*This describes deprecated functions - the SuSiE approach is more accurate and should be
used instead*

We load some simulated data.

```
library(coloc)
data(coloc_test_data)
attach(coloc_test_data) # contains D3, D4 that we will use in this vignette
```

```
## The following objects are masked from coloc_test_data (pos = 3):
##
## D1, D2, D3, D4
```

```
## The following objects are masked from coloc_test_data (pos = 4):
##
## D1, D2, D3, D4
```

```
## The following objects are masked from coloc_test_data (pos = 5):
##
## D1, D2, D3, D4
```

First, let us do a standard coloc (single causal variant) analysis to serve as a baseline comparison. The analysis concludes there is colocalisation, because it “sees” the SNPs on the left which are strongly associated with both traits. But it misses the SNPs on the right of the top left plot which are associated with only one trait.

```
library(coloc)
<- coloc.abf(dataset1=D3, dataset2=D4) my.res
```

```
## PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf
## 1.39e-15 2.84e-07 5.35e-12 9.76e-05 1.00e+00
## [1] "PP abf for shared variant: 100%"
```

`class(my.res)`

`## [1] "coloc_abf" "list"`

```
## print.coloc_abf
my.res
```

`## Coloc analysis of trait 1, trait 2`

```
##
## SNP Priors
```

```
## p1 p2 p12
## 1e-04 1e-04 1e-05
```

```
##
## Hypothesis Priors
```

```
## H0 H1 H2 H3 H4
## 0.9894755 0.005 0.005 2.45e-05 5e-04
```

```
##
## Posterior
```

```
## nsnps H0 H1 H2 H3 H4
## 5.000000e+01 1.386329e-15 2.843375e-07 5.351202e-12 9.763482e-05 9.999021e-01
```

`sensitivity(my.res,"H4 > 0.9")`

`## Results pass decision rule H4 > 0.9`

Even though the sensitivity analysis itself looks good, the Manhattan
plots suggest we are violating the assumption of a single causal variant
per trait.

We can use =finemap.signals= to test whether there are additional
signals after conditioning.

`finemap.signals(D3,method="cond")`

```
## s5 s38
## 8.268599 6.621488
```

`finemap.signals(D4,method="cond")`

```
## s5
## 6.557987
```

Note that every colocalisation conditions out every other signal except one for each trait. For that reason, trying to colocalise many signals per trait is not recommended. Instead, use pthr to set the significance (p value) required to call a signal. If you set if too low, you will capture signals that are non-significant, or too high and you will miss true signals. pthr=5e-8 would correspond to a genome-wide significance level for common variants in a European study, but we typically choose a slightly relaxed pthr=1e-6 on the basis that if there is one GW-significant signal in a region, we expect there is a greater chance for secondary signals to exist.

`finemap.signals(D3,method="cond",pthr=1e-20) ## too small`

`## NULL`

`finemap.signals(D4,method="cond",pthr=0.1) ## too big`

```
## s5 s31 s41
## 6.557987 2.736165 -3.713089
```

Now we can ask coloc to consider these as separate signals using the coloc.signals() function.

`<- coloc.signals(D3,D4,method="cond",p12=1e-6,pthr=1e-6) res `

```
## PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf
## 1.39e-14 2.84e-06 5.35e-11 9.75e-04 9.99e-01
## [1] "PP abf for shared variant: 99.9%"
## PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf
## 1.69e-08 2.59e-04 6.51e-05 1.00e+00 4.45e-06
## [1] "PP abf for shared variant: 0.000445%"
```

` res`

`## Coloc analysis of trait 1, trait 2`

```
##
## SNP Priors
```

```
## p1 p2 p12
## 1e-04 1e-04 1e-06
```

```
##
## Hypothesis Priors
```

```
## H0 H1 H2 H3 H4
## 0.9899255 0.005 0.005 2.45e-05 5e-05
```

```
##
## Posterior
```

```
## nsnps hit1 hit2 H0 H1 H2 H3
## 1: 50 s38 s5 1.685256e-08 2.589837e-04 6.505054e-05 0.99967150
## 2: 50 s5 s5 1.371408e-14 2.812772e-06 5.293606e-11 0.01085724
## H4
## 1: 4.450517e-06
## 2: 9.891399e-01
```

Note that because we are doing multiple colocalisations, sensitivity() needs to know which to consider:

`sensitivity(res,"H4 > 0.9",row=1)`

`## Results fail decision rule H4 > 0.9`

`sensitivity(res,"H4 > 0.9",row=2)`

`## Results pass decision rule H4 > 0.9`

Because these signals are truly independent, we could also split them by using masking, which doesn’t condition at all, but restricts the search space for colocalisation to SNPs which are not in LD with any-but-one of each signal SNP. Here you also need to consider r2thr which sets the maximum \(r^2\) between two SNPs for them to be considered independent.

`finemap.signals(D3,method="mask")`

```
## s5 s49
## 8.268599 4.926711
```

`finemap.signals(D4,method="mask")`

```
## s5
## 6.557987
```

`=coloc.signals(D3,D4,method="mask",p12=1e-6,pthr=1e-6,r2thr=0.01) resm`

```
## PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf
## 1.39e-14 2.84e-06 5.35e-11 9.75e-04 9.99e-01
## [1] "PP abf for shared variant: 99.9%"
```

`## 1`

`## dropping 0/50 : 0 + 0`

`## 2`

`## dropping 46/50 : 46 + 0`

` resm`

`## Coloc analysis of trait 1, trait 2`

```
##
## SNP Priors
```

```
## p1 p2 p12
## 1e-04 1e-04 1e-06
```

```
##
## Hypothesis Priors
```

```
## H0 H1 H2 H3 H4
## 0.9899255 0.005 0.005 2.45e-05 5e-05
```

```
##
## Posterior
```

```
## nsnps hit1 hit2 H0 H1 H2 H3
## 1: 50 s49 s5 1.237770e-04 1.354141e-04 4.777764e-01 0.52196383
## 2: 50 s5 s5 1.371408e-14 2.812772e-06 5.293606e-11 0.01085724
## H4
## 1: 5.542865e-07
## 2: 9.891399e-01
```

`sensitivity(resm,"H4 > 0.9",row=1)`

`## Results fail decision rule H4 > 0.9`

`sensitivity(resm,"H4 > 0.9",row=2)`

`## Results pass decision rule H4 > 0.9`