Inbreeding Depression Homozygosity

Inbreeding depression can adversely affect traits related to fitness, reproduction and productive performance. Although current research suggests that inbreeding levels are generally low in most goat breeds, the impact of inbreeding depression on phenotypes of economic interest has only been investigated in a few studies based on genealogical data.

inbreeding depression homozygosity

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We genotyped 1040 goats with the Goat SNP50 BeadChip. This information was used to estimate different molecular inbreeding coefficients and characterise runs of homozygosity and homozygosity patterns. We detected 38 genomic regions with increased homozygosity as well as 8 ROH hotspots mapping to chromosomes 1, 2, 4, 6, 14, 16 and 17. Eight hundred seventeen goats with available records for dairy traits were analysed to evaluate the potential consequences of inbreeding depression on milk phenotypes. Four regions on chromosomes 8 and 25 were significantly associated with inbreeding depression for the natural logarithm of the somatic cell count. Notably, these regions contain several genes related with immunity, such as SYK, IL27, CCL19 and CCL21. Moreover, one region on chromosome 2 was significantly associated with inbreeding depression for milk yield.

Although genomic inbreeding levels are low in Murciano-Granadina goats, significant evidence of inbreeding depression for the logarithm of the somatic cell count, a phenotype closely associated with udder health and milk yield, have been detected in this population. Minimising inbreeding would be expected to augment economic gain by increasing milk yield and reducing the incidence of mastitis, which is one of the main causes of dairy goat culling.

Inbreeding is defined as the mating of individuals that are related to each other more closely than the average relationship within the concerned population [1]. In livestock species, the magnitude of inbreeding has been traditionally measured through genealogical information [2]. However, pedigree-based estimates are affected by the depth of the pedigree [2] because founders are assumed to be unrelated and non-inbred [3]. Consequently, inbreeding produced by distant ancestors not included in the pedigree is systematically ignored [4]. Another disadvantage of quantifying inbreeding from pedigree data is that it provides bare expectations about the fraction of the genome which is identical-by-descent (IBD) [3]. With the advent of high-density arrays of single nucleotide polymorphisms (SNPs), it has become possible to estimate genomic inbreeding coefficients which circumvent these limitations [5]. Indeed, important advantages of genomic inbreeding coefficients over their genealogical counterparts are: (i) higher accuracy to differentiate among individuals within the same pedigree, since variation due to Mendelian sampling is captured [4], (ii) higher accuracy to quantify shared ancestry of genetic haplotypes [4], and (iii) the ability to map inbreeding to specific genomic regions [6]. Different types of genomic inbreeding coefficients have been implemented. While inbreeding coefficients based on the proportion of homozygous SNPs (FHOM) just reflect identity-by-state (IBS) allele-sharing proportions [6], coefficients (FROH) based on measuring the fraction of the genome covered by runs of homozygosity (ROH) estimate IBD allele sharing [4,5,6,7,8], making possible to disentangle recent from ancient inbreeding [3,4,5,6,7,8,9].

Several studies have used genomic methods to determine the levels of inbreeding in goat populations with a broad geographic distribution [19,20,21]. A recent investigation carried out by Bertolini et al. [19] revealed that short ROH (

The goals of the current work were: (i) to measure the levels of inbreeding in a Murciano-Granadina resource population by using different genomic coefficients, and (ii) to use this information to infer the impact of inbreeding depression on dairy phenotypes recorded in this population.

The proportion of homozygosity per site was estimated as the ratio between the number of animals with homozygous genotypes for a particular SNP divided by the number of animals genotyped for that SNP. A sliding window encompassing 30 SNPs was designed to estimate the average of this ratio, and chromosomal patterns of homozygosity were visualised as Manhattan plots using R [28].

The effects of inbreeding depression on dairy traits were investigated using data from 817 goats with available phenotypes (MY210, MY240, MY305, lnSCC, FP, PP and LP). Analyses were performed with the REMLF90 software [39] to implement a restricted maximum likelihood (REML) analysis approach, in which the phenotypic values of each trait in each individual are regressed onto its inbreeding coefficient using a linear mixed model. These analyses were performed to quantify inbreeding depression at the whole genome scale as well as on a chromosome and regional basis. The model was fitted as follows:

Here, \( \overlinex \) corresponds to the regression coefficient representing the effect of inbreeding over each trait, and μ0 is the coefficient of inbreeding corresponding to the null hypothesis (in this case is equal to 0), and s.e. is the standard error. The transformation of Z-scores into P-values was accomplished with the function pnorm() implemented on R [28].

The above analysis was performed by regressing each phenotype onto four genomic inbreeding coefficients (FHOM, FROH, FROHLong, and FROHShort). In order to detect genomic regions associated with inbreeding depression, analyses at the chromosomal level were performed for traits that in the whole-genome analysis were identified as significantly affected by inbreeding depression (P

A Proportion of individuals with homozygous genotypes for each SNP marker. The y-axis displays the proportion of individuals for which a specific SNP displays a homozygous genotype, while the x-axis corresponds to the positional coordinates of SNPs distributed in the 29 caprine autosomes. B ROH hotspots identified in the population of Murciano-Granadina goats under study. The y-axis displays the frequency at which a given SNP is found within a ROH in the population; while the x-axis corresponds to the positional coordinates of SNPs distributed in the 29 caprine autosomes. Markers above the red line are in the top 1% of each category (homozygosity or frequency of being within a ROH). Markers highlighted in green are located in genomic regions consistently identified as regions of high homozygosity and ROH hotspots

Boxplots depicting the magnitude and dispersion of molecular inbreeding FROH, FROHLong, FROHShort, FHOM, FIS and FYANG coefficients estimated in 1040 female Murciano-Granadina goats. Differences in magnitude between FHOM and the other molecular coefficients are due to the fact that they indicate identity-by-state and identity-by-descent allele-sharing proportions, respectively

The inbreeding coefficients FROH, FROHShort, and FROHLong of Murciano-Granadina goats were mainly in the range of 0 to 0.05. In their study, Bertolini et al. [19] reported that about 60% of a worldwide sample of goat breeds displayed low FROH coefficients (

MA, JJ, JVD and VL designed the study. JFA, JVD and AM coordinated all tasks involved in phenotype recording. VL did all DNA extractions. MGLS, MS and AF carried out homozygosity and inbreeding depression analyses, with the cooperation of DG. MGLS and MA wrote the first draft of the paper with the cooperation of MS and AF. All authors read and approved the content of the paper.

Inbreeding depression estimates (s.e: standard error) for the natural logarithm of the somatic cell count divided by 1000 (lnSCC) in specific regions of goat chromosomes (CHI) 8 and 25 and their 95% confidence intervals (C.I).

Pedigree information was traditionally used to assess inbreeding. The availability of high-density marker panels provides an alternative to assess inbreeding, particularly in the presence of incomplete and error-prone pedigrees. Assessment of autozygosity across chromosomal segments using runs of homozygosity (ROH) has emerged as a valuable tool to estimate inbreeding due to its general flexibility and ability to quantify the chromosomal contribution to genome-wide inbreeding. Unfortunately, the identification of ROH segments is sensitive to the parameters used during the search process. These parameters are heuristically set, leading to significant variation in the results. The minimum length required to identify an ROH segment has major effects on the estimation of inbreeding and inbreeding depression, yet it is arbitrarily set. To overcome this limitation, a search algorithm to approximate mutation enrichment was developed to determine the minimum length of ROH segments. It consists of finding genome segments with significant effect differences in trait means between animals with high and low burdens of autozygous intervals with a specific length. The minimum length could be determined heuristically as the smallest interval at which a significant signal is detected. The proposed method was tested in an inbred Hereford cattle population genotyped for 30,220 SNPs. Phenotypes recorded for six traits were used for the approximation of mutation loads. The estimated minimum length was around 1 Mb for yearling weight (YW) and average daily gain (ADG) and 4 Mb for birth weight and weaning weight. These trait-specific thresholds estimated using the proposed method could be attributed to a trait-dependent effect of homozygosity. The detection of significant inbreeding effects was well aligned with the estimated thresholds, especially for YW and ADG. Although highly deleterious alleles are expected to be more frequent in recent inbreeding (long ROH), short ROH segments ( 2ff7e9595c