Bayesian cophescan analysis under single causal variant assumption

```
cophe.single(
dataset,
querysnpid,
querytrait,
MAF = NULL,
pa = 3.82e-05,
pc = 0.00182,
p1 = NULL,
p2 = NULL,
p12 = NULL
)
```

- dataset
a list with specifically named elements defining the query trait dataset to be analysed.

- querysnpid
Id of the query variant, (id in dataset$snp)

- querytrait
Query trait name

- MAF
Minor allele frequency vector

- pa
prior probability that a non-query variant is causally associated with the query trait (cophescan prior), default 3.82e-5

- pc
prior probability that the query variant is causally associated with the query trait (cophescan prior), default 1.82e-3 (cophescan prior)

- p1
prior probability a SNP is associated with trait 1, (coloc prior), pc derived by using \(pc = p12/p1+p12\); use p1, p2, p12 only when pa and pc are unavailable (See vignettes)

- p2
prior probability a SNP is associated with trait 2, (coloc prior), pa derived by using \(pa = p2\)

- p12
prior probability a SNP is associated with both traits, (coloc prior), pc derived by using \(pc = p12/p1+p12\)

a list of two `data.frame`

s:

summary is a vector giving the number of SNPs analysed, and the posterior probabilities of Hn (no shared causal variant), Ha (two distinct causal variants) and Hc (one common causal variant)

results is an annotated version of the input data containing log Approximate Bayes Factors and intermediate calculations, and the posterior probability SNP.PP.Hc of the SNP being causal for the shared signal

*if*Hc is true. This is only relevant if the posterior support for Hc in summary is convincing.

This function calculates posterior probabilities of different causal variant configurations under the assumption of a single causal variant for each trait.

If regression coefficients and variances are available, it calculates Bayes factors for association at each SNP. If only p values are available, it uses an approximation that depends on the SNP's MAF and ignores any uncertainty in imputation. Regression coefficients should be used if available. Find more input data structure details in the coloc package

```
library(cophescan)
data(cophe_multi_trait_data)
query_trait_1 <- cophe_multi_trait_data$summ_stat[['Trait_1']]
querysnpid <- cophe_multi_trait_data$querysnpid
res.single <- cophe.single(query_trait_1, querysnpid = querysnpid, querytrait='Trait_1')
#> Running cophe.single...
#> SNP Priors
#> 0.96001823.82e-050.00182
#> Hypothesis Priors
#> 0.96001820.03816180.00182
#> 0.002090.3750.623
#> PP for causal query variant: 62.3%
summary(res.single)
#> nsnps PP.Hn PP.Ha PP.Hc lBF.Ha lBF.Hc querysnp
#> 1 1000 0.00208927 0.3749638 0.6229469 15.32189 11.96576 chr19-11182353
#> querytrait typeBF
#> 1 Trait_1 ABF
```