Forest Genetics and Tree Breeding in Sweden and SLU Research Program

Transcription

1 Forest Genetics and Tree Breeding in Sweden and SLU Research Program Harry Wu Faculty Professor of Forest Genetics and Tree Breeding Swedish University of Agriculture Sciences, Sweden

2 Content UPSC and Forest Genetics and Tree Breeding Forest Tree Breeding in Sweden SLU Forest Tree Breeding Research

3 Umeå Plant Science Centre (UPSC) Our 40 PIs and 6 research area Developmental Biology Prof. Rishikesh Bhalerao Prof. Ulrika Egertsdotter Dr. Urs Fischer Prof. Karin Ljung Prof. Ewa Mellerowicz Prof. Thomas Moritz Dr. Totte Niittylä Prof. Ove Nilsson Prof. Göran Sandberg Prof. Markus Schmid Prof. Björn Sundberg Dr. Hannele Tuominen Cell Biology Dr. Laszlo Bako Prof. Catherine Bellini Dr. Stephanie Robert Prof. Åsa Strand Genetics and Breeding Prof. Bengt Andersson Gull Dr. Anders Fries Dr. Rosario García-Gil Prof. Harry X Wu Photosynthesis/metabolism Dr. Maria E Eriksson Prof. Per Gardeström Prof. Petter Gustafsson Dr. Johannes Hanson Prof. Vaughan Hurry Prof. Stefan Jansson Dr. Olivier Keech Prof. Leszek Kleczkowski Prof. Göran Samuelsson Prof. Gunnar Wingsle Prof. Emeritus Gunnar Öquist Bioinformatics Dr. Torgeir R Hvidsten Dr. Nathaniel Street Ecophysiology Dr. Benedicte Albrechtsen Dr. Catherine Campbell Dr. Judith Felten Dr. Ulrika Ganeteg Prof. Annika Nordin Prof. Torgny Näsholm Prof. Erling Ögren Prof. Anita Sellstedt

4 Forest Genetic Program in SLU 4 PI + 6 Postdocs + 6 PhD students

5 But, we are backed up by Skogforsk Skogforsk is responsible for all forest tree breeding in Sweden

6 Forest tree breeding in Sweden Swedish forests Growing stock Forest area 23 m ha Birch 10% Other 7% Pine 38% Spruce 45%

7 Species in breeding programs Norway spruce Scots pine Birch Lodgepole pine Minor activities for other species e.g. larch, hybrid aspen, oak, ash, beech, cherry, sitka spruce, Douglas fir

8 Distribution of Scots Pine

9 Distribution of Norway Spruce

10 Large base material Species Scots pine Norway spruce Lodgepole pine Birch Size 6000 plus-trees 6000 plus-trees clones 200 plus-trees 1200 OP families 1400 plus-trees

11 Many breeding populations within species Species Scots pine Norway spruce No. of populations 24 (1000 trees) 22 (1000 trees) Lodgepole pine 11 Birch 6

12 Forest tree breeding in Sweden Breeding populations for - increased value production - preparedness for climatic change - conservation of genetic resources Photoperiod (latitude) Objective traits should be general and long-term Temperature climate (growing season in days)

13 Approach to long-term breeding Increased productivity Volume and survival Preparing for climatic changes Adapation Maintaning genetic diversity Within-family selection

14 Scots pine breeding Crossing 50 parents 2 families/parent 50 full-sib families Testing - alternative strategies 4 test sites A mix of: Progeny testing Phenotypic Clonal testing Pre-selection Selection 50 clones 1 per full-sib family

15 Many Genetic Trials for Scots Pine in Sweden ~430 trials

16 Norway spruce breeding Crossing 50 parents 2 fam/parent 50 full-sib families 2000 clones 40 per family 14 ramets per clone cuttings 4 test sites Selection 50 clones 1 per full-sib family

17 Summary of long-term breeding m Multiple-population breeding system, pops. m Some 50 individuals per population m Essentially within family selection m Double-pair mating, when possible as PAM m Clone and seedling (progeny) testing

18 Genetic evaluation Multi-trait, multi-site estimation of genetic parameters and variances (REML) Breeding value (BLUP) for single and multiple trait index using Mixed Model Equation (MME)

19 SLU breeding research focuses 1. Climate adaption of breeding population 2. Breeding from growth to wood quality traits 3. Optimal breeding strategy to deal with negative genetic correlation and inbreeding 4. Using new genomics and biotechnology

20 1. Climate adaption of breeding population Re-define optimal breeding zones Mega G x E patterns for Norway spruce and Scots pine and Biogeo-climatic mapping of populations Jenny Lundströmer - Industry PhD program

21 Implemented quantitative genetics study to redefine breeding zone 1.1 To examine effectiveness of spatial analysis in Swedish Norway spruce progeny trials 1.2 To dissect the G E patterns of Norway spruce trials in southern and central Sweden 1.3 To examine the cause of the G E patterns of Norway spruce

22 1.1 To examine effectiveness of spatial analysis in Swedish Norway spruce progeny trials Locaitons Southern SWeden 118 Number of trials Central Sweden 16 North Sweden 4 Total variables

23 Spatial analysis improves genetic gains Genetic gain Trait n Parental Offspring Height Diameter Pilodyn Frost damage Branch Straightness Insect Stems Bud burst

24 1.2 To dissect the G E patterns of Norway spruce trials in southern and central Sweden Locations of 20 Norway spruce trials in three deployment (Seed) Zones and 6 test series Latitude Factor analytic model was used y = Xβ + Z 0 u 0 + Z ge [L Ä I ng )f + δ] + e = Xβ + Z 0 u 0 + Z*ge f + Z geδ + e

25 Heat map and dendrogram of cross-site additive genetic correlations for height Test series S6 S2, S3 S1,S4, S5 Cut-off line

26 Can we re-define breeding zone: current seed zone are better definition for breeding zones 6 test series and 3 Seed zones 3 clusters Seed zone 6 Latitude Seed zone 7 Seed zone 8 Longitude

27 1.3 To examine the cause of the G E patterns of Norway spruce Frost damage is main reason cause GXE in southern Sweden Frost damage (field record) could explain 11% of additive genetic correlations Mean temperature of May and June less than 3.2 in May and June (spring frost) and mean temperature September and October (autumn frost) less than 1.3 could explain 27.8% variation of additive genetic correlation matrix

28 2. From growth to wood quality traits Quantitative Genetics Studies of Wood Quality traits Spruce population (500 half-sib families sampled and characterized for wood traits, 100 full-sib families phenotyped for stiffness) Scots pine population (120 families families) Lodgepole pine population (100 families)

29 Negative genetic correlation in Norway spruce Genetic correlation = between growth and wood stiffness 4 3 DBH and MOE Chen et al 2014 TGG

30 Negative genetic correlation in Scots pine Genetic correlation between growth and wood quality traits in Scots pine Hong et al. 2015

31 Negative genetic correlation in lodgepole pine Genetic correlation between growth and wood quality traits in lodgepole pine Övra Density EWD MFA MOE s DBH -0,24-0,53 0,8-0,51 EWP -0,67-0,61-0,18-0,42 Lagfors DBH -0,31-0,48 0,35-0,8 EWP -0,63-0,43-0,96 0,55 Hayatgheibi et al submitted

32 3. Development of breeding strategy to deal with negative genetic correlation 3.1 Breeding objective Estimating economic weights for growth and MOE 3.2 Breeding strategy Single and multiple breeding population

33 3.1 Breeding objective Estimating economic weights for growth and MOE by sawmiling study Bio-economical model Resource Wood and properties Link Relate Structural grades Product Through large scale sawing study for stem straightness, branch size, stiffness Irena Fundova - Industry PhD program

34 3.2 Breeding Strategy: Single VS multiple population strategy 100 genes, r= - 0.6, trait X, Y Multiple population strategy reduce negative correlation but with lower genetic gain Henrik et al, 2015 TGG

35 4. Using new genomics and biotechnology 4.1 Estimate QTL number and their effect 4.2 Design breeding strategy to deal with inbreeding depression in advanced generation 4.3 GWAS and genomic selection

36 Estimating the number of loci that control quantitative traits Marker effect follows exponential distribution Hall et al. TGG, 2016

37 Estimating the number of loci that control quantitative traits Estimated QTL number for 8 types of traits

38 What are optimal long-term tree breeding strategies under genomic model Wu et al. G3, 2016

39 Average genetic gain in breeding population Complete additivity Partial dominance Generation Generation

40 Genome-wide association and genomic selection Capture Mendelian segregation and IBS Use as a first-stage nursery (early) selection to increase selection intensity Use very early selection to shorten generation interval

41 GWAS and Genomic Selection 1. Genotyping plus tree exome sequencing of half-sib (500) and fullsib (120) trees exome sequenced 2. Phenotyping 1. 14,000 rametes of 4500 clones phenotyped for wood stiffness families phenotyped for disease resistance clones and 620 families for phenology and fecundity

42 Thanks for your attention