DOI: 10.5061/dryad.vx0k6djq8
title: An evaluation of inbreeding measures using a whole genome sequenced cattle pedigree

description: The estimation of the inbreeding coefficient (F) is essential for the
 study of inbreeding depression (ID) or for the management of populations
 under conservation. Several methods have been proposed to estimate the
 realized F using genetic markers, but it remains unclear which one should
 be used. Here we used whole-genome sequence data for 245 individuals from
 a Holstein cattle pedigree to empirically evaluate which estimators best
 capture homozygosity at variants causing ID, such as rare deleterious
 alleles or loci presenting heterozygote advantage and segregating at
 intermediate frequency. Estimators relying on the correlation between
 uniting gametes (FUNI) or on the genomic relationships (FGRM) presented
 the highest correlations with these variants. However, homozygosity at
 rare alleles remained poorly captured. A second group of estimators
 relying on excess homozygosity (FHOM), homozygous-by-descent segments
 (FHBD), runs-of-homozygosity (FROH) or on the known genealogy (FPED) was
 better at capturing whole genome homozygosity, reflecting the consequences
 of inbreeding on all variants, and for young alleles with low to moderate
 frequencies. The results indicate that FUNI and FGRM might present a
 stronger association with ID. However, the situation might be different
 when recessive deleterious alleles reach higher frequencies, such as in
 populations with a small effective population size. For locus specific
 inbreeding measures or at low marker density, the ranking of the methods
 can also change as FHBD makes better use of the information from
 neighbouring markers. Finally, we confirmed that genomic measures are in
 general superior to pedigree-based estimates. In particular, FPED was
 uncorrelated with locus specific homozygosity.
description: DNA samples were extracted
 from whole blood or semen using standard protocols. Sequencing was done on
 Illumina HiSeq 2000 instruments with a PCR free method to<br>
 prepare libraries with 550bp (DAMONA pedigree) insert sizes. Paired-end
 sequencing with read length of 2 x 100 base pairs was applied.
 The whole-genome sequence
 data was analyzed according to GATK Best Practice V3.4. Alignement of
 reads (FASTQ files) to the reference genome (Bos Taurus UMD 3.1) was done
 with BWA MEM (version 0.7.9a-r786, (Li 2013)) with the default settings.
 The sorted BAM had PCR duplicates detected using sambamba (v0,4,6) and
 Picard tools and bedtools were used to generate library statistics and
 coverage information. The obtained BAM files were then realigned around indels
 and recalibrated for base quality with Genome Analysis Toolkit (GATK
 2.7.4., (DePristo et al. 2011)). List of known SNP used for recalibration
 were obtained from DBSNP release 138. Variant calling was performed with
 GATK Haplotype caller in N+1 mode. For calibration of variant quality, a
 set of trusted SNP and indels was used. For SNPs, the set consisted in
 SNPs from the BovineHD (Illumina) and Axiom Genome-Wide BOS 1 (Affymetrix)
 commercial genotyping arrays. For indels, we selected a subset of indels
 identified in the DAMONA pedigree behaving like true Mendelian variants :
 presenting no parent-offspring incompatibilities (e.g. opposite
 homozygotes), no deviation from Hardy-Weinberg proportions (p &gt;
 0.05) and no deviation from expected genotypic proportions in<br>
 offspring of heterozygous parents (p &gt; 0.05). In addition, we
 compute the probability to observe no parent-offspring inconsistency if
 parental alleles were drawn at random and<br> conserved only indels
 with a probability below 1e-12 (to make sure that the absence of
 parent-offspring incompatibilities was not due by chance).<br>
  
description: The data is a subset from a pedigree of 743 sequenced Holstein
 cattle. The full data set has for instance been used in the following
 study:
 https://genome.cshlp.org/content/early/2020/06/26/gr.256172.119.short?rss=1 Original VCFs files are also available at https://www.ebi.ac.uk/ena/browser/view/PRJEB38336, under the name BPWG.vcf.gz. A readme file describing the files and their format is included in the folder. 13037955 bi-allelic SNP were selected for the associated study. From the sequenced pedigree we conserved individuals with a sequencing cover &gt; 15x (266).<br> We also removed one outlier with extreme whole genome heterozygosity. The data was divided in two subsets:<br> 1) genotype_parents.txt: a set of 145 sequenced parents from the second set of individuals<br> 2) genotype_targets.txt: a set of 100 sequenced targets with both parents sequenced parents and without sequenced offspring<br> These 245 sequenced individuals form thus trios. Format, per line:<br> 1) chromosome<br> 2) position in bp<br> 3) REF allele<br> 4) ALT allele<br> 5 and more) one genotype per individual, genotype recoding is = number of alternate alleles, 0 for REF/REF, 1 for REF/ALT, 2 for ALT/ALT, 9 for missing Genotype files at different marker density:<br> 1) typ_parents_50K.txt: the genotypes of the selected markers from the 50K genotyping array for the 145 parents<br> 2) typ_targets_50K.txt: the genotypes of the selected markers from the 50K genotyping array for the 100 targets<br> 3) typ_parents_LD.txt: the genotypes of the selected markers from the low-density genotyping array for the 145 parents Format, per line:<br> 1) chromosome<br> 2) position in bp<br> 3) position in bp (a second time a surrogate for name)<br> 4) REF allele<br> 5) ALT allele<br> 6 and more) one genotype per individual, genotype recoding is = number of alternate alleles, 0 for REF/REF, 1 for REF/ALT, 2 for ALT/ALT, 9 for missing The file parents_ids.txt and targets_ids.txt contain information on the individuals from the genotype files, in three columns:<br> 1) position in genotype file<br> 2) pedigree id from the individual (coded from 1 to 266)<br> 3) inbreeding coefficient estimated with the entire pedigree The file samples_relationships.txt provides pedigree id from parents when these are sequenced, with three columns:<br> 1) pedigree id from the individual<br> 2) pedigree id from the sire, when sequenced with 15x or more<br> 3) pedigree id from the dam, when sequenced with 15x or more <br> Annotation file annotation_VQSR4_VEP_S_NS.txt :<br> 1) chromosome<br> 2) position in bp<br> 3) SNP id<br> 4) REF allele<br> 5) ALT allele<br> 6) Annotation where M = missense; S = synonymous; I = intergenic or intronic; O = other; U = unknown (based on snpEff)<br> 7) Ancestral allele where R = ancestral is REF; A = ancestral is ALT; U = ancestral is unknown (based on information from Rocha et al., 2014)<br> 8) SNP observed in Belgian Blue Sample: 0 = no, 1 = yes<br> 9) SNP observed in TAAF cattle sample: 0 = no, 1 = yes<br> 10) Synonymous = 1; Missense variants = 2; other = 0 (based on Variant Effect Predictor)<br> 11) Impact from Variant Effect Predictor: 1 = MODIFIER; 2 = LOW; 3 = MODERATE; 4 = HIGH<br> 12) SIFT prediction from Variant Effect Predictor: 1 = tolerated; 2 = tolerated_low_confidence; 3 = deleterious; 4 = deleterious_low_confidence<br> 13) SIFT Score from Variant Effect Predictor (2 if missing)