Exploring Genetic and Environmental Influences on Phenotypes: ACE Model Innovations
This paper discusses future technologies for identifying all loci and alleles contributing to phenotypes, examining their additive effects, and utilizing regression analysis to predict phenotypes. It presents two thought experiments focused on measuring environmental influences (Xs) on phenotypes and stresses the importance of ensuring independence between unique and common environmental factors. The study highlights necessary conditions for the ACE model and addresses the implications of correlation among variables. A call to challenge established notions in this area is also made.
Exploring Genetic and Environmental Influences on Phenotypes: ACE Model Innovations
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Presentation Transcript
ACEProblems Greg Carey BGA, 2009 Minneapolis, MN
Thought Experiment 1: • Future technology identifies all loci and alleles that contribute to a phenotype. • Genotype a very large sample for all these loci. • Code the alleles for additive effects. • Regress the phenotypes on the additive codes. • Predicted values of the phenotypes are the additive genetic values = numerical estimates of latent variable A.
A21 A2j A11 A1i An1 Ank Locus 2 Locus 1 Locus n a11 a1i a21 a2j an1 ank ^ P
Assumption 1: • Phenotypes are influenced by concrete environmental events or Xs.
Thought Experiment 2: • Measure all the Xs for a large sample of individuals. • Regress the phenotype on all the Xs. • Predicted values equal the total environmental values = numerical estimates of the sum of latent variables C + E.
X1 X2 Xn b1 b2 bn ^ P
Problem at Hand: • If (C + E) = SbiXi, we should be able to find weights for C and weights for E so that:(1) C and E are uncorrelated in an individual;(2) the Es for siblings are uncorrelated.
X11 X12 E1 C1 P1
Necessary Condition 1: • Every X variable can be placed into one of two mutually exclusive classes—those predicting E and those predicting C. • (X variables can be either green or red).
X1e X1c E1 C1 P1
Necessary Condition 2: • X variables predicting the unique environment cannot be correlated with X variables predicting the common environment within an individual. • (No magenta correlations).
X1e X2c X2e X1c E2 E1 C1 C2 P1 P2
Necessary Condition 3: • No sibling correlations among the Xs for the unique environment. • (Green Xs cannot correlate across siblings or no green correlational paths).
X1e X2c X2e X1c E2 E1 C1 C2 P1 P2
Necessary Condition 4: • No X for sib 1’s unique environment can correlate with any X for sib 2’s common environment. • (No magenta correlational paths)
X1e X2c X2e X1c E2 E1 C1 C2 P1 P2
Necessary Condition 5: • When C1 = C2,
Necessary Condition 5: • When C1 = C2, • (With some algebra), a red X for sib 1 and itscounterpart for sib 2 must correlate 1.0.
Xjc X12 X1ke X11e X21e X1c X2ke E1 C E2 P1 P2
ACE Model Assumption: • Select any X variable.
ACE Model Assumption: • Select any X variable. • That X must correlate either 0.0 or 1.0 for the relatives.
ACE Model Assumption: • Select any X variable. • That X must correlate either 0.0 or 1.0 for the relatives. • It is not possible to have an X that correlates,say, .43 between sibs.
ACE Model Assumption: • Conversely, if peer substance abuse correlates .38 among sibs, then
ACE Model Assumption: • Conversely, if peer substance abuse correlates .38 among sibs, then • Peer substance abuse can NOT be an environmental influence on substance abuse.
What Happened? • In the beginning,
What Happened? • In the beginning, there was G1, G2, E1, and E2 (Jinks & Fulker, 1970).
What Happened? • In the beginning, there was G1, G2, E1, and E2 (Jinks & Fulker, 1970). • E2variance component morphed into variableC in path analysis.
What Happened? • In the beginning, there was G1, G2, E1, and E2 (Jinks & Fulker, 1970). • E2variance component morphed into variableC in path analysis. • E1variance component morphed into variableE in path analysis.
What Happened? • In the beginning, there was G1, G2, E1, and E2 (Jinks & Fulker, 1970). • E2variance component morphed into variableC in path analysis. • E1variance component morphed into variableE in path analysis. • Variance components G1 and G2 were eliminated and replaced with variable A.
What Happened? • In the process, we overlooked the fact that correlation (variance components) does notnecessarily imply causality.
School Res1 Res2 Pupil1 Pupil2
Can legitimately calculate: • Variance component for School.
Can legitimately calculate: • Variance component for School. • Orthogonal variance component for Error.
Can legitimately calculate: • Variance component for School. • Orthogonal variance component for Error. • Test of significance of the variance component for School.
Can legitimately calculate: • Variance component for School. • Orthogonal variance component for Error. • Test of significance of the variance component for School. • Intraclass correlation for School.
But is this causal? • Not necessarily!
Family1 Family2 School Res1 Res2 Pupil1 Pupil2
How Important Is This? • For the simple analysis of a single phenotype, no problem. • For some models of GE correlation, how does a variable (G) correlate with a variance component? • What about multivariate models?
Solution? Common andUniqueEnvironment
Solution? Shared andNonsharedEnvironment
Solution? Use Total Environment = C + E
a h A1 E2 E1 A2 b b b b e a a e P1 P2
$5,000 prize Bouchard Prize
$5,000 prize Bouchard Prize Prove me wrong or irrelevant
$5,000 prize Bouchard Prize Prove me wrong or irrelevant Equations, not words
