DOI: 10.5061/dryad.k0p2ngfb4
title: Data for: Genomic heterozygosity is associated with parasite abundance, but the effects are not mediated by host condition

description: Whether, when, and how genetic diversity buffers individuals and
 populations against infectious disease risk is a critical and open
 question for understanding wildlife disease and zoonotic disease risk.
 Several, but not all, studies have found negative relationships between
 infection and heterozygosity in wildlife. Since they can host multiple
 zoonotic infections, we sampled a population of wild deer mice (Peromyscus
 maniculatus), sequenced their genomes, and examined their fecal samples
 for coccidia and nematode eggs. We analyzed coccidia infection status,
 abundance, and coinfection status in relation to per-locus and
 per-individual measures of heterozygosity, as well as identified SNPs
 associated with infection status. Since heterozygosity might affect host
 condition, and condition is known to affect immunity, it was included as a
 co-variate in the per-individual analyses and as response variable in
 relation to heterozygosity. Not only did coccidia-infected individuals
 have lower levels of genome-wide per-locus diversity across all metrics,
 but we found an inverse relationship between genomic diversity and
 severity of coccidia infection. We also found weaker evidence that
 coinfected individuals had lower levels of private allelic variation than
 all other groups. In the per-individual analyses, relationships between
 heterozygosity and infection were marginal but followed the same negative
 trends. Condition was negatively correlated with infection, but was not
 associated with heterozygosity, suggesting that effects of heterozygosity
 on infection were not mediated by host condition in this system.
 Association tests identified multiple loci involved in the inflammatory
 response, with a particular role for NF-κB signaling, supporting previous
 work on the genetic basis of coccidia resistance. Taken together, we find
 that increased genome-wide neutral diversity, the presence of specific
 genetic variants, and improved condition positively impact infection
 status. Our results underscore the importance of considering host genomic
 variation as a buffer against infection, especially in systems that can
 harbor zoonotic diseases.
description: <strong>Field sampling methods</strong>
 Deer mice (<em>Peromyscus maniculatus</em>) were
 live-trapped at the Bernard Field Station in Claremont, CA from June 27 to
 August 9, 2019. Eight 40 × 40 m grids with traps spaced 10 m apart (25
 traps/grid) were established in paired locations throughout the sage-scrub
 habitat. At each grid, Sherman traps were baited with seeds for three
 nights in a row. Three-night trapping sessions were repeated at each grid
 every two weeks. Upon their first capture each session, mice were weighed,
 measured, and a retro-orbital blood sample was collected. Each mouse was
 also given an individually-numbered ear tag upon its first capture.
 Females were considered reproductive if they were pregnant or lactating,
 and males were classified as reproductive based on testes size. Fecal
 samples from each individual were collected from the traps, weighed, and
 stored in neutral-buffered formalin. Blood samples were kept on ice, then
 spun at 2000 g for 10 min to separate plasma. Plasma and pellet fractions
 were frozen separately at -80°C. Host trait and parasite infection
 data were used from each individual’s initial capture, while the blood
 samples sequenced sometimes came from subsequent recaptures. 
 <strong>Parasite and condition
 measurements</strong> To determine body
 condition, we regressed weight with body length then used the residuals as
 a measure of condition. The number of coccidia oocysts and helminth eggs
 per g of feces were counted using a modified McMaster egg fecal counting
 protocol (MAFF 1980). There were 9 uninfected and 22 coccidia-infected
 individuals. Following the methods of Ezenwa et al. (2021), we used a
 model-based approach to identify statistically significant clusters in the
 oocyst intensity data using the mclust package (Fraley et al. 2021). We
 then classified infected individuals into two groups based on these
 clusters [Low (&lt;100 oocysts per gram feces (opg)): n = 11; High
 (&gt;100 opg): n = 10]. For two individuals, feces weight data were
 not collected, so only infection status, not abundance, could be
 determined. One of these two individuals only had a single coccidia oocyst
 detected, so it was included in the Low infection severity category.
 Lastly, we classified individuals as whether or not they were coinfected
 with coccidia (<em>Eimeria</em> sp.) and gastrointestinal
 nematodes (<em>Aspicularis americana</em> (a pinworm) and 3
 stryongylid (hookworm) species) (Uninfected: n = 9; Low coccidia
 (&lt;100 opg): n = 9; High coccidia (&gt;100 opg): n = 7;
 Coinfected: n = 5).  <strong>Sequencing and
 bioinformatics workflow</strong> DNA was
 extracted from heparin-treated blood pellet samples with the Qiagen DNEasy
 Blood and Tissue Extraction Kit. DNA was quantified on a Qubit™ 2.0
 fluorometer using a dsDNA High Sensitivity Assay Kit (Invitrogen).
 Sequencing libraries were prepared by Admera Health with a KAPA Hyper Prep
 Kit. Individually barcoded libraries were sequenced on an Illumina NovaSeq
 S4 (paired end, 2 × 150 bp), at a target depth of ~200 million PE reads
 per sample. We conducted an initial assessment of read
 quality using FASTQC v0.11.8 with default values (Andrews 2010).
 TRIMMOMATIC v0.39 was then used to trim adapter sequences, the leading and
 trailing three bases, and low quality sequences ( leading:3 trailing:3
 slidingwindow:4:15 minlen:36; Bolger et al. 2014). Filtered sequences were
 mapped to the P. maniculatus genome (GCF_000500345.1_Pman_1.0_genomic.fna)
 using BWA mem v0.7.17 with default values (Li). After sorting (“sort”) and
 indexing (“index”) BAM files with SAMTOOLS v1.14 (Li et al. 2009), PICARD
 v 2.26.1 was used to remove duplicates (“MarkDuplicates”) and add read
 groups ("AddOrReplaceReadGroups";
 validation_stringency = lenient; Broad Institute 2019).
 We called variants in the parallel mode of FREEBAYES v1.3.5 with
 16 threads, resulting in an initial total of 153,221,207 variant sites
 (Garrison and Marth 2012). To retain only high quality SNPs, we first used
 vcftools 0.1.17 to filter variant sites with &gt; 5% missing data
 (–max-missing 0.95), quality scores &lt; 40 (–minQ 40), minimum
 depth &lt;8 (–minDP 8), and a minor allele frequency of at least 1.5%
 (–maf 0.015) (Danecek et al. 2011). We next filtered for allele balance,
 or the proportion of reads supporting a variant, to reduce paralogs and
 errors using the vcffilter tool in the vcflib package v1.0.1 (Garrison et
 al. 2021). We removed sites with an allele balance &lt;0.25
 or &gt;0.75, with the exception that we retained allele balances close
 to zero to keep variants where all individuals were homozygous for one or
 the other allele. Vcftools v0.1.17 was then used to retain only biallelic
 SNPs with a genotype quality &gt;20 (–min-alleles 2, –max-alleles 2,
 –minGQ 20, –remove-indels) and remove any loci that were out of
 Hardy–Weinberg Equilibrium (P&lt;0.01; –hwe 0.01) in Uninfected
 individuals (Danecek et al. 2011;). Linkage disequilibrium filtering was
 performed by pruning out sites with r2&gt;0.6 in 1000 site windows
 with bcftools v1.11 (+ prune -m 0.6 -w 1000; Li 2011; Danecek et al.
 2021). After filtering, 573,838 high quality SNPs were retained for
 downstream analyses. <strong>Genetic diversity
 analyses</strong> Per-individual genetic
 diversity metrics were calculated in vcftools v0.1.17 and included HO, HE,
 and FIS. Per-individual measures of diversity average across all loci in
 the genome for each individual (n = 31 individuals).
description: <em>BFS 2019 - rodent infection heterozygosity
 data.csv</em> contains capture data, infection data and
 heterozygosity data. <strong>Capture
 data:</strong> Trapping grid (GRID), grid site (Grid2), trap number
 (Trap), Species (PM = Peromyscus maniculatus), Sex (F = female, M =
 male),  Age (A = adult, SA = subadult), reproductive characteristics (VAG
 = perforate vagina, NIP = enlarged nipples indicative of lactation, PREG =
 pregnant, TES = enlarged testes, Repro = reproductive status, total length
 in mm, body length in mm, and weight in g. 
 <strong>Infection data:</strong> Coccidia oocysts per
 gram of feces at initial capture (Init.Coccidia.EPG, and log transformed
 in logCoccidiaEPG), nematode eggs per gram of feces at initial capture
 (Init.Nem.EPG and log transformed in logNemEPG), binary infected with
 coccidia or not (Infection.Status), coinfection status (Coinf.status),
 Coccidia severity (Coccidia.group), and length-weight residual condition
 (condition) <strong>Individual-level
 heterozygosity data:</strong> Number of sites analyzed (N_SITES),
 expected heterozygosity (He), standardized observed heterozygosity
 (Ho_standard), and standardized inbreeding coefficient
 (Fis_standard)
 <em>Pman_13Jul21.vcf.gz</em> This
 file contains SNP data.