R: Pedigree Reconstruction

sequoia {sequoia}

R Documentation

Pedigree Reconstruction

Description

Perform pedigree reconstruction based on SNP data, including parentage assignment and sibship clustering.

Usage

sequoia(
  GenoM = NULL,
  LifeHistData = NULL,
  SeqList = NULL,
  Module = "ped",
  Err = 1e-04,
  Tfilter = -2,
  Tassign = 0.5,
  MaxSibshipSize = 100,
  DummyPrefix = c("F", "M"),
  Complex = "full",
  Herm = "no",
  UseAge = "yes",
  args.AP = list(Flatten = NULL, Smooth = TRUE),
  mtSame = NULL,
  CalcLLR = TRUE,
  quiet = FALSE,
  Plot = NULL,
  StrictGenoCheck = TRUE,
  ErrFlavour = "version2.9",
  MaxSibIter = 42,
  MaxMismatch = NA,
  FindMaybeRel = FALSE
)

Arguments

`GenoM`	numeric matrix with genotype data: One row per individual, one column per SNP, coded as 0, 1, 2, missing values as a negative number or NA. You can reformat data with `GenoConvert`, or use other packages to get it into a genlight object and then use `as.matrix`.
`LifeHistData`	data.frame with up to 6 columns: ID max. 30 characters long Sex 1 = female, 2 = male, 3 = unknown, 4 = hermaphrodite, other numbers or NA = unknown BirthYear birth or hatching year, integer, with missing values as NA or any negative number. BY.min minimum birth year, only used if BirthYear is missing BY.max maximum birth year, only used if BirthYear is missing Year.last Last year in which individual could have had offspring. Can e.g. in mammals be the year before death for females, and year after death for males. "Birth year" may be in any arbitrary discrete time unit relevant to the species (day, month, decade), as long as parents are never born in the same time unit as their offspring, and only integers are used. Individuals do not need to be in the same order as in ‘GenoM’, nor do all genotyped individuals need to be included.
`SeqList`	list with output from a previous run, to be re-used in the current run. Used are elements ‘PedigreePar’, ‘LifeHist’, ‘AgePriors’, ‘Specs’, and ‘ErrM’, and these override the corresponding input parameters. Not all of these elements need to be present, and all other elements are ignored. If `SeqList$Specs` is provided, all input parameters with the same name as its items are ignored, except `Module`/`MaxSibIter`.
`Module`	one of pre Only input check, return `SeqList$Specs` dup Also check for duplicate genotypes par Also perform parentage assignment (genotyped parents to genotyped offspring) ped (Also) perform full pedigree reconstruction, including sibship clustering and grandparent assignment. By far the most time consuming, and may take several hours for large datasets. NOTE: Until 'MaxSibIter' is fully deprecated: if 'MaxSibIter' differs from the default (`42`), and 'Module' equals the default (`'ped'`), MaxSibIter overrides 'Module'.
`Err`	estimated genotyping error rate, as a single number, or a length 3 vector with P(hom\|hom), P(het\|hom), P(hom\|het), or a 3x3 matrix. See details below. The error rate is presumed constant across SNPs, and missingness is presumed random with respect to actual genotype. Using `Err` >5% is not recommended, and `Err` >10% strongly discouraged.
`Tfilter`	threshold log10-likelihood ratio (LLR) between a proposed relationship versus unrelated, to select candidate relatives. Typically a negative value, related to the fact that unconditional likelihoods are calculated during the filtering steps. More negative values may decrease non-assignment, but will increase computational time.
`Tassign`	minimum LLR required for acceptance of proposed relationship, relative to next most likely relationship. Higher values result in more conservative assignments. Must be zero or positive.
`MaxSibshipSize`	maximum number of offspring for a single individual (a generous safety margin is advised).
`DummyPrefix`	character vector of length 2 with prefixes for dummy dams (mothers) and sires (fathers); maximum 20 characters each. Length 3 vector in case of hermaphrodites (or default prefix 'H').
`Complex`	Breeding system complexity. Either "full" (default), "simp" (simplified, no explicit consideration of inbred relationships), "mono" (monogamous).
`Herm`	Hermaphrodites, either "no", "A" (distinguish between dam and sire role, default if at least 1 individual with sex=4), or "B" (no distinction between dam and sire role). Both of the latter deal with selfing.
`UseAge`	either "yes" (default), "no" (only use age differences for filtering), or "extra" (additional rounds with extra reliance on ageprior, may boost assignments but increased risk of erroneous assignments). Used during full reconstruction only.
`args.AP`	list with arguments to be passed on to `MakeAgePrior`, e.g. 'Discrete' (non-overlapping generations), 'MinAgeParent', 'MaxAgeParent'.
`mtSame`	NEW matrix indicating whether individuals (might) have the same mitochondrial haplotype (1), and may thus be matrilineal relatives, or not (0). Row names and column names should match IDs in 'GenoM'. Not all individuals need to be included and order is not important. Please report any issues. For details see the mtDNA vignette.
`CalcLLR`	TRUE/FALSE; calculate log-likelihood ratios for all assigned parents (genotyped + dummy; parent vs. otherwise related). Time-consuming in large datasets. Can be done separately with `CalcOHLLR`.
`quiet`	suppress messages: TRUE/FALSE/"verbose".
`Plot`	display plots from `SnpStats, MakeAgePrior`, and `SummarySeq`. Defaults (NULL) to TRUE when quiet=FALSE or "verbose", and FALSE when quiet=TRUE. If you get error 'figure margins too large', enlarge the plotting area (drag with mouse). Error 'invalid graphics state' can be dealt with by clearing the plotting area with dev.off().
`StrictGenoCheck`	Automatically exclude any individuals genotyped for <5 the unavoidable default up to version 2.4.1. Otherwise only excluded are (very nearly) monomorphic SNPs, SNPs scored for fewer than 2 individuals, and individuals scored for fewer than 2 SNPs.
`ErrFlavour`	function that takes `Err` (single number) as input, and returns a length 3 vector or 3x3 matrix, or choose from inbuilt options 'version2.9', 'version2.0', 'version1.3', or 'version1.1', referring to the sequoia version in which they were the default. Ignored if `Err` is a vector or matrix. See `ErrToM` for details.
`MaxSibIter`	DEPRECATED, use `Module` number of iterations of sibship clustering, including assignment of grandparents to sibships and avuncular relationships between sibships. Clustering continues until convergence or until MaxSibIter is reached. Set to 0 for parentage assignment only.
`MaxMismatch`	DEPRECATED AND IGNORED. Now calculated automatically using `CalcMaxMismatch`.
`FindMaybeRel`	DEPRECATED AND IGNORED, advised to run `GetMaybeRel` separately.

Details

For each pair of candidate relatives, the likelihoods are calculated of them being parent-offspring (PO), full siblings (FS), half siblings (HS), grandparent-grandoffspring (GG), full avuncular (niece/nephew - aunt/uncle; FA), half avuncular/great-grandparental/cousins (HA), or unrelated (U). Assignments are made if the likelihood ratio (LLR) between the focal relationship and the most likely alternative exceed the threshold Tassign.

Dummy parents of sibships are denoted by F0001, F0002, ... (mothers) and M0001, M0002, ... (fathers), are appended to the bottom of the pedigree, and may have been assigned real or dummy parents themselves (i.e. sibship-grandparents). A dummy parent is not assigned to singletons.

Full explanation of the various options and interpretation of the output is provided in the vignettes and on the package website, https://jiscah.github.io/index.html .

Value

A list with some or all of the following components, depending on Module. All input except GenoM is included in the output.

`AgePriors`	Matrix with age-difference based probability ratios for each relationship, used for full pedigree reconstruction; see `MakeAgePrior` for details. When running only parentage assignment (`Module="par"`) the returned AgePriors has been updated to incorporate the information of the assigned parents, and is ready for use during full pedigree reconstruction.
`args.AP`	(input) arguments used to specify age prior matrix. If a custom ageprior was provided via `SeqList$AgePrior`, this matrix is returned instead
`DummyIDs`	Dataframe with pedigree for dummy individuals, as well as their sex, estimated birth year (point estimate, upper and lower bound of 95% confidence interval; see also `CalcBYprobs`), number of offspring, and offspring IDs. From version 2.1 onwards, this includes dummy offspring.
`DupGenotype`	Dataframe, duplicated genotypes (with different IDs, duplicate IDs are not allowed). The specified number of maximum mismatches is used here too. Note that this dataframe may include pairs of closely related individuals, and monozygotic twins.
`DupLifeHistID`	Dataframe, row numbers of duplicated IDs in life history dataframe. For convenience only, but may signal a problem. The first entry is used.
`ErrM`	(input) Error matrix; probability of observed genotype (columns) conditional on actual genotype (rows)
`ExcludedInd`	Individuals in GenoM which were excluded because of a too low genotyping success rate (<50%).
`ExcludedSNPs`	Column numbers of SNPs in GenoM which were excluded because of a too low genotyping success rate (<10%).
`LifeHist`	(input) Dataframe with sex and birth year data. All missing birth years are coded as '-999', all missing sex as '3'.
`LifeHistPar`	LifeHist with additional columns 'Sexx' (inferred Sex when assigned as part of parent-pair), 'BY.est' (mode of birth year probability distribution), 'BY.lo' (lower limit of 95% highest density region), 'BY.hi' (higher limit), inferred after parentage assignment. 'BY.est' is NA when the probability distribution is flat between 'BY.lo' and 'BY.hi'.
`LifeHistSib`	as LifeHistPar, but estimated after full pedigree reconstruction
`NoLH`	Vector, IDs in genotype data for which no life history data is provided.
`Pedigree`	Dataframe with assigned genotyped and dummy parents from Sibship step; entries for dummy individuals are added at the bottom.
`PedigreePar`	Dataframe with assigned parents from Parentage step.
`Specs`	Named vector with parameter values.
`TotLikParents`	Numeric vector, Total likelihood of the genotype data at initiation and after each iteration during Parentage.
`TotLikSib`	Numeric vector, Total likelihood of the genotype data at initiation and after each iteration during Sibship clustering.
`AgePriorExtra`	As AgePriors, but including columns for grandparents and avuncular pairs. NOT updated after parentage assignment, but returned as used during the run.
`DummyClones`	Hermaphrodites only: female-male dummy ID pairs that refer to the same non-genotyped individual

List elements PedigreePar and Pedigree both have the following columns:

`id`	Individual ID
`dam`	Assigned mother, or NA
`sire`	Assigned father, or NA
`LLRdam`	Log10-Likelihood Ratio (LLR) of this female being the mother, versus the next most likely relationship between the focal individual and this female. See Details below for relationships considered, and see `CalcPairLL` for underlying likelihood values and further details)
`LLRsire`	idem, for male parent
`LLRpair`	LLR for the parental pair, versus the next most likely configuration between the three individuals (with one or neither parent assigned)
`OHdam`	Number of loci at which the offspring and mother are opposite homozygotes
`OHsire`	idem, for father
`MEpair`	Number of Mendelian errors between the offspring and the parent pair, includes OH as well as e.g. parents being opposing homozygotes, but the offspring not being a heterozygote. The offspring being OH with both parents is counted as 2 errors.

Genotyping error rate

The genotyping error rate Err can be specified three different ways:

A single number, which is combined with ErrFlavour by ErrToM to create a length 3 vector (next item). By default (ErrFlavour = 'version2.9'), P(hom|hom)=$(E/2)^2$, P(het|hom)=$E-(E/2)^2$, P(hom|het)=$E/2$.
a length 3 vector (NEW from version 2.6), with the probabilities to observe a actual homozygote as the other homozygote (hom|hom), to observe a homozygote as heterozygote (het|hom), and to observe an actual heterozygote as homozygote (hom|het). This assumes that the two alleles are equivalent with respect to genotyping errors, i.e. $P(AA|aa) = P(aa|AA)$, $P(aa|Aa)=P(AA|Aa)$, and $P(aA|aa)=P(aA|AA)$.
a 3x3 matrix, with the probabilities of observed genotype (columns) conditional on actual genotype (rows). Only needed when the assumption in the previous item does not hold. See ErrToM for details.

(Too) Few Assignments?

Possibly Err is much lower than the actual genotyping error rate.

Alternatively, a true parent will not be assigned when it is:

unclear who is the parent and who the offspring, due to unknown birth year for one or both individuals
unclear whether the parent is the father or mother
unclear if it is a parent or e.g. full sibling or grandparent, due to insufficient genetic data

And true half-siblings will not be clustered when it is:

unclear if they are maternal or paternal half-siblings
unclear if they are half-siblings, full avuncular, or grand-parental
unclear what type of relatives they are due to insufficient genetic data

All pairs of non-assigned but likely/definitely relatives can be found with GetMaybeRel. For a method to do pairwise 'assignments', see https://jiscah.github.io/articles/pairLL_classification.html ; for further information, see the vignette.

Disclaimer

While every effort has been made to ensure that sequoia provides what it claims to do, there is absolutely no guarantee that the results provided are correct. Use of sequoia is entirely at your own risk.

Website

https://jiscah.github.io/

Author(s)

Jisca Huisman, jisca.huisman@gmail.com

References

Huisman, J. (2017) Pedigree reconstruction from SNP data: Parentage assignment, sibship clustering, and beyond. Molecular Ecology Resources 17:1009–1024.

Examples

# ===  EXAMPLE 1: simulated data  ===
head(SimGeno_example[,1:10])
head(LH_HSg5)
# parentage assignment:
SeqOUT <- sequoia(GenoM = SimGeno_example, Err = 0.005,
                  LifeHistData = LH_HSg5, Module="par", Plot=TRUE)
names(SeqOUT)
SeqOUT$PedigreePar[34:42, ]

# compare to true (or old) pedigree:
PC <- PedCompare(Ped_HSg5, SeqOUT$PedigreePar)
PC$Counts["GG",,]


# parentage assignment + full pedigree reconstruction:
# (note: this can be rather time consuming)
SeqOUT2 <- sequoia(GenoM = SimGeno_example, Err = 0.005,
                  LifeHistData = LH_HSg5, Module="ped", quiet="verbose")
SeqOUT2$Pedigree[34:42, ]

PC2 <- PedCompare(Ped_HSg5, SeqOUT2$Pedigree)
PC2$Counts["GT",,]
PC2$Counts[,,"dam"]

# different kind of pedigree comparison:
ComparePairs(Ped1=Ped_HSg5, Ped2=SeqOUT$PedigreePar, patmat=TRUE)

# results overview:
SummarySeq(SeqOUT2)

# important to run with approx. correct genotyping error rate:
SeqOUT2.b <- sequoia(GenoM = SimGeno_example, #  Err = 1e-4 by default
                  LifeHistData = LH_HSg5, Module="ped", Plot=FALSE)
PC2.b <- PedCompare(Ped_HSg5, SeqOUT2.b$Pedigree)
PC2.b$Counts["GT",,]


## Not run: 
# ===  EXAMPLE 2: real data  ===
# ideally, select 400-700 SNPs: high MAF & low LD
# save in 0/1/2/NA format (PLINK's --recodeA)
GenoM <- GenoConvert(InFile = "inputfile_for_sequoia.raw",
                     InFormat = "raw")  # can also do Colony format
SNPSTATS <- SnpStats(GenoM)
# perhaps after some data-cleaning:
write.table(GenoM, file="MyGenoData.txt", row.names=T, col.names=F)

# later:
GenoM <- as.matrix(read.table("MyGenoData.txt", row.names=1, header=F))
LHdata <- read.table("LifeHistoryData.txt", header=T) # ID-Sex-birthyear
SeqOUT <- sequoia(GenoM, LHdata, Err=0.005)
SummarySeq(SeqOUT)

SeqOUT$notes <- "Trial run on cleaned data"  # add notes for future reference
saveRDS(SeqOUT, file="sequoia_output_42.RDS")  # save to R-specific file
writeSeq(SeqOUT, folder="sequoia_output")  # save to several plain text files

# runtime:
SeqOUT$Specs$TimeEnd - SeqOUT$Specs$TimeStart

## End(Not run)

[Package sequoia version 2.11.2 Index]