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\title{function to compute average homozygosity within a person}
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This function computes average homozygosity (inbreeding) for a set of
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people, across multiple markers. Can be used for Quality Control
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(e.g. contamination checks)
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hom(data, snpsubset, idsubset, snpfreq, n.snpfreq = 1000)
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hom(data, snpsubset, idsubset, snpfreq, n.snpfreq = 1000)
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\item{data}{Object of \link{gwaa.data-class} or \link{snp.data-class}}
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\item{data}{Object of \link{gwaa.data-class} or
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\item{snpsubset}{Subset of SNPs to be used}
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\item{idsubset}{People for whom average homozygosity is to be computed}
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\item{snpfreq}{when option weight="freq" used, you can provide
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fixed allele frequencies}
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\item{n.snpfreq}{when option weight="freq" used, you can provide
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a vector supplying the number of people used to estimate allele
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frequencies at the particular marker, or a fixed number}
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\item{idsubset}{People for whom average homozygosity is
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\item{snpfreq}{when option weight="freq" used, you can
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provide fixed allele frequencies}
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\item{n.snpfreq}{when option weight="freq" used, you can
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provide a vector supplying the number of people used to
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estimate allele frequencies at the particular marker, or
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A matrix with rows corresponding to the ID names and
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columns showing the number of SNPs measured in this
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person (NoMeasured), the number of measured polymorphic
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SNPs (NoPoly), homozygosity (Hom), expected homozygosity
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(E(Hom)), variance, and the estimate of inbreeding, F.
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This function computes average homozygosity (inbreeding)
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for a set of people, across multiple markers. Can be used
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for Quality Control (e.g. contamination checks)
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Homozygosity is measured as proportion of
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homozygous genotypes observed in a person.
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Inbreeding for person \eqn{i} is estimated with
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f_i = \frac{(O_i - E_i)}{(L_i - E_i)}
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f_i = ((O_i - E_i))/((L_i - E_i))
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where \eqn{O_i} is observed homozygosity, \eqn{L_i} is the number of SNPs
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measured in individual \eqn{i} and
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E_i = \Sigma_{j=1}^{L_i} (1 - 2 p_j (1 - p_j) \frac{T_{Aj}}{T_{Aj}-1})
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E_i = Sigma_(j=1)^(L_i) (1 - 2 p_j (1 - p_j) (T_(Aj))/(T_(Aj)-1))
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where \eqn{T_{Aj}} is the number of measured genotypes at locus \eqn{j};
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\eqn{T_{Aj}} is either estimated from data or provided by "n.snpfreq"
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parameter (vector). Allelic frequencies are either estimated from
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data or provided by the "snpfreq" vector.
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This measure is the same as used by PLINK (see reference).
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The variance (Var) is estimated as
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V_{i} = \frac(1)(N) \Sigma_k \frac{(x_{i,k} - p_k)^2}{(p_k * (1 - p_k))}
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where k changes from 1 to N = number of SNPs, \eqn{x_{i,k}} is
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a genotype of ith person at the kth SNP, coded as 0, 1/2, 1 and
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\eqn{p_k} is the frequency
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Only polymorphic loci with number of measured genotypes >1 are used
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This variance is used as diagonal of the genomic
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kinship matrix when using EIGENSTRAT method.
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You should use as many people and markers as possible when estimating
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inbreeding/variance from marker data.
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A matrix with rows corresponding to the ID names and columns
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showing the number of SNPs measured in this person (NoMeasured),
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the number of measured polymorphic SNPs (NoPoly),
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expected homozygosity (E(Hom)), variance, and
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the estimate of inbreeding, F.
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Purcell S. et al, (2007) PLINK: a toolset for whole genome association and population-based
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linkage analyses. Am. J. Hum. Genet.
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\author{Yurii Aulchenko, partly based on code by John Barnard}
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\code{\link{gwaa.data-class}},
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\code{\link{snp.data-class}}
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Homozygosity is measured as proportion of homozygous
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genotypes observed in a person.
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Inbreeding for person \eqn{i} is estimated with
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\deqn{ }{ f_i = ((O_i - E_i))/((L_i - E_i)) }\deqn{ f_i =
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\frac{(O_i - E_i)}{(L_i - E_i)} }{ f_i = ((O_i -
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E_i))/((L_i - E_i)) }\deqn{ }{ f_i = ((O_i - E_i))/((L_i
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where \eqn{O_i} is observed homozygosity, \eqn{L_i} is
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the number of SNPs measured in individual \eqn{i} and
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\deqn{ }{ E_i = Sigma_(j=1)^(L_i) (1 - 2 p_j (1 - p_j)
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(T_(Aj))/(T_(Aj)-1)) }\deqn{ E_i = \Sigma_{j=1}^{L_i} (1
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- 2 p_j (1 - p_j) \frac{T_{Aj}}{T_{Aj}-1}) }{ E_i =
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Sigma_(j=1)^(L_i) (1 - 2 p_j (1 - p_j)
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(T_(Aj))/(T_(Aj)-1)) }\deqn{ }{ E_i = Sigma_(j=1)^(L_i)
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(1 - 2 p_j (1 - p_j) (T_(Aj))/(T_(Aj)-1)) }
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where \eqn{T_{Aj}} is the number of measured genotypes at
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locus \eqn{j}; \eqn{T_{Aj}} is either estimated from data
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or provided by "n.snpfreq" parameter (vector). Allelic
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frequencies are either estimated from data or provided by
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This measure is the same as used by PLINK (see
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The variance (Var) is estimated as
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\deqn{ V_{i} = \frac{1}{N} \Sigma_k \frac{(x_{i,k} -
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p_k)^2}{(p_k * (1 - p_k))} }
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where k changes from 1 to N = number of SNPs,
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\eqn{x_{i,k}} is a genotype of ith person at the kth SNP,
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coded as 0, 1/2, 1 and \eqn{p_k} is the frequency of the
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Only polymorphic loci with number of measured genotypes
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>1 are used with this option.
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This variance is used as diagonal of the genomic kinship
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matrix when using EIGENSTRAT method.
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You should use as many people and markers as possible
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when estimating inbreeding/variance from marker data.
added
removed
man/hom.Rd
