parent.assign.fun: parent.assign.fun
In mghamilton/SNPpools: Maximum likelihood parentage assignment using quantitative genotypes
Description
Usage
Arguments
Value
References
Examples
View source: R/parent.assign.fun.R
Description
This function assigns parents to pooled samples using one of seven approaches:
'Least squares' method outlined in Henshall et al. 2014 (method = "Least_squares")
'Least squares minimum sum of squares' method whereby the sum of squares of all parental combinations are computed and
the combination with the minimum value identified. Refer to Hamilton 2020 (method = "Least_squares", beta.min.ss = TRUE)
'Quantitative maximum likelihood' method whereby the approach using quantititive genotypes for parentatge assignment
outlined in Henshall et al. 2014 is extended to pooled DNA samples. Refer to Hamilton 2020 (method = "Quantitative")
'Discrete maximum likelihood from genotype probabilities' method whereby discrete genotypes are derived from genotype probabilities and
used for parentatge assignment of pooled DNA samples by extending the discrete genotype maximum likelihood approach outlined in
Henshall et al. 2014 to pooled DNA samples. Refer to Hamilton 2020 (method = "Discrete", discrete.method = "geno.probs").
'Discrete maximum likelihood from genotype assignments' method whereby discrete genotypes are provided as input and
used for parentatge assignment of pooled DNA samples by extending the discrete genotype maximum likelihood approach outlined in
Henshall et al. 2014 to pooled DNA samples. Refer to Hamilton 2020 (method = "Discrete", discrete.method = "assigned.genos")
'Exclusion from genotype probabilities' method whereby discrete genotypes are derived from genotype probabilities and
used for parentatge assignment of pooled DNA samples by extending the exclusion approach outlined in
Henshall et al. 2014 to pooled DNA samples Refer to Hamilton 2020 (method = "Exclusion", discrete.method = "geno.probs").
'Exclusion from genotype assignments' method whereby discrete genotypes are provided as input and
used for parentatge assignment of pooled DNA samples by extending the exclusion approach outlined in
Henshall et al. 2014 to pooled DNA samples. Refer to Hamilton 2020 (method = "Exclusion", discrete.method = "assigned.genos")
Usage
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
parent.assign.fun(
method,
beta.min.ss = FALSE,
discrete.method = "geno.probs",
threshold.indiv = NULL,
threshold.pools = NULL,
snp.dat.indiv,
snp.dat.pools,
n.in.pools,
min.intensity = 0,
snp.error.assumed = NULL,
snp.error.underlying = NULL,
snp.param.indiv = NULL,
snp.param.pools = NULL,
min.sd = 0,
fams,
fam.set.combns = NULL,
fam.set.combns.by.pool = NULL,
skip.checks = FALSE
)
Arguments
method
is a vector of methods to be implemented (e.g. c("Quantitative", "Discrete", "Exclusion", "Least_squares"))
beta.min.ss
is a logical variable appicable to least_squares method only (default = FALSE).
If TRUE, the sum of squares of all parental combinations are computed and the combination with the minimum value is identified.
Refer to Hamilton 2020.
discrete.method
is a character variable applicable to the "Discrete" or "Exclusion" methods only
(default = "geno.probs"). It must equal either:
"geno.probs" in which case discrete genotypes for parents and pools are derived from genotype probabilities.
"assigned.genos" in which case discrete genotypes for parents and pools are obtained directly from the snp.dat.indiv and snp.dat.pools inputs.
threshold.indiv
is a numeric variable between 0 and 1 inclusive applicable to the "Discrete" or "Exclusion" methods only
when discrete.method = "geno.probs" (default = NULL). A discrete genotype is assigned to the the most likely genotype in
the quantitative ordered genotype probability matrix Gij if it is greater than threshold.indiv (or
threshold.indiv / 2 for the two heterozygous genotypes). Otherwise the genotype is deemed missing (refer to the left hand side of
page 5 of Henshall et al. 2014)
threshold.pools
is a numeric variable between 0 and 1 inclusive applicable to the "Discrete" or "Exclusion" methods only
when discrete.method = "geno.probs" (default = NULL). Equivalent to threshold.indiv for pooled DNA samples.
snp.dat.indiv
is a data frame with the following headings (class in parentheses):
'SAMPLE_ID' is the sample identifier. Samples must be from diploid individuals (i.e. not pools) (integer).
'SNP_ID' is the SNP identifier (character).
'INTENSITY_A' is the area/intensity for allele A (numeric).
'INTENSITY_B' is the area/intensity for allele B (numeric).
'A_ALLELE' is the base represented by allele A (i.e. 'A', 'C', 'G' or 'T') (character).
'B_ALLELE' is the base represented by allele B (i.e. 'A', 'C', 'G' or 'T') (character).
'GENOTYPE' is the SNP genotype call (e.g. 'AT', 'TT'). NA if missing (character).
snp.dat.pools
is a data frame with the following headings. Note that all pooled DNA samples
in this dataframe must be comprised of DNA from the same number of individuals (see n.in.pools) (class in parentheses):
SAMPLE_ID is the pooled sample identifier (integer).
SNP_ID is the SNP identifier (character).
INTENSITY_A is the signal intensity for allele A. Not required if method does not include 'Quantitative' or 'Least_squares' and discrete.method = "geno.probs" (numeric).
INTENSITY_B is the signal intensity for allele B. Not required if method does not include 'Quantitative' or 'Least_squares' and discrete.method = "geno.probs" (numeric).
GENOTYPE is the assigned unordered genotype. Not required if discrete.method = "geno.probs" (character).
n.in.pools
is an integer variable representing the number of individual that contributed DNA to each sample in snp.dat.pools
min.intensity
is a numeric variable (default = 0). If the square root of the sum of INTENSITY_A squared and
INTENSITY_B squared in snp.dat.indiv or snp.dat.pools is less than min.intensity then this record is excluded.
That is, observations that fall into an arc with a radius equal to min.intensity in the lower left of
signal intensity scatter plots are excluded.
snp.error.assumed
Must be one of (default = NULL):
NULL. Note that if snp.error.assumed is NULL then snp.error.underlying must not be NULL.
a numeric variable between 0 and 1, in which case the 'assumed error rate' (see Henshall et al 2014) is the same across all SNP.
a data frame with columns SNP_ID and SNP_ERROR_TILDE (see Henshall et al 2014).
snp.error.underlying
is not used if snp.error.assumed is not NULL (default = NULL). Must be either:
NULL.
a numeric variable between 0 and 1 inclusive. Used to comptute SNP_ERROR_TILDE from SNP_ERROR_HAT according
to the approach outlined on the left of page 5 of Henshall et al. 2014 using individual
(i.e. not pooled) data only. If snp.error.underlying = 0 then SNP_ERROR_TILDE = SNP_ERROR_HAT.
snp.param.indiv
is the output of snp.param.indiv.fun (default = NULL). That is, it is a data frame with the following
headings (class in parentheses):
'SNP_ID' is the SNP identifier (character).
'N_AA' is the count of homozygous A (AA) genotypes (integer).
'MEAN_P_AA' is the mean of allelic proportion for homozygous A genotypes (numeric).
'SD_P_AA' is the standard deviation of allelic proportion for homozygous A genotypes (numeric).
'N_AB' is the count of heterozygous (AB) genotypes (integer).
'MEAN_P_AB' is the mean of allelic proportion for heterozygous (AB) genotypes (numeric).
'SD_P_AB' is the standard deviation of allelic proportion for heterozygous (AB) genotypes (numeric).
'N_BB' is the count of homozygous B (BB) genotypes (integer).
'MEAN_P_BB' is the mean of allelic proportion for homozygous B genotypes (numeric).
'SD_P_BB' is the standard deviation of allelic proportion for homozygous B genotypes (numeric).
'WELCH_A' is the welsh statistic for the interval between MEAN_P_AA and MEAN_P_AB (numeric).
'WELCH_B' is the welsh statistic for the interval between MEAN_P_AB and MEAN_P_BB (numeric).
'A_ALLELE_FREQ' is the A allele frequency computed from genotype counts (numeric).
'B_ALLELE_FREQ' is the B allele frequency computed from genotype counts (numeric).
'A_ALLELE' is the base represented by allele A (i.e. 'A', 'C', 'G' or 'T') (character).
'B_ALLELE' is the base represented by allele B (i.e. 'A', 'C', 'G' or 'T') (character).
snp.param.pools
is the output of snp.param.pools.fun. That is, it is a data frame with the following headings (class in parentheses)
'SNP_ID' is the SNP identifier (character).
'MEAN_P_AAAA' is the mean of allelic proportion for homozygous A genotypes (numeric).
'SD_P_AAAA' is the standard deviation of allelic proportion for homozygous A genotypes (numeric).
'MEAN_P_AAAB' is the mean of allelic proportion for unordered AAAB genotypes (numeric).
'SD_P_AAAB' is the standard deviation of allelic proportion for unordered AAAB genotypes (numeric).
'MEAN_P_AABB' is the mean of allelic proportion for unordered AABB genotypes (numeric).
'SD_P_AABB' is the standard deviation of allelic proportion for unordered AABB genotypes (numeric).
'MEAN_P_ABBB' is the mean of allelic proportion for unordered ABBB genotypes (numeric).
'SD_P_ABBB' is the standard deviation of allelic proportion for unordered ABBB genotypes (numeric).
'MEAN_P_BBBB' is the mean of allelic proportion for homozygous B genotypes (numeric).
'SD_P_BBBB' is the standard deviation of allelic proportion for homozygous B genotypes (numeric).
'A_ALLELE' is the base represented by allele A (i.e. 'A', 'C', 'G' or 'T') (character).
'B_ALLELE' is the base represented by allele B (i.e. 'A', 'C', 'G' or 'T') (character).
min.sd
is a numberic variable defining a lower bound to be applied to estimates of the
standard deviation of allelic proportion for genotypes in snp.param.indiv and snp.param.pools (default = 0)
fams
is a data frame with the following headings (class in parentheses):
'FAMILY_ID' is the family identifier (integer).
'SIRE_ID' is the sire identifier (integer).
'DAM_ID' is the dam identifier (integer).
fam.set.combns
is a data frame with the following headings (class in parentheses). Note: if fam.set.combns = NULL
(see 'pooling by phenotype' example below),
FAMILY_ID is taken from the 'fams' and duplicated n.in.pools times, FAM_SET_ID = 1 for the first duplication of
FAMILY_IDs, 2 for the second etc and FAM_SET_COMBN_ID = 1 (default = NULL):
'FAM_SET_COMBN_ID' is the family set combination identifier (integer). A 'family set combination' is a combination of 'family sets'.
Each pooled sample must be associated with one only family set combination but a family set combination
may be assoicated with multiple pooled samples using the fam.set.combns.by.pool input below.
'FAM_SET_ID' is the family set identifier (integer). A 'family set' is a group of families of which one is known to be the true family
of one of the individuals in a pooled sample. Within each 'family set combination' there must be a 'family set'
for each individual in a pooled sample (i.e. if n.in.pools = 2 there must be two family sets in each family set combination)
'FAMILY_ID' is the family identifier (integer).
fam.set.combns.by.pool
is a data frame linking pooled samples with family set combinations.
It has the following headings (class in parentheses). Note: if fam.set.combns is NULL (see 'pooling by phenotype' example below),
fam.set.combns.by.pool is made NULL.
If fam.set.combns.by.pool = NULL, FAM_SET_COMBN_ID = 1 and SAMPLE_ID is taken from the
'snp.dat.pools' input (default = NULL):
SAMPLE_ID is the pooled sample identifier (integer).
'FAM_SET_COMBN_ID' is the family set combination identifier (integer).
skip.checks
is a logical variable. If FALSE parent.assign.fun data checks are not undertaken.
Value
Primary outputs
most.like.parents.quant
Applicable when method = "Quantitative". Identifies the most likely parental combination and delta LODs for
individual parents. Second most likely (alternative) parents are also presented. Refer to Hamilton 2020. Example fields for n.in.pools = 2:
SAMPLE_ID (integer).
PARENT_COMBN_ID (integer).
MISS_PARENT_SNP_DATA_PROP (numeric).
MISS_POOL_SNP_DATA_PROP (numeric).
NO_MISS_PARENT_OR_POOL_PROP (numeric).
MIN_LOGL (numeric).
MIN_LOGL_SNP (character).
MAX_LOGL (numeric).
MAX_LOGL_SNP (character).
RANGE_5_TO_95_LOGL (numeric).
LOD (numeric).
FAM_SET_COMBN_ID (integer).
PARENT_1 (integer).
PARENT_2 (integer).
PARENT_3 (integer).
PARENT_4 (integer).
FAM_COMBN_ID (integer).
FAMILY_ID_1 (integer).
FAMILY_ID_2 (integer).
PARENT_1_DELTA_LOD (logical).
PARENT_2_DELTA_LOD (logical).
PARENT_3_DELTA_LOD (numeric).
PARENT_4_DELTA_LOD (numeric).
ALT_PARENT_1 (logical).
ALT_PARENT_2 (logical).
ALT_PARENT_3 (numeric).
ALT_PARENT_4 (numeric).
ALT_PARENT_COMBN_1 (logical).
ALT_PARENT_COMBN_2 (logical).
ALT_PARENT_COMBN_3 (integer).
ALT_PARENT_COMBN_4 (integer).
ALT_FAM_COMBN_1 (integer).
ALT_FAM_COMBN_2 (integer).
ALT_FAM_COMBN_3 (integer).
ALT_FAM_COMBN_4 (integer).
most.like.parents.discrete
Applicable when method = "Discrete". Identifies the most likely parental
combination and delta LODs for individual parents. Second most likely (alternative) parents are also presented.
Refer to Hamilton 2020. Example fields for n.in.pools = 2:
SAMPLE_ID (integer).
PARENT_COMBN_ID (integer).
MISS_PARENT_SNP_DATA_PROP (numeric).
MISS_POOL_SNP_DATA_PROP (numeric).
NO_MISS_PARENT_OR_POOL_PROP (numeric).
MIN_LOGL (numeric).
MIN_LOGL_SNP (character).
MAX_LOGL (numeric).
MAX_LOGL_SNP (character).
RANGE_5_TO_95_LOGL (numeric).
LOD (numeric).
FAM_SET_COMBN_ID (integer).
PARENT_1 (integer).
PARENT_2 (integer).
PARENT_3 (integer).
PARENT_4 (integer).
FAM_COMBN_ID (integer).
FAMILY_ID_1 (integer).
FAMILY_ID_2 (integer).
PARENT_1_DELTA_LOD (logical).
PARENT_2_DELTA_LOD (logical).
PARENT_3_DELTA_LOD (numeric).
PARENT_4_DELTA_LOD (numeric).
ALT_PARENT_1 (logical).
ALT_PARENT_2 (logical).
ALT_PARENT_3 (numeric).
ALT_PARENT_4 (numeric).
ALT_PARENT_COMBN_1 (logical).
ALT_PARENT_COMBN_2 (logical).
ALT_PARENT_COMBN_3 (integer).
ALT_PARENT_COMBN_4 (integer).
ALT_FAM_COMBN_1 (integer).
ALT_FAM_COMBN_2 (integer).
ALT_FAM_COMBN_3 (integer).
ALT_FAM_COMBN_4 (integer).
most.like.parents.excl
Applicable when method = "Exclusion". Identifies the most likely parental
combination. Refer to Hamilton 2020. Second most likely (alternative) parental
combination is also presented.
Example fields for n.in.pools = 2:
SAMPLE_ID (integer).
PARENT_COMBN_ID (integer).
MISMATCHES (integer).
SNP_COUNT (integer).
MISMATCH_PROP (numeric).
MISMATCH_PROP_SE (numeric).
MISMATCH_PROP_Z (numeric).
FAM_COMBN_ID (integer).
FAMILY_ID_1 (integer).
FAMILY_ID_2 (integer).
PARENT_1 (integer).
PARENT_2 (integer).
PARENT_3 (integer).
PARENT_4 (integer).
ALT_PARENT_COMBN_ID (integer).
ALT_FAM_COMBN_ID (integer).
ALT_MISMATCHES (integer).
ALT_SNP_COUNT (integer).
ALT_MISMATCH_PROP (numeric).
ALT_MISMATCH_PROP_SE (numeric).
ALT_MISMATCH_PROP_Z (numeric).
most.like.parents.excl.non.dup
Applicable when method = "Exclusion". Identifies the most likely parental
combination - simplified output with multiple combinations with the same number of mismatches (duplicated
SAMPLE_IDs) removed. Refer to Hamilton 2020. Example fields for n.in.pools = 2:
SAMPLE_ID (integer).
MISMATCHES (integer).
SNP_COUNT (integer).
MISMATCH_PROP (numeric).
MISMATCH_PROP_SE (numeric).
MISMATCH_PROP_Z (numeric).
FAMILY_ID_1 (integer).
FAMILY_ID_2 (integer).
PARENT_1 (integer).
PARENT_2 (integer).
PARENT_3 (integer).
PARENT_4 (integer).
beta
Applicable when method = "Least_squares". Identifies the most likely parental combination:
SAMPLE_ID (integer).
SIRE_ID (integer).
DAM_ID (integer).
FAMILY_ID (integer).
BETA_STAR Refer to Henshall et al. 2014 (numeric).
BETA_HAT Refer to Henshall et al. 2014 (numeric).
BETA_HAT_CONSTRAINED Constrained to have equal contributions from each FAMILY_SET_ID. Refer to Hamilton 2020
BETA_MIN_SS Applicable when beta.min.ss = "TRUE". Constrained beta with minimum sum of squares is retained. Beta constrained to have equal contributions from each FAMILY_SET_ID. Refer to Hamilton 2020
Primary plots
bar.png
Bar plot of BETA_HAT by FAMILY_ID. Applicable to 'Least_squares' method only. Output in a directory named 'Results' on the current working directory.
discrete.png
Scatter plot of '5-95 percentile range of log-likelihood ratios' against 'Log odds (LOD) scores' for each
possible family combination. Ideally there is an isolated point in the bottom right represtenting the
correct family combination. Applicable to 'Discrete' method only. Output in a directory named
'Results/lod.scatter' on the current working directory.
quantitative.png
Scatter plot of '5-95 percentile range of log-likelihood ratios' against 'Log odds (LOD) scores' for each
possible family combination. Ideally there is an isolated point in the bottom right represtenting the
correct family combination. Applicable to 'Quantitative' method only. Output in a directory named
'Results/lod.scatter' on the current working directory.
Intermediate outputs
'Dij' Applicable when method = "Discrete" or "Exclusion". Refer to Henshall et al. 2014
#:
'dkj' Applicable when method = "Discrete" or "Exclusion". Refer to Hamilton 2020
#:
'dklj.adj' Applicable when method = "Discrete" or "Exclusion". Refer to Hamilton 2020 (dkj.star)
#:
'fkj.and.weight' Applicable when method = "Least_squares". Refer to Henshall et al. 2014
#:
'Gij' Applicable when method = "Discrete" or ""Quantitative". Refer to Henshall et al. 2014
#:
'gkj' Applicable when method = "Discrete" or ""Quantitative". Refer to Hamilton 2020
#:
'gklj.adj' Applicable when method = "Discrete" or ""Quantitative". Refer to Hamilton 2020 (gkj.star)
#:
'flj.probs' Applicable when discrete.method = "geno.probs". Refer to Hamilton 2020 (fj)
#:
'flj.geno' Applicable when discrete.method = "assigned.genos". Refer to Hamilton 2020 (fj)
#:
'lambda.kj' Applicable when method = "Discrete" or ""Quantitative". Refer to Hamilton 2020.
#Example output for n.in.pools = 2#:
'lod.duos.discrete' Applicable when method = "Discrete". Refer to Hamilton 2020.
#:
'lod.duos.quant' Applicable when method = "Quantitative". Refer to Hamilton 2020.
#:
'logl.duos.discrete' Applicable when method = "Discrete". Refer to Hamilton 2020. Only outputted for the final SAMPLE_ID
#:
'logl.duos.quant' Applicable when method = "Quantitative". Refer to Hamilton 2020. Only outputted for the final SAMPLE_ID
#:
'mismatches' Applicable when method = "Exclusion". Refer to Hamilton 2020.
#:
'mismatches.by.snp' Applicable when method = "Exclusion". Refer to Hamilton 2020.
#Example output for n.in.pools = 2:
'nlj.probs' Applicable when discrete.method = "geno.probs". Refer to Hamilton 2020 (nj).
#Example output for n.in.pools = 2:
'nlj.geno' Applicable when discrete.method = "geno.probs". Refer to Hamilton 2020 (nj).
# Example output for n.in.pools = 2:
'parent.combns':
'phi.ij' Applicable when method = "Discrete" or ""Quantitative". Refer to Henshall et al. 2014
#:
'snp.error.probs' Applicable when discrete.method = "geno.probs". Refer to Henshall et al. 2014
#:
'snp.error.geno' Applicable when discrete.method = "assigned.genos". Refer to Henshall et al. 2014
#:
'tclj.adj.quant' Applicable when method = "Quantitative". Refer to Hamilton 2020 (tcj.star).
#Example output for n.in.pools = 2:
'tclj.adj.discrete' Applicable when method = "Discrete". Refer to Hamilton 2020 (tcj.star).
#Example output for n.in.pools = 2:
'tclj.discrete' Applicable when method = "Discrete". Refer to Hamilton 2020 (tcj).
#Example output for n.in.pools = 2:
'tclj.ls' Applicable when method = "Least_squares". Refer to Hamilton 2020 (tcj).
3:
'tclj.quant' Applicable when method = "Quantitative". Refer to Hamilton 2020 (tcj).
#Example output for n.in.pools = 2::
'Xl.mat' List Applicable when method = "Quantitative". Refer to Henshall et al 2014 (X)
#:
References
Henshall JM, Dierens, L Sellars MJ (2014) Quantitative analysis of low-density SNP data for parentage assignment and estimation of family contributions to pooled samples. Genetics Selection Evolution 46, 51. https://doi 10.1186/s12711-014-0051-y
Hamilton MG (2020) Maximum likelihood parentage assignment using quantitative genotypes
Examples
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
#' #Retrieve data for 'pooling by phenotype' example from Hamilton 2020
data(shrimp.snp.dat.indiv)
data(shrimp.snp.dat.pools)
data(shrimp.fams)
#Compute SNP parameters
shrimp.snp.param.indiv <- snp.param.indiv.fun(shrimp.snp.dat.indiv)
shrimp.snp.param.pools <- snp.param.pools.fun(shrimp.snp.param.indiv, n.in.pools = 2)
#Assign parentage using the quantitative maximum likelihood method
parent.assign.fun(method= "Quantitative",
snp.dat.indiv = shrimp.snp.dat.indiv,
snp.dat.pools = shrimp.snp.dat.pools,
n.in.pools = 2,
snp.error.assumed = 0.01,
snp.param.indiv = shrimp.snp.param.indiv,
snp.param.pools = shrimp.snp.param.pools,
fams = shrimp.fams)
#Retrieve data for 'pooling for individual parentage assignment' example from Hamilton 2020
data(ab.snp.dat.indiv)
data(ab.snp.dat.pools)
data(ab.fams)
data(ab.fam.set.combns)
data(ab.fam.set.combns.by.pool)
#Compute SNP parameters
ab.snp.param.indiv <- snp.param.indiv.fun(ab.snp.dat.indiv)
ab.snp.param.pools <- snp.param.pools.fun(ab.snp.param.indiv, n.in.pools = 3)
#Assign parentage using the quantitative maximum likelihood method
parent.assign.fun(method= "Quantitative",
snp.dat.indiv = ab.snp.dat.indiv,
snp.dat.pools = ab.snp.dat.pools,
n.in.pools = 3,
snp.error.assumed = 0.01,
snp.param.indiv = ab.snp.param.indiv,
snp.param.pools = ab.snp.param.pools,
fams = ab.fams,
fam.set.combns = ab.fam.set.combns,
fam.set.combns.by.pool = ab.fam.set.combns.by.pool)
#Retrieve data for small worked example from Hamilton 2020
data(Ham.snp.dat.indiv)
data(Ham.snp.dat.pools)
data(Ham.fams)
data(Ham.fam.set.combns)
data(Ham.fam.set.combns.by.pool)
#Compute SNP parameters
Ham.snp.param.indiv <- snp.param.indiv.fun(Ham.snp.dat.indiv)
Ham.snp.param.pools <- snp.param.pools.fun(Ham.snp.param.indiv, n.in.pools = 2)
#Assign parentage using the least squares method
parent.assign.fun(method = "Least_squares",
beta.min.ss = TRUE,
snp.dat.indiv = Ham.snp.dat.indiv,
snp.dat.pools = Ham.snp.dat.pools,
n.in.pools = 2,
snp.error.assumed = 0.01,
snp.param.indiv = Ham.snp.param.indiv,
snp.param.pools = Ham.snp.param.pools,
fams = Ham.fams,
fam.set.combns = Ham.fam.set.combns,
fam.set.combns.by.pool = Ham.fam.set.combns.by.pool)
#Assign parentage using the quantitative maximum likelihood method
parent.assign.fun(method= "Quantitative",
threshold.indiv = 0.98,
threshold.pools = 0.98,
snp.dat.indiv = Ham.snp.dat.indiv,
snp.dat.pools = Ham.snp.dat.pools,
n.in.pools = 2,
snp.error.assumed = 0.01,
snp.param.indiv = Ham.snp.param.indiv,
snp.param.pools = Ham.snp.param.pools,
fams = Ham.fams,
fam.set.combns = Ham.fam.set.combns,
fam.set.combns.by.pool = Ham.fam.set.combns.by.pool)
#Assign parentage using the discrete maximum likelihood method
#(discrete.method = "geno.probs")
parent.assign.fun(method= "Discrete",
discrete.method = "geno.probs",
threshold.indiv = 0.98,
threshold.pools = 0.98,
snp.dat.indiv = Ham.snp.dat.indiv,
snp.dat.pools = Ham.snp.dat.pools,
n.in.pools = 2,
snp.error.assumed = 0.01,
snp.param.indiv = Ham.snp.param.indiv,
snp.param.pools = Ham.snp.param.pools,
fams = Ham.fams,
fam.set.combns = Ham.fam.set.combns,
fam.set.combns.by.pool = Ham.fam.set.combns.by.pool)
#Assign parentage using the discrete maximum likelihood method
#(discrete.method = "assigned.genos")
parent.assign.fun(method= "Discrete",
discrete.method = "assigned.genos",
snp.dat.indiv = Ham.snp.dat.indiv,
snp.dat.pools = Ham.snp.dat.pools,
n.in.pools = 2,
snp.error.assumed = 0.01,
fams = Ham.fams,
fam.set.combns = Ham.fam.set.combns,
fam.set.combns.by.pool = Ham.fam.set.combns.by.pool)
#Assign parentage using the exclusion method
#(discrete.method = "geno.probs")
parent.assign.fun(method= "Exclusion",
discrete.method = "geno.probs",
threshold.indiv = 0.98,
threshold.pools = 0.98,
snp.dat.indiv = Ham.snp.dat.indiv,
snp.dat.pools = Ham.snp.dat.pools,
n.in.pools = 2,
snp.error.assumed = 0.01,
snp.param.indiv = Ham.snp.param.indiv,
snp.param.pools = Ham.snp.param.pools,
fams = Ham.fams,
fam.set.combns = Ham.fam.set.combns,
fam.set.combns.by.pool = Ham.fam.set.combns.by.pool)
#Assign parentage using the exclusion method
#(discrete.method = "assigned.genos")
parent.assign.fun(method= "Exclusion",
discrete.method = "assigned.genos",
snp.dat.indiv = Ham.snp.dat.indiv,
snp.dat.pools = Ham.snp.dat.pools,
n.in.pools = 2,
fams = Ham.fams,
fam.set.combns = Ham.fam.set.combns,
fam.set.combns.by.pool = Ham.fam.set.combns.by.pool)
#Assign parentage using multiple methods
#(discrete.method = "geno.probs")
parent.assign.fun(method = c("Least_squares", "Quantitative", "Discrete", "Exclusion"),
beta.min.ss = TRUE,
discrete.method = "geno.probs",
threshold.indiv = 0.98,
threshold.pools = 0.98,
snp.dat.indiv = Ham.snp.dat.indiv,
snp.dat.pools = Ham.snp.dat.pools,
n.in.pools = 2,
snp.error.assumed = 0.01,
snp.param.indiv = Ham.snp.param.indiv,
snp.param.pools = Ham.snp.param.pools,
fams = Ham.fams,
fam.set.combns = Ham.fam.set.combns,
fam.set.combns.by.pool = Ham.fam.set.combns.by.pool)
mghamilton/SNPpools documentation built on Feb. 13, 2021, 12:52 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Value References Examples
View source: R/parent.assign.fun.R
Description
This function assigns parents to pooled samples using one of seven approaches:
'Least squares' method outlined in Henshall et al. 2014 (method = "Least_squares")
'Least squares minimum sum of squares' method whereby the sum of squares of all parental combinations are computed and the combination with the minimum value identified. Refer to Hamilton 2020 (method = "Least_squares", beta.min.ss = TRUE)
'Quantitative maximum likelihood' method whereby the approach using quantititive genotypes for parentatge assignment outlined in Henshall et al. 2014 is extended to pooled DNA samples. Refer to Hamilton 2020 (method = "Quantitative")
'Discrete maximum likelihood from genotype probabilities' method whereby discrete genotypes are derived from genotype probabilities and used for parentatge assignment of pooled DNA samples by extending the discrete genotype maximum likelihood approach outlined in Henshall et al. 2014 to pooled DNA samples. Refer to Hamilton 2020 (method = "Discrete", discrete.method = "geno.probs").
'Discrete maximum likelihood from genotype assignments' method whereby discrete genotypes are provided as input and used for parentatge assignment of pooled DNA samples by extending the discrete genotype maximum likelihood approach outlined in Henshall et al. 2014 to pooled DNA samples. Refer to Hamilton 2020 (method = "Discrete", discrete.method = "assigned.genos")
'Exclusion from genotype probabilities' method whereby discrete genotypes are derived from genotype probabilities and used for parentatge assignment of pooled DNA samples by extending the exclusion approach outlined in Henshall et al. 2014 to pooled DNA samples Refer to Hamilton 2020 (method = "Exclusion", discrete.method = "geno.probs").
'Exclusion from genotype assignments' method whereby discrete genotypes are provided as input and used for parentatge assignment of pooled DNA samples by extending the exclusion approach outlined in Henshall et al. 2014 to pooled DNA samples. Refer to Hamilton 2020 (method = "Exclusion", discrete.method = "assigned.genos")
Usage
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | parent.assign.fun(
method,
beta.min.ss = FALSE,
discrete.method = "geno.probs",
threshold.indiv = NULL,
threshold.pools = NULL,
snp.dat.indiv,
snp.dat.pools,
n.in.pools,
min.intensity = 0,
snp.error.assumed = NULL,
snp.error.underlying = NULL,
snp.param.indiv = NULL,
snp.param.pools = NULL,
min.sd = 0,
fams,
fam.set.combns = NULL,
fam.set.combns.by.pool = NULL,
skip.checks = FALSE
)
|
Arguments
method |
is a vector of methods to be implemented (e.g. c("Quantitative", "Discrete", "Exclusion", "Least_squares")) |
beta.min.ss |
is a logical variable appicable to least_squares method only (default = FALSE). If TRUE, the sum of squares of all parental combinations are computed and the combination with the minimum value is identified. Refer to Hamilton 2020. |
discrete.method |
is a character variable applicable to the "Discrete" or "Exclusion" methods only (default = "geno.probs"). It must equal either:
|
threshold.indiv |
is a numeric variable between 0 and 1 inclusive applicable to the "Discrete" or "Exclusion" methods only when discrete.method = "geno.probs" (default = NULL). A discrete genotype is assigned to the the most likely genotype in the quantitative ordered genotype probability matrix Gij if it is greater than threshold.indiv (or threshold.indiv / 2 for the two heterozygous genotypes). Otherwise the genotype is deemed missing (refer to the left hand side of page 5 of Henshall et al. 2014) |
threshold.pools |
is a numeric variable between 0 and 1 inclusive applicable to the "Discrete" or "Exclusion" methods only when discrete.method = "geno.probs" (default = NULL). Equivalent to threshold.indiv for pooled DNA samples. |
snp.dat.indiv |
is a data frame with the following headings (class in parentheses):
|
snp.dat.pools |
is a data frame with the following headings. Note that all pooled DNA samples in this dataframe must be comprised of DNA from the same number of individuals (see n.in.pools) (class in parentheses):
|
n.in.pools |
is an integer variable representing the number of individual that contributed DNA to each sample in snp.dat.pools |
min.intensity |
is a numeric variable (default = 0). If the square root of the sum of INTENSITY_A squared and INTENSITY_B squared in snp.dat.indiv or snp.dat.pools is less than min.intensity then this record is excluded. That is, observations that fall into an arc with a radius equal to min.intensity in the lower left of signal intensity scatter plots are excluded. |
snp.error.assumed |
Must be one of (default = NULL):
|
snp.error.underlying |
is not used if snp.error.assumed is not NULL (default = NULL). Must be either:
|
snp.param.indiv |
is the output of snp.param.indiv.fun (default = NULL). That is, it is a data frame with the following headings (class in parentheses):
|
snp.param.pools |
is the output of snp.param.pools.fun. That is, it is a data frame with the following headings (class in parentheses)
|
min.sd |
is a numberic variable defining a lower bound to be applied to estimates of the standard deviation of allelic proportion for genotypes in snp.param.indiv and snp.param.pools (default = 0) |
fams |
is a data frame with the following headings (class in parentheses):
|
fam.set.combns |
is a data frame with the following headings (class in parentheses). Note: if fam.set.combns = NULL (see 'pooling by phenotype' example below), FAMILY_ID is taken from the 'fams' and duplicated n.in.pools times, FAM_SET_ID = 1 for the first duplication of FAMILY_IDs, 2 for the second etc and FAM_SET_COMBN_ID = 1 (default = NULL):
|
fam.set.combns.by.pool |
is a data frame linking pooled samples with family set combinations. It has the following headings (class in parentheses). Note: if fam.set.combns is NULL (see 'pooling by phenotype' example below), fam.set.combns.by.pool is made NULL. If fam.set.combns.by.pool = NULL, FAM_SET_COMBN_ID = 1 and SAMPLE_ID is taken from the 'snp.dat.pools' input (default = NULL):
|
skip.checks |
is a logical variable. If FALSE parent.assign.fun data checks are not undertaken. |
Value
Primary outputs
most.like.parents.quant
Applicable when method = "Quantitative". Identifies the most likely parental combination and delta LODs for
individual parents. Second most likely (alternative) parents are also presented. Refer to Hamilton 2020. Example fields for n.in.pools = 2:
SAMPLE_ID (integer).
PARENT_COMBN_ID (integer).
MISS_PARENT_SNP_DATA_PROP (numeric).
MISS_POOL_SNP_DATA_PROP (numeric).
NO_MISS_PARENT_OR_POOL_PROP (numeric).
MIN_LOGL (numeric).
MIN_LOGL_SNP (character).
MAX_LOGL (numeric).
MAX_LOGL_SNP (character).
RANGE_5_TO_95_LOGL (numeric).
LOD (numeric).
FAM_SET_COMBN_ID (integer).
PARENT_1 (integer).
PARENT_2 (integer).
PARENT_3 (integer).
PARENT_4 (integer).
FAM_COMBN_ID (integer).
FAMILY_ID_1 (integer).
FAMILY_ID_2 (integer).
PARENT_1_DELTA_LOD (logical).
PARENT_2_DELTA_LOD (logical).
PARENT_3_DELTA_LOD (numeric).
PARENT_4_DELTA_LOD (numeric).
ALT_PARENT_1 (logical).
ALT_PARENT_2 (logical).
ALT_PARENT_3 (numeric).
ALT_PARENT_4 (numeric).
ALT_PARENT_COMBN_1 (logical).
ALT_PARENT_COMBN_2 (logical).
ALT_PARENT_COMBN_3 (integer).
ALT_PARENT_COMBN_4 (integer).
ALT_FAM_COMBN_1 (integer).
ALT_FAM_COMBN_2 (integer).
ALT_FAM_COMBN_3 (integer).
ALT_FAM_COMBN_4 (integer).
most.like.parents.discrete
Applicable when method = "Discrete". Identifies the most likely parental
combination and delta LODs for individual parents. Second most likely (alternative) parents are also presented.
Refer to Hamilton 2020. Example fields for n.in.pools = 2:
SAMPLE_ID (integer).
PARENT_COMBN_ID (integer).
MISS_PARENT_SNP_DATA_PROP (numeric).
MISS_POOL_SNP_DATA_PROP (numeric).
NO_MISS_PARENT_OR_POOL_PROP (numeric).
MIN_LOGL (numeric).
MIN_LOGL_SNP (character).
MAX_LOGL (numeric).
MAX_LOGL_SNP (character).
RANGE_5_TO_95_LOGL (numeric).
LOD (numeric).
FAM_SET_COMBN_ID (integer).
PARENT_1 (integer).
PARENT_2 (integer).
PARENT_3 (integer).
PARENT_4 (integer).
FAM_COMBN_ID (integer).
FAMILY_ID_1 (integer).
FAMILY_ID_2 (integer).
PARENT_1_DELTA_LOD (logical).
PARENT_2_DELTA_LOD (logical).
PARENT_3_DELTA_LOD (numeric).
PARENT_4_DELTA_LOD (numeric).
ALT_PARENT_1 (logical).
ALT_PARENT_2 (logical).
ALT_PARENT_3 (numeric).
ALT_PARENT_4 (numeric).
ALT_PARENT_COMBN_1 (logical).
ALT_PARENT_COMBN_2 (logical).
ALT_PARENT_COMBN_3 (integer).
ALT_PARENT_COMBN_4 (integer).
ALT_FAM_COMBN_1 (integer).
ALT_FAM_COMBN_2 (integer).
ALT_FAM_COMBN_3 (integer).
ALT_FAM_COMBN_4 (integer).
most.like.parents.excl
Applicable when method = "Exclusion". Identifies the most likely parental
combination. Refer to Hamilton 2020. Second most likely (alternative) parental
combination is also presented.
Example fields for n.in.pools = 2:
SAMPLE_ID (integer).
PARENT_COMBN_ID (integer).
MISMATCHES (integer).
SNP_COUNT (integer).
MISMATCH_PROP (numeric).
MISMATCH_PROP_SE (numeric).
MISMATCH_PROP_Z (numeric).
FAM_COMBN_ID (integer).
FAMILY_ID_1 (integer).
FAMILY_ID_2 (integer).
PARENT_1 (integer).
PARENT_2 (integer).
PARENT_3 (integer).
PARENT_4 (integer).
ALT_PARENT_COMBN_ID (integer).
ALT_FAM_COMBN_ID (integer).
ALT_MISMATCHES (integer).
ALT_SNP_COUNT (integer).
ALT_MISMATCH_PROP (numeric).
ALT_MISMATCH_PROP_SE (numeric).
ALT_MISMATCH_PROP_Z (numeric).
most.like.parents.excl.non.dup
Applicable when method = "Exclusion". Identifies the most likely parental
combination - simplified output with multiple combinations with the same number of mismatches (duplicated
SAMPLE_IDs) removed. Refer to Hamilton 2020. Example fields for n.in.pools = 2:
SAMPLE_ID (integer).
MISMATCHES (integer).
SNP_COUNT (integer).
MISMATCH_PROP (numeric).
MISMATCH_PROP_SE (numeric).
MISMATCH_PROP_Z (numeric).
FAMILY_ID_1 (integer).
FAMILY_ID_2 (integer).
PARENT_1 (integer).
PARENT_2 (integer).
PARENT_3 (integer).
PARENT_4 (integer).
beta
Applicable when method = "Least_squares". Identifies the most likely parental combination:
SAMPLE_ID (integer).
SIRE_ID (integer).
DAM_ID (integer).
FAMILY_ID (integer).
BETA_STAR Refer to Henshall et al. 2014 (numeric).
BETA_HAT Refer to Henshall et al. 2014 (numeric).
BETA_HAT_CONSTRAINED Constrained to have equal contributions from each FAMILY_SET_ID. Refer to Hamilton 2020
BETA_MIN_SS Applicable when beta.min.ss = "TRUE". Constrained beta with minimum sum of squares is retained. Beta constrained to have equal contributions from each FAMILY_SET_ID. Refer to Hamilton 2020
Primary plots
bar.png
Bar plot of BETA_HAT by FAMILY_ID. Applicable to 'Least_squares' method only. Output in a directory named 'Results' on the current working directory.
discrete.png
Scatter plot of '5-95 percentile range of log-likelihood ratios' against 'Log odds (LOD) scores' for each possible family combination. Ideally there is an isolated point in the bottom right represtenting the correct family combination. Applicable to 'Discrete' method only. Output in a directory named 'Results/lod.scatter' on the current working directory.
quantitative.png
Scatter plot of '5-95 percentile range of log-likelihood ratios' against 'Log odds (LOD) scores' for each possible family combination. Ideally there is an isolated point in the bottom right represtenting the correct family combination. Applicable to 'Quantitative' method only. Output in a directory named 'Results/lod.scatter' on the current working directory.
Intermediate outputs
'Dij' Applicable when method = "Discrete" or "Exclusion". Refer to Henshall et al. 2014 #:
'dkj' Applicable when method = "Discrete" or "Exclusion". Refer to Hamilton 2020 #:
'dklj.adj' Applicable when method = "Discrete" or "Exclusion". Refer to Hamilton 2020 (dkj.star) #:
'fkj.and.weight' Applicable when method = "Least_squares". Refer to Henshall et al. 2014 #:
'Gij' Applicable when method = "Discrete" or ""Quantitative". Refer to Henshall et al. 2014 #:
'gkj' Applicable when method = "Discrete" or ""Quantitative". Refer to Hamilton 2020 #:
'gklj.adj' Applicable when method = "Discrete" or ""Quantitative". Refer to Hamilton 2020 (gkj.star) #:
'flj.probs' Applicable when discrete.method = "geno.probs". Refer to Hamilton 2020 (fj) #:
'flj.geno' Applicable when discrete.method = "assigned.genos". Refer to Hamilton 2020 (fj) #:
'lambda.kj' Applicable when method = "Discrete" or ""Quantitative". Refer to Hamilton 2020. #Example output for n.in.pools = 2#:
'lod.duos.discrete' Applicable when method = "Discrete". Refer to Hamilton 2020. #:
'lod.duos.quant' Applicable when method = "Quantitative". Refer to Hamilton 2020. #:
'logl.duos.discrete' Applicable when method = "Discrete". Refer to Hamilton 2020. Only outputted for the final SAMPLE_ID #:
'logl.duos.quant' Applicable when method = "Quantitative". Refer to Hamilton 2020. Only outputted for the final SAMPLE_ID #:
'mismatches' Applicable when method = "Exclusion". Refer to Hamilton 2020. #:
'mismatches.by.snp' Applicable when method = "Exclusion". Refer to Hamilton 2020. #Example output for n.in.pools = 2:
'nlj.probs' Applicable when discrete.method = "geno.probs". Refer to Hamilton 2020 (nj). #Example output for n.in.pools = 2:
'nlj.geno' Applicable when discrete.method = "geno.probs". Refer to Hamilton 2020 (nj). # Example output for n.in.pools = 2:
'parent.combns':
'phi.ij' Applicable when method = "Discrete" or ""Quantitative". Refer to Henshall et al. 2014 #:
'snp.error.probs' Applicable when discrete.method = "geno.probs". Refer to Henshall et al. 2014 #:
'snp.error.geno' Applicable when discrete.method = "assigned.genos". Refer to Henshall et al. 2014 #:
'tclj.adj.quant' Applicable when method = "Quantitative". Refer to Hamilton 2020 (tcj.star). #Example output for n.in.pools = 2:
'tclj.adj.discrete' Applicable when method = "Discrete". Refer to Hamilton 2020 (tcj.star). #Example output for n.in.pools = 2:
'tclj.discrete' Applicable when method = "Discrete". Refer to Hamilton 2020 (tcj). #Example output for n.in.pools = 2:
'tclj.ls' Applicable when method = "Least_squares". Refer to Hamilton 2020 (tcj). 3:
'tclj.quant' Applicable when method = "Quantitative". Refer to Hamilton 2020 (tcj). #Example output for n.in.pools = 2::
'Xl.mat' List Applicable when method = "Quantitative". Refer to Henshall et al 2014 (X) #:
References
Henshall JM, Dierens, L Sellars MJ (2014) Quantitative analysis of low-density SNP data for parentage assignment and estimation of family contributions to pooled samples. Genetics Selection Evolution 46, 51. https://doi 10.1186/s12711-014-0051-y
Hamilton MG (2020) Maximum likelihood parentage assignment using quantitative genotypes
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 | #' #Retrieve data for 'pooling by phenotype' example from Hamilton 2020
data(shrimp.snp.dat.indiv)
data(shrimp.snp.dat.pools)
data(shrimp.fams)
#Compute SNP parameters
shrimp.snp.param.indiv <- snp.param.indiv.fun(shrimp.snp.dat.indiv)
shrimp.snp.param.pools <- snp.param.pools.fun(shrimp.snp.param.indiv, n.in.pools = 2)
#Assign parentage using the quantitative maximum likelihood method
parent.assign.fun(method= "Quantitative",
snp.dat.indiv = shrimp.snp.dat.indiv,
snp.dat.pools = shrimp.snp.dat.pools,
n.in.pools = 2,
snp.error.assumed = 0.01,
snp.param.indiv = shrimp.snp.param.indiv,
snp.param.pools = shrimp.snp.param.pools,
fams = shrimp.fams)
#Retrieve data for 'pooling for individual parentage assignment' example from Hamilton 2020
data(ab.snp.dat.indiv)
data(ab.snp.dat.pools)
data(ab.fams)
data(ab.fam.set.combns)
data(ab.fam.set.combns.by.pool)
#Compute SNP parameters
ab.snp.param.indiv <- snp.param.indiv.fun(ab.snp.dat.indiv)
ab.snp.param.pools <- snp.param.pools.fun(ab.snp.param.indiv, n.in.pools = 3)
#Assign parentage using the quantitative maximum likelihood method
parent.assign.fun(method= "Quantitative",
snp.dat.indiv = ab.snp.dat.indiv,
snp.dat.pools = ab.snp.dat.pools,
n.in.pools = 3,
snp.error.assumed = 0.01,
snp.param.indiv = ab.snp.param.indiv,
snp.param.pools = ab.snp.param.pools,
fams = ab.fams,
fam.set.combns = ab.fam.set.combns,
fam.set.combns.by.pool = ab.fam.set.combns.by.pool)
#Retrieve data for small worked example from Hamilton 2020
data(Ham.snp.dat.indiv)
data(Ham.snp.dat.pools)
data(Ham.fams)
data(Ham.fam.set.combns)
data(Ham.fam.set.combns.by.pool)
#Compute SNP parameters
Ham.snp.param.indiv <- snp.param.indiv.fun(Ham.snp.dat.indiv)
Ham.snp.param.pools <- snp.param.pools.fun(Ham.snp.param.indiv, n.in.pools = 2)
#Assign parentage using the least squares method
parent.assign.fun(method = "Least_squares",
beta.min.ss = TRUE,
snp.dat.indiv = Ham.snp.dat.indiv,
snp.dat.pools = Ham.snp.dat.pools,
n.in.pools = 2,
snp.error.assumed = 0.01,
snp.param.indiv = Ham.snp.param.indiv,
snp.param.pools = Ham.snp.param.pools,
fams = Ham.fams,
fam.set.combns = Ham.fam.set.combns,
fam.set.combns.by.pool = Ham.fam.set.combns.by.pool)
#Assign parentage using the quantitative maximum likelihood method
parent.assign.fun(method= "Quantitative",
threshold.indiv = 0.98,
threshold.pools = 0.98,
snp.dat.indiv = Ham.snp.dat.indiv,
snp.dat.pools = Ham.snp.dat.pools,
n.in.pools = 2,
snp.error.assumed = 0.01,
snp.param.indiv = Ham.snp.param.indiv,
snp.param.pools = Ham.snp.param.pools,
fams = Ham.fams,
fam.set.combns = Ham.fam.set.combns,
fam.set.combns.by.pool = Ham.fam.set.combns.by.pool)
#Assign parentage using the discrete maximum likelihood method
#(discrete.method = "geno.probs")
parent.assign.fun(method= "Discrete",
discrete.method = "geno.probs",
threshold.indiv = 0.98,
threshold.pools = 0.98,
snp.dat.indiv = Ham.snp.dat.indiv,
snp.dat.pools = Ham.snp.dat.pools,
n.in.pools = 2,
snp.error.assumed = 0.01,
snp.param.indiv = Ham.snp.param.indiv,
snp.param.pools = Ham.snp.param.pools,
fams = Ham.fams,
fam.set.combns = Ham.fam.set.combns,
fam.set.combns.by.pool = Ham.fam.set.combns.by.pool)
#Assign parentage using the discrete maximum likelihood method
#(discrete.method = "assigned.genos")
parent.assign.fun(method= "Discrete",
discrete.method = "assigned.genos",
snp.dat.indiv = Ham.snp.dat.indiv,
snp.dat.pools = Ham.snp.dat.pools,
n.in.pools = 2,
snp.error.assumed = 0.01,
fams = Ham.fams,
fam.set.combns = Ham.fam.set.combns,
fam.set.combns.by.pool = Ham.fam.set.combns.by.pool)
#Assign parentage using the exclusion method
#(discrete.method = "geno.probs")
parent.assign.fun(method= "Exclusion",
discrete.method = "geno.probs",
threshold.indiv = 0.98,
threshold.pools = 0.98,
snp.dat.indiv = Ham.snp.dat.indiv,
snp.dat.pools = Ham.snp.dat.pools,
n.in.pools = 2,
snp.error.assumed = 0.01,
snp.param.indiv = Ham.snp.param.indiv,
snp.param.pools = Ham.snp.param.pools,
fams = Ham.fams,
fam.set.combns = Ham.fam.set.combns,
fam.set.combns.by.pool = Ham.fam.set.combns.by.pool)
#Assign parentage using the exclusion method
#(discrete.method = "assigned.genos")
parent.assign.fun(method= "Exclusion",
discrete.method = "assigned.genos",
snp.dat.indiv = Ham.snp.dat.indiv,
snp.dat.pools = Ham.snp.dat.pools,
n.in.pools = 2,
fams = Ham.fams,
fam.set.combns = Ham.fam.set.combns,
fam.set.combns.by.pool = Ham.fam.set.combns.by.pool)
#Assign parentage using multiple methods
#(discrete.method = "geno.probs")
parent.assign.fun(method = c("Least_squares", "Quantitative", "Discrete", "Exclusion"),
beta.min.ss = TRUE,
discrete.method = "geno.probs",
threshold.indiv = 0.98,
threshold.pools = 0.98,
snp.dat.indiv = Ham.snp.dat.indiv,
snp.dat.pools = Ham.snp.dat.pools,
n.in.pools = 2,
snp.error.assumed = 0.01,
snp.param.indiv = Ham.snp.param.indiv,
snp.param.pools = Ham.snp.param.pools,
fams = Ham.fams,
fam.set.combns = Ham.fam.set.combns,
fam.set.combns.by.pool = Ham.fam.set.combns.by.pool)
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.