformatics tool

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Abstract

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Background

Methods

Insect materials

Phenotyping

The Thai Rice Resource

Bioinformatic analyses

Further analyses

Results

Discussion

Conclusions

Abbreviations

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References

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