Relatedness

NGSrelate - estimation of IBD probabilities

In order to estimate kinship coefficient then population allele frequencies are needed. These can be estimated from data if you can multiple individuals. For some individuals, for example most human populations, there are publicly available data. If you can obtain population allele frequencies or have a many samples from your population then we recommend that you use NGSrelate has works with ANGSD output. From the estimated IBD probabilities you can then infer the relationship. Below is a table of the expected IBD sharing probabilities assuming no inbreeding

Relationship	$K_{0}$	$K_{1}$	$K_{2}$
Parent-Offspring	$0$	$1$	$0$
Full siblings	$0.25$	$0.5$	$0.25$
Half siblings	$0.5$	$0.5$	$0$
First cousins	$0.75$	$0.25$	$0$
Unrelated	$1$	$0$	$0$

NGSrelate has its very own website http://www.popgen.dk/software/index.php/NgsRelate

IBS/genotype distribution

If you do not have population allele frequencies the you cannot estimate kinship coefficients. However, you can still make some claims about the relationship of your samples based on IBS patterns. Below is an example of IBS patterns between two individuals where we ignore the allele types. G is the genotype that counts for example the number of derived or non-reference alleles. Basically it is the 2D SFS where the is just 1 individual in each of the two populations

		ind2
ind1	$G=0$	$G=1$	$G=2$
$G=0$	$A$	$D$	$G$
$G=1$	$B$	$E$	$H$
$G=2$	$C$	$F$	$I$

Relationship	Expected ratio
Parent-Offspring	$0$	$1$	$0$
Full siblings	$0.25$	$0.5$	$0.25$
Half siblings	$0.5$	$0.5$	$0$
First cousins	$0.75$	$0.25$	$0$
Unrelated	${\frac {E}{C+G}}=2$	$0$	$0$

#R code go get expected IBS pattern
getEst<-function(k=c(1,0,0),f=0.5){
    p<-f
    q<-1-f
    m0<-rbind(
        c(p^4,2*p^3*q,p^2*q^2),
        c(2*p^3*q,4*p^2*q^2,2*p*q^3),
        c(q^4,2*q^3*p,p^2*q^2)
        )
   m1<-rbind(
        c(p^3,p^2*q,0),
        c(p^2*q,p^2*q+q^2*p,p*q^2),
        c(0,q^2*p,q^3)
        )
    m2<-rbind(
        c(p^2,0,0),
        c(0,2*p*q,0),
        c(0,0,q^2)
        )

return(k[1]*m0+k[2]*m1+k[3]*m2)

}

getEst(k=c(1,0,0),f=0.5)
       [,1]  [,2]   [,3]
[1,] 0.0625 0.125 0.0625
[2,] 0.1250 0.250 0.1250
[3,] 0.0625 0.125 0.0625