ibs {GenABEL} | R Documentation |

Given a set of SNPs, computes a matrix of average IBS for a group of people

ibs(data, snpsubset, idsubset, cross.idsubset, weight="no", snpfreq)

`data` |
object of `snp.data-class` |

`snpsubset` |
Index, character or logical vector with subset of SNPs to run analysis on.
If missing, all SNPs from `data` are used for analysis. |

`idsubset` |
IDs of people to be analysed.
If missing, all people from `data` are used for analysis. |

`cross.idsubset` |
Parameter allowing parallel implementation. Not to be used normally. If supplied together with idsubset, the ibs/kinship for all pairs between idsubset and cross.idsubset computed. |

`weight` |
"no" for direct IBS computations, "freq" to weight by allelic frequency |

`snpfreq` |
when option weight="freq" used, you can provide fixed allele frequencies |

This function facilitates quality control of genomic data. E.g. people with exteremly high (close to 1) IBS may indicate duplicated samples (or twins), simply high values of IBS may indicate relatives.

When weight "freq" is used, IBS for a pair of people i and j is computed as

*
f_{i,j} = Σ_k \frac{(x_{i,k} - p_k) * (x_{j,k} - p_k)}{(p_k * (1 - p_k))}
*

where k changes from 1 to N = number of SNPs GW, *x_{i,k}* is
a genotype of ith person at the kth SNP, coded as 0, 1/2, 1 and
*p_k* is the frequency
of the "+" allele. This apparently provides an unbiased estimate of the
kinship coefficient.

Only with "freq" option monomorphic SNPs are regarded as non-informative.

ibs() operation may be very lengthy for a large number of people.

A (Npeople X Npeople) matrix giving average IBS (kinship) values between a pair below the diagonal and number of SNP genotype measured for both members of the pair above the diagonal.

On the diagonal, homozygosity 0.5*(1+inbreeding) is provided.

attr(computedobject,"Var") returns variance (replaing the
diagonal when the object is used by `egscore`

Yurii Aulchenko

`check.marker`

,
`summary.snp.data`

,
`snp.data-class`

data(ge03d2c) # compute IBS based on a random sample of 1000 autosomal marker a <- ibs(ge03d2c,snps=sample(autosomal(ge03d2c),1000,replace=FALSE)) a[1:5,1:5] mds <- cmdscale(as.dist(1-a)) plot(mds) # identify smaller cluster of outliers km <- kmeans(mds,centers=2,nstart=1000) cl1 <- names(which(km$cluster==1)) cl2 <- names(which(km$cluster==2)) if (length(cl1) > length(cl2)) cl1 <- cl2; cl1 # PAINT THE OUTLIERS IN RED points(mds[cl1,],pch=19,col="red") # compute genomic kinship matrix to be used with e.g. polygenic, mmscore, etc a <- ibs(ge03d2c,snps=sample(autosomal(ge03d2c),1000,replace=FALSE),weight="freq") a[1:5,1:5] # now replace diagonal with EIGENSTRAT-type of diaganal to be used for egscore diag(a) <- hom(ge03d2c[,autosomal(ge03d2c)])$Var a[1:5,1:5]

[Package *GenABEL* version 1.6-7 Index]