R/qtl2ggplot Vignette
Whole Genome Allele Scan
Load example DO data from web. For convenience, we attach package
‘ggplot2’ for the autoplot function. Functions from ‘qtl2’
are explicitly referenced with prefix qtl2::.
Download ‘qtl2’ cross2 object.
DOex <-
qtl2::read_cross2(
file.path(
"https://raw.githubusercontent.com/rqtl",
"qtl2data/master/DOex",
"DOex.zip"))With multiple alleles, it is useful to examine an additive allele model. Download pre-calculated allele probabilities (~5 MB) as follows:
tmpfile <- tempfile()
file <- paste0("https://raw.githubusercontent.com/rqtl/",
"qtl2data/master/DOex/DOex_alleleprobs.rds")
download.file(file, tmpfile)
apr <- readRDS(tmpfile)
unlink(tmpfile)Alternatively, calculate these directly.
Genome allele scan.
Summary of peaks.
## lodindex lodcolumn chr pos lod
## 1 1 OF_immobile_pct 2 96.84223 10.173313
## 2 1 OF_immobile_pct 3 15.02006 5.971503
## 3 1 OF_immobile_pct X 74.57257 6.939151New summary method:
## # A tibble: 3 × 5
## pheno chr pos lod marker
## <chr> <fct> <dbl> <dbl> <chr>
## 1 OF_immobile_pct 2 96.8 10.2 backupUNC020000070
## 2 OF_immobile_pct 3 15.0 5.97 backupUNC030729939
## 3 OF_immobile_pct X 74.6 6.94 UNC200000454The basic plot of genome scan,
and the grammar of graphics (ggplot2) version.
Genome Allele Scan for Chr 2
Subset to chr 2.
Scan chromosome and summarize peak
## lodindex lodcolumn chr pos lod
## 1 1 OF_immobile_pct 2 96.84223 10.17331
Examine coefficients for the 8 alleles
New summary method:
## # A tibble: 1 × 14
## pheno chr pos lod marker A B C D E F G
## <chr> <fct> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 OF_immobil… 2 96.8 10.2 backu… -6.93 -3.80 -6.27 -5.97 -30.1 -14.3 -3.87
## # ℹ 2 more variables: H <dbl>, intercept <dbl>
Plot allele effects over LOD scan.
Examine just some of the founder effects, without centering.
SNP Association Mapping
For SNP association mapping, be sure to use the genotype allele pair
probabilities pr rather than the additive model allele
probabilities apr. Download pre-calculated genotype
probabilities (~19 MB) and subset to Chr 2.
tmpfile <- tempfile()
file <- paste0("https://raw.githubusercontent.com/rqtl/",
"qtl2data/master/DOex/DOex_genoprobs.rds")
download.file(file, tmpfile)
pr <- readRDS(tmpfile)
unlink(tmpfile)
pr <- subset(pr, chr = "2")Or, alternatively, calculate directly using the subsetted
DOex.
Download SNP information from web.
filename <- file.path("https://raw.githubusercontent.com/rqtl",
"qtl2data/master/DOex",
"c2_snpinfo.rds")
tmpfile <- tempfile()
download.file(filename, tmpfile, quiet=TRUE)
snpinfo <- readRDS(tmpfile)
unlink(tmpfile)Or alternatively, use query function approach.
snpdb_file <- system.file("extdata", "cc_variants_small.sqlite", package="qtl2")
query_variant <- qtl2::create_variant_query_func(snpdb_file)
snpinfo <- query_variant("2", 96.5, 98.5)The SNP routines in ‘qtl2ggplot’ can distinguish SNP variants
artificially add type to snpinfo with about
20% DEL to show how variants get plotted.
variants <- c("snp","indel","SV","INS","DEL","INV")
snpinfo$type <-
factor(
sample(
c(sample(variants[-1], 5000, replace = TRUE),
rep("snp", nrow(snpinfo) - 5000))),
variants)Perform SNP association mapping. It is possible to use
qtl2::scan1snps instead, which bundles these three
routines, but we want to have the SNP probabilities for later use.
snpinfo <- qtl2::index_snps(DOex$pmap, snpinfo)
snppr <- qtl2::genoprob_to_snpprob(pr, snpinfo)
scan_snppr <- qtl2::scan1(snppr, DOex$pheno)Plot results.
Plot just subset of distinct SNPs
Highlight the top snps (with LOD within 1.5 of max). Show as open circles of size 1.
Strain Distribution Pattern (SDP) Scan
SNP assocation mapping is more useful with plots that emphasized the
strain distribution pattern (SDP), which separate out SNPs based on
their SDP and plot the top patterns. For instance sdp = 52
corresponds to pattern ABDGH:CEF. That is, the SNP genotype
"AA" resulting from qtl2::genoprob_to_snpprob
applied to pr corresponds to any of the 36 allele pairs
with the two alleles drawn from the reference (ref) set of
ABDGH (15 pairs:
AA, AB, AD, AG, AH, BB, BD, BG, BH, DD, DG, DH, GG, GH, HH),
"BB" has two alleles from the alternate (alt)
set CEF (6 pairs: CC, CE, CF, EE, EF, FF), and
"AB" has one from each for the heterogeneous
(het) set (15 pairs: AC, AE, ..., HF). There
are 255 possible sdps, but only a few (4 in our example)
that need be examined carefully. One can think of these as a subset of
markers for genome scan, where interest is only in those SNPS following
a particular sdp; as with genome scans, we can fill in for
missing data. That is, only a few SNPs may show a particular pattern,
but key differences might be seen nearby if we impute SNPs of the same
pattern. Here we highlight SDPs in SNPs within 3 of max; connect with
lines.
Highlight only top SDP patterns in SNPs.
Looking at all SNPS is more useful than just focusing on mapped SNPs.
Genes in Peak Region
Download Gene info for DOex from web via RDS.
filename <- file.path("https://raw.githubusercontent.com/rqtl",
"qtl2data/master/DOex",
"c2_genes.rds")
tmpfile <- tempfile()
download.file(filename, tmpfile, quiet=TRUE)
gene_tbl <- readRDS(tmpfile)
unlink(tmpfile)Or alternatively use query function approach.
dbfile <- system.file("extdata", "mouse_genes_small.sqlite", package="qtl2")
query_genes <- qtl2::create_gene_query_func(dbfile, filter="(source=='MGI')")
gene_tbl <- query_genes("2", 96.5, 98.5)Plot genes. These can be aligned with the SNP association map or SDP scans.
Multiple Phenotypes
Plot routines (except scan patterns for now) can accommodate multiple
phenotypes. At present, it is best to stick to under 10. In the preambl
of this document, a second phenotype, asin, was artifically
created for illustration purposes.
Create artificial second phenotype as arcsic sqrt of first one.
Genome scans for multile phenotypes
Redo genome allele scans on both phenotypes.
## lodindex lodcolumn chr pos lod
## 1 1 OF_immobile_pct 2 96.84223 10.173313
## 2 2 asin 2 96.84223 9.422816Similar summary using new summary method:
## # A tibble: 2 × 5
## pheno chr pos lod marker
## <chr> <fct> <dbl> <dbl> <chr>
## 1 OF_immobile_pct 2 96.8 10.2 backupUNC020000070
## 2 asin 2 96.8 9.42 backupUNC020000070
SNP scans for multiple phenotypes
Redo SNP scans on both phenotypes.
Using new summary method. The summary includes a range
(min and max) for pos, as there
could be multiple SNPs across a range of positions.
## # A tibble: 8 × 7
## pheno max_pos min_pos lod sdp pattern snp_id
## <chr> <dbl> <dbl> <dbl> <int> <chr> <chr>
## 1 OF_immobile_pct 97.0 96.9 7.31 52 ABDGH:CEF 3 SNPs
## 2 OF_immobile_pct 96.8 96.8 7.04 48 ABCDGH:EF 12 SNPs
## 3 asin 96.8 96.8 6.55 48 ABCDGH:EF 12 SNPs
## 4 asin 97.0 96.9 6.49 52 ABDGH:CEF 3 SNPs
## 5 OF_immobile_pct 98.2 96.9 5.99 16 ABCDFGH:E 25 SNPs
## 6 OF_immobile_pct 96.9 96.9 5.97 20 ABDFGH:CE 9 SNPs
## 7 asin 97.0 96.9 5.56 56 ABCGH:DEF 88 SNPs
## 8 asin 97.0 96.9 5.32 60 ABGH:CDEF 69 SNPsPlot results.
Note that in the autoplot (using
qtl2ggplot), the hilit points for the second
trait are fewer than with the plot (using package ‘qtl2’).
This is because the maxlod for the faceted
autoplot is across both traits, and the other points for
the second trait are too low.
Distinguish high values by color but leave others gray.
autoplot(scan_snppr, snpinfo, 1:2,show_all_snps = FALSE,
facet_var = "pheno", drop_hilit = 2,
col=8, col_hilit=1:2, cex=2) +
geom_hline(yintercept = max(scan_snppr) - 2, col = "darkgrey", linetype = "dashed")
### SDP scans for multiple phenotypes
autoplot(scan_snppr, snpinfo, 1:2, patterns = "hilit", cex=2,
drop_hilit=3, facet = "pheno", scales = "free")
autoplot(scan_snppr, snpinfo, 1:2, patterns = "hilit",
show_all_snps = TRUE, cex=2,
drop_hilit=3, facet = "pattern")
LOD peaks for multiple phenotypes
## lodindex lodcolumn chr pos lod ci_lo ci_hi
## 1 1 OF_immobile_pct 2 96.84223 10.173313 91.60175 103.8778
## 2 2 asin 2 96.84223 9.422816 91.60175 103.8778
Coefficients for multiple phenotypes
New summary method:
## # A tibble: 2 × 14
## pheno chr pos lod marker A B C D E F
## <chr> <fct> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 OF_imm… 2 96.8 10.2 backu… -7.49 -4.36 -6.83 -6.53 -30.6 -14.8
## 2 asin 2 96.8 9.42 backu… -0.108 -0.0661 -0.0936 -0.109 -0.392 -0.209
## # ℹ 3 more variables: G <dbl>, H <dbl>, intercept <dbl>
## # A tibble: 2 × 14
## pheno chr pos lod marker A B C D E F
## <chr> <fct> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 OF_imm… 2 96.8 10.2 backu… -7.49 -4.36 -6.83 -6.53 -30.6 -14.8
## 2 asin 2 96.8 9.42 backu… -0.108 -0.0661 -0.0936 -0.109 -0.392 -0.209
## # ℹ 3 more variables: G <dbl>, H <dbl>, intercept <dbl>Coefficients for 36 allele pairs
This last section shows some very noisy images of coefficients for the 36 allele pairs. Generally, these will not be useful unless the cross is quite large. See also package ‘qtl2pattern’.
QTL effects for 36 allele pair model. Note that they are quite
unstable, and the 36 allele pair max LOD is far from the peak for the
additive (haplotype) model. Only showing effects with at least one
E allele. Plots are truncated at +/-100 for viewability.
Note also that ‘qtl2ggplot’ routines have some centering built in.
Find coefficients for 36 allele pair genome scan.
## Warning in qtl2::scan1coef(pr, DOex$pheno): Considering only the first
## phenotype.All 36 allele pair QTL effects.
The autoplot is centered by default (so mean across all
alleles is mean of trait) to make coefficient plots easier to view. This
can be turned off with the hidden center option.
autoplot(coefs36, DOex$pmap, ylim=c(-100,100), center = FALSE,
colors = NULL, legend.position = "none")
Only 8 allele pair QTL effects that contain E.
tmp <- qtl2ggplot:::modify_object(coefs36,
coefs36[, stringr::str_detect(dimnames(coefs36)[[2]], "E")])
autoplot(tmp, DOex$pmap, ylim=c(-100,100))