bs148h 6 September 2007 Read: Text pgs , ch 22 & 23

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1 bs148h 6 September 2007 Read: Text pgs , ch 22 & 23 phenotypic variation selection within generations heritability across generations phenotypic response across generations partitioning variance: heritability and IQ fluctuating selection on finch beaks evolution of influenza A haemagglutinin genotype & phenotype An organism exposes its phenotype not its genotype to the environment. Phenotypic selection within a generation requires phenotypic variation within the pop. Genetic response across generations (evolution of pop) requires: a correlation between genotypes & phenotypes heritability. Heritability is an estimate of the proportion of phenotypic variation in the population that results from different genes (additive genetic variation), ( nature ) rather than different environmental effects ( nurture ) (+ arcane genetic interactions). phenotypic variation within populations is ubiquitous, esp for continuous (quantitative) traits, ex height. Ex: a quantitative trait: Raven et al. Fig 13.16: height of a sample of 50 female Vet students It is conventional & convenient to describe samples or pops with a graph called a frequency distribution 1. Divide the range of data into bins (x axis); ex: 5 cm 2. Pile individuals into the bins, making a bar graph 3. and/or draw a line through the tops of the bars 4. Label the y axis to show the # indiv s in each bin 5. Relabel the y axis to show the proportion in each bin; do this by dividing the # in each bin by the total n (ex: 50) This is convenient for comparing different graphs; they all have the same y scale & sum up to 1 ave = mean = student height (cm) We can describe a frequency distribution with a few descriptive statistics, like: a. the mean (average) b. various measures of variation around the mean, ex: variance, std deviation, etc. phenotypic variation At this point, we can see and describe the distribution of the phenotypic trait, but, the variation around the mean is just random, noise until we can relate deviations from the mean to some other, explanatory variable: ex: can we explain why this indiv is above ave? Well parents and offspring have similar genes, so if genes have something to do with height, Hyp: taller than ave offspring from taller than ave parents? Does parental height explain any of the var in offspring height? How can we see if offspring deviations from ave are related to (correlated with; explained by ) parental deviations from ave? (cm)

2 heritability: offspring & parent deviations from ave. It is obvious from inspection that deviations of parents and offspring are correlated How can we get a more precise, quantitative measure of the extent to which offspring trait deviation from mean can be predicted from knowing parental trait deviation from mean? This is exactly why Galton invented the statistical procedure linear regression. (cm) The procedure fits a line y = a x + b through the data that pivots around the point (mean y, mean x) to find the slope a that best predicts (minimizes the sum of vertical deviations ( errors ) squared) how much you expect particular y s will deviate from ave given the amount particular x s deviate from ave. In our student height ex, the slope is a 0.75 So, if we have a pair (or sample) of parents that are about 8cm below ave, how much would you predict/expect their offspring to deviate from ave? if y = a x + b, then (y ave y) = a (x ave x) ex: (y ave y) = 0.75 ( 8 cm) = 6cm AVE x AVE y selection & response across generations (quantitative genetics perspective) Note: if y = a x + b, then (y ave y) = a (x ave x) {just translating the origin}. Let x s be the ave trait value of the breeding parents; while x 0 is the ave trait value for all adults; call this diff the selection differential S = (x s x 0 ) If the breeding parents are on ave 8cm shorter than all adults in their generation, then the selection differential is S = 8cm. Let y r be the ave trait value observed in offspring; while y 0 is the ave trait value exp if no selection; call this diff the response to selection R = (y r y 0 ) If the slope = a, how much should we expect the the next generation (offspring) to respond R to this selection S? (cm) expect: R = a x 0 (x s x o ) = a S, ex: R = = 6cm * The basic quantitative genetic expression for phenotypic evolution is: R = h 2 S; one way to estimate heritability is: h 2 = R / S = slope a So, the slope a is one estimate of the heritability h 2 {What if parents and offspring have similar environments as well as similar genes?} * technically, we would convert the units to standard deviations and not work in cm y 0 selection, heritability & response The slope of the linear regression line is one estimate of heritability. 1. If knowing parents trait deviation tells nothing about offspring trait deviation (ex all var is env noise ), dots fall in circular cloud, slope = 0, heritability h 2 = 0, and the response R to any selection S is If knowing parents trait deviation tells everything about offspring trait deviation (ex all var is genetic), dots fall along a 45 line, slope = 1, heritability h 2 = 1, and the response R = selection S. Heritability: a simple example of P O regression Influence of heredity and environment on bone density in adolescent boys: A parent offspring study Nordstrom P, Lorentzon R Osteoporosis International 10: (4) The purpose of the present parent offspring study was to investigate the influence of heredity and environment on bone density in young men. Fifty families including a father, mother and one son were investigated. Bone mineral density (BMD) of the total body was measured using dual energy X ray absorptiometry. Heritability estimates were obtained as regression coefficients {slope = a} with the boys' adjusted BMD as dependent variable and the adjusted midparent bone density (father BMD + mother BMD)/2 as independent variable. Accordingly, heritability explained 34 54% of the variation in the sons' BMD. 3. Galton called this regression because offspring are rarely as deviant as their parents; they regress toward the mean, slope < 1, heritability h 2 < 1 The non experimental description of correlations leaves potentially confounding variables... Parents & Offspring (& sibs) may have similar environments as well as similar genes. ex: Do well-fed (tall) parents have well fed (tall) offspring? Do milk-drinking (high BMD) parents have milk drinking (high BMD) offspring?

3 The heritability of IQ. Devlin B, Daniels M, Roeder K Nature 388: (6641) IQ heritability, the portion of a population's IQ variability attributable to the effects of genes remains controversial. What we have are patterns of Meta-analysis of 212 previous studies shows more or less phenotypic similarity (test score), corr w/ more & less genotypic similarity Corr & & environmental There are various ways to est h 2 from these correlations, including the midparent-offspring regression, which gives h 2 0.5, however Covariance {correlations} between relatives may be due not only to shared genes, but also to shared environments... {including shared womb environment} Heritability estimates of intelligence in twins: Effect of chorion type. Jacobs et al. BEHAVIOR GENETICS 31 (2): MAR monochorion-mz twins resembled each other more than dichorion-mz twins. So, shared environments can inflate est. of shared genes (h 2 ), but does it really matter whether the h 2 for IQ is 0.3 or 0.5? Picture obtained from Farnoosh Tayyari, The Genetic Basis of Intelligence Genotype by Environment Interaction in Adolescents Cognitive Aptitude P.K. Harden et al. Behav Genet (2007) 37: we investigate genotype environment (G E) interaction in the cognitive aptitude of 839 {same sex, monozygotic MZ & dizygotic DZ} twin pairs who completed the National Merit Scholastic Qualifying Test... Table 1... twin pair correlations by zygosity {note: MZ twins share more genes than DZ and they have more similar test scores} Parents reported their level of education and the annual family income in a written questionnaire. In the {arcane statistical} models... the variance in the cognitive aptitude factor attributable to additive genetic or shared environmental influences is a function of the measured environment.... Shared environmental influences were stronger for adolescents from poorer homes, {among poor families, twin scores are correlated across families, but MZ twins reared together not much more similar than DZ twins} while genetic influences were stronger for adolescents from more affluent homes. {among wealthy families, MZ twins reared together are much more similar than DZ twins} how much of the var in scores can we explain by knowing if MZ or DZ how much of the var in scores can we explain by knowing if same or diff home? {with G E: heritability h 2 depends on the environment think about the implications} within species Evolutionary beacon. Medium ground finch beaks wax and wane with climate shifts. CREDIT: JOSEPH W. DOUGHERTY Unpredictable evolution in a 30-year study of Darwin's finches. Grant PR, Grant BR SCIENCE 296: Abstract: Evolution can be predicted in the short term {response} from a knowledge of selection and inheritance. However, in the long term evolution is unpredictable because environments, which determine the directions and magnitudes of selection coefficients, fluctuate unpredictably. {& heritability might change} From 1972 to 2001, Geospiza fortis (medium ground finch) and Geospiza scandens (cactus finch) changed several times in body size and two beak traits. Natural selection occurred frequently in both species and varied from unidirectional to oscillating, episodic to gradual. {Volume 296, Number 5568, 26 Apr 2002, pp }, On page 707 of this issue the Grants review 30 years of evolution among Darwin's finches. {this is the Science version of News & Views, called News of the Week}

4 Fig 1. Morphological trajectories {evol of phenotypic traits} of adult Geospiza fortis {medium ground finch} medium ground finch response selection 77 Fig 2. Standardized selection differentials, calculated for each sample surviving from year x to year x+1. Positive values indicate selection for large size or pointed beaks; {unpredictably fluctuating selection} Figure 3. Predicted and observed evolutionary responses to natural selection on beak size ( ) and shape ( ) in G. fortis and beak size in G. scandens ( ). Predictions are the products of standardized selection differentials and heritabilities. Predicted and observed values are correlated (r = 0.832, n = 10, P = ). {we can predict short-term response if we know selection & heritability, but we can t predict selection (or heritability) in long run This is a challenge for predicting responses to global climate chance or long-term evolution of pathogens} Mapping the Antigenic and Genetic Evolution of Influenza Virus {A H3N2} Smith, et al. Science 305, , 16 July 2004 Much of the burden of infectious disease today is caused by antigenically variable pathogens that can escape from immunity induced by prior infection or vaccination. Fig. 4. (A) antigenic {phenotypic} evolution. Circles give the average antigenic distance to A/Bilthoven/16190/68 antigen color-coded with the antigenic cluster colors. {this is a measure of antibody-antigen binding to viral surface glycoprotein hemagglutinin (HA) ; predicts effectiveness of different vaccines } (C) amino acid substitution distances {genetic evolution} calculated from the genetic map Nelson MI, Holmes EC NATURE REVIEWS GENETICS 8 (3): MAR we review our current understanding of the evolutionary biology of human influenza A virus... The genome of influenza A virus is composed of eight segments that can be exchanged through reassortment. Wild waterfowl are the reservoir hosts for type A influenza viruses, harbouring numerous antigenically distinct subtypes (serotypes) of the two main viral antigens (surface glycoproteins), haemagglutinin (HA)... responsible for entry into host cells, {key in} and neuraminidase (NA)... involved in budding new virions from infected cells. {key out} Natural selection favours amino-acid variants of the HA and NA proteins that allow the virus to evade immunity, infect more hosts and proliferate. This continual change in antigenic structure is called antigenic drift. The degree to which immunity induced by one strain is effective against another is mostly dependent on the antigenic difference between the strains; the antigenic structure has changed significantly over time, a process known as antigenic drift, and in most years, the influenza vaccine has to be updated to ensure sufficient efficacy against newly emerging variants; thus, the analysis of antigenic differences is critical for surveillance and vaccine strain selection. At the phylogenetic scale, the selective turnover of amino-acid variants is thought to produce the phylogenetic tree of the HA1 domain. The apparent regularity of this phylogenetic pattern has generated much interest, because of the potential to predict the future course of viral evolution and, in doing so, aid vaccine strain selection. Koelle et al. Science 2006, 314:

5 ... the definitive signature of positive selection in the virus HA protein... is defined by an increased frequency of non-synonymous substitutions, which reflects the continual fixation of (advantageous) amino-acid replacements Box 1 Measuring selection pressures in influenza virus Selection pressures in influenza A virus are often quantified as the ratio of non-synonymous (dn) to synonymous (ds) substitutions per nucleotide site, with dn > ds being indicative of positive selection and dn < ds being indicative of purifying selection Box 2 Antigenic mapping and antigenic cluster jumps One of the notable features of influenza A virus is that it is possible to compare patterns of genetic and antigenic change through time, thereby providing a tentative association between genotype and phenotype. Ongoing antigenic change can be measured using data obtained from a haemagglutinin inhibition assay, which assesses the ability of influenza viruses to agglutinate red blood cells, and the corresponding ability of ferret antisera that has been raised against a set of viral isolates to inhibit agglutination. Application of antigenic mapping to longitudinally sampled isolates of A/H3N2 showed that viral isolates tended to fall into discrete clusters that were defined by amino-acid differences at known (and often positively selected) antigenic sites, and that each cluster remained dominant for approximately 3 years. Smith et al Severe influenza pandemics can occur following a sudden antigenic shift when a reassortment event generates a novel combination of HA and NA antigens to which the population is immunologically naive. {reassortment is a mutation}... a detailed phylogenetic analysis of 413 complete viral genomes from New York, sampled over a 7-year period, revealed 14 reassortment events... Accurate estimations of evolutionary rates at both nucleotide and amino-acid levels are central to resolving many long-standing questions... including the relative roles of natural selection versus genetic drift, the origins of the 1918 H1N1 pandemic virus, and the ecology of the virus in its avian reservoir. The main determinant of variation in substitution rates among influenza viruses seems to be the strength of immune selection pressure; background mutation rates are generally similar among RNA viruses, at approximately one mutation at each genome replication. Selection pressures generally reflect the length of time that an influenza virus subtype has been associated with a particular host species. So, older influenza A viruses evolve more slowly (at non-synonymous sites) in the reservoir avian species with which they might have co-adapted, whereas newly emergent viruses in humans and domestic poultry, evolve more rapidly (through positive selection) to evade host immunity i and achieve efficient transmission in new host species. The clock-like consistency of the winter incidence peaks of influenza virus represents one of the strongest examples of seasonality in infectious disease. However, the reasons... are unknown. Various theories have been proposed to explain how seasonal change might stimulate influenza activity: transmission rates might increase during school terms and winter crowding, the stability of the virus might be enhanced by cooler temperatures, or host immunity might decline during colder weather {or dry sinuses} All of these hypotheses remain largely untested. There are only two models for the origin of humans: evolution and creation. If creation occurred, it did so just once and there will be no "second acts." If evolution occurs, it does so too slowly to be observed. Both theories are accepted on faith by those who believe in them. Neither theory can be tested scientifically because neither model can be observed or repeated. {note the distinction between the phylogenetic history of macroevol and the current ongoing process of microevolution}