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RESEARCH SCHOOL GENETICS /

FORSKARSKULE GENETIKKTormod Ådnøy, leader

5.9.2007

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Welcome!Program today

1415 On the Research school genetics at UMB. Tormod Ådnøy

1438 ’Lille lørdag’ – Åsmund Bjørnstad 1440 Group work – Who we are, what we know,

and what we want. Groups of 3-5 participants. 1455 Brief summing up by the youngest in every

group. 1500 Pizza, beer, .. (free) (Husdyrkantina)

Discussions over tables, and all together. (Genetic small talk permitted)

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What we are An application for funding (web - in Norwegian) / 100.000 nok

But people and ideas are more important than money:

Email list: 42 PhD, 45 Advisors and researchers

Web page http://www.umb.no/22912

Preliminary board (3 PhD, 2 advisors)

– Silje Brenna Hansen (PhD Cigene)

– Marianne Haraldsen (PhD IHA – Forskargruppe genetikk og avl)

– Simen Rød Sandve (PhD IPM – Genetikk og plantebiologi)

– Morten Lillemo (postdoc IPM)

– Tormod Ådnøy, leader research school (assoc.prof. IHA)

A secretary: Anne Golten, IHA

This gathering today, and first Wednesday every month

Focus on PhD students

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What we will be remains to be seen … Send an email for new participants to join the school

– So far membership is not exclusive

– Meetings are open

Peer review groups? Reader groups? Nordic collaboration? Research grant applications? Include MSc students? ECTS for some activities? Presentation of own work for others in the Research

school – May help self-image

– Will give useful training

Future courses in the Research school? Summer courses? ...

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What I will talk about now:

‘SCHOOL’• Institution• Knowledge• Feeling ok• Clowns• Paradigms

‘GENETICS’– Genes

• DNA, mRNA, ..• SNP• Genotypes,

haplotypes• Regulatory nets

– BREEDING• Regression• Additive

relationship

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GENETICS Gene

– Gene maps, DNA, mRNA, … , amino acids and proteins, …

– SNPs identifying genes in single individuals– We know a lot more now than some years ago

Molecular lab people have a lot of information – and will have a lot more!How can it be used?

Can it be used to find the best future individual for a trait we want to improve?

What combination of genes is best?

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How is the gene expressed in a trait?

We don’t see a gene’s value, we see an individual’s complete genome’s value!

Genotype value for the trait

How do we express a gene’s value

– or

How do we know which genes to combine to have a better individual in the future?

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If two individuals were the same except two alleles in a locus

We could say that the difference in the two individuals’ genotype values was the difference of the two allele effects

But the alleles may interact with other genes, or the environment

And normally we have a lot more differences between two individuals than just two different alleles

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Numbers of genotypes

..very many

How do we know which individuals/genotypes to select for future breeding?

– What is you answer?

May we predict what value a not yet existing genotype (of infinitely many) will get for a trait?

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.. to have better individuals in the future ..

Select for single gene effects

Select for haplotype effects

Select for combination of gene effects (dominance, epistasis, heterosis)?

Select for best genotypes today = breeding

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Casein genes in Norwegian goats

DNA from 436 bucks in national breeding scheme

– Analyzed 39 snps (single nucleotide polymorphisms) in 4 casein genes (on same chromosome)

Haplotypes deduced from snp genotypes and relationship

Milk (kg), and protein-, fat-, lactose-% from daughters in Goat dairy control

» Hayes,Ben; Hagesæther,Nina; Ådnøy,Tormod; Pellerud,Grunde; Berg,Paul R.; Lien,Sigbjørn (2006): Haplotype structure of casein genes in Norwegian goats and effects on production traits. Genetics 174, 455-464.

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Casein snp genotypes – excerpt of the 436 bucks

Buck CS

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70

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_9

88

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_9

91

8

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S1

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67

3

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12

-F3

CS

N1

S1

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12

-0

CS

N1

S1

ex1

7_

16

86

0

CS

N2

exo

n7

_1

18

01

CS

N2

exo

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_1

17

70

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20

71

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53

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86

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76

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S2

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7

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7

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Pro

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83

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m_

85

2

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Pro

m_

94

2

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Pro

m_

99

1

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74

CS

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40

CS

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Pro

m_

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91

CS

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38

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99

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50

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36

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 381996043 A C ? ? AG TC AG CT ? DEL.C AG CG A A C T ? A G AG TA TC C ? ? G G G A C ? G T T A TC GT G1996446 A C GA AG AG TC AG CT ? DEL.C AG CG A DEL.A C T C A G AG ? TC C ? AT G G G ? ? T G T T A TC ? G1996846 A CT GA ? AG TC AG ? ? C AG CG GA DEL.A TC CT C GA G A T C C C A GA GA G AT TC TA ? TG TG A T G G1997305 A CT GA AG AG TC AG CT ? C AG CG GA DEL.A TC CT C GA G A T C C C A A A G T T A C ? G A T G G1997769 A C G G G C G T C ? A C A DEL C T C A G A T C C CT AT GA A G T TC TA C TG G A T G G

2003668 A C G G G C G T C C A C A DEL C T C A ? A T C C CT AT GA ? G AT TC TA GC TG TG A TC GT G

2003670 A C G G G C G ? C C A C A DEL C T C A ? A T C C C A A ? G T T A C TG G A T G G

2003671 A C GA ? AG TC AG ? CG DEL.C AG CG A DEL.A C T C A ? AG TA TC C CT AT G G G A C T G T T A TC GT G2003673 A C G ? G C G T C C A C A DEL C T C A ? A T C C T T G ? G A C T G T T A C T G2003674 A CT GA ? AG TC AG CT ? C AG CG GA DEL.A TC CT C GA ? A T C C C A A ? G T T A C ? G A T G G2003841 A ? GA ? AG TC AG ? ? C AG CG GA DEL.A TC ? C GA ? A T C C C A A G G T T A C TG G A T G G2003842 A C GA ? AG TC AG CT ? DEL.C AG CG A DEL.A C T C A ? AG TA TC C CT AT G ? G A C T G T T A TC GT G2003843 A C G ? G C G ? C C A C A DEL C T C A ? AG TA TC C C A G ? G A C T G T T G T G A2001181 AG C ? ? AG TC AG CT ? ? AG CG GA DEL.A TC T ? ? G A T ? C ? AT ? GA ? AT TC TA GC TG TG A TC GT ?2001182 A C ? AG AG TC AG CT ? DEL.C AG CG A A C T C A G AG TA TC C C A G GA G AT C T GC T T A T G G2001185 A C G G G C G T C C A C A DEL C T ? A G A T C C CT AT GA GA G AT TC TA GC TG TG A TC GT G2001186 A C G G G C ? ? C C A C A DEL C T C A G A T C C CT AT GA GA G AT TC TA ? TG TG A TC GT G2001187 A C G G G C G T C C A C A DEL C T C A G A T C C C A G G G A C T G T T AG T G GA2001213 A C G G G C G T C C A C A DEL C T C ? G A T C C CT AT G G G A C T G T T A T G G2001232 A C GA ? AG TC AG CT CG DEL.C AG CG A DEL.A C T C A ? AG TA TC C C A GA ? G AT TC TA ? TG TG A T G G1997782 A C G ? G C G T C C A C A DEL C T C A G A T C C CT AT ? G G A C T G T T A TC ? G1998307 A C G G G C G T C C A C A DEL C T C A G A T C C CT AT GA GA GA AT TC TA ? TG TG A T G G1998429 A C G G G C G T C C A ? A DEL C T C A G A T C C CT AT GA GA G AT TC TA ? TG TG A TC GT G1998450 A C G G G C G T C C A C A DEL C T C A G A T C C CT AT GA GA G AT TC TA GC TG TG A TC GT G1998456 A C G G G C G ? C C A C A DEL C T ? A G A T C C CT AT G G G A C T G T T AG TC GT GA1998590 A C G G G C ? T ? C A C A DEL C T ? A ? ? TA TC C C A GA G G AT TC TA ? TG TG AG T G GA1998607 A C G G G C G T C C A C A DEL C T C A ? A T C C C A GA ? G AT TC TA GC TG TG A T G G1998735 A C G G G C G ? C C A C A DEL C T C A G A T C C CT ? GA GA G AT TC ? ? TG TG A TC GT G1998745 A C G G G C G T C C A C A DEL C T C A G A T C C ? AT GA GA G AT TC TA GC TG TG A TC GT G1999093 A C G ? G C G T C C A C A DEL.A C T C A G A T C C CT AT GA GA G AT TC TA ? TG ? A TC GT G

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Results haplotypes – effects on fat-, protein-% and milk, not significant for lactose%

9 11

19 176 1

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-0,08

-0,06

-0,04

-0,02

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0,06

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Haplotype

Eff

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DY

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Fat %

Prot %

Milk

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Results of single snp – not significant

-0,025

-0,02

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SNP

% P

rote

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Freqent SNP

Rare SNP

Allele 6 in SNP14

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Finding gene effects / Number of genotypes

We deduced 21 haplotypes on bucks, and found additive effects on daughters’ production.

All possible combinations of 39 snps is 339>1018, but number of individuals observed was 436. All genotypes may not be modeled, only the ones observed.

Modeling additive effects of all 39 snp-s simultaneously led to collinearity problems, but we could analyze for one snp at a time.

Even to find all haplotype combinations represented in a sample will be difficult: 21+21*20/2=231 potential genotypes. (Some haplotypes are rare.) How important is haplotype dominance?

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BREEDING To generate the best future individuals – We want to

change the population mean

– Info used:

• Phenotypic observations

• Additive relationship

..best genotypes/ population (for a future environment)

– Given

• Existing populations,

• Existing knowledge about the populations,

• Existing techniques for breeding (AI, blup, ..)

Focus is on population, less on individuals

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How do we know which individuals/genotypes to select for future breeding?

– Select best phenotypes – Mass selection

– Select individuals with best offspring –

– Other methods

Breeders use genes as an alibi – they don’t need them!

Statistics: linear regression of offspring phenotypes on parents’ phenotypes

Additive inheritance of genes is a motive for relationship matrix

– Include info on genes» Meuwissen, T. H. E., Hayes, B. J., & Goddard, M. E. (2001).

Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819-1829.

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The infinitesimal model

Many genes

Small effects

Independently distributed

No change in gene frequency

Equilibrium of gene frequencies

All assumptions are violated in breeding programs, normally

‘Shaky foundation of Fisherian genetics’ – SWO

Why does it work so well?

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Additivity

A crucial question:

To what extent are gene effects additive?

How does deviations from additivity affect the Parent-Offspring relationship: Cov (P,O) =0.5*Additive variance ?

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Genotype values

KKKk

kk

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Two additive loci (aH=2, aK=3)

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Genetic variance – pure additive model

For two loci with two alleles each (H h K k), and only additive gene effects (in figure: aH=2 aK=3, while ah=ak=0):

Let Hi=1 when H-allele is present, Hi=0 when h-allele Then the genotype value is

y(i,j)= [H(i)+H(j)]*aH + [K(i)+K(j)]*aK

(0, 2, 4, …, 10 in figure)

The mean genotype value is

EY= sum p(i,j) * y(i,j) = 2*pH*aH + 2*pK*aK The variance of the genotype values, with random mating and

same disequilibrium in parents’ gametes (’D’= dHK=pHK-pH*pK)

VY= E(Y2)+(EY)2= 2*pH*ph*aH2+2*pK*pk*aK2+4dHK*aH*aK

= VY0 + VYd

Avery, P. J. & Hill, W. G. (1978). The effect of linkage disequilibrium on the genetic variance of a quantitative trait. Adv. Appl. Prob. 4-6. /

Ådnøy, T. (1981). Selection in few-locus models / Seleksjon i få-lokus modellar. PhD-dissertation at Dept Mathem Statist, Agric Univ Norway. 1-218.

Even in the additive model, disequilibrium over loci will change the variance.Linkage, selection, .. may lead to disequilibrium.

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If selection is for an additive trait we should expect that the best allele is fixed in every locus

– Should be no genetic variation left

This does not happen normally

– There is genetic variation left for most traits even after much selection

– Why?

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Bridging the gap: genes – phenotypes (Cigene in eVita)

Arne Gjuvsland (Cigene) PhD dissertation October 2:

linking regulatory gene networks to additivity and dominance

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SCHOOL To learn

– Something is not known by students

– It is normal that students don’t know – that’s why they attend a school

The most important is the process in the students’ heads

Transfer of knowledge – from lectures, books, ..

– It helps to know what you already know

Generation of knowledge

– Important science may generate new ’schools’ (paradigms)

» ’The shaky foundations of Fisherian genetics’ SWO

Creativeness is good in a research school / new ideas

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To feel OK

Emotions are important for learning» We learn more when we fell ok

We (most of us) need to know if what we are doing is good/relevant/useful/

– We may not always rely on our self-evaluation

– We need external evaluations» Norwegians are good at belittling themselves

– We need to compare to what others do

What do you need to trust that you are doing ok?

• If I tell you you’re clever – do you believe me?

Others’ input may correct our learning – make us better students

Don’t be afraid to tell what you don’t know!

– Helps other feel helpful / builds their self-image

Clowns help us relax

– May help us see ourselves in a new light

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I have talked about

Genetics

Breeding

School

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Litle laurdag / Lille lørdag Wednesday=little Saturday

Now professor Åsmund Bjørnstad will tell a story?

Fanfare!!

In comes the clown??

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Groups

– Divide in groups

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GROUP ASSIGNMENT

Who we are

– Present yourself to the group

• Name, birth, occupation, …

What we know

– What techniques do you use? What courses are you taking?

• Variance components, Linear models, Molecular lab, mRNA, micromatrices, HFA401, …

– How does your discipline find the ’best’ individuals for the future?

What we want

– How can a research school be useful?

– What can we contribute yourself and what can we get/buy from others?

– Present two topics where you think our school may be helpful to the whole group at 1455. (By youngest in group.)

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Food and drink

Pizza

– 8 assorted kinds

– 1 vegetarian

– 1 ’muslim’

Salad with vinaigrette

One bottle of drink

– Apple drink

– Clausthaler Beer without alcohol

– Green Tuborg

I need two voluntaries to help with the dishes afterwards

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