DOI: 10.5061/dryad.w9ghx3fst
title: Data From: what mandrills leave behind: using fecal samples to characterize the major histocompatibility complex in a threatened primate

description: The major histocompatibility complex (MHC) can be useful in guiding
 conservation planning because of its influence on immunity, fitness, and
 reproductive ecology in vertebrates. The mandrill (Mandrillus sphinx) is a
 threatened primate endemic to central Africa. Considerable research in
 this species has shown that the MHC is important for disease resistance,
 mate choice, and reproductive success. However, all previous MHC research
 in mandrills has focused on an inbred semi-captive population, so their
 genetic diversity may have been underestimated. Here we expand our current
 knowledge of mandrill MHC variation by performing next-generation
 sequencing of non-invasively collected fecal samples from a large wild
 horde in central Gabon. We observe MHC lineages and alleles shared with
 other primates, and we uncover 45 putative new class II MHC DRB alleles,
 including representatives of the DRB9 pseudogene, which has not previously
 been identified in mandrills. We also document methodological challenges
 associated with fecal samples in NGS-based MHC research. Even with high
 read depth, the replicability of alleles from fecal samples was lower than
 that of tissue samples, and allele assignments are inconsistent between
 sample types. Further, the common assumption that variants with very high
 read depth should represent true alleles does not appear to be reliable
 for fecal samples. Nevertheless, the use of degraded DNA in the present
 study still enabled significant progress in quantifying immunogenetic
 diversity and its evolution in wild primates.
description: Further details of data analysis can be found in the publication
 manuscript, but in brief, samples were collected from a wild population of
 mandrills (<em>Mandrillus sphinx</em>) in Lopé National Park,
 Gabon. The majority of the samples are non-invasively collected feces, but
 samples of blood and plucked hair were also collected from anesthetized
 animals. Each sample was PCR-amplified for a 157-base fragment of the DRB
 gene of the class II major histocompatibility complex, then sequenced on
 Illumina Miseq. An initial Miseq run was performed in 2018, containing 192
 fecal samples. In 2019, a Miseq Nano run was performed, containing the 24
 samples of blood and plucked hair along with 23 replicate fecal samples
 from the 2018 sequencing run. In 2021, the third sequencing run was
 performed using standard Miseq, and this run included nine replicates of
 the blood and hair samples and 183 replicated fecal samples.
 To quantify repeatability between runs, each Illumina run was
 processed separately. Pre-processing was performed using the ampliSAT
 pipeline (Sebastian et al. 2016), and alleles were assigned to each sample
 in each run using the degree of change method (Lighten et al. 2014). For
 samples that were replicated between runs, a replicability score
 (R<sub>A</sub>) was calculated based on the proportion of
 variants that appeared in both runs. A custom Python script was used to
 compare allele assignments of samples in each pair of Illumina
 runs. To generate consensus allele assignments for each
 sample, each Illumina run was reanalyzed in the AmpliSAT pipeline, using
 more relaxed parameters in order to minimize the chance of allelic
 dropout. Then, a custom Python script was used to extract sequence
 variants that replicated between runs for each sample. This set of
 replicated variants for each sample was then screened for errors such as
 replicated chimeric sequences. After discarding such sequence artifacts,
 the set of variants that remained was considered the individual's
 "true" allele assignment.
description: Most files can be opened using an open source text editor such as
 JEdit or Notepad++. The .csv files will be easist to view in a spreadsheet
 such as Excel or Google Sheets, but they can also be opened in a text
 editor if needed. Users may wish to open fasta files using an open source
 genetics software such as MEGA-X.
description: # Data From: What mandrills leave behind: using fecal samples to
 characterize the major histocompatibility complex in a threatened primate
 --- Jan. 12, 2024 One aim of this study was to quantify replicability of
 Illumina sequencing results from fecal samples and higher-quality samples
 of blood and plucked hair. Samples of both types were sequenced in at
 least two independent Illumina runs, and an allele assignment was
 determined using the degree of change method(Lighten et al. 2014). We then
 calculated the proportion of sequence variants that replicated between
 runs for each sample to represent a replicability score(RA), and compared
 average RA scores for feces vs blood/hair. The data used for these tests
 are contained in the folder 'DOC_assignments_per_run'. We also
 tested whether replicability is related to sequence depth or the
 sequence's rank within the amplicon. These data are also available in
 this package. A second aim of this study was to characterize functional
 genetic diversity in the wild mandrill population and to assess the
 presence of trans-species polymorphism. To that end, we generated
 consensus MHC-DRB allele assignments for 181 mandrills from Lope National
 Park (172 fecal samples and 9 samples oxblood and plucked hair). Each
 sample was sequenced in two independent Illumina runs, and consensus
 assignments were determined by extracting variants that appeared in both
 runs and then screening those variants for sequencing error. The allele
 assignments and other supporting data are contained here. These can be
 used to replicate the analyses of genetic diversity from the manuscript:
 creating a phylogeny, identifying super types and sites under positive
 selection, and comparisons with other primate species. ## Description of
 the Data and file structure FOLDER 'consensus_fasta_files' This
 folder contains fasta files of each mandrill's putative consensus MHC
 allele assignments determined in the study (generated using the script
 'generate_consensus_MHC_assignments.py', followed by manual
 error screening). Fecal samples (n=172) all have the prefix
 "LP," followed by a 3 digit code (ex-LP596.fas). Blood/hair
 samples (n=9) have a 2-digit code representing the individual animal, then
 three letters (hmb, for example), showing the sample type (hair-h,
 blood-b), sex (male-m, female-f), and age class (b-reproductive adult). Ex
 NG-hmb.fas FOLDER 'DOC_assignments_per_run' This folder
 contains: 1. subfolder '2018MiSeq','2019MiSeqNano',
 and '2021MiSeq'. These are results of the degree of change
 method of allele assignment in fasta format for each sample in each
 Illumina run. These data were used to generate replicability scores (RA)
 by inputting them to a custom Python code
 ('MHC_replicability_test.py') that produced the .csv files
 described in point 2 below. 2. 3 csv files ending in
 'rep-test-out.csv' (generated using the code
 'MHC_replicability_test.py'. These include the results of the
 replicability tests described above, for each pair of Illumina runs. Data
 are organized in rows, where each row is a sample. Columns are as follows:
 sampleID sample used for replicability test \#sharedalleles number of
 DOC-identified alleles appearing in both runs for this sample
 \#2018alleles number of DOC-identified alleles appearing in the 2018 run
 for this sample \#2021alleles number of DOC-identified alleles appearing
 in the 2021 run for this sample replicability.score proportion of alleles
 that replicated (#sharedalleles divided by
 (#2018alleles+#2021alleles-#sharedalleles)) 2018shared.freq average
 frequency of alleles in the 2018 run that also appeared in the 2021 run
 ([variant depth in 2018/amplicon depth in 2018]*100). NA if no replicated
 variants 2018shared.sings the number of alleles in the 2018 run that
 appear in only one animal, but are replicated for this sample in the 2021
 run 2021shared.freq average frequency of alleles in the 2021 run that also
 appeared in the 2018 run ([variant depth in 2021/amplicon depth in
 2021]*100). NA if no replicated variants 2021shared.sings the number of
 alleles in the 2021 run that appear in only one animal, but are replicated
 for this sample in the 2018 run 2018only.freq average frequency of alleles
 identified for this sample that only appeared in the 2018 run ([variant
 depth in 2018/amplicon depth in 2018]*100) 2018only.sings the number of
 alleles identified for this sample in the 2018 run that are unique to this
 sample (i.e. only detected in one animal, not replicated between seq.
 runs) 2021only.freq average frequency of alleles identified for this
 sample that only appeared in the 2021 run ([variant depth in 2018/amplicon
 depth in 2018]*100) 2021only.sings the number of alleles identified for
 this sample in the 2021 run that are unique to this sample (i.e. only
 detected in one animal, not replicated between seq. runs) FILE
 '2021_seqfreqs.csv' and '2018_seqfreqs.csv' (generated
 using the code 'allelefrequency_repstatus_per_run.py'). These
 files can be used to recreate the logistic regression anaylses in the
 manuscript that tested whether a variant's replicability is related
 to its depth or its rank within the amplicon. Data in each file
 corresponds to 2021 and 2018 Miseq sequencing runs, and lists all
 DOC-identified alleles present in each sample in the relevant run. Column
 headings are as follows: sampleID the sample containing the variant. Each
 sample will have multiple rows, one for each of its DOC-identified alleles
 allele name of a variant in the sample (may not match variant names from
 fasta files--recommend to disregard this column) frequency frequency of
 the variant within the amplicon ([variant's depth/amplicon
 depth]*100) replication a binary value indicating whether or not the
 allele replicated in the other sequencing run rank rank of the variant,
 when ranked by depth (ranked from high to low, 1....n, where n=number of
 alleles possessed by the sample) FILE
 'all_identified_alleles.fas' is a fasta file of all alleles
 identified in the focal population. These can be used to recreate the
 analyses of genetic diversity from the manuscript: creating a phylogeny,
 identifying supertypes and sites under positive selection, and comparisons
 with other primate species. FILE 'consensus_alleles.csv' is a
 master file containing all alleles and all samples with allele
 assignments. Generally, samples are in columns and alleles are in rows,
 with some exceptions. The first three rows contain data about each sample
 (amplicon depth, depth of all alleles, and number of alleles identified).
 Depths listed are from the 2021 Illumina run. The first six columns have
 the following headings: SEQUENCE allele sequence LENGTH allele length
 SAMPLES number of samples possessing the allele Clade clade 1 (putative
 loci DRB1-6) or clade 2 (putative DRB9 locus) ST supertype, designated
 1-6, representing predicted functional group based on physiochemical
 properties of the sequence's putative antigen binding amino acid
 sites Allele Name designated allele name all subsequent columns represent
 individual samples SCRIPT 'allelefrequency_repstatus_per_run.py'
 Python code used to compare fasta files of the same samples in two
 Illumina runs. For each run, for all variants in all samples, data is
 extracted that shows 1) within-amplicon frequency of variant, 2) variank
 rank within amplicon, 3) whether the variant replicated. Produced files
 '2018_seqfreqs.csv' and '2021_seqfreqs.csv.' SCRIPT
 'generate_consensus_MHC_assignments.py' Python code used to
 extract each sample's sequence variants that replicated between
 Illumina runs. This was applied to two folders of ampliSAT-generated fasta
 files. Resulting variants were then aligned in MEGA-X and visually
 screened for sequencing error. After removal of artifical sequences,
 resulting fasta files were stored in the 'consensus_fasta_files'
 folder. SCRIPT 'MHC_replicability_test.py' Python code to
 calculate replicability scores (RA) from pairs of ampliSAT-generated fasta
 files. For each sample, the output shows the number of variants that
 replicated between runs, number of variants appearing in each sequencing
 run, number of singletons, and the average frequencies of these variants.
 This code produced the 'rep-test-out.csv' files described above.
 ## Sharing/access Information Raw NGS data may be uploaded to the Genbank
 SRA in the future. This document will be updated to reflect this.
 Microsatellite data used in the study is forthcoming in a separate
 DataDryad upload.