Dario Grattapaglia

Genomic prediction of complex phenotypes:Driving innovation in the Brazilian forest based 

industry

Ron Sederoff’s “legacy” at the IUFRO Tree Biotechnology - Brazil – 2011

Actually just a small part...

Ron Sederoff, the “father of tree biotechnology”

The global area of planted forests is still very smallcompared to the wood demand

8% of world’s forest area 2% of land use 270 million hectares but is has grown in the last 20 years

FAO Global Forest Resource Assessment 2015

Eucalyptus: a global tree

Brazil

Preserved areas and other uses 

Arable Land All Crops

5

529 mi ha

315 mi ha

72 mi ha7 mi ha

Planted Forests

851 mi ha

Source: IBGE(2011)

Less than 1%

Land use in Brazil

Eucalypts “fiber farms” in tropical sites

Current realized average mean annual increment  (MAI) in industrially managed 

Eucalyptus  forests in Brazil

45 m3/ha/year 

Loblolly pine in SE USA15 m3/ha/year 

Productivity at rotation age

6 years  ‐ 270 m3/ha

Evolution of eucalypt planted forest productivity in Brazil

7

Evolution of forest productivity

Productivity tripled in 50 years

TechnologiesTree breeding

Clonal propagation

Soil managementMechanized harvest

Nutrition

Minimal cultivation (No till farming)

Integrated Pest managementCombined biological and chemical

Shared knowledge through networksCompanies/Universities/Embrapa

60 70 90 00

1525

30

40

Dec.

m³ /

 ha / yr

1970 :  170,000 ha

Land area needed toproduce 1 milliontons of cellulose

2000 :  100,000 ha

Challenges for planted forestsmore wood on less land

Source: WBCSD, WWF, FAO. 

Projected 9.5 billion people

10 billion m³ of wood needed

Consumption: 3X current 

50% has to come from planted forests

+ 250 million hectares of planted forests

Increased income and consumption in developing countries

Increased demand for products and servicesfrom forests

Who ?How ?Where ?

Forest tree breeding

• Trees are largely undomesticated, lots of genetic variation

• Long breeding cycles, poor juvenile mature correlations

• Logistically complex, large areas, multiple sites

• Late expressing traits and delayed flowering

• Extended time‐lag between the breeding investment and 

the deployment of genetically improved material

• Costly operation, more susceptible to changes in market 

demands, business objectives and climate change

The breeder's equation

Genetic gain =   i * r * A

L i = selection intensityr = selection accuracy (correlation between estimated breeding value and true BV)A = additive genetic standard deviation (additive genetic variation available)L = breeding cycle length

Advanced breeding and selection has a great impact on forest productivity  

Currently planted elite clone – age 2

Newly selected elite clone – age 2

Photograph: Fibria

Steps taken for the selection of new elite clonesSelection parameters: trunk and crown form, % bark,  productivity Adt/ha; wood

quality; disease resistance, financial margin

Hybrid mating Selection of best families and best trees in hybridprogeny trials (growth, form, pilodyn density and NIRS)

Best trees are felled Production of cutting for first clonal trial

Selection of top clones in first clonal trial(growth, form, density and NIRS)

Production of cutting for expanded clonal trial

Selection of top clones in expanded clonal trial(growth, density, disease resistance, wood quality)

Production of cutting for minicutting expansion

Production of clonal plants for planting

Commercial forests

Even in fast growing Eucalyptus this process takes between 12 and 16 yearsEarly selection methods for late expressing traits and hard to measure traits would 

be very useful especially wood quality and disease resistance 

Biometricians: did not believe that Mendelian

genetics can explain complex traits

The longest-standing question in genetics:How does genetic variation contribute to

phenotypic variation?

Molecular biologistsbelieve on the widespread existence

of single genes of large effects controlling complex phenotypes

Mendelians: focused on discrete, monogenic

phenotypes

Quantitative geneticistsdevised statistical methods to

treat complex traits by partitioning variances

Debate was resolved in a 1918 paper by R.A. Fisher: the “infinitesimal model”

Many genes affect a trait, producing a continuous, normally distributed phenotype in the population

GENOMICS now allows convergence !!

THE CONVERGENCE OF GENOMICS AND QUANTITATIVE GENETICS

In advanced tree breeding we are moving from trying to discover genes and determine their individual effects, to dealing with the full aggregate 

effect of the entire genome

Genomics and prediction

Genomic Selection: put in a simple concept

Select on thousands of DNA markers across the entire genomeso that ALL gene effects are captured in a predictive model

“GENES” DNA markers

• SNP data• SNP dataGENOTYPESGENOTYPES

•Trait data• Trait dataPHENOTYPESPHENOTYPES

Predictive model

Y =  Xb + Zh + e

Predictive model

Y =  Xb + Zh + e

Cross validation

SELECTION CANDIDATES (Young seedlings genotyped but not phenotyped)(e.g. 100 full or half‐sib families of 100 offspring each = 10,000 seedlings)

• SNP data• SNP dataGENOTYPESGENOTYPES

Predictive model updating

GENOMIC SELECTION CYCLE

Elite clones

Selected seedlings (top 5% ranked by GS) 

Field trial and phenotype

Clonal trial of top ranked GEGV seedlings

Flower induction of top ranked GEBV seedlings

BREEDING

DEVELOPMENT OF PREDICTIVE MODEL

(e.g.  progeny trial N ~ 2,000 of a breeding population with Ne ~ 60)

Training population

Validation population

Van Eenennaam 2014 Ann. Rev. Animal Biosciences

Conventional progeny test based breeding

Genomic Selection based program – Genomic Bulls

Genomic Selection: an operational technology in animal breeding

Cenibra population Ne = 11 Fibria population Ne = 51

Trait Diameter Height Wood 

Density

Pulp 

Yield

Diameter Height Wood 

Density

Pulp 

Yield

Heritability from pedigree 0.53 0.42 0.59 0.38 0.56 0.48 0.42 0.47

Number of individuals  780 780 820 594 920 920 920 650

Predictive ability 0.54 0.51 0.60 0.54 0.55 0.46 0.42 0.38

Accuracy of Genomic BLUP 0.74 0.79 0.78 0.88 0.73 0.66 0.65 0.55

Accuracy of phenotypic BLUP 0.80 0.76 0.83 0.73 0.82 0.79 0.77 0.74

Resende et al. 2012 New PhytologistResende et al. 2012 New Phytologist

We started Genomic Selection in fores trees in 2007 Eucalyptus – first experimental results in 2009

We started Genomic Selection in fores trees in 2007 Eucalyptus – first experimental results in 2009

• Genomic prediction matched phenotypic prediction for all traits• Predictive ability across populations was very low (< 0.2)• Variable genetic background and G x E confounded• GS models should be population and environment specific

• Genomic prediction matched phenotypic prediction for all traits• Predictive ability across populations was very low (< 0.2)• Variable genetic background and G x E confounded• GS models should be population and environment specific

Eucalyptus genome

Myburg, Grattapaglia, Tuskan et al. 2014

Genome size: 640 Mbp605 Mbp (94%) in 11 chromosomes

36,376 predicted protein‐coding genes

Genomic Selection requires a highly efficient DNA marker platform 

Development of a DNA CHIP for Eucalyptus

• For long term implementation of GS in Eucalyptus we developed a DNA marker platform with:– Genome‐wide DNA marker density– High reproducibility and portability of data– Informative for the BIG TEN Eucalyptus species– Speed of data delivery – Public access, worldwide use – Low cost per sample

• “Crowd funding”: eucalypt forest companies worldwide• We sequenced the genome of 240 eucalyptu trees form 12 

different species planted worldwide

The EuCHIP provides high quality DNA data for 60 thousand markers in the genome 

Homozygous AA

Heterozygous AG

Homozygous GG

Missing data

• Automated genotyping with stringent genotype declaration parameters• Minimal human intervention in data editing (removal of bad samples)• Reproducibility above 99.99% within and between experiments• User friendly data files; immediately usable into common softwares

GENOMIC PREDICTIONS

OF 15 GROWTH AND WOOD TRAITS

Good correlations between predicted and 

observed dataas good as or better that direct phenotypic 

measurements following independent 

cross validation OBSERVED

PRED

ICTE

D

Resende, R.T. et al. 2017 Heredity

Average genomic value

of the top twenty genomically selected

trees

Mean annual volume growth

Basic wood density

CellulosePulp Yield

Probability of rejecting the null hypothesis (=1%) that Genomic Selection would

select randomly

Genomic Selection successfully

identifies the top trees

Results in other forest tree species followedGood genomic prediction abilities across species and traits

• Loblolly pine (Pinus taeda)• Public dataset and no differences among models (Resende et al. 2012)• Prediction driven by relatedness (Zapata‐Valenzuela et al. 2012)• GRM better to separate additive and non‐additive effects (Munoz et al. 2014)

• White spruce (Picea glauca)• Prediction strongly dependent on relatedness (Beaulieu et al. 2014a)• Prediction accuracy across environments varies with trait (Beaulieu et al. 2014b)

• Interior spruce (PIcea glauca x engelmannii)• P.A.s were good within but unreliable across environments (El‐Dien et al. 2015)• Major G x Age effect on P.A.s; no difference across models (Ratcliffe et al. 2015)

• Maritime pine (Pinus pinaster)• Training on parents and progeny; no difference across models (Isik et al. 2016)• Good predictions across generations (Bartholomé et al. 2016)

AND MORE RESULTS ARE COMING....

Genomic Selection tree breeding

Time gain: significantly accelerate breeding cycles

Improved precision for hard to select or late expressing  

traits (ex. wood quality, stem form)

Selection for ALL traits simultaneously in ALL plants

Higher selection intensity

A ‘back of the envelope’ financial analysis

CONVENTIONAL Eucalyptus BREEDING          18 YEARSCONVENTIONAL Eucalyptus BREEDING          18 YEARS

GENOMIC SELECTIONBREEDING

GENOMIC SELECTIONBREEDING

• How much does GS cost ? •~500k US$/generation• How much is it worth having wood that provides 1% higher pulp yield nine years ahead of time in a 1 Millionton pulp mill? •10K ton x 800 US$ x 9 years =  72 M US$

• How much does GS cost ? •~500k US$/generation• How much is it worth having wood that provides 1% higher pulp yield nine years ahead of time in a 1 Millionton pulp mill? •10K ton x 800 US$ x 9 years =  72 M US$

TIME SAVINGS: 9 YEARS

GARTNER HYPE CYCLE OF NEW TECHNOLOGIES

GENOMIC SELECTION IN FOREST TREES: WHERE ARE WE NOW?

Genomic selection in forest trees – current research

Multi‐trait selection: GS index based on economic value

Inbreeding and reduction of diversity   Better management of inbreeding by specifying the Mendelian term  Greater impact of reduction of diversity surrounding QTLs due to “hitch‐hiking” Weighed GS to reduce loss of rare alleles

"Moving target” environment“PAST PERFORMANCE IS NO GUARANTEE OF FUTURE RETURNS" “Training” in expected future environments (climate change)

Predictive model updating Counterbalance decay of relationship and LD Change in trait architecture and environment; continuous validation 

Logistics: flower induction, people, detailed case‐by‐case cost/benefit analysis

“Essentially, all models are wrong, but some are useful”

George E. P. Box    1919‐2013

“There are an awful lot of ways for predictions to go wrong thanks to bad 

incentives and bad methods”Nate Silver 2012

“It’s difficult to make predictions, especially about the future”

Niels Bohr 1885 ‐ 1962

Genomic Selection in EucalyptusCompanies already investing in this new breeding

technology in Brazil and the world using the EuCHIP60K

Genomic Selection in EucalyptusCompanies already investing in this new breeding

technology in Brazil and the world using the EuCHIP60K

AcknowledgmentsAcknowledgments

Marcos Resende

MarcioResende

Carolina Sansaloni

Cesar Petroli

Danielle Faria

Andrzej Kilian

OrzenilBonfim

Funding

DArT projects

AlexandreMissiaggia

ElizabeteTakahashi

PhenotypingBioinfo ‐ EuCHIP60K

GS Prediction and GWAS

Shawn Mansfield 

UBC

Eduardo Cappa

Matias KirstPatricio MuñozLeandro Neves

Collaborations

Bruno Lima

Daniel Pomp Harry WuPar Ingvarsson

Biyue Tan Barbara Muller

Thanks!

dario.grattapaglia@embrapa.br