Use of Genomic data in animal breeding. Theo Meuwissen

Transcription

1 Use of Genomic data in animal breeding Theo Meuwissen

2 Genome: Introduction totality of genes on all chromosomes (1930s) DNA discovered in the 1950s Genomics: Investigations into the structure and function of very large numbers of genes undertaken in a simultaneous fashion

3 Genomics: a new discipline large scale research high throughput new technologies requires subdisciplines: structural genomics comparative genomics functional genomics bioinformatics

4 Why so much interest? expectations are high for: new approaches for drug discovery new understandings on cancer formation new approaches to genetic engineering disease resistance improved nutrient contents of food new selection methods involving new traits

5 genes, markers & maps gene = inherited physical entity affecting traits alleles = alternative forms of genes allele frequency=# allele A / total # of alleles segregation = sampling of alleles in meiosis genes are lying on chromosomes locus = position on chromosome polymorphic gene = more than 1 allele

6 terminology (2) genotype = allelic constitution at 1,..,all loci homozygous=same 2 alleles at locus X heterozygous= 2 different alleles at locus X phenotype = recordable effect of genotype quantitative traits: continuous variation qualitative traits: 2 (or few) alternative forms

7 Genetic markers locus whose alleles can be identified polymorphic locus No biological function Like a flag on the genome molecular genetic techniques: RFLP (polymorphisms in DNA sequence) cutting by restriction enzymes separating DNA fragments based on size on gel

8 microsatelites: Markers (2) frequent and randomly distributed highly polymorphic SNP huge numbers of SNPs (but difficult to find) typing by microarray technology 2 allelic typing is automated

9 Recombination parent M1N1 / M2N2 non-recombinant gametes: M1N1: ½ (1-r) M2N2: ½ (1-r) - recombinant gametes: M1N2: ½r M2N1: ½r

10 position on chroms: distances physical distances: # of kbase pairs recombination distance: r (max. r = ½) genetic (map) distances: d (in cmorgan) 1 cm = on average 1 recomb. / 100 meiosis

11 Quantitative Trait Locus (QTL) Quantitative Trait: Trait that can be measured (e.g. in kg) and accuracy of measurement can be improved Is complex trait: Affected by many genes Affected by environment QTL = Gene for Quantitative Traits Difficult to find due to complex trait nature Approximate estimate of position

12 QTL genotypes & values QQ : m+a (m= mean = effect of all other QTL Qq : m+d no dominance. d=0; complete dominance d=a qq : m-a qq Qq QQ -a 0 +d +a

13 Values of offspring of bull Sire MQ / mq (assume additive QTL: d=0) non-recombinant gametes: MQ: ½ (1-r) ; value = ½ *a mq: ½ (1-r) ; value = - ½ *a - recombinant gametes: -Mq: ½r ; value = -½ *a mq: ½r ; value = ½ *a

14 100 daughters of sire Effect of dam is population average = 0 r =0.1 Daughters inherit: M m Q (45) ½a (5) ½a q (5) -½a (45) -½a Average value: ½a*40/50 -½a*40/50 Difference: M dgtrs m dgtrs = a * 40/50 = a*(1-2r)

15 M dgtrs m dgtrs = a*(1-2r) Difference depends on : QTL effect (a) Recombination between QTL and Marker (r) If QTL and marker unlinked (r= ½: M dgtrs m dgtrs =0 Find marker with biggest difference: Will be closest to the QTL

16 Markers Assisted Selection (MAS) Suppose I know for a elite bull-sire: M dgtrs m dgtrs = 100 kg milk Bull-sire has young sons that I want to progeny test (costs 50,000 Euro per son) Would I test sons which inherited m allele from the elite sire? MAS: select animals (sons) with M allele

17 Linkage Equilibrium (LE) Alleles at locus 1 are randomly associated with alleles at locus 2 LinkageEquilibrium A a Loc1 Q pq (1 p)q q q (1 q)p (1 p)(1 q) (1 q) p (1 p)

18 Linkage Disequilibrium (LD) Alleles at locus 1 are non-randomly associated with alleles at locus 2 LD A a Loc1 Q pq+d (1 p)q D q q (1 q)p D (1 p)(1 q)+d (1 q) p (1 p) D = freq(aq)-pq R 2 = D 2 / [p(1-p)q(1-q)]

19 LD Generated by: Genetic drift, small effective size mutation Selection Migration between populations Broken down by: Recombination So: if LD high, may expect loci being close LD mapping / association mapping

20 Association mapping for QTL Regression model: in humans y = μ + b*x + e y=records μ=overall mean b=regression coefficient x=snp genotype (0,1,2)

21 Spurious associations If SNP allele A more present in families x,y,z fams x,y,z also happen to have high growth Then: association between x,y,z even without the SNP being on the same chromosome as the QTL Population stratification: Bull Blackstar had good growth and SNP allele A and contributed heavy to families x,y,z

22 TDT test Avoids spurious association y = μ + fam + b*x + e fam uses a lot of degrees of freedom Requires family data Relatively low power of the test

23 GWAS in humans Use the simple regression y = μ + b*x + e Avoid spurious associations by: Big sample size across entire population Sample within population (no mixtures of pops) Perform on a genome-wide scale Millions of SNPs Need very low P value due to multiple testing problem

24 Multiple testing problem Usual statistical tests : accepts P=.01 probability of mistake If we do 100 such tests: How many mistakes do we make? Bonferroni correction: Use P=0.01/100=10-4 For GWAS: P=0.01/10 6 =10-8 And use validation data-set outside the exp. data Needs to be significant usual P=.01 or.001 level Avoid multiple-testing and spurious association

25 Humans : missing heritability Human height, Crohn s disease,schizophrenia Heritability high: 60-80% Dense SNP genotyping: ~1 milj SNPs Large data sets: ~60,000 detected SNPs/genes GWAS: Genetic variance explained: ~5% Why: infinitessimal model (+ few large genes) Many mutations, small effects Genomic selection: all V g => better than 30 SNPs

26 Association mapping in animals SNP chip available for cattle, pigs, horses, dogs, chicken, sheep Not as dense as in humans humans Ne very large => short LD segments Animal recent Ne small => long LD segments Within breed: 50,000 SNP seems enough Between breed: many more (~300,000) Confirmation in independent sample: Not often performed, but usefull to avoid false positives

27 Association mapping in animals Family structure too important to be ignored y = μ + b*x + polygenic + e Var(polygenic) = A*σ a 2 A may be based on pedigree or on SNP data Requires var. comp. analysis for every SNP ASREML gives t-statistic for b but their are issues with degrees of freedom of t-test

28 Conclusions Markers are flags on chromosome QTL are genes coding for quantitative traits Quantitative traits are complex traits Influenced by many genes & environment Sire with MQ/mq genotype: M-daughters > m-daughters Marker Assisted Selection: Select animals that inherited M marker allele

29 Conclusions (2) GWAS in humans: Based on LD between SNP and QTL Large no. of SNPs; large sample size Many genes with small effect Carefull : spurious associations GWAS in animals: Not as big SNPchip needed (if used within breed Need polygenic effect to avoid spurious assoc. Needs confirmation in independent sample