Shirakawa Institute of Animal Genetics Trinity College Dublin

Shirakawa Institute of Animal Genetics Trinity College Dublin

Shirakawa Institute of Animal Genetics Trinity College Dublin KARI-TRC Functional genomics to identify genes and networks Shirakawa Institute of influencing survivalAnimal Genetics following Trypanosome Trinity College

challenge. Dublin KARI-TRC Origins of NDama and Boran cattle Boran NDama Bovins Cattle Glossines

Tsetse Bovins Cattle et andGlossines tsetse Studying the tolerant/susceptible phenotype has problems: Separating cause from effect Separating relevant from irrelevant. Dominance of the what is happening to this weeks trendy gene/protein/cytokine? approach.

A gene mapping approach, by definition, points to the true genetic cause of the difference between resistant and susceptible ARHGA15 on BTA2 remains a candidate B) List of genes in the human on HSA2 A) Anaemia QTL HSA Start (bp) Description Ext_Gene ID Bta_cM 2 137107153 Bos taurus microsatellite

BM4440 60.3 2 137647798 Q9C0I4 2 137825761Bos taurus microsatellite DIK4673 59.3 2 137914894Bos taurus microsatellite MNB187 59.3 2

138555540Histamine N-methyltransferase (EC 2.1.1.8) (HMT). HNMT [Source:Uniprot/SWISSPROT;Acc:P50135] 2 139093103 NP_001001664 2 139262224Neurexophilin 2 precursor (Fragment). [Source:Uniprot/SWISSPROT;Acc:O95156] NXPH2 2 140117845Bos taurus microsatellite DIK4025 56.9 2 140709091Bos taurus microsatellite

RM356 56.9 2 140822732Low-density lipoprotein receptor-related protein LRP1B 1B precursor (Low- density lipoprotein receptor-related protei 2 143468951Kynureninase (EC 3.7.1.3) (L-kynurenine hydrolase). KYNU [Source:Uniprot/SWISSPROT;Acc:Q16719] 2 143720695ARHGAP15; uncharacterized bone marrow protein ARHGAP15 BM046 [Homo sapiens]. [Source:RefSeq;Acc:NM_018460] 2

143736439Bos taurus microsatellite DIK2705 52.9 2 144537053glycosyltransferase-like 1; PRO0159 protein; NP_078935 glycosyltransferase-like domain containing 1 [Homo sapiens]. [S 2 144979317Zinc finger homeobox protein 1b (Smad interacting ZFHX1Bprotein 1) (SMADIP1) (HRIHFB2411). [Source:Uniprot/SWIS 2 145045361Bos taurus microsatellite BMS1300 50.6 2

145937413Bos taurus microsatellite DIK2496 49.6 2 148202704Bos taurus microsatellite BMS2053 47.2 2 148436296Activin receptor type II precursor (EC 2.7.1.37) ACVR2 (ACTR-II) (ACTRIIA). [Source:Uniprot/SWISSPROT;Acc:P27037] 2 148525464Origin recognition complex subunit 4. [Source:Uniprot/SWISSPROT;Acc:O43929] ORC4L

2 149236074enhancer of polycomb homolog 2 [Homo sapiens]. EPC2 [Source:RefSeq;Acc:NM_015630] 2 149377400Bos taurus microsatellite DIK4077 41.6 2 149466919Kinesin heavy chain isoform 5C (Kinesin heavy KIF5C chain neuron-specific 2). [Source:Uniprot/SWISSPROT;Acc:O60 2 149720484 NP_808879

2 150012007 NP_919298 2 150172491Bos taurus microsatellite DIK1140 46.3 2 150251661 C2orf25 2 150540230 NP_694586 2

150838279Bos taurus microsatellite BMS2782 45.3 2 151150220Rho-related GTP-binding protein RhoE (Rho8)ARHE (Rnd3). [Source:Uniprot/SWISSPROT;Acc:P61587] 2 151283669Bos taurus microsatellite DIK2853 45.3 2 151753288Bos taurus microsatellite BMS803 44.5

NA profiles indicate that RAC1 the target modulated by ARHGAP differently expressed in Boran and NDama cattle. Alignment of NDama ARHGAP15 with homologues H P mutation at AA282 Cow NDama KFITRRPSLKTLQEKGLIKDQIFGSPLHTLCEREKSTVPRFVKQCIEAVEK Cow Boran

KFITRRPSLKTLQEKGLIKDQIFGSHLHTLCEREKSTVPRFVKQCIEAVEK Human KFISRRPSLKTLQEKGLIKDQIFGSHLHTVCEREHSTVPWFVKQCIEAVEK Pig KFITRRPSLKTLQEKGLIKDQIFGSHLHTVCERENSTVPRFVKQCIEAVEK Chicken KFISRRPSLKTLQEKGLIKDQIFGSHLHLVCEHENSTVPQFVRQCIKAVER

Salmon KFISRRPSMKTLQEKGIIKDRVFGCHLLALCEREGTTVPKFVRQCVEAVEK Gene frequency N'Dama (n = 35) Boran (n = 28) 282P-Allele 0.990 0.125 282H-Allele 0.010 0.875

Genotype + Phenotype -> Genetic region -> polymorphism ->understanding ->exploitation We examined the entire genome for regions involved in one phenotype But can we move to the next level and screen multiple phenotypes simultaneously ? Livestock are by definition adapted to the landscape they inhabit.

Landscape Temperature, altitude, rainfall etc Disease challenge Nutritional challenge Human selection Farming system In Europe these are extremely homogeneous In Africa they are extremely stratified and extremely unstable Different genotypes respond differently in a given environment. Following trypanosome challenge Ndama live while Boran die.

And we can see their genomes responding differently Principle components analysis of data from genome-wide expression analysis comparing gene expression in liver of Ndama (red) vs Boran (blue) in response to infection with T. congolense. Light colour day 29 post infection, dark day 32 post infection. Components 1 and 2. (Components 3 and 4 separate by day post infection) And the same data for spleen. The biggest effect we see (after tissue) is breed. Principle components analysis of data from genome-wide expression analysis comparing gene expression in spleen of Ndama (red) vs Boran (blue) in response to infection with T. congolense. Light colour day 29 post infection, dark day 32 post infection. Components 1 and 2. (Components 3 and 4 separate by day post infection)

Mouse time course. Liver. So we need to understand the fit between the livestock genotype and the landscape in which they function. Example Build a road Develop a vaccine Improve (or shut off) market access Change the climate! Livestock Landscape Genomics Any change in the landscape changes the

optimal livestock type There is information in the distribution of livestock genotypes across the environment. The tools -genetic, GIS, farm system analysis - are available now to allow us to ask what features of the genome is exposed to selection by what factors in the environment. The next level of genome scanning. There is information in the distribution of livestock genotypes across the environment. Boran

NDama Bovins Cattle Glossines Tsetse Bovins Cattle et andGlossines tsetse To extract information from distribution is a challenge. Input:

High density SNP data Detailed metadata on individual animals GIS data and derived disease/climate information Farming systems analysis Output: Predictions about consequences of change to landscapes Tools to manage landscapes for agriculture To extract information from distribution is a challenge. Data collection

- metadata Data management Data QC Data integration Data display tools

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