1. Genome Evolution. Amos Tanay 2009 Genome evolution Lecture 9: Quantitative traits

2. Genome Evolution. Amos Tanay 2009 Every meaningful evolutionary traits is ultimately quantitative F. Galton Continuous traits: Weight, height, milk yeild, growth rate Categorical traits: Number of offspring, petals, ears on a stalk of corn Threshold traits: disease (the underlying liability toward the trait) Ultimately, fitness is a quantitative trait, so what is special about it? Historically, research on genetics and directed selection were distinct from evolutionary theory Currently, a quantitative approach to molecular evolution and population genetics is a major frontier in evolutionary research

3. Genome Evolution. Amos Tanay 2009 The basic observation: heritability A linear fit would try to minimize the mean square deviation: Heritability is defined: (dividing because only one parent is considered) This is the “narrow sense” heritability

4. Genome Evolution. Amos Tanay 2009 Artificial selection Over 100 years of an ongoing selection experiments 12 48 %Oil From 4.6% to 20.4% oil What kinds of evolutionary dynamics allow for such rapid increase in the trait?

5. Genome Evolution. Amos Tanay 2009 Artificial selection Selection can work by exploiting existing polymorphic sites SNP data suggest that at least 50 genes were involved in the corn selection or by fixating new mutations Theory suggest that fixation of all strong effects should occur rapidly – 20 generations. Later one should see fixation of alleles with smaller effect or new mutations Remainder- Theorem (Kimura): One strong candidate for introducing mutations are repetitive elements. The corn population is of tiny size (60) Selection is enhanced due to the threshold effect

6. Genome Evolution. Amos Tanay 2009 Limits to artificial selection After some (variable number of) generations, artificial selection stop increasing the trait One reason for that can be the exhaustion of polymorphism This is frequently not the case, since reversing the selection is frequently shown to have an effect – meaning polymorphisms is present Another reason for converging trait values is selection on other traits (fertility!) Using many allele affecting the trait, artificial selection can reach trait values that are practically never observed in the original population Not all traits can be artificially selected: in 1960, Maynard-Smith and Sondhi showed they could not select for asymmetric body plan in flies by choosing flies with excess of dorsal bristles on the left side This suggest that some traits are strongly stabilized Artificial selection can proceed non-linearly: starting and stopping A main possible reason for that is that recombination of strongly linked alleles takes time J. Maynard-Smith

7. Genome Evolution. Amos Tanay 2009 Truncation selection M MSMS S = M S - M R = M’ - M M’ Response: Differential: The selection differential is generally larger than the selection response This is because some of the selected offsprings are of high trait value due to non-genetic effects Another reason is that the genotype of the selected offspring is modified by segregation and recombination We redefine (realized) heritability as the ratio between selection differential and selection response

8. Genome Evolution. Amos Tanay 2009 Back to genetics: two loci 4 3 2 1 0 2 2 3 1 AA Aa aa BBBbbb M S = 0.8 Selecting class 0 and 1 1/16 2/16 4/16 p(A)=p(B) = 0.2 After selection: M’ = 0.8 Yielding: Generally: M=2(p(A)+p(B)) Assume additive selected trait h 2 = 1 and 4 4 2 2 0 4 2 4 2 AA Aa aa BBBbbb M S = 12/7 Selecting class 2 1/16 2/16 4/16 p(A)=p(B) =2/7 After selection: M’ =96/49 Yielding: Assume dominant selected trait h 2 =17/21=0.81 and

9. Genome Evolution. Amos Tanay 2009 Continuous traits M MSMS B Z T (M S -M)/    AA AA’ A’A’ We now assume each genotype have a distribution of trait values The variability may be a consequence of environmental factors or other loci m – mean a – additivity d = dominance Cov(pheno, number of A alleles)= Var(number of A alleles)=

10. Genome Evolution. Amos Tanay 2009 Continuous traits M MSMS B Z T (M S -M)/    AA AA’ A’A’ Selecting on a threshold over the mean of the population (T) Thresh relative to AA normal distrib Thresh relative to AA’ normal distrib Thresh Relative to A’A’ normal distrib The “fitness” equals the ratio between the areas beyond the threshold Assuming small differences we have rectangular areas:

11. Genome Evolution. Amos Tanay 2009 Allele frequency change M MSMS B Z T (M S -M)/    AA AA’ A’A’ We showed before (lecture 3): Average fitness is the area B: Selection Intensity Allele frequency Phenotype to genotype regression

12. Genome Evolution. Amos Tanay 2009 Mean Phenotype M MSMS B Z T (M S -M)/    AA AA’ A’A’ Selection Intensity Allele frequency Phenotype to genotype regression

13. Genome Evolution. Amos Tanay 2009 Phenotype variation Phenotypes in natural environment can be modeled as a combination of genotype and environmental effects: More carefully, the genotype effect on phenotype may is a function of the environment, and the additive form may be wrong For example, gene expression of stress related genes depends on the genotype differently for different stresses Understanding QTL evolution Mapping phenotypes to QTL

14. Genome Evolution. Amos Tanay 2009 Genetic analysis of genome-wide variation in human gene expression (Morely et al. 2004 ) 14 CEPH families (of ~8 members each) 3554 variable expression genes (in lymphoblastoid cells) 2756 SNPs (just a few!) Alternatively: 94 unrelated CEPH grandfathers Testing linkage of expression and SNPs in the large family trees yield linkage for ~1000 phenotypes The test on families use the genealogical structure (SIBPAL - h ttp://darwin.cwru.edu/ ) Alternative test on unrelated individuals use simple correlation of the 0,1,2 individual Difficulties: multiple testing vs low resolution Reporting on loci that are linked with many QTLs

15. Genome Evolution. Amos Tanay 2009 Variability in B-cells response to irradiation Mapped eQTL 15 CEPH families (of ~8 members each) Low resolution ~3000 SNPS, and high resolution HapMap SNPS, 3280 responding genes – different time points during irradiation Follow up molecular biology experiments Genetic analysis of radiation-induced changes in human gene expression (Smirnov 2009)

16. Genome Evolution. Amos Tanay 2009 Genetic Dissection of Transcriptional Regulation in Budding Yeast (Brem et al 2002) Crossing two budding yeast strains Fully genotyping, testing expression (later in different conditions) Hundred of variably expressed genes Using the compact yeast genome help deciding linkage Using the well-characterized biology of yeast helps explain linkage

17. Genome Evolution. Amos Tanay 2009 Building association to groups of genes instead of single genes (Litvin et al 2009) ©2009 by National Academy of Sciences Identifying regulatory mechanisms using individual variation reveals key role for chromatin modification (Lee et al 2008)

18. Genome Evolution. Amos Tanay 2009 Schadt EE et al. 2005 (and many publications following it) R – expression L – locus genoetype C - phenotype Looking for gene expression traits that explain QTLs – stands between genetic loci and some disease trait of interest Applied to obesity linkage (in mice) Further development use more data (not just expression), or gene subnetworks Ultimate goal is to build a model explaining phenotype by genotype through molecular phenotypes Figure 2. Strong gametic phase disequilibrium between genes with significant cis-acting eQTLs simulates independence events. (a) The Ppox and Ifi203 gene expression traits have strong cis-acting eQTLs with lod scores of 29.2 and 17.4, respectively, at the positions indicated. The physical locations of these genes on chromosome 1 are also shown aligned next to the genetic map. (b) Scatter plot of the mean-log (ML) expression ratios for Ppox and Ifi203 in the BXD data set. The two genes are positively correlated, with a correlation coefficient of 0.75. This correlation is probably induced by the two genes having closely linked eQTLs and not a result of any functional relationship. (c) Twenty-one genes physically residing on chromosome 1 were identified with strong cis-acting eQTL (corresponding lod scores > 10.0) 3. Pearson correlation coefficients were computed for the mean log expression ratios between each of the 210 possible pairs of genes. The absolute value of each of the correlations is plotted here against the distance (cM) separating the cis-acting eQTLs for each pair. The pattern in this plot indicates that the magnitude of correlation is directly proportional to the distance between the cis-acting eQTLs, which are coincident with the physical locations of the genes (correlation coefficient = 0.82). This is precisely the relationship we would expect if the correlation structures were attributed to linkage disequilibrium between the eQTLs. The Ppox-Ifi203 pair is highlighted by the red dot. Possible modes of causality or interaction Positive correlation suggests linked eQTLs Correlation between genetic distance and correlation suggests LD effect

19. Genome Evolution. Amos Tanay 2009 (a) The horizontal axis shows the frequency from the NMR spectrum expressed as chemical shift from right to left (, ppm). The vertical axis indicates genetic locations (cM) on chromosomes 1 to X. The lod scores between each genotype and each metabolite are color coded. Significant linkages between genomic locations and regions of the plasma NMR profile are present in the aliphatic region (0.5 to 4.5 ppm) and the aromatic region (>5.5 ppm). Resonances corresponding to the anesthetics and their degradation products were withdrawn as described in Methods. (b,c) Genome-wide linkage mapping across the full metabonomic spectrum for marker D14Wox10 (b) and linkage data across the genome for the metabolite 7.86 (c). Direct quantitative trait locus mapping of mammalian metabolic phenotypes in diabetic and normoglycemic rat models (Dunas et al. 2007) Crossing two rat strains: diabitic and normal 2000 microsatellite and SNP markers Using NMR to perform metabolic profiling – looking for linkage explaining metabolic abnormalities