Package 'qtl2'

Description

Draw line segments at significance thresholds for a genome scan plot

Usage

add_threshold(map, thresholdA, thresholdX = NULL, chr = NULL, gap = NULL, ...)

Arguments

Value

None.

Examples

iron <- read_cross2(system.file("extdata", "iron.zip", package="qtl2"))

map <- insert_pseudomarkers(iron$gmap, step=5)
probs <- calc_genoprob(iron, map, error_prob=0.002)
Xcovar <- get_x_covar(iron)
out <- scan1(probs, iron$pheno[,1], Xcovar=Xcovar)
# run just 3 permutations, as a fast illustration
operm <- scan1perm(probs, iron$pheno[,1], addcovar=Xcovar,
                   n_perm=3, perm_Xsp=TRUE, chr_lengths=chr_lengths(map))

plot(out, map)
add_threshold(map, summary(operm), col="violetred", lty=2)

Description

Basic summaries of a cross2 object.

Usage

n_ind(cross2)

n_ind_geno(cross2)

n_ind_pheno(cross2)

n_ind_covar(cross2)

n_ind_gnp(cross2)

ind_ids(cross2)

ind_ids_geno(cross2)

ind_ids_pheno(cross2)

ind_ids_covar(cross2)

ind_ids_gnp(cross2)

n_chr(cross2)

n_founders(cross2)

founders(cross2)

chr_names(cross2)

tot_mar(cross2)

n_mar(cross2)

marker_names(cross2)

n_pheno(cross2)

pheno_names(cross2)

n_covar(cross2)

covar_names(cross2)

n_phenocovar(cross2)

phenocovar_names(cross2)

Arguments

Value

Variously a number, vector of numbers, or vector of character strings.

Functions

• n_ind(): Number of individuals (either genotyped or phenotyped)

• n_ind_geno(): Number of genotyped individuals

• n_ind_pheno(): Number of phenotyped individuals

• n_ind_covar(): Number of individuals with covariate data

• n_ind_gnp(): Number of individuals with both genotype and phenotype data

• ind_ids(): IDs of individuals (either genotyped or phenotyped)

• ind_ids_geno(): IDs of genotyped individuals

• ind_ids_pheno(): IDs of phenotyped individuals

• ind_ids_covar(): IDs of individuals with covariate data

• ind_ids_gnp(): IDs of individuals with both genotype and phenotype data

• n_chr(): Number of chromosomes

• n_founders(): Number of founder strains

• founders(): Names of founder strains

• chr_names(): Chromosome names

• tot_mar(): Total number of markers

• n_mar(): Number of markers on each chromosome

• marker_names(): Marker names

• n_pheno(): Number of phenotypes

• pheno_names(): Phenotype names

• n_covar(): Number of covariates

• covar_names(): Covariate names

• n_phenocovar(): Number of phenotype covariates

• phenocovar_names(): Phenotype covariate names

See Also

summary.cross2()

Description

Identify batches of columns of a matrix that have the same pattern of missing values.

Usage

batch_cols(mat, max_batch = NULL)

Arguments

Value

A list containing the batches, each with two components: cols containing numeric indices of the columns in the corresponding batch, and omit containing a vector of row indices that have missing values in this batch.

See Also

batch_vec()

Examples

x <- rbind(c( 1,  2,  3, 13, 16),
           c( 4,  5,  6, 14, 17),
           c( 7, NA,  8, NA, 18),
           c(NA, NA, NA, NA, 19),
           c(10, 11, 12, 15, 20))
batch_cols(x)

Description

Split a vector into batches, each no longer than batch_size and creating at least n_cores batches, for use in parallel calculations.

Usage

batch_vec(vec, batch_size = NULL, n_cores = 1)

Arguments

Value

A list of vectors, each no longer than batch_size, and with at least n_cores componenets.

See Also

batch_cols()

Examples

vec_split <- batch_vec(1:304, 50, 8)
vec_split2 <- batch_vec(1:304, 50)

Description

Calculate Bayes credible intervals for a single LOD curve on a single chromosome, with the ability to identify intervals for multiple LOD peaks.

Usage

bayes_int(
  scan1_output,
  map,
  chr = NULL,
  lodcolumn = 1,
  threshold = 0,
  peakdrop = Inf,
  prob = 0.95,
  expand2markers = TRUE
)

Arguments

Details

We identify a set of peaks defined as local maxima that exceed the specified threshold, with the requirement that the LOD score must have dropped by at least peakdrop below the lowest of any two adjacent peaks.

At a given peak, if there are ties, with multiple positions jointly achieving the maximum LOD score, we take the average of these positions as the location of the peak.

The default is to use threshold=0, peakdrop=Inf, and prob=0.95. We then return results a single peak, no matter the maximum LOD score, and give a 95% Bayes credible interval.

Value

A matrix with three columns:

• ci_lo - lower bound of interval

• pos - peak position

• ci_hi - upper bound of interval

Each row corresponds to a different peak.

See Also

lod_int(), find_peaks(), scan1()

Examples

# read data
iron <- read_cross2(system.file("extdata", "iron.zip", package="qtl2"))


# insert pseudomarkers into map
map <- insert_pseudomarkers(iron$gmap, step=1)

# calculate genotype probabilities
probs <- calc_genoprob(iron, map, error_prob=0.002)

# grab phenotypes and covariates; ensure that covariates have names attribute
pheno <- iron$pheno
covar <- match(iron$covar$sex, c("f", "m")) # make numeric
names(covar) <- rownames(iron$covar)
Xcovar <- get_x_covar(iron)

# perform genome scan
out <- scan1(probs, pheno, addcovar=covar, Xcovar=Xcovar)

# 95% Bayes credible interval for QTL on chr 7, first phenotype
bayes_int(out, map, chr=7, lodcolum=1)

Description

For each individual at each genomic position, calculate the entropy of the genotype probability distribution, as a quantitative summary of the amount of missing information.

Usage

calc_entropy(probs, quiet = TRUE, cores = 1)

Arguments

Details

We calculate -sum(p log_2 p), where we take 0 log 0 = 0.

Value

A list of matrices (each matrix is a chromosome and is arranged as individuals x markers).

Examples

grav2 <- read_cross2(system.file("extdata", "grav2.zip", package="qtl2"))

probs <- calc_genoprob(grav2, error_prob=0.002)
e <- calc_entropy(probs)
e <- do.call("cbind", e) # combine chromosomes into one big matrix

# summarize by individual
mean_ind <- rowMeans(e)

# summarize by marker
mean_marker <- colMeans(e)

Description

Use the genotype probabilities calculated with calc_genoprob() to calculate genotyping error LOD scores, to help identify potential genotyping errors (and problem markers and/or individuals).

Usage

calc_errorlod(cross, probs, quiet = TRUE, cores = 1)

Arguments

Details

Let $O_k$ denote the observed marker genotype at position $k$, and $g_k$ denote the corresponding true underlying genotype.

Following Lincoln and Lander (1992), we calculate LOD = $log_{10} [ Pr(O_k | g_k = O_k) / Pr(O_k | g_k \ne O_K) ]$

Value

A list of matrices of genotyping error LOD scores. Each matrix corresponds to a chromosome and is arranged as individuals x markers.

References

Lincoln SE, Lander ES (1992) Systematic detection of errors in genetic linkage data. Genomics 14:604–610.

See Also

calc_genoprob()

Examples

iron <- read_cross2(system.file("extdata", "iron.zip", package="qtl2"))
probs <- calc_genoprob(iron, error_prob=0.002, map_function="c-f")
errorlod <- calc_errorlod(iron, probs)

# combine into one matrix
errorlod <- do.call("cbind", errorlod)

Description

Calculate genotype frequencies, by individual or by marker

Usage

calc_geno_freq(probs, by = c("individual", "marker"), omit_x = TRUE)

Arguments

Value

If omit_x=TRUE, the result is a matrix of genotype frequencies; columns are genotypes and rows are either individuals or markers.

If necessary (that is, if omit_x=FALSE, the data include the X chromosome, and the set of genotypes on the X chromosome are different than on the autosomes), the result is a list with two components (for the autosomes and for the X chromosome), each being a matrix of genotype frequencies.

See Also

calc_raw_geno_freq(), calc_het()

Examples

iron <- read_cross2(system.file("extdata", "iron.zip", package="qtl2"))
p <- calc_genoprob(iron, err=0.002)

# genotype frequencies by marker
tab_g <- calc_geno_freq(p, "marker")

# allele frequencies by marker
ap <- genoprob_to_alleleprob(p)
tab_a <- calc_geno_freq(ap, "marker")

Description

Uses a hidden Markov model to calculate the probabilities of the true underlying genotypes given the observed multipoint marker data, with possible allowance for genotyping errors.

Usage

calc_genoprob(
  cross,
  map = NULL,
  error_prob = 0.0001,
  map_function = c("haldane", "kosambi", "c-f", "morgan"),
  lowmem = FALSE,
  quiet = TRUE,
  cores = 1
)

Arguments

Details

Let $O_k$ denote the observed marker genotype at position $k$, and $g_k$ denote the corresponding true underlying genotype.

We use the forward-backward equations to calculate $\alpha_{kv} = \log Pr(O_1, \ldots, O_k, g_k = v)$ and $\beta_{kv} = \log Pr(O_{k+1}, \ldots, O_n | g_k = v)$

We then obtain $Pr(g_k | O_1, \ldots, O_n) = \exp(\alpha_{kv} + \beta_{kv}) / s$ where $s = \sum_v \exp(\alpha_{kv} + \beta_{kv})$

Value

An object of class "calc_genoprob": a list of three-dimensional arrays of probabilities, individuals x genotypes x positions. (Note that the arrangement is different from R/qtl.) Also contains four attributes:

• crosstype - The cross type of the input cross.

• is_x_chr - Logical vector indicating whether chromosomes are to be treated as the X chromosome or not, from input cross.

• alleles - Vector of allele codes, from input cross.

• alleleprobs - Logical value (FALSE) that indicates whether the probabilities are compressed to allele probabilities, as from genoprob_to_alleleprob().

See Also

insert_pseudomarkers()

Examples

grav2 <- read_cross2(system.file("extdata", "grav2.zip", package="qtl2"))
gmap_w_pmar <- insert_pseudomarkers(grav2$gmap, step=1)
probs <- calc_genoprob(grav2, gmap_w_pmar, error_prob=0.002)

Description

Construct vectors of logical indicators that indicate which positions correspond to locations along a grid

Usage

calc_grid(map, step = 0, off_end = 0, tol = 0.01)

Arguments

Details

The function insert_pseudomarkers(), with stepwidth="fixed", will insert a grid of pseudomarkers, to a marker map. The present function gives a series of TRUE/FALSE vectors that indicate which positions fall on the grid. This is for use with probs_to_grid(), for reducing genotype probabilities, calculated with calc_genoprob(), to just the positions on the grid. The main value of this is to speed up genome scan computations in the case of very dense markers, by focusing on just a grid of positions rather than on all marker locations.

Value

A list of logical (TRUE/FALSE) vectors that indicate, for a marker/pseudomarker map created by insert_pseudomarkers() with step>0 and stepwidth="fixed", which positions correspond to he locations along the fixed grid.

See Also

insert_pseudomarkers(), probs_to_grid(), map_to_grid()

Examples

iron <- read_cross2(system.file("extdata", "iron.zip", package="qtl2"))
gmap_w_pmar <- insert_pseudomarkers(iron$gmap, step=1)
grid <- calc_grid(iron$gmap, step=1)

Description

Calculate heterozygosites, by individual or by marker

Usage

calc_het(probs, by = c("individual", "marker"), omit_x = TRUE, cores = 1)

Arguments

Details

calc_het() looks at the genotype names (the 2nd dimension of the dimnames of the input probs), which must be two-letter names, and assumes that when the two letters are different it's a heterozygous genotype while if they're the same it's a homozygous genotype

Value

The result is a vector of estimated heterozygosities

See Also

calc_raw_het(), calc_geno_freq()

Examples

iron <- read_cross2(system.file("extdata", "iron.zip", package="qtl2"))
p <- calc_genoprob(iron, err=0.002)

# heterozygosities by individual
het_ind <- calc_het(p)

# heterozygosities by marker
het_mar <- calc_het(p, "marker")

Description

Calculate genetic similarity among individuals (kinship matrix) from conditional genotype probabilities.

Usage

calc_kinship(
  probs,
  type = c("overall", "loco", "chr"),
  omit_x = FALSE,
  use_allele_probs = TRUE,
  quiet = TRUE,
  cores = 1
)

Arguments

Details

If use_allele_probs=TRUE (the default), we first convert the genotype probabilities to allele probabilities (using genoprob_to_alleleprob()).

We then calculate $\sum_{kl}(p_{ikl} p_{jkl})$ where $k$ = position, $l$ = allele, and $i,j$ are two individuals.

For crosses with just two possible genotypes (e.g., backcross), we don't convert to allele probabilities but just use the original genotype probabilities.

Value

If type="overall" (the default), a matrix of proportion of matching alleles. Otherwise a list with one matrix per chromosome.

Examples

grav2 <- read_cross2(system.file("extdata", "grav2.zip", package="qtl2"))
map <- insert_pseudomarkers(grav2$gmap, step=1)
probs <- calc_genoprob(grav2, map, error_prob=0.002)
K <- calc_kinship(probs)

# using only markers/pseudomarkers on the grid
grid <- calc_grid(grav2$gmap, step=1)
probs_sub <- probs_to_grid(probs, grid)
K_grid <- calc_kinship(probs_sub)

Description

Calculate minor allele frequency from raw SNP genotypes in founders, by founder strain or by marker

Usage

calc_raw_founder_maf(cross, by = c("individual", "marker"))

Arguments

Value

A vector of minor allele frequencies, one for each founder strain or marker.

See Also

recode_snps(), calc_raw_maf()

Examples

## Not run: 
# load example data and calculate genotype probabilities
file <- paste0("https://raw.githubusercontent.com/rqtl/",
               "qtl2data/main/DOex/DOex.zip")
DOex <- read_cross2(file)
DOex_maf <- calc_raw_founder_maf(DOex)

## End(Not run)

Description

Calculate genotype frequencies from raw SNP genotypes, by individual or by marker

Usage

calc_raw_geno_freq(cross, by = c("individual", "marker"), cores = 1)

Arguments

Value

A matrix of genotypes frequencies with 3 columns (AA, AB, and BB) and with rows being either individuals or markers.

See Also

calc_raw_maf(), calc_raw_het(), recode_snps(), calc_geno_freq()

Examples

## Not run: 
# load example data and calculate genotype probabilities
file <- paste0("https://raw.githubusercontent.com/rqtl/",
               "qtl2data/main/DOex/DOex.zip")
DOex <- read_cross2(file)
gfreq <- calc_raw_geno_freq(DOex)

## End(Not run)

Description

Calculate estimated heterozygosity for each individual from raw SNP genotypes

Usage

calc_raw_het(cross, by = c("individual", "marker"))

Arguments

Value

A vector of heterozygosities, one for each individual or marker.

See Also

recode_snps(), calc_raw_maf(), calc_raw_founder_maf(), calc_raw_geno_freq(), calc_het()

Examples

## Not run: 
# load example data and calculate genotype probabilities
file <- paste0("https://raw.githubusercontent.com/rqtl/",
               "qtl2data/main/DOex/DOex.zip")
DOex <- read_cross2(file)
DOex_het <- calc_raw_het(DOex)

## End(Not run)

Description

Calculate minor allele frequency from raw SNP genotypes, by individual or by marker

Usage

calc_raw_maf(cross, by = c("individual", "marker"))

Arguments

Value

A vector of minor allele frequencies, one for each individual or marker.

See Also

recode_snps(), calc_raw_founder_maf(), calc_raw_het(), calc_raw_geno_freq()

Examples

## Not run: 
# load example data and calculate genotype probabilities
file <- paste0("https://raw.githubusercontent.com/rqtl/",
               "qtl2data/main/DOex/DOex.zip")
DOex <- read_cross2(file)
DOex_maf <- calc_raw_maf(DOex)

## End(Not run)

Description

Calculate the strain distribution patterns (SDPs) from the strain genotypes at a set of SNPs.

Usage

calc_sdp(geno)

Arguments

Value

A vector of strain distribution patterns: integers between 1 and $2^n - 2$ where $n$ is the number of strains, whose binary representation indicates the strain genotypes.

See Also

invert_sdp(), sdp2char()

Examples

x <- rbind(m1=c(3, 1, 1, 1, 1, 1, 1, 1),
           m2=c(1, 3, 3, 1, 1, 1, 1, 1),
           m3=c(1, 1, 1, 1, 3, 3, 3, 3))
calc_sdp(x)

Description

This is like base::cbind() but using row names to align the rows and expanding with missing values if there are rows in some matrices but not others.

Usage

cbind_expand(...)

Arguments

Value

The matrices combined by columns, using row names to align the rows, and expanding with missing values if there are rows in some matrices but not others.

Examples

df1 <- data.frame(x=c(1,2,3,NA,4), y=c(5,8,9,10,11), row.names=c("A", "B", "C", "D", "E"))
df2 <- data.frame(w=c(7,8,0,9,10), z=c(6,NA,NA,9,10), row.names=c("A", "B", "F", "C", "D"))
cbind_expand(df1, df2)

Description

Join multiple genotype probability objects, as produced by calc_genoprob(), for the same set of individuals but different chromosomes.

Usage

## S3 method for class 'calc_genoprob'
cbind(...)

Arguments

Value

An object of class "calc_genoprob", like the input; see calc_genoprob().

See Also

rbind.calc_genoprob()

Examples

grav2 <- read_cross2(system.file("extdata", "grav2.zip", package="qtl2"))
map <- insert_pseudomarkers(grav2$gmap, step=1)
probsA <- calc_genoprob(grav2[1:5,1:2], map, error_prob=0.002)
probsB <- calc_genoprob(grav2[1:5,3:4], map, error_prob=0.002)
probs <- cbind(probsA, probsB)

Description

Join multiple scan1() results for different phenotypes; must have the same map.

Usage

## S3 method for class 'scan1'
cbind(...)

Arguments

Details

If components addcovar(), Xcovar, intcovar, weights do not match between objects, we omit this information.

If hsq present but has differing numbers of rows, we omit this information.

Value

An object of class '"scan1", like the inputs, but with the lod score columns from the inputs combined as multiple columns in a single object.

See Also

rbind.scan1(), scan1()

Examples

grav2 <- read_cross2(system.file("extdata", "grav2.zip", package="qtl2"))
map <- insert_pseudomarkers(grav2$gmap, step=1)
probs <- calc_genoprob(grav2, map, error_prob=0.002)
phe1 <- grav2$pheno[,1,drop=FALSE]
phe2 <- grav2$pheno[,2,drop=FALSE]

out1 <- scan1(probs, phe1) # phenotype 1
out2 <- scan1(probs, phe2) # phenotype 2
out <- cbind(out1, out2)

Description

Column-bind multiple scan1perm objects with the same numbers of rows.

Usage

## S3 method for class 'scan1perm'
cbind(...)

Arguments

Details

The aim of this function is to concatenate the results from multiple runs of a permutation test with scan1perm(), generally with different phenotypes and/or methods, to be used in parallel with rbind.scan1perm().

Value

The combined column-binded input, as an object of class "scan1perm"; see scan1perm().

See Also

rbind.scan1perm(), scan1perm(), scan1()

Examples

# read data
iron <- read_cross2(system.file("extdata", "iron.zip", package="qtl2"))


# insert pseudomarkers into map
map <- insert_pseudomarkers(iron$gmap, step=1)

# calculate genotype probabilities
probs <- calc_genoprob(iron, map, error_prob=0.002)

# grab phenotypes and covariates; ensure that covariates have names attribute
pheno <- iron$pheno
covar <- match(iron$covar$sex, c("f", "m")) # make numeric
names(covar) <- rownames(iron$covar)
Xcovar <- get_x_covar(iron)

# permutations with genome scan (just 3 replicates, for illustration)
operm1 <- scan1perm(probs, pheno[,1,drop=FALSE], addcovar=covar, Xcovar=Xcovar, n_perm=3)
operm2 <- scan1perm(probs, pheno[,2,drop=FALSE], addcovar=covar, Xcovar=Xcovar, n_perm=3)

operm <- cbind(operm1, operm2)

Description

Join multiple genotype imputation objects, as produced by sim_geno(), for the same individuals but different chromosomes.

Usage

## S3 method for class 'sim_geno'
cbind(...)

Arguments

Value

An object of class "sim_geno", like the input; see sim_geno().

See Also

rbind.sim_geno(), sim_geno()

Examples

grav2 <- read_cross2(system.file("extdata", "grav2.zip", package="qtl2"))
map <- insert_pseudomarkers(grav2$gmap, step=1)
drawsA <- sim_geno(grav2[1:5,1:2], map, error_prob=0.002, n_draws=4)
drawsB <- sim_geno(grav2[1:5,3:4], map, error_prob=0.002, n_draws=4)
draws <- cbind(drawsA, drawsB)

Description

Join multiple viterbi objects, as produced by viterbi(), for the same set of individuals but different chromosomes.

Usage

## S3 method for class 'viterbi'
cbind(...)

Arguments

Value

An object of class "viterbi", like the input; see viterbi().

See Also

rbind.viterbi(), viterbi()

Examples

grav2 <- read_cross2(system.file("extdata", "grav2.zip", package="qtl2"))
map <- insert_pseudomarkers(grav2$gmap, step=1)
gA <- viterbi(grav2[1:5,1:2], map, error_prob=0.002)
gB <- viterbi(grav2[1:5,3:4], map, error_prob=0.002)
g <- cbind(gA, gB)

Description

A vector of 8 colors for use with the mouse Collaborative Cross and Diversity Outbreds.

Details

CCorigcolors are the original eight colors for the Collaborative Cross founder strains. CCaltcolors are a slightly modified version, but still not color-blind friendly. CCcolors are derived from the the Okabe-Ito color blind friendly palette in Wong (2011) Nature Methods doi:10.1038/nmeth.1618.

Source

https://csbio.unc.edu/CCstatus/index.py?run=AvailableLines.information

Examples

data(CCcolors)
data(CCaltcolors)
data(CCorigcolors)

Description

Check the integrity of the data within a cross2 object.

Usage

check_cross2(cross2)

Arguments

Details

Checks whether a cross2 object meets the specifications. Problems are issued as warnings.

Value

If everything is correct, returns TRUE; otherwise FALSE, with attributes that give the problems.

Examples

grav2 <- read_cross2(system.file("extdata", "grav2.zip", package="qtl2"))
check_cross2(grav2)

Description

Perform a chi-square test for independence for all pairs of columns of a matrix.

Usage

chisq_colpairs(x)

Arguments

Value

A matrix of size p x p, where p is the number of columns in the input matrix x, containing the chi-square test statistics for independence, applied to pairs of columns of x. The diagonal of the result will be all NAs.

Examples

z <- matrix(sample(1:2, 500, replace=TRUE), ncol=5)
chisq_colpairs(z)

Description

Calculate chromosome lengths for a map object

Usage

chr_lengths(map, collapse_to_AX = FALSE)

Arguments

Details

We take diff(range(v)) for each vector, v.

Value

A vector of chromosome lengths. If collapse_to_AX=TRUE, the result is a vector of length 2 (autosomal and X chromosome lengths).

See Also

scan1perm()

Description

Clean an object by removing messy values

Usage

clean(object, ...)

Arguments

Value

Input object with messy values cleaned up

See Also

clean.scan1(), clean.calc_genoprob()

Description

Clean up genotype probabilities by setting small values to 0 and for a genotype column where the maximum value is rather small, set all values in that column to 0.

Usage

clean_genoprob(
  object,
  value_threshold = 0.000001,
  column_threshold = 0.01,
  ind = NULL,
  cores = 1,
  ...
)

## S3 method for class 'calc_genoprob'
clean(
  object,
  value_threshold = 0.000001,
  column_threshold = 0.01,
  ind = NULL,
  cores = 1,
  ...
)

Arguments

Details

In cases where a particular genotype is largely absent, scan1coef() and fit1() can give unstable estimates of the genotype effects. Cleaning up the genotype probabilities by setting small values to 0 helps to ensure that such effects get set to NA.

At each position and for each genotype column, we find the maximum probability across individuals. If that maximum is < column_threshold, all values in that genotype column at that position are set to 0.

In addition, any genotype probabilities that are < value_threshold (generally < column_threshold) are set to 0.

The probabilities are then re-scaled so that the probabilities for each individual at each position sum to 1.

If ind is provided, the function is applied only to the designated subset of individuals. This may be useful when only a subset of individuals have been phenotyped, as you may want to zero out genotype columns where that subset of individuals has only negligible probability values.

Value

A cleaned version of the input genotype probabilities object, object.

Examples

iron <- read_cross2(system.file("extdata", "iron.zip", package="qtl2"))


# calculate genotype probabilities
probs <- calc_genoprob(iron, error_prob=0.002)

# clean the genotype probabilities
# (doesn't really do anything in this case, because there are no small but non-zero values)
probs_clean <- clean(probs)

# clean only the females' genotype probabilities
probs_cleanf <- clean(probs, ind=names(iron$is_female)[iron$is_female])

Description

Clean scan1 output by replacing negative values with NA and remove rows where all values are NA.

Usage

clean_scan1(object, ...)

## S3 method for class 'scan1'
clean(object, ...)

Arguments

Value

The input object with negative values replaced with NAs and then rows with all NAs removed.

Examples

iron <- read_cross2(system.file("extdata", "iron.zip", package="qtl2"))

pr <- calc_genoprob(iron)
out <- scan1(pr, iron$pheno)

out <- clean(out)

Description

Count the number of matching genotypes between all pairs of individuals, to look for unusually closely related individuals.

Usage

compare_geno(cross, omit_x = FALSE, proportion = TRUE, quiet = TRUE, cores = 1)

Arguments

Value

A square matrix; diagonal is number of observed genotypes for each individual. The values in the lower triangle are the numbers of markers where both of a pair were genotyped. The values in the upper triangle are either proportions or counts of matching genotypes for each pair (depending on whether proportion=TRUE or ⁠=FALSE⁠). The object is given class "compare_geno".

Examples

grav2 <- read_cross2(system.file("extdata", "grav2.zip", package="qtl2"))
cg <- compare_geno(grav2)
summary(cg)

Description

Compare two sets of genotype probabilities for one individual on a single chromosome.

Usage

compare_genoprob(
  probs1,
  probs2,
  cross,
  ind = 1,
  chr = NULL,
  minprob = 0.95,
  minmarkers = 10,
  minwidth = 0,
  annotate = FALSE
)

Arguments

Details

The function does the following:

• Reduce the probabilities to a set of common locations that also appear in cross.

• Use maxmarg() to infer the genotype at every position using each set of probabilities.

• Identify intervals where the two inferred genotypes are constant.

• Within each segment, compare the observed SNP genotypes to the founders' genotypes.

Value

A data frame with each row corresponding to an interval over which probs1 and probs2 each have a fixed inferred genotype. Columns include the two inferred genotypes, the start and end points and width of the interval, and when founder genotypes are in cross, the proportions of SNPs where the individual matches each possible genotypes.

See Also

plot_genoprobcomp()

Examples

iron <- read_cross2(system.file("extdata", "iron.zip", package="qtl2"))
iron <- iron[1,"2"]   # subset to first individual on chr 2
map <- insert_pseudomarkers(iron$gmap, step=1)

# in presence of a genotyping error, how much does error_prob matter?
iron$geno[[1]][1,3] <- 3
pr_e <- calc_genoprob(iron, map, error_prob=0.002)
pr_ne <- calc_genoprob(iron, map, error_prob=1e-15)

compare_genoprob(pr_e, pr_ne, iron, minmarkers=1, minprob=0.5)

Description

Compare two marker maps, identifying markers that are only in one of the two maps, or that are in different orders on the two maps.

Usage

compare_maps(map1, map2)

Arguments

Value

A data frame containing

• marker - marker name

• chr_map1 - chromosome ID on map1

• pos_map1 - position on map1

• chr_map2 - chromosome ID on map2

• pos_map2 - position on map2

Examples

# load some data
iron <- read_cross2( system.file("extdata", "iron.zip", package="qtl2") )
gmap <- iron$gmap
pmap <- iron$pmap

# omit a marker from each map
gmap[[7]] <- gmap[[7]][-3]
pmap[[8]] <- pmap[[8]][-7]
# swap order of a couple of markers on the physical map
names(pmap[[9]])[3:4] <- names(pmap[[9]])[4:3]
# move a marker to a different chromosome
pmap[[10]] <- c(pmap[[10]], pmap[[1]][2])[c(1,3,2)]
pmap[[1]] <- pmap[[1]][-2]

# compare these messed-up maps
compare_maps(gmap, pmap)

Description

Convert a cross object from the R/qtl format to the R/qtl2 format

Usage

convert2cross2(cross)

Arguments

Value

Object of class "cross2". For details, see the R/qtl2 developer guide.

See Also

read_cross2()

Examples

library(qtl)
data(hyper)
hyper2 <- convert2cross2(hyper)

Description

Estimate the numbers of crossovers in each individual on each chromosome.

Usage

count_xo(geno, quiet = TRUE, cores = 1)

Arguments

Value

A matrix of crossover counts, individuals x chromosomes, or (if the input was the output of sim_geno()) a 3d-array of crossover counts, individuals x chromosomes x imputations.

See Also

locate_xo()

Examples

iron <- read_cross2(system.file("extdata", "iron.zip", package="qtl2"))

map <- insert_pseudomarkers(iron$gmap, step=1)
pr <- calc_genoprob(iron, map, error_prob=0.002, map_function="c-f")
g <- maxmarg(pr)
n_xo <- count_xo(g)

# imputations
imp <- sim_geno(iron, map, error_prob=0.002, map_function="c-f", n_draws=32)
n_xo_imp <- count_xo(imp)
# sums across chromosomes
tot_xo_imp <- apply(n_xo_imp, c(1,3), sum)
# mean and SD across imputations
summary_xo <- cbind(mean=rowMeans(tot_xo_imp),
                    sd=apply(tot_xo_imp, 1, sd))

Description

Create a function that will connect to a SQLite database of gene information and return a data frame with gene information for a selected region.

Usage

create_gene_query_func(
  dbfile = NULL,
  db = NULL,
  table_name = "genes",
  chr_field = "chr",
  start_field = "start",
  stop_field = "stop",
  name_field = "Name",
  strand_field = "strand",
  filter = NULL
)

Arguments

Details

Note that this function assumes that the database has start and stop fields that are in basepairs, but the selection uses positions in Mbp, and the output data frame should have start and stop columns in Mbp.

Also note that a SQLite database of MGI mouse genes is available at figshare: doi:10.6084/m9.figshare.5286019.v7

Value

Function with three arguments, chr, start, and end, which returns a data frame with the genes overlapping that region, with start and end being in Mbp. The output should contain at least the columns Name, chr, start, and stop, the latter two being positions in Mbp.

Examples

# create query function by connecting to file
dbfile <- system.file("extdata", "mouse_genes_small.sqlite", package="qtl2")
query_genes <- create_gene_query_func(dbfile, filter="(source=='MGI')")
# query_genes will connect and disconnect each time
genes <- query_genes("2", 97.0, 98.0)

# connect and disconnect separately
library(RSQLite)
db <- dbConnect(SQLite(), dbfile)
query_genes <- create_gene_query_func(db=db, filter="(source=='MGI')")
genes <- query_genes("2", 97.0, 98.0)
dbDisconnect(db)

Description

Create a table of snp information from a cross, for use with scan1snps().

Usage

create_snpinfo(cross)

Arguments

Value

A data frame of SNP information with the following columns:

• chr - Character string or factor with chromosome

• pos - Position (in same units as in the "map" attribute in genoprobs.

• snp - Character string with SNP identifier (if missing, the rownames are used).

• sdp - Strain distribution pattern: an integer, between 1 and $2^n - 2$ where $n$ is the number of strains, whose binary encoding indicates the founder genotypes SNPs with missing founder genotypes are omitted.

See Also

index_snps(), scan1snps(), genoprob_to_snpprob()

Examples

## Not run: 
# load example data and calculate genotype probabilities
file <- paste0("https://raw.githubusercontent.com/rqtl/",
               "qtl2data/main/DO_Recla/recla.zip")
recla <- read_cross2(file)
snpinfo <- create_snpinfo(recla)

# calculate genotype probabilities
pr <- calc_genoprob(recla, error_prob=0.002, map_function="c-f")

# index the snp information
snpinfo <- index_snps(recla$pmap, snpinfo)

# sex covariate
sex <- setNames((recla$covar$Sex=="female")*1, rownames(recla$covar))

# perform a SNP scan
out <- scan1snps(pr, recla$pmap, recla$pheno[,"bw"], addcovar=sex, snpinfo=snpinfo)

# plot the LOD scores
plot(out$lod, snpinfo, altcol="green3")

## End(Not run)

Description

Create a function that will connect to a SQLite database of founder variant information and return a data frame with variants for a selected region.

Usage

create_variant_query_func(
  dbfile = NULL,
  db = NULL,
  table_name = "variants",
  chr_field = "chr",
  pos_field = "pos",
  id_field = "snp_id",
  sdp_field = "sdp",
  filter = NULL
)

Arguments

Details

Note that this function assumes that the database has a pos field that is in basepairs, but the selection uses start and end positions in Mbp, and the output data frame should have pos in Mbp.

Also note that a SQLite database of variants in the founder strains of the mouse Collaborative Cross is available at figshare: doi:10.6084/m9.figshare.5280229.v3

Value

Function with three arguments, chr, start, and end, which returns a data frame with the variants in that region, with start and end being in Mbp. The output should contain at least the columns chr and pos, the latter being position in Mbp.

Examples

# create query function by connecting to file
dbfile <- system.file("extdata", "cc_variants_small.sqlite", package="qtl2")
query_variants <- create_variant_query_func(dbfile)
# query_variants will connect and disconnect each time
variants <- query_variants("2", 97.0, 98.0)

# create query function to just grab SNPs
query_snps <- create_variant_query_func(dbfile, filter="type=='snp'")
# query_variants will connect and disconnect each time
snps <- query_snps("2", 97.0, 98.0)

# connect and disconnect separately
library(RSQLite)
db <- dbConnect(SQLite(), dbfile)
query_variants <- create_variant_query_func(db=db)
variants <- query_variants("2", 97.0, 98.0)
dbDisconnect(db)

Description

Calculate the eigen decomposition of a kinship matrix, or of a list of such matrices.

Usage

decomp_kinship(kinship, cores = 1)

Arguments

Details

The result contains an attribute "eigen_decomp".

Value

The eigen values and the transposed eigen vectors, as a list containing a vector values and a matrix vectors.

Examples

iron <- read_cross2(system.file("extdata", "iron.zip", package="qtl2"))

map <- insert_pseudomarkers(iron$gmap, step=1)
probs <- calc_genoprob(iron, map, error_prob=0.002)
K <- calc_kinship(probs)

Ke <- decomp_kinship(K)

Description

Drop a vector of markers from a cross2 object.

Usage

drop_markers(cross, markers)

Arguments

Value

The input cross with the specified markers removed.

See Also

pull_markers(), drop_nullmarkers(), reduce_markers(), find_dup_markers()

Examples

grav2 <- read_cross2(system.file("extdata", "grav2.zip", package="qtl2"))
markers2drop <- c("BH.342C/347L-Col", "GH.94L", "EG.357C/359L-Col", "CD.245L", "ANL2")
grav2_rev <- drop_markers(grav2, markers2drop)

Description

Drop markers with no genotype data (or no informative genotypes)

Usage

drop_nullmarkers(cross, quiet = FALSE)

Arguments

Details

We omit any markers that have completely missing data, or if founder genotypes are present (e.g., for Diversity Outbreds), the founder genotypes are missing or are all the same.

Value

The input cross with the uninformative markers removed.

See Also

drop_markers(), pull_markers()

Examples

grav2 <- read_cross2(system.file("extdata", "grav2.zip", package="qtl2"))
# make a couple of markers missing
grav2$geno[[2]][,c(3,25)] <- 0
grav2_rev <- drop_nullmarkers(grav2)

Description

Estimate the heritability of a set of traits via a linear mixed model, with possible allowance for covariates.

Usage

est_herit(
  pheno,
  kinship,
  addcovar = NULL,
  weights = NULL,
  reml = TRUE,
  cores = 1,
  ...
)

Arguments

Details

We fit the model $y = X \beta + \epsilon$ where $\epsilon$ is multivariate normal with mean 0 and covariance matrix $\sigma^2 [h^2 (2 K) + I]$ where $K$ is the kinship matrix and $I$ is the identity matrix.

If weights are provided, the covariance matrix becomes $\sigma^2 [h^2 (2 K) + D]$ where $D$ is a diagonal matrix with the reciprocal of the weights.

For each of the inputs, the row names are used as individual identifiers, to align individuals.

If reml=TRUE, restricted maximum likelihood (reml) is used to estimate the heritability, separately for each phenotype.

Additional control parameters include tol for the tolerance for convergence, quiet for controlling whether messages will be display, max_batch for the maximum number of phenotypes in a batch, and check_boundary for whether the 0 and 1 boundary values for the estimated heritability will be checked explicitly.

Value

A vector of estimated heritabilities, corresponding to the columns in pheno. The result has attributes "sample_size", "log10lik" and "resid_sd".

Examples

# read data
iron <- read_cross2(system.file("extdata", "iron.zip", package="qtl2"))


# insert pseudomarkers into map
map <- insert_pseudomarkers(iron$gmap, step=1)

# calculate genotype probabilities
probs <- calc_genoprob(iron, map, error_prob=0.002)

# kinship matrix
kinship <- calc_kinship(probs)

# grab phenotypes and covariates; ensure that covariates have names attribute
pheno <- iron$pheno
covar <- match(iron$covar$sex, c("f", "m")) # make numeric
names(covar) <- rownames(iron$covar)

# perform genome scan
hsq <- est_herit(pheno, kinship, covar)

Description

Uses a hidden Markov model to re-estimate the genetic map for an experimental cross, with possible allowance for genotyping errors.

Usage

est_map(
  cross,
  error_prob = 0.0001,
  map_function = c("haldane", "kosambi", "c-f", "morgan"),
  lowmem = FALSE,
  maxit = 10000,
  tol = 0.000001,
  quiet = TRUE,
  save_rf = FALSE,
  cores = 1
)

Arguments

Details

The map is estimated assuming no crossover interference, but a map function (by default, Haldane's) is used to derive the genetic distances.

Value

A list of numeric vectors, with the estimated marker locations (in cM). The location of the initial marker on each chromosome is kept the same as in the input cross.

Examples

grav2 <- read_cross2(system.file("extdata", "grav2.zip", package="qtl2"))

gmap <- est_map(grav2, error_prob=0.002)

Description

Identify sets of markers with identical genotype data.

Usage

find_dup_markers(cross, chr, exact_only = TRUE, adjacent_only = FALSE)

Arguments

Details

If exact.only=TRUE, we look only for groups of markers whose pattern of missing data and observed genotypes match exactly. One marker (chosen at random) is selected as the name of the group (in the output of the function).

If exact.only=FALSE, we look also for markers whose observed genotypes are contained in the observed genotypes of another marker. We use a pair of nested loops, working from the markers with the most observed genotypes to the markers with the fewest observed genotypes.

Value

A list of marker names; each component is a set of markers whose genotypes match one other marker, and the name of the component is the name of the marker that they match.

See Also

drop_markers(), drop_nullmarkers(), reduce_markers()

Examples

grav2 <- read_cross2(system.file("extdata", "grav2.zip", package="qtl2"))
dup <- find_dup_markers(grav2)
grav2_nodup <- drop_markers(grav2, unlist(dup))

Description

Find IBD segments (regions with a lot of shared SNP genotypes) for a set of strains

Usage

find_ibd_segments(geno, map, min_lod = 15, error_prob = 0.001, cores = 1)

Arguments

Details

For each strain pair on each chromosome, we consider all marker intervals and calculate a LOD score comparing the two hypotheses: that the strains are IBD in the interval, vs. that they are not. We assume that the two strains are homozygous at all markers, and use the model from Broman and Weber (1999), which assumes linkage equilibrium between markers and uses a simple model for genotype frequencies in the presence of genotyping errors or mutations.

Note that inference of IBD segments is heavily dependent on how SNPs were chosen to be genotyped. (For example, were the SNPs ascertained based on their polymorphism between a particular strain pair?)

Value

A data frame whose rows are IBD segments and whose columns are:

• Strain 1

• Strain 2

• Chromosome

• Left marker

• Right marker

• Left position

• Right position

• Left marker index

• Right marker index

• Interval length

• Number of markers

• Number of mismatches

• LOD score

References

Broman KW, Weber JL (1999) Long homozygous chromosomal segments in reference families from the Centre d’Étude du Polymorphisme Humain. Am J Hum Genet 65:1493–1500.

Examples

## Not run: 
# load DO data from Recla et al. (2014) Mamm Genome 25:211-222.
recla <- read_cross2("https://raw.githubusercontent.com/rqtl/qtl2data/main/DO_Recla/recla.zip")

# grab founder genotypes and physical map
fg <- recla$founder_geno
pmap <- recla$pmap

# find shared segments
(segs <- find_ibd_segments(fg, pmap, min_lod=10, error_prob=0.0001))

## End(Not run)

Description

For a particular SNP, find the name of the corresponding indexed SNP.

Usage

find_index_snp(snpinfo, snp)

Arguments

Value

A vector of SNP IDs (the corresponding indexed SNPs), with NA if a SNP is not found.

See Also

find_marker()

Examples

## Not run: 
# load example data and calculate genotype probabilities
file <- paste0("https://raw.githubusercontent.com/rqtl/",
               "qtl2data/main/DO_Recla/recla.zip")
recla <- read_cross2(file)

# founder genotypes for a set of SNPs
snpgeno <- rbind(m1=c(3,1,1,3,1,1,1,1),
                 m2=c(3,1,1,3,1,1,1,1),
                 m3=c(1,1,1,1,3,3,3,3),
                 m4=c(1,3,1,3,1,3,1,3))
sdp <- calc_sdp(snpgeno)
snpinfo <- data.frame(chr=c("19", "19", "X", "X"),
                      pos=c(40.36, 40.53, 110.91, 111.21),
                      sdp=sdp,
                      snp=c("m1", "m2", "m3", "m4"), stringsAsFactors=FALSE)

# update snp info by adding the SNP index column
snpinfo <- index_snps(recla$pmap, snpinfo)

# find indexed snp for a particular snp
find_index_snp(snpinfo, "m3")

## End(Not run)

Description

Find gaps between markers in a genetic map

Usage

find_map_gaps(map, min_gap = 50)

Arguments

Value

Data frame with 6 columns: chromosome, marker to left of gap, numeric index of marker to left, marker to right of gap, numeric index of marker to right, and the length of the gap.

See Also

reduce_map_gaps()

Examples

iron <- read_cross2(system.file("extdata", "iron.zip", package="qtl2"))
find_map_gaps(iron$gmap, 40)

Description

Find markers closest to specified set of positions, or within a specified interval.

Usage

find_marker(map, chr, pos = NULL, interval = NULL)

Arguments

Details

If pos is provided, interval should not be, and vice versa.

If pos is provided, then chr and pos should either be the same length, or one of them should have length 1 (to be expanded to the length of the other).

If interval is provided, then chr should have length 1.

Value

A vector of marker names, either closest to the positions specified by pos, or within the interval defined by interval.

See Also

find_markerpos(), find_index_snp(), pull_genoprobpos(), pull_genoprobint()

Examples

iron <- read_cross2(system.file("extdata", "iron.zip", package="qtl2"))

# find markers by their genetic map positions
find_marker(iron$gmap, c(8, 11), c(37.7, 56.9))

# find markers by their physical map positions (two markers on chr 7)
find_marker(iron$pmap, 7, c(44.2, 108.9))

# find markers in an interval
find_marker(iron$pmap, 16, interval=c(35, 80))

Description

Find positions of markers within a cross object

Usage

find_markerpos(cross, markers, na.rm = TRUE)

Arguments

Value

A data frame with chromosome and genetic and physical positions (in columns "gmap" and "pmap"), with markers as row names. If the input cross is not a cross2 object but rather a map, the output contains chr and pos.

See Also

find_marker()

Examples

iron <- read_cross2(system.file("extdata", "iron.zip", package="qtl2"))

# find markers
find_markerpos(iron, c("D8Mit294", "D11Mit101"))

Description

Find peaks in a set of LOD curves (output from scan1()

Usage

find_peaks(
  scan1_output,
  map,
  threshold = 3,
  peakdrop = Inf,
  drop = NULL,
  prob = NULL,
  thresholdX = NULL,
  peakdropX = NULL,
  dropX = NULL,
  probX = NULL,
  expand2markers = TRUE,
  sort_by = c("column", "pos", "lod"),
  cores = 1
)

Arguments

Details

For each lod score column on each chromosome, we return a set of peaks defined as local maxima that exceed the specified threshold, with the requirement that the LOD score must have dropped by at least peakdrop below the lowest of any two adjacent peaks.

At a given peak, if there are ties, with multiple positions jointly achieving the maximum LOD score, we take the average of these positions as the location of the peak.

Value

A data frame with each row being a single peak on a single chromosome for a single LOD score column, and with columns

• lodindex - lod column index

• lodcolumn - lod column name

• chr - chromosome ID

• pos - peak position

• lod - lod score at peak

If drop or prob is provided, the results will include two additional columns: ci_lo and ci_hi, with the endpoints of the LOD support intervals or Bayes credible wintervals.

See Also

scan1(), lod_int(), bayes_int()

Examples

# read data
iron <- read_cross2(system.file("extdata", "iron.zip", package="qtl2"))


# insert pseudomarkers into map
map <- insert_pseudomarkers(iron$gmap, step=1)

# calculate genotype probabilities
probs <- calc_genoprob(iron, map, error_prob=0.002)

# grab phenotypes and covariates; ensure that covariates have names attribute
pheno <- iron$pheno
covar <- match(iron$covar$sex, c("f", "m")) # make numeric
names(covar) <- rownames(iron$covar)
Xcovar <- get_x_covar(iron)

# perform genome scan
out <- scan1(probs, pheno, addcovar=covar, Xcovar=Xcovar)

# find just the highest peak on each chromosome
find_peaks(out, map, threshold=3)

# possibly multiple peaks per chromosome
find_peaks(out, map, threshold=3, peakdrop=1)

# possibly multiple peaks, also getting 1-LOD support intervals
find_peaks(out, map, threshold=3, peakdrop=1, drop=1)

# possibly multiple peaks, also getting 90% Bayes intervals
find_peaks(out, map, threshold=3, peakdrop=1, prob=0.9)

Description

Fit a single-QTL model at a single putative QTL position and get detailed results about estimated coefficients and individuals contributions to the LOD score.

Usage

fit1(
  genoprobs,
  pheno,
  kinship = NULL,
  addcovar = NULL,
  nullcovar = NULL,
  intcovar = NULL,
  weights = NULL,
  contrasts = NULL,
  model = c("normal", "binary"),
  zerosum = TRUE,
  se = TRUE,
  hsq = NULL,
  reml = TRUE,
  blup = FALSE,
  ...
)

Arguments

Details

For each of the inputs, the row names are used as individual identifiers, to align individuals.

If kinship is absent, Haley-Knott regression is performed. If kinship is provided, a linear mixed model is used, with a polygenic effect estimated under the null hypothesis of no (major) QTL, and then taken as fixed as known in the genome scan.

If contrasts is provided, the genotype probability matrix, $P$, is post-multiplied by the contrasts matrix, $A$, prior to fitting the model. So we use $P \cdot A$ as the $X$ matrix in the model. One might view the rows of A^-1 as the set of contrasts, as the estimated effects are the estimated genotype effects pre-multiplied by A^-1.

The ... argument can contain several additional control parameters; suspended for simplicity (or confusion, depending on your point of view). tol is used as a tolerance value for linear regression by QR decomposition (in determining whether columns are linearly dependent on others and should be omitted); default 1e-12. maxit is the maximum number of iterations for converence of the iterative algorithm used when model=binary. bintol is used as a tolerance for converence for the iterative algorithm used when model=binary. eta_max is the maximum value for the "linear predictor" in the case model="binary" (a bit of a technicality to avoid fitted values exactly at 0 or 1).

Value

A list containing

• coef - Vector of estimated coefficients.

• SE - Vector of estimated standard errors (included if se=TRUE).

• lod - The overall lod score.

• ind_lod - Vector of individual contributions to the LOD score (not provided if kinship is used).

• fitted - Fitted values.

• resid - Residuals. If blup==TRUE, only coef and SE are included at present.

References

Haley CS, Knott SA (1992) A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity 69:315–324.

Kang HM, Zaitlen NA, Wade CM, Kirby A, Heckerman D, Daly MJ, Eskin E (2008) Efficient control of population structure in model organism association mapping. Genetics 178:1709–1723.

See Also

pull_genoprobpos(), find_marker()

Examples

# read data
iron <- read_cross2(system.file("extdata", "iron.zip", package="qtl2"))


# insert pseudomarkers into map
map <- insert_pseudomarkers(iron$gmap, step=5)

# calculate genotype probabilities
probs <- calc_genoprob(iron, map, error_prob=0.002)

# grab phenotypes and covariates; ensure that covariates have names attribute
pheno <- iron$pheno[,1]
covar <- match(iron$covar$sex, c("f", "m")) # make numeric
names(covar) <- rownames(iron$covar)

# scan chromosome 7 to find peak
out <- scan1(probs[,"7"], pheno, addcovar=covar)

# find peak position
max_pos <- max(out, map)

# genoprobs at max position
pr_max <- pull_genoprobpos(probs, map, max_pos$chr, max_pos$pos)

# fit QTL model just at that position
out_fit1 <- fit1(pr_max, pheno, addcovar=covar)

Description

Read a csv file via data.table::fread() using a particular set of options, including the ability to transpose the result.

Usage

fread_csv(
  filename,
  sep = ",",
  na.strings = c("NA", "-"),
  comment.char = "#",
  transpose = FALSE,
  rownames_included = TRUE
)

Arguments

Details

Initial two lines can contain comments with number of rows and columns. Number of columns includes an ID column; number of rows does not include the header row.

The first column is taken to be a set of row names

Value

Data frame

See Also

fread_csv_numer()

Examples

## Not run: mydata <- fread_csv("myfile.csv", transpose=TRUE)

Description

Read a csv file via data.table::fread() using a particular set of options, including the ability to transpose the result. This version assumes that the contents other than the first column and the header row are strictly numeric.

Usage

fread_csv_numer(
  filename,
  sep = ",",
  na.strings = c("NA", "-"),
  comment.char = "#",
  transpose = FALSE,
  rownames_included = TRUE
)

Arguments

Details

Initial two lines can contain comments with number of rows and columns. Number of columns includes an ID column; number of rows does not include the header row.

The first column is taken to be a set of row names

Value

Data frame

See Also

fread_csv()

Examples

## Not run: mydata <- fread_csv_numer("myfile.csv", transpose=TRUE)

Description

Reduce genotype probabilities (as calculated by calc_genoprob()) to allele probabilities.

Usage

genoprob_to_alleleprob(probs, quiet = TRUE, cores = 1)

Arguments

Value

An object of class "calc_genoprob", like the input probs, but with probabilities collapsed to alleles rather than genotypes. See calc_genoprob().

Examples

iron <- read_cross2(system.file("extdata", "iron.zip", package="qtl2"))
gmap_w_pmar <- insert_pseudomarkers(iron, step=1)
probs <- calc_genoprob(iron, gmap_w_pmar, error_prob=0.002)
allele_probs <- genoprob_to_alleleprob(probs)

Description

For multi-parent populations, convert use founder genotypes at a set of SNPs to convert founder-based genotype probabilities to SNP genotype probabilities.

Usage

genoprob_to_snpprob(genoprobs, snpinfo)

Arguments

Details

We first split the SNPs by chromosome and use snpinfo$index to subset to non-equivalent SNPs. snpinfo$interval indicates the intervals in the genotype probabilities that contain each. For SNPs contained within an interval, we use the average of the probabilities for the two endpoints. We then collapse the probabilities according to the strain distribution pattern.

Value

An object of class "calc_genoprob", like the input genoprobs, but with imputed genotype probabilities at the selected SNPs indicated in snpinfo$index. See calc_genoprob().

If the input genoprobs is for allele probabilities, the probs output has just two probability columns (for the two SNP alleles). If the input has a full set of $n(n+1)/2$ probabilities for $n$ strains, the probs output has 3 probabilities (for the three SNP genotypes). If the input has full genotype probabilities for the X chromosome ($n(n+1)/2$ genotypes for the females followed by $n$ hemizygous genotypes for the males), the output has 5 probabilities: the 3 female SNP genotypes followed by the two male hemizygous SNP genotypes.

See Also

index_snps(), calc_genoprob(), scan1snps()

Examples

## Not run: 
# load example data and calculate genotype probabilities
file <- paste0("https://raw.githubusercontent.com/rqtl/",
               "qtl2data/main/DO_Recla/recla.zip")
recla <- read_cross2(file)
recla <- recla[c(1:2,53:54), c("19","X")] # subset to 4 mice and 2 chromosomes
probs <- calc_genoprob(recla, error_prob=0.002)

# founder genotypes for a set of SNPs
snpgeno <- rbind(m1=c(3,1,1,3,1,1,1,1),
                 m2=c(1,3,1,3,1,3,1,3),
                 m3=c(1,1,1,1,3,3,3,3),
                 m4=c(1,3,1,3,1,3,1,3))
sdp <- calc_sdp(snpgeno)
snpinfo <- data.frame(chr=c("19", "19", "X", "X"),
                      pos=c(40.36, 40.53, 110.91, 111.21),
                      sdp=sdp,
                      snp=c("m1", "m2", "m3", "m4"), stringsAsFactors=FALSE)

# identify groups of equivalent SNPs
snpinfo <- index_snps(recla$pmap, snpinfo)

# collapse to SNP genotype probabilities
snpprobs <- genoprob_to_snpprob(probs, snpinfo)

# could also first convert to allele probs
aprobs <- genoprob_to_alleleprob(probs)
snpaprobs <- genoprob_to_snpprob(aprobs, snpinfo)

## End(Not run)

Description

For a set objects with IDs as row names (or, for a vector, just names), find the IDs that are present in all of the objects.

Usage

get_common_ids(..., complete.cases = FALSE)

Arguments

Details

This is used (mostly internally) to align phenotypes, genotype probabilities, and covariates in preparation for a genome scan. The complete.cases argument is used to omit individuals with any missing covariate values.

Value

A vector of character strings for the individuals that are in common.

Examples

x <- matrix(0, nrow=10, ncol=5); rownames(x) <- LETTERS[1:10]
y <- matrix(0, nrow=5, ncol=5);  rownames(y) <- LETTERS[(1:5)+7]
z <- LETTERS[5:15]
get_common_ids(x, y, z)

x[8,1] <- NA
get_common_ids(x, y, z)
get_common_ids(x, y, z, complete.cases=TRUE)

Description

Get the matrix of covariates to be used for the null hypothesis when performing QTL analysis with the X chromosome.

Usage

get_x_covar(cross)

Arguments

Details

For most crosses, the result is either NULL (indicating no additional covariates are needed) or a matrix with a single column containing sex indicators (1 for males and 0 for females).

For an intercross, we also consider cross direction. There are four cases: (1) All male or all female but just one direction: no covariate; (2) All female but both directions: covariate indicating cross direction; (3) Both sexes, one direction: covariate indicating sex; (4) Both sexes, both directions: a covariate indicating sex and a covariate that is 1 for females from the reverse direction and 0 otherwise.

Value

A matrix of size individuals x no. covariates.

Examples

iron <- read_cross2(system.file("extdata", "iron.zip", package="qtl2"))
xcovar <- get_x_covar(iron)

Description

Turn imputed genotypes into phased genotypes along chromosomes by attempting to pick the phase that leads to the fewest recombination events.

Usage

guess_phase(cross, geno, deterministic = FALSE, cores = 1)

Arguments

Details

We randomly assign the pair of alleles at the first locus to two haplotypes, and then work left to right, assigning alleles to haplotypes one locus at a time seeking the fewest recombination events. The results are subject to arbitrary and random choices. For example, to the right of a homozygous region, either orientation is equally reasonable.

Value

If input cross is phase-known (e.g., recombinant inbred lines), the output will be the input geno. Otherwise, the output will be a list of three-dimensional arrays of imputed genotypes, individual x position x haplotype (1/2).

See Also

maxmarg()

Examples

iron <- read_cross2(system.file("extdata", "iron.zip", package="qtl2"))
gmap <- insert_pseudomarkers(iron$gmap, step=1)
probs <- calc_genoprob(iron, gmap, error_prob=0.002)
imp_geno <- maxmarg(probs)
ph_geno <- guess_phase(iron, imp_geno)

Description

For a set of SNPs and a map of marker/pseudomarkers, partition the SNPs into groups that are contained within common intervals and have the same strain distribution pattern, and then create an index to a set of distinct SNPs, one per partition.

Usage

index_snps(map, snpinfo, tol = 0.00000001)

Arguments

Details

We split the SNPs by chromosome and identify the intervals in the map that contain each. For SNPs within tol of a position at which the genotype probabilities were calculated, we take the SNP to be at that position. For each marker position or interval, we then partition the SNPs into groups that have distinct strain distribution patterns, and choose a single index SNP for each partition.

Value

A data frame containing the input snpinfo with three added columns: "index" (which indicates the groups of equivalent SNPs), "interval" (which indicates the map interval containing the SNP, with values starting at 0), and on_map (which indicates that the SNP is within tol of a position on the map). The rows get reordered, so that they are ordered by chromosome and position, and the values in the "index" column are by chromosome.

See Also

genoprob_to_snpprob(), scan1snps(), find_index_snp()

Examples

## Not run: 
# load example data and calculate genotype probabilities
file <- paste0("https://raw.githubusercontent.com/rqtl/",
               "qtl2data/main/DO_Recla/recla.zip")
recla <- read_cross2(file)

# founder genotypes for a set of SNPs
snpgeno <- rbind(m1=c(3,1,1,3,1,1,1,1),
                 m2=c(1,3,1,3,1,3,1,3),
                 m3=c(1,1,1,1,3,3,3,3),
                 m4=c(1,3,1,3,1,3,1,3))
sdp <- calc_sdp(snpgeno)
snpinfo <- data.frame(chr=c("19", "19", "X", "X"),
                      pos=c(40.36, 40.53, 110.91, 111.21),
                      sdp=sdp,
                      snp=c("m1", "m2", "m3", "m4"), stringsAsFactors=FALSE)

# update snp info by adding the SNP index column
snpinfo <- index_snps(recla$pmap, snpinfo)

## End(Not run)

Description

Insert pseudomarkers into a map of genetic markers

Usage

insert_pseudomarkers(
  map,
  step = 0,
  off_end = 0,
  stepwidth = c("fixed", "max"),
  pseudomarker_map = NULL,
  tol = 0.01,
  cores = 1
)

Arguments

Details

If stepwidth="fixed", a grid of pseudomarkers is added to the marker map.

If stepwidth="max", a minimal set of pseudomarkers are added, so that the maximum distance between adjacent markers or pseudomarkers is at least step. If two adjacent markers are separated by less than step, no pseudomarkers will be added to the interval. If they are more then step apart, a set of equally-spaced pseudomarkers will be added.

If pseudomarker_map is provided, then the step, off_end, and stepwidth arguments are ignored, and the input pseudomarker_map is taken to be the set of pseudomarker positions.

Value

A list like the input map with pseudomarkers inserted. Will also have an attribute "is_x_chr", taken from the input map.

See Also

calc_genoprob(), calc_grid()

Examples

iron <- read_cross2(system.file("extdata", "iron.zip", package="qtl2"))
gmap_w_pmar <- insert_pseudomarkers(iron$gmap, step=1)

Description

Linear interpolation of genotype probabilities, mostly to get two sets onto the same map for comparison purposes.

Usage

interp_genoprob(probs, map, cores = 1)

Arguments

Details

We reduce probs to the positions present in map and then interpolate the genotype probabilities at additional positions in map by linear interpolation using the two adjacent positions. Off the ends, we just copy over the first or last value unchanged.

In general, it's better to use insert_pseudomarkers() and calc_genoprob() to get genotype probabilities at additional positions along a chromosome. This function is a very crude alternative that was implemented in order to compare genotype probabilities derived by different methods, where we first need to get them onto a common set of positions.

Value

An object of class "calc_genoprob", like the input, but with additional positions present in map. See calc_genoprob().

See Also

calc_genoprob()

Examples

iron <- read_cross2(system.file("extdata", "iron.zip", package="qtl2"))

probs <- calc_genoprob(iron, iron$gmap, error_prob=0.002)

# you generally wouldn't want to do this, but this is an illustration
map <- insert_pseudomarkers(iron$gmap, step=1)
probs_map <- interp_genoprob(probs, map)

Description

Use interpolate to convert from one map to another

Usage

interp_map(map, oldmap, newmap)

Arguments

Value

Object of same form as input map but in the units as in newmap.

Examples

# load example data
iron <- read_cross2(system.file("extdata", "iron.zip", package="qtl2"))

# positions to interpolate from cM to Mbp
tointerp <- list("7" = c(pos7.1= 5, pos7.2=15, pos7.3=25),
                 "9" = c(pos9.1=20, pos9.2=40))

interp_map(tointerp, iron$gmap, iron$pmap)

Description

Calculate the matrix of SNP genotypes from a vector of strain distribution patterns (SDPs).

Usage

invert_sdp(sdp, n_strains)

Arguments

Value

Matrix of SNP genotypes, markers x strains, coded as 1 (AA) and 3 (BB). Markers with values other than 1 or 3 are omitted, and monomorphic markers, are omitted.

See Also

sdp2char(), calc_sdp()

Examples

sdp <- c(m1=1, m2=12, m3=240)
invert_sdp(sdp, 8)

Description

Estimate the locations of crossovers in each individual on each chromosome.

Usage

locate_xo(geno, map, quiet = TRUE, cores = 1)

Arguments

Value

A list of lists of estimated crossover locations, with crossovers placed at the midpoint of the intervals that contain them.

See Also

count_xo()

Examples

iron <- read_cross2(system.file("extdata", "iron.zip", package="qtl2"))
map <- insert_pseudomarkers(iron$gmap, step=1)
pr <- calc_genoprob(iron, map, error_prob=0.002, map_function="c-f")
g <- maxmarg(pr)
pos <- locate_xo(g, iron$gmap)