Genome scanning for non model
forest species ~linking a gap
between genetic and phenotypic
diversity~
Japan International Research Center for Agricultural Sciences
University of Tsukuba

Naoki TANIThe 11th AP-BON Workshop @ Double trees by Hilton, KL

2019-6-21

How genetic diversity supports biodiversity?
We believe that larger genetic diversity demonstrates higherresilience.

Ecosystem
HOW?
Selection works against phenotypes, not for all genotypes.

Biodiversity

Genetic diversity

For example,
Drought causes less survival for water demand individuals
Diseases cause less survival for susceptible individualsLimited number of genetic markers show neutral against
genetic diversity

Linking between genotype and phenotype also require forbiodiversity conservation

Amount of genetic variation is NOT equal throughout genome

Selective sweep

Elimination of genetic variation due topositive natural selection

Results of two weed rice
Li et al. 2017 Nature Genetics

Phenotype is positively selected by environments, and genetic variation of the genes
and flanking regions is eliminated.

Conventional methods to identify phenotype – genotype relationship
Quantitative traits loci (QTL) mapping
At least 2 generations, ideally 3 generations arerequired.
Segregation of quantitative phenotypic traits isanalyzed on linkage map composed by geneticmarkers.

Limitation of QTL mapping
Such as for long-lived woody plant, it is very
difficult to make 2 or 3 generation pedigree.
Identification of QTL is based on recombinationof F1 individuals. Non-segregated QTLs are notsurveyed.
Mauricion 2001, Nature Genetics

Linkage disequilibrium (LD)Consider only two SNPs

Probabilities foreach haplotype inpopulation

ABAbRaBRabBbApAqB

pA(1-q)Ba(1-p)aqB(1-p)a(1-q)b

We can estimate distance between two loci on chromosome fromfrequency of two multi-locus genotypes
Multi-generation pedigrees are not required!

Genome-Wide Association Study (GWAS)

Association of observed phenotypes is assessed by linear model.
Now, large amount of SNP loci is required.

Obtaining many SNP loci by ddRADseq technology
Genomic DNA

Restriction enzymes (Mse1; Pst1)

1) Fragmentation/digestion2) Adapter ligation

Restriction Sequencing Multiplexing Reversesite
read primer barcode adaptor

Forward Multiplexing Sequencing Restriction
adaptorbarcode read primer
site3) Pooled DNA, randomly sheared DNA, size selection4) Library amplification

Liu et al 2017, PeerJTargets

Illumina HiSeq sequencing
1) Cluster Amplification

2) Sequencing

Sequencing
reagents

Flow cell
Bridge amplification
cyclesSequencing cycles

Data is exported to an output fileClusters3) Alignment and Data Analysis

Digital image

Text File

Merits and demerits of RADseq and MIGseq
RADseq generates more SNP loci thanMIGseq
RADseq require larger amount and higherquality DNA than MIGseq

Reads

Reference genome

Liu et al 2017, PeerJ

Applications of GWAS
Phenotypes should be measured from uniform environment with some genetic diversity.

Experimental crop field

Fish farming

Tree clonal test

Cattle faming

Applications of GWAS for adaptation to climate change
Selection of ecologicallyimportant species

Climate change threaten
tropical forest

Utilize genetic resources toadapt climate change
Phenotype:
Growth traits
Physiological traits

Increasing hightemperature stress
Tropical forest occupies only 710 % of land,
But it stores 40-50 % of carbon ofland vegetation.

In South-east Asia, dipterocarps
occupies nearly 80% of canopy
trees.

Selection of genetic
resources to adapt

Mitigation

Increasing drought
stressPhenotype measurementDifficulty of GWAS in tropical forest
Q: Can phenotypes be measured from
uniform environment in tropical forest?
A: No, such test forest is very limited. But wefound some.

Common garden experiment in FRIM
Species: Shorea leprosula

Progeny trial in central Kalimantan
Species: 5 species
S. leprosula, S. platyclados,
S. Macrophylla etc.

Location of progeny trial
Location : Sari Bumi Kusuma forest concession,
Central Kalimantan, Indonesia
Planted in : May, 2006 under ITTO project
Source: Seed (collected from SBK region)
Design: RCBD

Plant materials and phenotype mearsurment
1 Phenotype data assessment

384 individuals from 77 families (mother trees)2

Leaf sampling collection

1) Total height3) Clear bole

4) Branch angle

Dry leaf samples(kept in silica gel during the transp. carried to Japan)

Genomic DNA extraction
2) Diameter

SNP discovery after NGS
We use single flow cell of illumine Hiseq X, which outputs 800 – 900 Gb, 2.6 -3.0 billion reads.

Decompressing;
Demultiplexing raw sequence files

Raw sequence processing;Quality filtering;
Base trimming

Sequenced files(GZ)
zcat; gunzip; gzip
Decompressed sequence files(FASTQ)FASTQC; Trimmomatic
Processed sequence files(FASTQ)
BWA; SAMtools

Read mapping/ Alignment

Aligned sequence(SAM; BAM)FreeBayes; VCFtools

SNP calling(VCF)

SNP filtering
exclude >40% missing data;
MAF > 0.05;
HWE p value < 0.001;
thin 1000 bp

VCFtools
Filtered SNP genotype(VCF)

Total
5,424 SNP loci weregenotyped

Statistical pipeline after NGS
ANALYSIS
GENETIC POPULATION STRUCTURE
Probabilistic Principal Component Analysis (PPCA)
Principal Coordinate Analysis (PCoA)
Principal Component Analysis (PCA)

LINKAGE DISEQUILIBRIUM (LD)ASSOCIATION ANALYSIS (MLM (Q+K model))(incorporated both population structure and marker basedkinship)
GENE FUNCTION ANNOTATIONGENOMIC PREDICTION
1) Ridge regression (RR)
2) Least Absolute Shrinkage and Selection Operator (LASSO)
3) Elastic Net

TOOLS
R packages
GenAlex v6.5
(Peakall & Smouse, 2012)
TASSEL 5.0(Bradbury et al., 2007)
TASSEL 5.0 (Bradbury et al., 2007)
LOESS in R packages(Locally weighted polynomial regression )(Cleveland, 1979)
rrBLUP in R packages (Endelman, 2011)
qqman R package (Turner, 2014)
Genome annotation tools
Beagle package ver. 4.1 (Browning et al.,2015)
glmnet in R packages(Friedman et al., 2010)

Genetic structure
Probabilistic Principal Component Analysis (PPCA) showed weak genetic structure.

Linkage disequilibrium (LD)LD decay at 0.1 R^2 is ?? kbp in progeny trial forest of Shorea
platyclados

Slow LD is caused mainly by:
1) Short breeding history

Half-sib progeny (F1)

2) Low recombination rate
Oraguzie et al., 2007; Yuan et al., 2018

If rapid LD decay observed,

LD decay of S. platyclados using the ???? SNP markers dataset

Cover more candidate genes possibly
related to the traits

Slower LD decay help us to detect genotype – phenotype
relationship with limited SNP loci.

Spatial analysis of phenotypes before GWAS
Progeny trials

Cover large area:
Large size of testing population(77 families with multiple individuals (4 trees plot/family)
Large field space needed for individual trees(plant spacing of S.platyclados: 6 m x 3 m)

Large physical area exhibits:
heterogeneous global and micro-environmental variation
RCB Design

Site is divided into several blocks (replications) separating
environmental variation from genetic variation
Block 3Block 1

Spatial analysis
Improve the accuracy ofbreeding value prediction

Block 4Block 2

Covering variance of microsite environmental factor (ex:soil depth, nutrition) in the local gradient variations withinblocks (patches) and global gradient trend along the rowand column of the trial between blocks (Bian et al., 2017; Chen et al.,2018)

Spatial analysis on breedR package
Before spatial analysis
Environmental heterogeneity :High variance within block and between block
Block 3 Block 4

Block 1

Block 2After spatial analysis
Spatial analysis reduce the error variance

Diameter of S. platyclados before andafter spatial correction

Summary of phenotype
Phenotypic variation of the progeny trial of Shorea platyclados

Distribution of diameter and height at 12 years oldGenome Wide Association Study (GWAS)
Association analysis based on MLM(Q+K model)Incorporated both of
population structure & marker based kinship

MLM approach, greatly reduced false positives, as shown in quantile-quantile plots

Many SNP loci are associated with diameter (phenotype). However, -log10(2) (0.01%)
level can not distinguish between association and false positive, because of the large
number of SNP loci.
Multiple comparison is required. However, after controlling P value by Bonferroni andFDR, no significant SNP loci is detected.
Many small effect loci may associate the diameter growth.

Genome Wide Prediction (Selection)
SNP loci can be used forgenetic markers for selection

Sweet and starchy of corn kernel of 2,145 USA corn
inbred line, Romay et al. (2013) Genome biology

But, when there is no strong
associated loci, how ?

Model training
DNA marker datay = Xb + Zu + e
Phenotypic data

Genotyping

Genomic selection and enrichment planting…
…
…

Model trainingy = Xb + Zu + e

Progeny trial(384)Training pop(284)

Randomly
select 100
individuals

Test pop(100)

VerifyWe can use SNP
genotypes forselection!

Phenotyping in future
Our analysis is now ongoing…..
Measured phenotypes are limited in growth related traits.
Genomic selection can enhance growth of dipterocarp plantation, which cancontribute to return of forestry, mitigation of climate change.
In near future, we measure physiological traits, which is highly related to environmental
changes, such as climate change.
Leaf flushing timing
Photosynthesis activity
Water potential in leaf
Root catabolism speed
Etc.
We can efficiently utilize genetic resources through genomic selection and planting formitigation and adaptation of climate change!

Research member and acknowledgement
U Gadja Mada
U Tsukuba
FFPRI
FRIM
JIRCAS

Widiyatno, Indrioko S, Utomo S, Na’iem M
Sawitri, Tsumura Y,Uchiyama K
Lee SL, Ng KKS
Oka H, Yamamoto Y, Tani N
Supported by PT Sari Bumi Kusuma

www.jircas.go.jp