Approaches Challenge Improve livestock productivity - Changing/moving the production systems (e.g. from crop-livestock/pastoral systems to industrial ones) - Making better use of the local genetic resources by Keep it in the hoof, Move it (or lose it), Put some in the bank, Match breeds to environments (Séré et al. 2008. AGRI)
Keep it on the hoof : Encouraging the continuing sustainable use of traditional breeds and in situ conservation by providing market-driven incentive, public policy... Move it or loose it : Enable access and safe movement of AnGR within and between countries, regions and continents Put some in the bank : ex-situ in vitro conservation of AnGR, long-term insurance Match breeds to environment : Understanding the match between livestock populations, breeds and genes with the environment (including the economic landscape)
Livestock landscape genomics and Genomic selection The future for livestock production in the tropics? GLOBALDIV Final International Workshop February 8 and 9, 2011 Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland Olivier Hanotte Olivier.hanotte@nottingham.ac.uk
Landscape Genetics/Genomics Interdisciplinary - spatial statistics, landscape ecology, population genetics and molecular biology etc...) Landscape genetics (Manel et al. 2003): aim to study interaction between landscape features and microevolutionary processes (gene flow, genetic drift), as well as to correlate allele frequencies with the environment to understand the effect of the environment on the adaptive component of genetic diversity. Manel et al. (2010). Molecular Ecology 19: 3760-3772.
Landscape Genetics/Genomics Landscape genomics (Luikart et al. 2003, Joost et al. 2007): correlation between genomic data (.e.g. genome wide scan) and environmental parameters to identify genes (chromosomal fragment) alleles (haplotypes) under selection. Typically landscape genetics/genomics will include the comparison of adaptive and neutral variation to quantify the effects landscape features, including environmental variables, on spatial genetic variation and they will involve the detection of signature of selection at the genome (and causative mutation).
The expectation is that it will pave the way to the understanding of how an population/species will be capable to respond to environmental changes (major ef Does not require direct measurement phenotypic data on the adaptive traits of interests! Ideal for wild species... Environmental traits Mendelian - Quantitative traits (major effects) Applied to livestock: Livestock landscape genetic/genomics
Genomic selection Genomic selection (Meuwissen et al. 2001): selection of animals for breeding based on estimated breeding values calculated from the joint effects (additive effects) of genetic markers covering the whole genome in LD for the trait of interest. Design for the livestock breeds of the industrialized production systems (intensive) with accurate recording of breeds phenotypes Required known genotypes and phenotypes for references populations Quantitative traits (QTL) Complex traits
Goddard and Hayes Nature Review Genetics (2009) Purpose: breed productivity improvement through the calculation of individual genomic breeding values Two advantages: All the genetic variance for a trait can be tracked by the markers panel Effect of marker alleles can be estimated on a population basis rather than within family
Challenges of genomic selection Need of large reference population to accurately estimate SNP effects (several thousands USDA 6,700 dairy bulls... 0.8 accuracy of genomic breeding values). Beef and sheep (chicken?) industries involvement of multiple breeds and crosses require larger number of SNP compared to dairy herds (30-40,000 versus > 300,000). Driven by the private sector Genomic selection Goddard and Hayes Nature Review Genetics (2009)
Can Livestock landscape genomics and Genomic selection Contribute to the improvement of livestock productivity in the tropics?
Proof of principle 2009 Purpose: Selecting cattle adapted to predicted changes in the environment by studying the sensitivity of milk production to environmental conditions (feeding level and temperature humidity). Three data sets: - Discovery: 62343 Holstein Friesian sired by 798 bulls - First validation data set: 23603 cows sired by 453 bulls - Second validation data set: 35293 Jersey sired by 364 bulls
Temperature Humidity Dairy farms Level of feeding Weather stations
Livestock landscape genomics component Discovery set Holstein - Friesian Illumina BovineSNP50 beadchip 39048 SNP markers ( ~ 56,000) Validation sets Ṯwo parameters for each trait Average milk production at the mean level of the environmental variable Sensitivity of the milk yield to changes in the envirommental variable
Livestock landscape genomics component Chromosomal distributions Fine mapping and candidate genes Sensitivity of milk production THI BTA29 Fibroblast growth factor 4 Regulator of mammary epithelial cell Apoptosis - Sensitivity to feeding level Pathways for carbohydrate and phopholipid metabolism BT9
Genomic selection component Predicted response in daily milk production of daughters to temperature humidity index (THI) for the two most extreme sires from the data set. In a climate change scenario where the THI increases significantly, sire 2 should be selected for breeding as the milk yield of his daughters is relatively insensitive to THI. B. Predicted response in daily milk production of daughters to herd average daily milk production (HTDMY), a surrogate for the level of feeding, for two sires from the data set. With low levels of feeding, eg. low inputs of grain, sire 2 could be considered as his daughters produce more milk than the daughters of sire 2 at very low levels of feeding.
Livestock landscape genomics and Genomic selection For the tropics The case of Africa cattle
The case of African cattle
The case of African cattle
Hanotte et al. Science 2002 The case of African cattle
The case of African cattle Zebu admixture level
The case of African cattle
The case of African cattle
Etc... The case of African cattle
The case of African cattle Pastoral Crops-livestock
Starting point Trait Bta 1 Bta 2 Bta 4 Bta 7 Bta 8 Bta 13 Bta 14 Bta 16 Bta 17 Bta 20 Bta 22 Bta 23 Bta 24 Bta 25 Bta 26 Bta 27 Bta 28 Bta 29 PCVI D*** (R) BWI (A) R** BWM (A) (A) R* D* R* PCVM A* R* A* PCVF R** PCVIF R*** R** PCVIM R*** D** R* D* R* PCVFM (D) (D) R* PCVV R*** R** (D) R* PCVD150 A* D** R*** (R) A* A* R** PCVD100 (A) (R) D* R* R*** D** R*** BWF/I A* D* BWD (A) D** (R) R* PARMLn R*** PARLnM D** (D) (R) D* DR (R) D* D* - Genetic model: A = additive, R = Recessive, D= dominant, D = overdominant, R = negative overdominance - Level of significance: ( ) = P < 0.1, * = P < 0.0185 (FDR 20%), ** = P < 0.0043 (FDR 10%), *** = P < 0.0008 (FDR 5%) - Origin of the allele conferring higher trypanotolerance or increase in body weight: Green N'Dama, Red Boran Hanotte et al. PNAS 2003
Environmental constraints to livestock productivity in Africa - Climatic - Parasitic infectious diseases - Feeding
AIMS Remedy the widely recognised lack of baseline epidemiological data on the dynamics and impacts of infectious diseases of cattle in East Africa. Determine whether the negative impacts of different infections are independent of one another, testing the hypothesis that their impacts may be synergistic or (in some instances) antagonistic. Determine whether positive traits (e.g. resistance to infection and/or disease, good condition, immunological status etc.) cluster in certain individuals: the good-cow/bad-cow hypothesis.
Mary Ndila PhD Student 2009 June 2012 Population genomics and signature of selection for disease resistances in African indigenous cattle
Study site KEY LOWER MIDLANDS 1 (LM1) LOWER MIDLANDS 2 (LM2) LOWER MIDLANDS 3 (LM3) UPPER MIDLANDS (UM3) SUBLOCATIONS
Study design
R 2 = 0.9879 R 2 = 0.9966 European taurine R 2 = 0.9984 R 2 = 0.9995
R 2 = 0.9331 R 2 = 0.9819 R 2 = 0.9923 Asian zebu R 2 = 0.9973
R 2 = 0.2365 R 2 = 0.6137 R 2 = 0.7921 African taurine R 2 = 0.9248
Goddard and Hayes Nature Review Genetics (2009) Wraag (2010) Unpublished
Coat patterns Uniform Roan Dappled Pied Brindled Spotted
Coat patterns Roan Piedbald Uniform Dappled Spotted Brindle GENE LOCATION Chr 5 mast cell growth factor (MGF) locus (Seitz et al., 1999) Chr 6 Spotted locus encompassing KIT gene (Grosz&MacNeil 1999;Fontanesi et al.,2009) Chr 6 Spotted locus encompassing KIT gene (Grosz&MacNeil 1999;Fontanesi et al.,2009) Chr 18 Agouti locus (Girardot et al., 2006) Coat colour Gene location White coat colour chr 6 KIT v kit Hardy Zuckerman 4 feline sarcoma viral oncogene homolog black /brown coat colour Chr18 Extension locus melanocortin 1 receptor (MC1R) gene
Association dead/alive SNP.Name Chrom osome Position P1df Pc1df N ARS.BFGL.NGS. 81795 10 49981642 7.80E 07 8.86E 07 542 BFGL.NGS.1136 45 22 7042758 1.85E 05 2.04E 05 521 ARS.BFGL.NGS. 67591 13 28183389 3.52E 05 3.86E 05 542 Hapmap48667. BTA.16502 18 20531300 4.09E 05 4.47E 05 543 Hapmap44009. BTA.114471 21 12051183 6.51E 05 7.09E 05 543 BFGL.NGS.1130 05 2 1.24E+08 6.59E 05 7.18E 05 528 BTA.115525.no. rs 11 94101466 7.44E 05 8.09E 05 543 ARS.BFGL.NGS. 78058 19 8307212 7.54E 05 8.20E 05 543 BTA.34893.no.r s 14 4209266 7.62E 05 8.29E 05 501 BTA.106866.no. rs 6 28730349 7.84E 05 8.86E 07 543 Preliminary results
Livestock landscape genomics Genomic selection Diversity of livestock genotypes Population genetics structure Human selection Natural selection Mapping of genomic and environmental diversity Livestock genotype tailored to the local production environment New technologies Genome association study Genomic selection
Science 328, 1640-1641 (2010)
Georges Louis Leclerc Comte de Buffon 1707-1788 Carl von Linné Carolus Linnaeus 1707-1778