PowerPoint Presentation€¦ · PPT file · Web view · 2009-07-22nitrilase 1. [Source:RefSeq;Acc:NM_012049] NM_012049. ENSMUSG00000006412.2. Prefoldin subunit 2....

Trinity College Dublin

KARI-TRC

Shirakawa Institute of Animal Genetics

Trinity College Dublin

KARI-TRC

Shirakawa Institute of Animal Genetics

Functional genomics to identify genes and networks

influencing survival following Trypanosome

challenge.

Bovins

Bovins et GlossinesGlossines

CattleTsetseCattle and tsetse

Origins of N’Dama and Boran cattle

N’DamaBoran

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

A) Anaemia QTL

B) List of genes in the human on HSA2HSA Start (bp) Description Ext_Gene ID Bta_cM

2 137107153 Bos taurus microsatellite BM4440 60.32 137647798 Q9C0I42 137825761Bos taurus microsatellite DIK4673 59.32 137914894Bos taurus microsatellite MNB187 59.32 138555540Histamine N-methyltransferase (EC 2.1.1.8) (HMT). [Source:Uniprot/SWISSPROT;Acc:P50135]HNMT2 139093103 NP_0010016642 139262224Neurexophilin 2 precursor (Fragment). [Source:Uniprot/SWISSPROT;Acc:O95156]NXPH22 140117845Bos taurus microsatellite DIK4025 56.92 140709091Bos taurus microsatellite RM356 56.92 140822732Low-density lipoprotein receptor-related protein 1B precursor (Low- density lipoprotein receptor-related protein-deleted in tumor) (LRP- DIT). [Source:Uniprot/SWISSPROT;Acc:Q9NZR2]LRP1B2 143468951Kynureninase (EC 3.7.1.3) (L-kynurenine hydrolase). [Source:Uniprot/SWISSPROT;Acc:Q16719]KYNU2 143720695ARHGAP15; uncharacterized bone marrow protein BM046 [Homo sapiens]. [Source:RefSeq;Acc:NM_018460]ARHGAP152 143736439Bos taurus microsatellite DIK2705 52.92 144537053glycosyltransferase-like 1; PRO0159 protein; glycosyltransferase-like domain containing 1 [Homo sapiens]. [Source:RefSeq;Acc:NM_024659]NP_0789352 144979317Zinc finger homeobox protein 1b (Smad interacting protein 1) (SMADIP1) (HRIHFB2411). [Source:Uniprot/SWISSPROT;Acc:O60315]ZFHX1B2 145045361Bos taurus microsatellite BMS1300 50.62 145937413Bos taurus microsatellite DIK2496 49.62 148202704Bos taurus microsatellite BMS2053 47.22 148436296Activin receptor type II precursor (EC 2.7.1.37) (ACTR-II) (ACTRIIA). [Source:Uniprot/SWISSPROT;Acc:P27037]ACVR22 148525464Origin recognition complex subunit 4. [Source:Uniprot/SWISSPROT;Acc:O43929]ORC4L2 149236074enhancer of polycomb homolog 2 [Homo sapiens]. [Source:RefSeq;Acc:NM_015630]EPC22 149377400Bos taurus microsatellite DIK4077 41.62 149466919Kinesin heavy chain isoform 5C (Kinesin heavy chain neuron-specific 2). [Source:Uniprot/SWISSPROT;Acc:O60282]KIF5C2 149720484 NP_8088792 150012007 NP_9192982 150172491Bos taurus microsatellite DIK1140 46.32 150251661 C2orf252 150540230 NP_6945862 150838279Bos taurus microsatellite BMS2782 45.32 151150220Rho-related GTP-binding protein RhoE (Rho8) (Rnd3). [Source:Uniprot/SWISSPROT;Acc:P61587]ARHE2 151283669Bos taurus microsatellite DIK2853 45.32 151753288Bos taurus microsatellite BMS803 44.5

ARHGA15 on BTA2 remains a candidate

mRNA profiles indicate that RAC1 the target modulated by ARHGAP15is differently expressed in Boran and N’Dama cattle.

N'Dama (n = 35)Boran (n = 28)282P-Allele 0.990 0.125282H-Allele 0.010 0.875

Gene frequency

H P mutation at AA282

Alignment of N’Dama ARHGAP15 with homologues

Cow NDama KFITRRPSLKTLQEKGLIKDQIFGSPLHTLCEREKSTVPRFVKQCIEAVEK Cow Boran KFITRRPSLKTLQEKGLIKDQIFGSHLHTLCEREKSTVPRFVKQCIEAVEK Human KFISRRPSLKTLQEKGLIKDQIFGSHLHTVCEREHSTVPWFVKQCIEAVEK Pig KFITRRPSLKTLQEKGLIKDQIFGSHLHTVCERENSTVPRFVKQCIEAVEK Chicken KFISRRPSLKTLQEKGLIKDQIFGSHLHLVCEHENSTVPQFVRQCIKAVER Salmon KFISRRPSMKTLQEKGIIKDRVFGCHLLALCEREGTTVPKFVRQCVEAVEK

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

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)

Different genotypes respond differently in a given environment.

Following trypanosome challenge N’dama 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 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)

And the same data for spleen.

The biggest effect we see (after tissue) is breed.

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.

Bovins

Bovins et GlossinesGlossines

CattleTsetseCattle and tsetse

N’DamaBoran

There is information in the distribution of livestock genotypes across the environment.

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 analysisOutput:

Predictions about consequences of change to landscapes

Tools to manage landscapes for agriculture

Unique probe of genome function

To extract information from distribution is a challenge.

• Data collection

- metadata • Data management• Data QC• Data integration• Data display tools• Statistics !

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