**Note that you can cite this work as: **

*Jolicoeur-Martineau, A., Wazana, A., Szekely, E., Steiner, M., Fleming, A. S., Kennedy, J. L., Meaney M. J. & Greenwood, C.M. (2017). Alternating optimization for GxE modelling with weighted genetic and environmental scores: examples from the MAVAN study. arXiv preprint arXiv:1703.08111.*

*Jolicoeur-Martineau, A., Belsky, J., Szekely, E., Widaman, K. F., Pluess, M., Greenwood, C., & Wazana, A. (2017). Distinguishing differential susceptibility, diathesis-stress and vantage sensitivity: beyond the single gene and environment model. arXiv preprint arXiv:1712.04058.*

From version 1.2.0 of the LEGIT package, we introduce a function to test for different types of gene-by-environment interactions (GxE) as per Belsky et al. (2013). See our preprint: https://psyarxiv.com/27uw8. We want to test if the GxE reflects diathesis stress, differential susceptibility or vantage sensitivity.

The figure below is a graphical representation of the different types of interactions assuming a STRONG model:

`knitr::include_graphics("https://raw.githubusercontent.com/AlexiaJM/LEGIT/master/images/GxE_testing_strong.png")`

The figure below is a graphical representation of the different types of interactions assuming a WEAK model:

`knitr::include_graphics("https://raw.githubusercontent.com/AlexiaJM/LEGIT/master/images/GxE_testing_weak.png")`

To test for differential susceptibility, Widaman et al. (2013) estimate the cross-over point of the model. This can be done easily with LEGIT by simply adding a negative intercept to the environment E. This can be seen by the following: \[y = 2 + 3(E-c) + 5G(E-c) \\ E = q_1e_1 + q_2e_2 + ... + q_le_l\] is equivalent to \[y = 2 + 3E' + 5GE' \\ E' = -c + q_1e_1 + q_2e_2 + ... + q_le_l\]

To test for vantage sensitivity and diathesis-stress, we can use the model above but fix the cross-over point to the expected minimum and maximum of \(E\) respectively.

Let’s look at a two-way interaction model with a cross-over point and a continuous outcome:

\[\mathbf{g}_j \sim Binomial(n=1,p=.30) \\ j = 1, 2, 3, 4\] \[\mathbf{e}_l \sim 10 Beta(\alpha=2,\beta=2) \\ l = 1, 2, 3\] \[\mathbf{g} = .30\mathbf{g}_1 + .10\mathbf{g}_2 + .20\mathbf{g}_3 + .40\mathbf{g}_4 \] \[ \mathbf{e} = .45\mathbf{e}_1 + .35\mathbf{e}_2 + .20\mathbf{e}_3\] \[\mathbf{\mu} = 3 + \beta_E(\mathbf{e}-c) + 2\mathbf{g}(\mathbf{e}-c) \] where \(c\) and \(\beta_E\) depend on the model assumed. \(c = 0\) represents vantage sensitivity, \(c = 10\) represents diathesis-stress. We set \(c=5\) to represents differential susceptibility. \(\beta_E = 0\) assumes a STRONG model while \(\beta_E \ne 0\) assumes a WEAK model.

\[ y \sim Normal(\mu,\sigma=1)\]

Note that the environmental score E is in the range [0,10].

When assuming vantage sensitivity WEAK, we let \(c=0\) so that: \[\mathbf{\mu} = 3 + (\mathbf{e}-0) + 2\mathbf{g}(\mathbf{e}-0) \]

We generate N=250 observations and test all 4 possible models based on the BIC (other choices are also available: AIC, AICc, cross-validation and cross-validated AUC). It is important to not include G or E in the model formula because they will be added automatically. We start by assuming that we don’t know the true minimum of the environmental score, thus we use option *crossover = c(“min”,“max”)* to find the minimum/maximum from the observed data (this is the default).

```
set.seed(777)
library(LEGIT)
ex_dia = example_with_crossover(N=250, c=0, coef_main = c(3,1,2), sigma=1, seed=7)
GxE_test_BIC = GxE_interaction_test(data=ex_dia$data, genes=ex_dia$G, env=ex_dia$E, formula_noGxE = y ~ 1, crossover = c("min","max"), criterion="BIC")
GxE_test_BIC$results
```

```
## BIC crossover crossover 95%
## Vantage sensitivity WEAK "772.27" "0.45" ""
## Differential susceptibility WEAK "775.08" "-0.4" "( -0.73 / -0.07 )"
## Differential susceptibility STRONG "870.68" "2.22" "( 1.79 / 2.64 )"
## Vantage sensitivity STRONG "930.4" "0.44" ""
## Diathesis-stress WEAK "971.12" "9.75" ""
## Diathesis-stress STRONG "1123.61" "9.74" ""
## G + E only "1237.56" NA ""
## G only "1285.07" NA ""
## E only "1308.15" NA ""
## Intercept only "1341.59" NA ""
## Within observable range?
## Vantage sensitivity WEAK ""
## Differential susceptibility WEAK "No"
## Differential susceptibility STRONG "No"
## Vantage sensitivity STRONG ""
## Diathesis-stress WEAK ""
## Diathesis-stress STRONG ""
## G + E only ""
## G only ""
## E only ""
## Intercept only ""
## % of observations below crossover
## Vantage sensitivity WEAK "0"
## Differential susceptibility WEAK "0"
## Differential susceptibility STRONG "0.004"
## Vantage sensitivity STRONG "0"
## Diathesis-stress WEAK "1"
## Diathesis-stress STRONG "1"
## G + E only NA
## G only NA
## E only NA
## Intercept only NA
```

Here we see the results. Notice that vantage sensitivity WEAK is the model with the lowest BIC. Differential susceptibility WEAK has a slightly worse fit and the crossover point is not within observable range, thus we reject it. Note that we only show the element *results* of the output, but the 6 models (weak/strong vantage sensitivity, diathesis-stress and differential susceptibility) are also returned.

Considering that we know that the minimum and maximum of E are 0 and 10 respectively, we can also refit the models with *crossover = c(0, 10)*.

```
GxE_test_BIC_ = GxE_interaction_test(data=ex_dia$data, genes=ex_dia$G, env=ex_dia$E, formula_noGxE = y ~ 1, crossover = c(0, 10), criterion="BIC")
GxE_test_BIC_$results
```

```
## BIC crossover crossover 95%
## Vantage sensitivity WEAK "770.07" "0" ""
## Differential susceptibility WEAK "775.08" "-0.4" "( -0.73 / -0.07 )"
## Differential susceptibility STRONG "870.68" "2.22" "( 1.79 / 2.64 )"
## Vantage sensitivity STRONG "948.33" "0" ""
## Diathesis-stress WEAK "965.08" "10" ""
## Diathesis-stress STRONG "1125.2" "10" ""
## G + E only "1237.56" NA ""
## G only "1285.07" NA ""
## E only "1308.15" NA ""
## Intercept only "1341.59" NA ""
## Within observable range?
## Vantage sensitivity WEAK ""
## Differential susceptibility WEAK "No"
## Differential susceptibility STRONG "No"
## Vantage sensitivity STRONG ""
## Diathesis-stress WEAK ""
## Diathesis-stress STRONG ""
## G + E only ""
## G only ""
## E only ""
## Intercept only ""
## % of observations below crossover
## Vantage sensitivity WEAK "0"
## Differential susceptibility WEAK "0"
## Differential susceptibility STRONG "0.004"
## Vantage sensitivity STRONG "0"
## Diathesis-stress WEAK "1"
## Diathesis-stress STRONG "1"
## G + E only NA
## G only NA
## E only NA
## Intercept only NA
```

We obtain similar results.

We can then plot the best model:

`plot(GxE_test_BIC$fits$vantage_sensitivity_WEAK, xlim=c(0,10), ylim=c(3,13),cex.leg=1.4, cex.axis=1.5, cex.lab=1.5)`

When assuming vantage sensitivity STRONG, we let \(c=0\) and \(\beta_E=0\) so that: \[\mathbf{\mu} = 3 + 2\mathbf{g}(\mathbf{e}-0) \]

We generate N=250 observations and test all 6 possible models based on the BIC assuming we don’t know what is the minimum environmental score.

```
ex_dia_s = example_with_crossover(N=250, c=0, coef_main = c(3,0,2), sigma=1, seed=7)
GxE_test_BIC = GxE_interaction_test(data=ex_dia_s$data, genes=ex_dia_s$G, env=ex_dia_s$E, formula_noGxE = y ~ 1, crossover = c("min","max"), criterion="BIC")
GxE_test_BIC$results
```

```
## BIC crossover crossover 95%
## Differential susceptibility STRONG "770.17" "-0.5" "( -1.22 / 0.23 )"
## Vantage sensitivity STRONG "771.77" "0.45" ""
## Vantage sensitivity WEAK "772.93" "0.45" ""
## Differential susceptibility WEAK "775.68" "-0.45" "( -1.18 / 0.27 )"
## Diathesis-stress WEAK "967.38" "9.74" ""
## Diathesis-stress STRONG "1104.84" "9.75" ""
## G + E only "1170.07" NA ""
## G only "1171.24" NA ""
## Intercept only "1247.53" NA ""
## E only "1248.73" NA ""
## Within observable range?
## Differential susceptibility STRONG "No"
## Vantage sensitivity STRONG ""
## Vantage sensitivity WEAK ""
## Differential susceptibility WEAK "No"
## Diathesis-stress WEAK ""
## Diathesis-stress STRONG ""
## G + E only ""
## G only ""
## Intercept only ""
## E only ""
## % of observations below crossover
## Differential susceptibility STRONG "0"
## Vantage sensitivity STRONG "0"
## Vantage sensitivity WEAK "0"
## Differential susceptibility WEAK "0"
## Diathesis-stress WEAK "1"
## Diathesis-stress STRONG "1"
## G + E only NA
## G only NA
## Intercept only NA
## E only NA
```

We conclude that vantage sensitivity STRONG is the best model.

We can then plot the best model:

`plot(GxE_test_BIC$fits$vantage_sensitivity_STRONG, xlim=c(0,10), ylim=c(3,13),cex.leg=1.4, cex.axis=1.5, cex.lab=1.5)`

When assuming differential susceptibility WEAK, we let \(c=5\) (and use 8 instead of 3 as intercept to keep a similar outcome range) so that: \[\mathbf{\mu} = 8 + (\mathbf{e}-5) + 2\mathbf{g}(\mathbf{e}-5) \]

We generate N=250 observations and test all 6 possible models based on the BIC assuming we don’t know what is the minimum environmental score.

```
ex_ds = example_with_crossover(N=250, c=5, coef_main = c(3+5,1,2), sigma=1, seed=7)
GxE_test_BIC = GxE_interaction_test(data=ex_ds$data, genes=ex_ds$G, env=ex_ds$E, formula_noGxE = y ~ 1, crossover = c("min","max"), criterion="BIC")
GxE_test_BIC$results
```

```
## BIC crossover crossover 95%
## Differential susceptibility WEAK "776.06" "4.83" "( 4.5 / 5.16 )"
## Diathesis-stress WEAK "834.53" "9.74" ""
## Vantage sensitivity WEAK "835.05" "0.45" ""
## Differential susceptibility STRONG "864.07" "4.78" "( 4.37 / 5.2 )"
## E only "1011.14" NA ""
## G + E only "1011.79" NA ""
## Intercept only "1149.96" NA ""
## G only "1153.2" NA ""
## Diathesis-stress STRONG "1157.61" "6.95" ""
## Vantage sensitivity STRONG "1159.69" "-2.28" ""
## Within observable range?
## Differential susceptibility WEAK "Yes"
## Diathesis-stress WEAK ""
## Vantage sensitivity WEAK ""
## Differential susceptibility STRONG "Yes"
## E only ""
## G + E only ""
## Intercept only ""
## G only ""
## Diathesis-stress STRONG ""
## Vantage sensitivity STRONG ""
## % of observations below crossover
## Differential susceptibility WEAK "0.488"
## Diathesis-stress WEAK "1"
## Vantage sensitivity WEAK "0"
## Differential susceptibility STRONG "0.476"
## E only NA
## G + E only NA
## Intercept only NA
## G only NA
## Diathesis-stress STRONG "1"
## Vantage sensitivity STRONG "0.044"
```

We conclude that differential susceptibility WEAK is the best model.

We can then plot the best model:

`plot(GxE_test_BIC$fits$diff_suscept_WEAK, xlim=c(0,10), ylim=c(3,13),cex.leg=1.4, cex.axis=1.5, cex.lab=1.5)`

When assuming differential susceptibility STRONG, we let \(c=5\) and \(\beta_E=0\) (and use 8 instead of 3 as intercept to keep a similar outcome range) so that: \[\mathbf{\mu} = 8 + 2\mathbf{g}(\mathbf{e}-5) \]

We generate N=250 observations and test all 6 possible models based on the BIC assuming we don’t know what is the minimum environmental score.

```
ex_ds_s = example_with_crossover(N=250, c=5, coef_main = c(3+5,0,2), sigma=1, seed=7)
GxE_test_BIC = GxE_interaction_test(data=ex_ds_s$data, genes=ex_ds_s$G, env=ex_ds_s$E, formula_noGxE = y ~ 1, crossover = c("min","max"), criterion="BIC")
GxE_test_BIC$results
```

```
## BIC crossover crossover 95%
## Differential susceptibility STRONG "771.2" "4.84" "( 4.13 / 5.55 )"
## Differential susceptibility WEAK "776.64" "4.84" "( 4.12 / 5.55 )"
## Diathesis-stress WEAK "834.4" "9.74" ""
## Vantage sensitivity WEAK "834.9" "0.44" ""
## E only "848.02" NA ""
## G + E only "851.11" NA ""
## Intercept only "895.99" NA ""
## G only "899.81" NA ""
## Diathesis-stress STRONG "909.24" "6.77" ""
## Vantage sensitivity STRONG "913.1" "-2.45" ""
## Within observable range?
## Differential susceptibility STRONG "Yes"
## Differential susceptibility WEAK "Yes"
## Diathesis-stress WEAK ""
## Vantage sensitivity WEAK ""
## E only ""
## G + E only ""
## Intercept only ""
## G only ""
## Diathesis-stress STRONG ""
## Vantage sensitivity STRONG ""
## % of observations below crossover
## Differential susceptibility STRONG "0.484"
## Differential susceptibility WEAK "0.484"
## Diathesis-stress WEAK "1"
## Vantage sensitivity WEAK "0"
## E only NA
## G + E only NA
## Intercept only NA
## G only NA
## Diathesis-stress STRONG "1"
## Vantage sensitivity STRONG "0.036"
```

We conclude that differential susceptibility STRONG is the best model.

We can then plot the best model:

`plot(GxE_test_BIC$fits$diff_suscept_STRONG, xlim=c(0,10), ylim=c(3,13),cex.leg=1.4, cex.axis=1.5, cex.lab=1.5)`