Quantitative Genetics Flashcards
Change in persective in Quanatative genetics
Before = looking at perspective of “measured genotypes”
- Before = looking at variation in S/R varies as a function of traits
- Pop gene = looking at allele frequncey
NOW = Moving to look at phenotypes directley –
not keeping track of underlying alleles
Reconciling Mendle
Reconcile understanding of genetics (Mendle) With fact that most varaition is not as discrete as the trait he used
- Most traits = more complex (not just short vs. tall)
Needed to reconcile trait varaition with mendelian genetics
Quantative genetics (overall)
How does heritability operate in traits with continous quanative values
- QG = study of continous penotypic varaition
Subfeild of evolutionary biology focused on understanding the evolution of continous phenotypuc varaition
Continous Phenotypic varaition
Take any numeric value
How do discrete units (genes) results in continuous phenotypic variation?
- Environmental condition acting on genetic variation
- Environmental variation = Get continuous range of variation
- Envirnmental vraition acting on top of genetoc varaition
- Epistasis –> interaction between 2 loci –> interaction
- More than one gene
- Many genes = get continuous trait perhaps without environmental variation
- Could be epistasis or additive –> could lead to continous
- Additive = influnece indepndtley = contubute independentley
Ex. Height – based on genotype and environment (condition of soil drive)
Discrete units of heredity
One allele or another – Diploid/haploid = genetic variation = discrete (either or)
Discrete units of heredity
One allele or another – Diploid/haploid = genetic variation = discrete (either or)
Loci and number of genotypes
1 locus (2 alleles) = 3 gentypes –> can have 3 phenotypes (if control for envirnmnet)
2 genes (2 loci) = can get 5 genotypes
6 genes = get much larger number of genotypes
More loci = get complete continiuity amount of phenotypes
- More genes = more continous phenotypes appear because increase number of genotypes
***True in nature – many polygenetic
Distribution of traits (based on genotype)
If genotypes is only driver of phenotype = get continous bell curve distrabution based on genotype alone
More loci = get complete continiuity amount of phenotypes
- If increase genotypic states = get bell curve based on genotypic varaition alone
Example quanative trait
- Height
We know heretible – genetic is hard to detect because so many genes involoved
- See highly hertible but due to small effect of many genes scattered throughout genome
- Have lots of genes of fairley small effect
2. Skin color –> polygenic component
- Can have continous distrbution with fewer genes
- Have around 5 genes that ave strong effect that play a role
- 5 genes –> in the same envirnmnet = produce ocntinous varaition in melanin concentration in skin
- Less genes of strong effect
TRAITS = POLYGENIC
What affects most traits
Many traits = affecetd by both genetic and environmental
- Many phenotype have contibution of envirnment and genetic
- MOst traits = result of bot polygenic effects and envirnmntal effects
Quanatative genetics = allows us to look at both simultaneously
What traits have no envirnmental impact
Usually traits at molecular level
Example – Carb molecule
Polygenic/Quantative
Many genes are determined by allelic varaition at multiple loci = Polygenic or qunatative traiots
Use of quanatative traits
Quanatative genetics = gives us the toolkut to understand the link between multi locus genotypes and phenotypes
Allows us to understand how NS operates on these traits
QG = explains patterms of =heretability + explains how NS acts on fitness when have complex variation
Assumption in continous traits
Continous traits = assume traits influneced by multiple genes
Quanatative trait
Continous varaible + result of multiple genes = polygenic affects of the traits
Example Polygenic + envirnmental effect
- Warms
Selecting for thermal reaction norms
- Temperature affects outcome + genotype affects outcome – G X E
Example of trait with continous varaition driven by envirnment (temeprature) and genotype
- Gulls – Polygenic + envirnmntal affects drives contonous varaition
- heretibility = influences genotype on gull size
- ALSO have envirnmental varaition (rain + soil + temp.)
- Genotype of plant could affect gull size (envirnmental affect from perspective of fly)
- Gall = have envirnmental varaition based on plant genotype (not own genomes = envirnmental varaition for fly)
NS on gall Size
We looked at fitness vs. Gall size –> NS is likley acting oin it –> How dow we scale NS determantistic force here
Before = did it at phenotypic level
NOW – can you preseict what will happen at te next generation
What do we need to know to predict what will happen with NS in the next generation?
- How many phenotypes are possible (measure of varaition in trait)
- Need to know if phenotypic varaition is heretible + how heretible it is
- Need to know how heretible traits are
Overall: Need to know phenotypic varaition + how much varaiation is heretible
- With this = can predict what will happen to phenotype across generations without having to know all genes and alleles involoved
- Need to know the relationship between the phenotype and fitness AND the proportion of phenotypic varaition driven by fitness
Variation
Here we mean varaince in statistical sense
- If it is contionous – how muc varaince is there in continous traits
Calculating Varaition
Overall: Square deviation from expected mean
- Avergage deiviation of individula trait value vs. popultion mean –> avergae of how far off indiviudal is from mean
Varaince = 1/n-1 X (SUm of (Xi - X/)^2
1/n-1 (Sum) – sum X 1/n –> Summing and dividing by number of samples = getting an average
- The sum is the deviation of each data point X from the mean - squared
(Xi-X/)^2 –
- Difference if Xi trait value from mean trait value X/ –> Looking at average difference of deviation from mean
- Looking at distance from mean
Square – because –> to make all posible numbers = can look at variation on both sides –> what matters is tge distance from mean not if it is larger or smaller
***Varaince = easy to measure in popultion
***Can do it for any trait value
Low vs. high varaince
Low varaince = everyone is closer to being the same
High varaince = far from mean
Overall phenotypic varaition
Vp – overall trait varaince
What affects Vp
Vp = result of the additive effects of genetic and envirnmental components of phenotypic varaince
- Result of polygenic affects + envirnmental affects
Genes drive varaition + envirnment drives varaition –> Do so in ADDITIVE WAY
- Varaition by genes and varaition by envirnment add together
Findiong Vp
Gene and envirnemnt affect Vp in adidtive way –> Additive relationship between genes and envirnment (easy to comvine genetic and envirnmental varaince)
Vp = Vg + VE
- Vg = variance effects due to genes
- Ve –> Varaince due to envinment
VGXE = can be determined too BUT that seperate source of variance also forms an additive component of Vp and is often lumped in with Ve
- VGXE = another aditive effect –> also adds in additive way to total phenotypic variance
Vp = Vg + Ve = VGXE
What can NS act on
NS can only act on heretible varaition to cause chnage in one generation to the next
- Whether something is heritable or not is not just yes or no –> genes are heretible BUT envirnment is not heretible –> NEED to quantofy partial heretible effects to know how phenotype can change due to NS
- The combination of Bg and Ve results in partial heredity
Heretible varaition = part of Vp we are concerned with
Value of heretibility
0 -1
0 – there is no relationship between parent and offsrping phenotype
- Offspring are not correlated to parents
- All envirnment – no varaince due to genetics
1 – Offspring phenotypes are expactlet like parents
- Phenotype is expactley like parents – if know parent know offspring phenotype
Heretibility
Broad sense heretibility
H^2 = Vg/Vp
Proportion of total phenotypuc variance (Vp) made up of genetic varaince
- Proportion of total varaince that is ascribed to genetic varaince
Narrow sense – proportion of phenotypic varaition that is passed between parents and offspring in a straight foward way (easily predictable way)
- due to additive affects
- Proportion of all genetic varaition due to additive affects
h^2 = VA/Vp
Broad vs. Narrow sense heretibility
Broad = encompasses all of the gentic input to the trait
- Ecompasses all potential genetic affects acting on phenotype
- All varaition due to any type of genetic affect
- Includes genetic affects that it is hard for NS to act on
- Braod = not measure of heretibiulity critical to making predictions
H^2 = Vg/Vp
Narrow sense – proportion of phenotypic varaition that is passed between parents and offspring in a straight foward way (easily predictable way)
- due to additive affects
- Proportion of all genetic varaition due to additive affects
- Part that matters –> component of Vp that is due additive
h^2 = VA/Vp
Breaking down Vg
Vg = can be broken down into different components
- Can break down genetic variance
Vg = Va + Vd + Vi
Va = additive genetic varaince
Vd = dominance genetic varaince
Vi = epistatic interaction genetic varaince
***Not hard to measure them independently
- Hard to get in nature
What drives narrow sense heretibility
Va = drives narrow sense heretibility
- Affects parent offsrping relationshio
Vd and Vi = interfere with direct parent phenotype to offspring phenotype relationshio
- Interfere with some of parental contirbution to your phenotype = interfere with direct realtionship
- they’re difficult to measure without
knowing the genotypes.
- More importantly – they interfere with the straightforward prediction of the effects of natural selection
How to measure additive genetic varaince
Hard to do directlet –> hard to fo without finding h^2
Don’t know Va without knowing h = we measure h^2 directley and then get Va
Measuring h^2
Heretibility involoved the correlation between parent and offsrping phenotypes –> most straightfoward way to get h^2 = through midparent regressions
- easy way to get h^2 = to compare parent to offspring phenotype using mid point regression
Mid point regression
Take parent average phenotype
- Expect the offsrping the match the average of the parents –> use expectation to see gow well data matches
- Estimates h^2
h^2 = equal to the slope of a linear regression bteween the midpoint phenotype (average of mother and father) and the offsrping phenotype (or mean offsrping phenotype for multiple sublings)
Overall in regression = looking at average parent vs. Avergae offspring
Regression
Seeing how Y=mx + b fits varaibles –> finding linear relationship between varaibles
NAME = has to do with motivation to do this – has to do with heretibility
- On avergae children = look more like popultion mean not like parents = regression to popultion mean –> regression away from parental values and towards popultion
- Complete regression = no heretibility
- No regression = parent phenotypes are like offspring phenotypes
Results of mid point regression
Overall in regression = looking at average parent vs. Avergae offspring
Left = NO statistical coreelation –> parent phenotypoe dies not predict offspring phenotype
- h = 0
Middle = Have trivial amount of heretibility
- Parent phenotype predicts something but not completley
- h = 0.5
Right – h = 1
Value of heretibility = value of regression line
- Slope = 0 –> like popultion mean not parent –> h = 0
- Slope = 1 –> h = 1 –> all varaince driven by additive genetics
- h = 0.5 –> 1/2 varaince due to parent genotypes; 1/2 variance due to envirnmnetal non-additive components
Example real mid point regression
- Height – found h^2 = 0.84
MEANS 84% of varaince in height = due to genes inherited by parents
16% = due to others
SHOWS many genes involoved + strong phenotypic affects of strong straight foward parent vs. offspring
- Looking at behavior in spiders – looking at how social they are
- Looked at how the spiders were together
Results: Y = 0.66X + 4.47
- h^2 = 0.66 – 66% of phenotypic variance due to additive genetic varaition
***Looking at parent traits vs. offsrping traits –> plot –> find h^2
Getting data for midpoint regressions
In the real world we might not have perfect data for mid-parent regressions –> it is often hard to know who the father is
-Sometimes hard to know parent sitution (might not know who the father is)
INSTEAD – we can measure h^2 using a single parent/offsrping regressions (can do it based on only mom and offspring = single parent regression)
- Single parent/offsrping regression = under estimate heretidity by exactleyt half –> THEN h^2 is equal to 2X the slope in single parent regression
- Only accounts for 1/2 genetic varaition = underestimate h by extacley half –> h^2 will be the slope X2 (because know underestimate by exactly 1/2)
Complications for measuring heretibility
- Might be hard to get data for mid-parent regression
- Parent and offsrping are likley to share envirnmental varaition
- parents and offsrping share envirnment that other members don’t share not only genes
- Shared components of envirnments vraiation that conturbutes to parent vs. offsrping relationship pairs = complicates h^2
- Can be envirnment rather than genes
Example – shared envirnment – might share where they live –> like if they gave a lot of food in area – parents get the food + kids get the food
- Experiment – forced bird pairs to raise foster bids – can takie into account envirnmental varaition to rule out envirnmental contribution of varaibility – canges slope of regression
h^2 = the upper limit of hereteibility in popultions –> because can be due to envirnment rather than genes = h^2 measured in mid-point regression = shows the upper limit of varaibility because some amount of slope is because of shared envirnment
Other ways to measure hereibility
- Sibilings vs. half siblings –> Sibling studies predicted on known coefcient of relatness (r)
- Twin studies – humans
Sibling studies predicted on known coreffcianets of relatness
coefeciant of relatedness = r
Traits = nested 1/2 sibling design
- Have males (Sires) and females (Dams)
Have males = multiple females = get different sets of offsrping – get full sibling and half siblings
- Differences in degree of genetic relatedness –> Gives null (compare to data to see Va)
Full sinlings = X2 similar vs 1/2 siblings –> Using ANOVA –> ANY PHENOTYPIC VARAINCE IN HERETIBILITY –> can produce affect Va from other components of varaince
- Uses a nested ANOVA framework to parse
apart components of variance. VA equals
four times the phenotypic variance among
half sibling families
Overall: gives us ways to break Va down
Issues:
1. need organisms you can get into design
2. need 30 males –> each mate with 3 females –> 270 offsrping –> very statistical = need big sample size
- works well with small organisms (can many in lab)
3. Hard to do with many wild animals
Full vs. half siblings
Differences in degree of genetic relatedness –> Gives null (compare to data to see Va)
Full siblings = share 50% of genes – 50% chance siblings has alleles (50% allele is shared)
- r = 0.5
1/2 siblings = share 25% of genes –> can share more genes from mom or not more
- r = 0.25
Twin studies
Done in humans –> compare identical (monozygotic) to fraternal (dizygotic) twins
- can do mid parent regression but not other routes
- Take advantage of known relationship in coreficants of relationships in groups of indviuals
- Compare mono vs. dizygotic twins –> how much phenotypic varaition between mono and how much phenotypic varaition between dizygotic
Mono = share all genes –> Share same genes + Samne envirnment
- r = 1.0
Dizygotic = share 1/2 of their genes –> Same relatness as other siblings BUT experince the same envirnmental varaintion (similar to how mono share envirnment)
- r - 0.5
Why look at twins
What is h^2 specific to?
Prameters = refer only to the specific popultion (genetic makeup) in a specific envirnment
- Specific to that envinment –> if put popultion in new envirnment = change Ve = chnage h^2
- Specific to popultion AND specific to popultion in that envirnment that it is in – only for popultion in that envirnment
When we measure heretibility it is only value within that popultion – it is useless for making comparisons between popultions
Affect of dominance and epistasis
Interferes with ability of offspring/parent phenotype
Use of heretibility
if we know heritability of trait = we can predict how NS will affect it
- Need to know h^2 to know what will happen to trait in next generation
In order to know how something will change over time –> need to know h^2 -
Use of Breeder’s equation
We can predict a particular strength of selection using Breeder’s equation
Breeder’s equation
R = h^2s
R = response to selection in the offspring generation
- Phenotypic response –> change in mean trait value from one generation to next due to h^2
S = selection differential in the parental generation
- Select subset from parental generation to S/R – get difference between overall mean and selected mean = get S
Response to selection = due to strength of selection + heritability of trait
h^2 – not actually squaring something – just put squared to differentate from other times h value is used
(Called breeder’s equation because used in breeding in plants)
What restricts selection
restriction on selection – limited becaused h^2 is not perfect – not 1
Response to selection = due to strength of selection + heritability of trait
What is S in breeders
Selection differential
- Difference in traits mean in parents vs. the trait mean in the selected individuals
***S = within overall population mean
In situations where selection is based on survival (or choice for reproduction in artificial selection program) – S equals the difference between mean trait value of survivors (or individuals selected to breed) and the overall mean of the starting population
Easy to see in breeding programs – choose who survives/reproduces
Example S
Experiment
Blue + Red = overall population – mean trait value
Blue =
Red = ines that reproduce to impose selection difference between blue mean and red mean
Artifical selection in muce tail length
t/ = mean tail length of the population of mice
t* = mean tail length of the mice chosen to reproduce
Difference between t/ and t* = S
Example S #2
Gall example
White arrow = mean gall diameter of the population
- White = mean all populations
Black arrow = the mean of the survivors
- Black = mean popultion of gall that survive = only ones that reproduce
***Have a binary on if you survive or not
Distance between the two = S
Selection Gradient
The relationship between the trait value and the relative fitness
- Scalable into complex situations
More flexible way to think about strength on phenotype
Can account for continous fitness varaition + varying fitness varying as a function of the trait
- Look at fecindity – relationship between varaibles = selection gradient
***Acts in the breeder’s equation in the same way
Use of selection gradient
Can use selection gradient to apply our quantitative genetics tools to more realistic complex fitness situation
Can use technique in more complex settings
Selection Differential vs. Selection gradient
Differential = trait as binary survive or not survive
- Just have number of individuals
Selection gradient = Continuous relative fitness
- Slope = selection gradient
Can convert between the two
Example selection gradient
Flowing time
Relative fitness = number of seeds
Earlier flowing time = increase # of seeds = increase fitness
- Mathematical relationship = selection gradient
Linear selection gradient
Selection gradient = do not need to be linear
R in Breeders
Response to selection – change in the population mean from one generation to the next
R vs. S
R < S because h^2 is less than 1
- The response in selection R is smaller than S because h^2 is less than 1
Response on selection = sub standard based on h^2
S and R in graph
Look at Mid parent regression – parent phenotypes + offspring phenotypes – same plot used to get h^2
- Look at distance of parental phenotype and distance of offspring phenotype
X Axis – push trait (pushing parent trait) by X much and see chnage in offspring (See how much Y chnages)
Graph – pushing P –> P* and seeing chnage in Y value (Change in offsrping trait value)
- When look at slope – look at how likley offspring match parent (slope is h^2)
- Look to see for X push on X = get Y (get that much increase) in offspring
- Run across mid parent regression that rise along using h^2
- See what happens if increase parent trait by X much – see chnage in Y value – change in offspring
h^2 = slope
Example selection on quanatative trait
Mean gall diametere in the population is 25 mm
Due to high numbers of birds that year the mean diametere of the survivors is 21 mm
h^2 = 0.18
What will the mean gall diameter be in the next generation?
Galls – we are solving for R – how R relates to the parental population
Mean gall diametere in the popultion is 25 mm (overall parental pop. mean)
h^2 = 0.18 –> 18% pf selection is due to genetics
Survivors = 21 mm
R = h^2s
S = 21 - 25 = -4 (moving down)
R = 0.18 X (-4) = -0.72
New mean of gall size = Starting + R = 25 + (-0.72) = 24.28
- Comparison if to the parental mean (25 NOT 21) –> impose R on the overall population mean not the selection mean
Overall: based on h^2 not much shift –> have some shift but not so much considering selection
- Don’t have huge shift because a lot that links parent and offsrping is not genetic
***Reall
Rise / run in selection gradient
Really just looking at rise over run for change (-4) = run – look at how much rise there is
- S = run - R = Rise
18 = 25h^2
h^2 = 25/18 –> h^2 is slope = rise/run
Example #2 – Selection gradient
Mean gall = 24.27 mm
No Birds nest nearby – selection largeley from parasistoids – mean survivors = 27.5 mm
h^2 = 0.18
What will the mean gall diameter be in the next generation
Here = Positive R = adding to parental value
Selection for larger vs. selection for smaller trait value
Selection for larger trait value = Positive S
Selection for smaller trait value = Negitive S
S = gives directionality
What can we use artifical selection experiments for + Breeders equation?
We can also use equation to estimate the heretibility from artifical selection experiments
- Can run selection experiment and use Breeders to calculate h^2 –> Might want to know if there is Va to select in the future
Example – If running an agricultural breeding program to imrpove corn
- Mean percent popped = 60%
- You think there is a lot if varaince in phenotype and you want to know how much is genetic – want to know how much you can change trait
- Breed – take from popultion with 85% popping
GRAPH – parental vs. Offspring
- Offspring increase popping rate (60% –> 78%)
- Slope = h^2 – get slope with rise/run
- Positive slope because increase trait value
- S = run
- R = Rise
For 0.6 –> 0.85 increase in X (increase in S) = get 60 –> 78 increase in Y (increase in R)
18 = 25h^2
h^2 = 25/18 –> h^2 is slope = rise/run
Example – using breeders to find h^2
In initial popultion the mean for percent of kernels popped was only 60%
Selected for individuals with mean of 85%
Offspring of the selected had mean of 78%
What is narrow sense heretibility?
18 = 25h^2
h^2 = 25/18 –> h^2 is slope = rise/run
h^2 = 0.72
25 – 85 - 65
18 – 78 - 65
Finding R and S
S = selected mean - starting mean
- Gives directionality
R = offsrping mean = starting mean
- NOT using selected mean
S = gives directionality
R = gives response
Qunatative genetics (overall)
Know heretibility without knowing underlying genotypes
What does it mean when a complex trait has a genetic component? – How do we interpret when we find a gene for a trait
NEED TO BE CAREFUL – h^2 represtns the upper limit of heretuibility + other varaibles that are hard to break down
- Over estimate h^2 because of shared envirnment (looks like heretibility but heretibility but really shared parent offsrping envrirnment not shared genes)
Example – Birds that are in area (area might be warmer) = get more sunlight = birds in area are faster and bigger BUT they share the trait because of shared envirnmnet
- Outcome is due to envirnmental similarity
- Also applyes if have same diet in parent and offspring
Subtle envirnmnetal effects
- Maternal Effects
- Epigenetics
Maternal Effects
Envirnment of mom affects not gene
- Resemble mom but resemblance is due to envirnmnet that shapes aspect of development
Example – Egg size is based on food mom had not moms genes – due to moms envirnment not genes BUT different moms have different envirnments thats leads to difefrences that look genetic
Epigenetics
Trangenerations epigenetics – Trangenercation effects
- opperates in the lab –> studies on lab organisms now studied in humans
Affects that we are learning more about
OVERALL – ssRNA for a gene regulates or RNAi regulates – the effect of RNAi is seen after generation after intial generation
- Aleter gene expression that gets passed down to offspring not just in the 1st generation
- Get connection between parent vs. offsrping dur to epigenetics not heretible changes
Disentagling genetic varaition from shared envirnmental
NOT trivial
KNOW there are some issues with measuring additive genetics varaiation in human traits
Example – Complex genetic heretibility
Nutritien and transgeneration effects
Experiment – knew pedigrees + relationships + Knew there had been major famines
Epidimeologists looked at the health outcomes in grandchildren of people who went through the famine
Results: Strong effects + complex
- Found difference between men/women – difference on if a man/female went through famine = difference in sex of grandchildren
IF a Males = less usbstantial effect if grandfather went through famine
If Females – more substantial effect if grandmother went through famine
SHOWS envirnmental is complex – heretibility is very complicated –> effects exist in huamns but very complex + hard to predict
- Very complicated – difefrent sexes are very different
Straight fowards envirnmnetal effects
Envirnmental – NOT always straight fowards envinmental effects
Example – Maternal effects and transgenerational epigenetics effects on human phenotypes may be more common that previously thought
Effect of complex envinrmental effects + Shared envirnment efefcts
ALL complicated our ability to quantify genetic components of traits within human popultopms
- Complicates ability to make inferences within popultions we measure – we often overestimate heretibility
Measuring narrow sense heribility = far from trivial –> espcially complex traits in humans because can’t do the same experiments in humans
Even when we feel we can draw string conlsuioons about heretibility we are still limited to talking about the role of genetics in THAT popultion
- Strong heretibilility parameters = tell us about proportion of Va in popultion in THAT envirnment –> only have how much genetics drive phenotype in that envirnment
- Doesn’t say anything about between popultions –> hard to look at heretibility across different asopects of human society
Example on heretibility in THAT envirnment
Even when we feel we can draw string conlsuioons about heretibility we are still limited to talking about the role of genetics in THAT popultion
- Strong heretibilility parameters = tell us about proportion of Va in popultion in THAT envirnment –> only have how much genetics drive phenotype in that envirnment
- Doesn’t say anything about between popultions –> hard to look at heretibility across different asopects of human society
Example – You are intersted in imporving beed yeilds through selective breeding
Start = go to different cattle in popultion and look at phenotypic varaition in beef yeild in cattle to get an idea of Va
- Get idea of genetic differences in popultion to leverage in experiment
THEN – you gi to a couple of large farming operations to measure additive genetic varaince
IF in popultion 1 – mean mass = 450; Vp = 10,000
IF popultion 2 – Means mass = 850; Vp = 10,000
Difference in mean BUT similar amounts of phenotypic varaince
GRAPH (off vs. Parent) – same slope but one mean is 450 (lowser) and other mean is 850 (higher)
- Results –> heretible trait – strong relationship
- h^2 = 1 in both popultions = within popultion all of the varaition is driven by Va (high heretibility but big difference in trait values)
- Both slopes = 1 = h^2 = 1–> have 100% Vp explained by Va
- If select to imporve beef yeild –> is one popultion genetically bettwe fir beef yeild (if H^2 is 100% in both) – is there genetic difefrence between popultions
On the surface = seems to be geentic difefrence but the data does NOT suggest that
Have we found genetic difefrence?
h^2 us high = explained by Va –> tells us little about
envirnmental varaince within popultion –> genetics could be the same but both popultions are in difefrent –> if foo is good in one and bad in other –> the differebnce is due to envirnment
We don’t know if Va is what is difefrent between popultions
Appliying heretibility in THAT envirnment to humans
Compare humans popultions –> diferent traits value parameters don’t tell you difference in genes
We don’t know if Va is what is difefrent between popultions
Human popultions = not in homeostasis –> have lots of envinmental variation
- Envirnmental varaition can play out in one generation or across generations
Envirnmental varaition makes saying things about genetic differnces meaningless
If people say heretible and differnces is genetics —> They are using a misapplication of biology
Each comparison only works for varaition in popultion you are measuring in that time
Example Misuse of heretibiloity
Murray – pepetuating ideas of human groups based on heretibility
Wrote about bell cirbe about intelligneve as heretible trait + distrubution in society
leavergaed things about heretibility of traits and implying ideas are determinaistic –> idea that certain outcomes if IQ is not envirnmmetal driving then it must be due to genetics = not our probelm to fix
Lots of varaition varied with envirnment that within and btewen popultions –> just because intelignece can have heretibility doesn’t mean they are hardwired into genes
It is easier to change envirnment –> faster than giving up
Realization of QG –> closed idea of taking Murray seriously
- He would reducve the amount of varaition (if only genetics) = bad + have huge envirnmental components + affects societal outcomes without racist outcomes
