Global assessment of effective population sizes: Consistent taxonomic differences in meeting the 50/500 rule.

Effective population size (Ne) is a particularly useful metric for conservation as it affects genetic drift, inbreeding and adaptive potential within populations. Current guidelines recommend a minimum Ne of 50 and 500 to avoid short-term inbreeding and to preserve long-term adaptive potential respectively. However, the extent to which wild populations reach these thresholds globally has not been investigated, nor has the relationship between Ne and human activities. Through a quantitative review, we generated a dataset with 4610 georeferenced Ne estimates from 3829 populations, extracted from 723 articles. These data show that certain taxonomic groups are less likely to meet 50/500 thresholds and are disproportionately impacted by human activities; plant, mammal and amphibian populations had a <54% probability of reaching N ̂ e = 50 and a <9% probability of reaching N ̂ e = 500. Populations listed as being of conservation concern according to the IUCN Red List had a smaller median N ̂ e than unlisted populations, and this was consistent across all taxonomic groups. N ̂ e was reduced in areas with a greater Global Human Footprint, especially for amphibians, birds and mammals, however relationships varied between taxa. We also highlight several considerations for future works, including the role that gene flow and subpopulation structure plays in the estimation of N ̂ e in wild populations, and the need for finer-scale taxonomic analyses. Our findings provide guidance for more specific thresholds based on Ne and help prioritise assessment of populations from taxa most at risk of failing to meet conservation thresholds.

Deep learning for automated detection of generalized paroxysmal fast activity in Lennox-Gastaut syndrome.

OBJECTIVES: Generalized paroxysmal fast activity (GPFA) is a key electroencephalographic (EEG) feature of Lennox-Gastaut Syndrome (LGS). Automated analysis of scalp EEG has been successful in detecting more typical abnormalities. Automatic detection of GPFA has been more challenging, due to its variability from patient to patient and similarity to normal brain rhythms. In this work, a deep learning model is investigated for detection of GPFA events and estimating their overall burden from scalp EEG. METHODS: Data from 10 patients recorded during four ambulatory EEG monitoring sessions are used to generate and validate the model. All patients had confirmed LGS and were recruited into a trial for thalamic deep-brain stimulation therapy (ESTEL Trial). RESULTS: The correlation coefficient between manual and model estimates of event counts was r2 = 0.87, and for total burden was r2 = 0.91. The average GPFA detection sensitivity was 0.876, with an average false-positive rate of 3.35 per minute. There was no significant difference found between patients with early or delayed deep brain stimulation (DBS) treatment, or those with active vagal nerve stimulation (VNS). CONCLUSIONS: Overall, the deep learning model was able to accurately detect GPFA and provide accurate estimates of the overall GPFA burden and electrographic event counts, albeit with a high false-positive rate. SIGNIFICANCE: Automated GPFA detection may enable automated calculation of EEG biomarkers of burden of disease in LGS.

Population Genomics of New Zealand Pouched Lamprey (kanakana; piharau; Geotria australis).

Pouched lamprey (Geotria australis) or kanakana/piharau is a culturally and ecologically significant jawless fish that is distributed throughout Aotearoa New Zealand. Despite its importance, much remains unknown about historical relationships and gene flow between populations of this enigmatic species within New Zealand. To help inform management, we assembled a draft G. australis genome and completed the first comprehensive population genomics analysis of pouched lamprey within New Zealand using targeted gene sequencing (Cyt-b and COI) and restriction site-associated DNA sequencing (RADSeq) methods. Employing 16 000 genome-wide single nucleotide polymorphisms (SNPs) derived from RADSeq (n = 186) and sequence data from Cyt-b (766 bp, n = 94) and COI (589 bp, n = 20), we reveal low levels of structure across 10 sampling locations spanning the species range within New Zealand. F-statistics, outlier analyses, and STRUCTURE suggest a single panmictic population, and Mantel and EEMS tests reveal no significant isolation by distance. This implies either ongoing gene flow among populations or recent shared ancestry among New Zealand pouched lamprey. We can now use the information gained from these genetic tools to assist managers with monitoring effective population size, managing potential diseases, and conservation measures such as artificial propagation programs. We further demonstrate the general utility of these genetic tools for acquiring information about elusive species.

Across-country genomic predictions in Norwegian and New Zealand Composite sheep populations with similar development history.

The goal of this study was to assess the feasibility of across-country genomic predictions in Norwegian White Sheep (NWS) and New Zealand Composite (NZC) sheep populations with similar development history. Different training populations were evaluated (i.e., including only NWS or NZC, or combining both populations). Predictions were performed using the actual phenotypes (normalized) and the single-step GBLUP via Bayesian inference. Genotyped NWS animals born in 2016 (N = 267) were used to assess the accuracy and bias of genomic estimated breeding values (GEBVs) predicted for birth weight (BW), weaning weight (WW), carcass weight (CW), EUROP carcass classification (EUC), and EUROP fat grading (EUF). The accuracy and bias of GEBVs differed across traits and training population used. For instance, the GEBV accuracies ranged from 0.13 (BW) to 0.44 (EUC) for GEBVs predicted including only NWS, from 0.06 (BW) to 0.15 (CW) when including only NZC, and from 0.10 (BW) to 0.41 (EUC) when including both NWS and NZC animals in the training population. The regression coefficients used to assess the spread of GEBVs (bias) ranged from 0.26 (BW) to 0.64 (EUF) for only NWS, 0.10 (EUC) to 0.52 (CW) for only NZC, and from 0.42 (WW) to 2.23 (EUC) for both NWS and NZC in the training population. Our findings suggest that across-country genomic predictions based on ssGBLUP might be possible for NWS and NZC, especially for novel traits.

Genetic variation in skin traits in New Zealand lambs.

BACKGROUND: This study explored the genetic variability in the New Zealand sheep population for economically important skin traits. Skins were collected at slaughter from two progeny test flocks, resulting in 725 skins evaluated for grain strain, flatness, crust leather strength and overall suitability for shoe leather. DNA profiles collected from skins post-slaughter were matched to individual animals using previously collected high-density genotypes. RESULTS: Considerable phenotypic variation for skin traits was observed, with around 40% of the skins being identified as suitable for high-value shoe leather production. Several key traits associated with leather production, including flatness, tear strength, grain strength and grain strain were found to be moderate to highly heritable (h2 = 0.28-0.82). There were no major significant genome-wide association study (GWAS) peaks associated with many of the traits examined, however, one single-nucleotide polymorphism (SNP) reached significance for the flatness of the skin over the hindquarters. CONCLUSION: This research confirms that suitable lamb skins can be bred for use as high-value shoe leather. While moderately to highly heritable, skin traits in New Zealand lambs appear to be polygenic with no genes of major effect underlaying the traits of interest. Given the complex nature of these traits, the identification and selection of animals with higher-value skins may be enabled by geomic selection. © 2022 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Computer-assisted EEG diagnostic review for idiopathic generalized epilepsy.

Epilepsy diagnosis can be costly, time-consuming, and not uncommonly inaccurate. The reference standard diagnostic monitoring is continuous video-electroencephalography (EEG) monitoring, ideally capturing all events or concordant interictal discharges. Automating EEG data review would save time and resources, thus enabling more people to receive reference standard monitoring and also potentially heralding a more quantitative approach to therapeutic outcomes. There is substantial research into the automated detection of seizures and epileptic activity from EEG. However, automated detection software is not widely used in the clinic, and despite numerous published algorithms, few methods have regulatory approval for detecting epileptic activity from EEG. This study reports on a deep learning algorithm for computer-assisted EEG review. Deep convolutional neural networks were trained to detect epileptic discharges using a preexisting dataset of over 6000 labelled events in a cohort of 103 patients with idiopathic generalized epilepsy (IGE). Patients underwent 24-hour ambulatory outpatient EEG, and all data were curated and confirmed independently by two epilepsy specialists (Seneviratne et al., 2016). The resulting automated detection algorithm was then used to review diagnostic scalp EEG for seven patients (four with IGE and three with events mimicking seizures) to validate performance in a clinical setting. The automated detection algorithm showed state-of-the-art performance for detecting epileptic activity from clinical EEG, with mean sensitivity of >95% and corresponding mean false positive rate of 1 detection per minute. Importantly, diagnostic case studies showed that the automated detection algorithm reduced human review time by 80%-99%, without compromising event detection or diagnostic accuracy. The presented results demonstrate that computer-assisted review can increase the speed and accuracy of EEG assessment and has the potential to greatly improve therapeutic outcomes. This article is part of the Special Issue "NEWroscience 2018".

Male-female relatedness at specific SNP-linkage groups influences cryptic female choice in Chinook salmon (Oncorhynchus tshawytscha).

In a range of taxa, the relatedness between mates influences both pre- and post-mating processes of sexual selection. However, relatively little is known about the genetic loci facilitating such a bias, with the exception of the major histocompatibility complex. Here, we performed tightly controlled replicated in vitro fertilization trials to explore the impact of relatedness on two possible mechanisms of cryptic female choice (CFC) in Chinook salmon (Oncorhynchus tshawytscha). We tested (i) whether relatedness of mates, assessed using 682 single nucleotide polymorphisms (SNPs) on 29 SNP-linkage groups (LGs), biases a male's sperm velocity in ovarian fluid (a parameter previously shown to predict male fertilization success), and (ii) whether relatedness of mates governs fertilization success via other mechanisms, probably via sperm-egg interactions. We found that relatedness on three LGs explained the variation in sperm velocity, and relatedness on two LGs explained fertilization success, which might indicate the presence of genes important in sperm-ovarian fluid and sperm-egg interactions in these genomic regions. Mapping of the SNPs on these LGs to the rainbow trout genome revealed two genes that affect fertility in humans and represent candidate genes for further studies. Our results thereby provide a novel contribution to the understanding of the mechanism of CFC.

Estimation of linkage disequilibrium and effective population size in New Zealand sheep using three different methods to create genetic maps.

BACKGROUND: Investments in genetic selection have played a major role in the New Zealand sheep industry competitiveness. Selection may erode genetic diversity, which is a crucial factor for the success of breeding programs. Better understanding of linkage disequilibrium (LD) and ancestral effective population size (Ne) through quantifying this diversity and comparison between populations allows for more informed decisions with regards to selective breeding taking population genetic diversity into account. The estimation of N e can be determined via genetic markers and requires knowledge of genetic distances between these markers. Single nucleotide polymorphisms (SNP) data from a sample of 12,597 New Zealand crossbred and purebred sheep genotyped with the Illumina Ovine SNP50 BeadChip was used to perform a genome-wide scan of LD and N e . Three methods to estimate genetic distances were investigated: 1) M1: a ratio fixed across the whole genome of one Megabase per centiMorgan; 2) M2: the ratios of genetic distance (using M3, below) over physical distance fixed for each chromosome; and, 3) M3: a genetic map of inter-SNP distances estimated using CRIMAP software (v2.503). RESULTS: The estimates obtained with M2 and M3 showed much less variability between autosomes than those with M1, which tended to give lower N e results and higher LD decay. The results suggest that N e has decreased since the development of sheep breeds in Europe and this reduction in Ne has been accelerated in the last three decades. The N e estimated for five generations in the past ranged from 71 to 237 for Texel and Romney breeds, respectively. A low level of genetic kinship and inbreeding was estimated in those breeds suggesting avoidance of mating close relatives. CONCLUSIONS: M3 was considered the most accurate method to create genetic maps for the estimation of LD and Ne. The findings of this study highlight the history of genetic selection in New Zealand crossbred and purebred sheep and these results will be very useful to understand genetic diversity of the population with respect to genetic selection. In addition, it will help geneticists to identify genomic regions which have been preferentially selected within a variety of breeds and populations.

Genetic diversity of a New Zealand multi-breed sheep population and composite breeds' history revealed by a high-density SNP chip.

BACKGROUND: Knowledge about the genetic diversity of a population is a crucial parameter for the implementation of successful genomic selection and conservation of genetic resources. The aim of this research was to establish the scientific basis for the implementation of genomic selection in a composite Terminal sheep breeding scheme by providing consolidated linkage disequilibrium (LD) measures across SNP markers, estimating consistency of gametic phase between breed-groups, and assessing genetic diversity measures, such as effective population size (Ne), and population structure parameters, using a large number of animals (n = 14,845) genotyped with a high density SNP chip (606,006 markers). Information generated in this research will be useful for optimizing molecular breeding values predictions and managing the available genetic resources. RESULTS: Overall, as expected, levels of pairwise LD decreased with increasing distance between SNP pairs. The mean LD r2 between adjacent SNP was 0.26 ± 0.10. The most recent effective population size for all animals (687) and separately per breed-groups: Primera (974), Lamb Supreme (380), Texel (227) and Dual-Purpose (125) was quite variable. The genotyped animals were outbred or had an average low level of inbreeding. Consistency of gametic phase was higher than 0.94 for all breed pairs at the average distance between SNP on the chip (~4.74 kb). Moreover, there was not a clear separation between the breed-groups based on principal component analysis, suggesting that a mixed-breed training population for calculation of molecular breeding values would be beneficial. CONCLUSIONS: This study reports, for the first time, estimates of linkage disequilibrium, genetic diversity and population structure parameters from a genome-wide perspective in New Zealand Terminal Sire composite sheep breeds. The levels of linkage disequilibrium indicate that genomic selection could be implemented with the high density SNP panel. The moderate to high consistency of gametic phase between breed-groups and overlapping population structure support the pooling of the animals in a mixed training population for genomic predictions. In addition, the moderate to high Ne highlights the need to genotype and phenotype a large training population in order to capture most of the haplotype diversity and increase accuracies of genomic predictions. The results reported herein are a first step toward understanding the genomic architecture of a Terminal Sire composite sheep population and for the optimal implementation of genomic selection and genome-wide association studies in this sheep population.

Prediction of genomic breeding values for growth, carcass and meat quality traits in a multi-breed sheep population using a HD SNP chip.

BACKGROUND: New Zealand has some unique Terminal Sire composite sheep breeds, which were developed in the last three decades to meet commercial needs. These composite breeds were developed based on crossing various Terminal Sire and Maternal breeds and, therefore, present high genetic diversity compared to other sheep breeds. Their breeding programs are focused on improving carcass and meat quality traits. There is an interest from the industry to implement genomic selection in this population to increase the rates of genetic gain. Therefore, the main objectives of this study were to determine the accuracy of predicted genomic breeding values for various growth, carcass and meat quality traits using a HD SNP chip and to evaluate alternative genomic relationship matrices, validation designs and genomic prediction scenarios. A large multi-breed population (n = 14,845) was genotyped with the HD SNP chip (600 K) and phenotypes were collected for a variety of traits. RESULTS: The average observed accuracies (± SD) for traits measured in the live animal, carcass, and, meat quality traits ranged from 0.18 ± 0.07 to 0.33 ± 0.10, 0.28 ± 0.09 to 0.55 ± 0.05 and 0.21 ± 0.07 to 0.36 ± 0.08, respectively, depending on the scenario/method used in the genomic predictions. When accounting for population stratification by adjusting for 2, 4 or 6 principal components (PCs) the observed accuracies of molecular breeding values (mBVs) decreased or kept constant for all traits. The mBVs observed accuracies when fitting both G and A matrices were similar to fitting only G matrix. The lowest accuracies were observed for k-means cross-validation and forward validation performed within each k-means cluster. CONCLUSIONS: The accuracies observed in this study support the feasibility of genomic selection for growth, carcass and meat quality traits in New Zealand Terminal Sire breeds using the Ovine HD SNP chip. There was a clear advantage on using a mixed training population instead of performing analyzes per genomic clusters. In order to perform genomic predictions per breed group, genotyping more animals is recommended to increase the size of the training population within each group and the genetic relationship between training and validation populations. The different scenarios evaluated in this study will help geneticists and breeders to make wiser decisions in their breeding programs.

Assessing accuracy of imputation using different SNP panel densities in a multi-breed sheep population.

BACKGROUND: Genotype imputation is a key element of the implementation of genomic selection within the New Zealand sheep industry, but many factors can influence imputation accuracy. Our objective was to provide practical directions on the implementation of imputation strategies in a multi-breed sheep population genotyped with three single nucleotide polymorphism (SNP) panels: 5K, 50K and HD (600K SNPs). RESULTS: Imputation from 5K to HD was slightly better (0.6 %) than imputation from 5K to 50K. Two-step imputation from 5K to 50K and then from 50K to HD outperformed direct imputation from 5K to HD. A slight loss in imputation accuracy was observed when a large fixed reference population was used compared to a smaller within-breed reference (including all 50K genotypes on animals from different breeds excluding those in the validation set i.e. to be imputed), but only for a few animals across all imputation scenarios from 5K to 50K. However, a major gain in imputation accuracy for a large proportion of animals (purebred and crossbred), justified the use of a fixed and large reference dataset for all situations. This study also investigated the loss in imputation accuracy specifically for SNPs located at the ends of each chromosome, and showed that only chromosome 26 had an overall imputation (5K to 50K) accuracy for 100 SNPs at each end higher than 60 % (r2). Most of the chromosomes displayed reduced imputation accuracy at least at one of their ends. Prediction of imputation accuracy based on the relatedness of low-density genotypes to those of the reference dataset, before imputation (without running an imputation software) was also investigated. FIMPUTE V2.2 outperformed BEAGLE 3.3.2 across all imputation scenarios. CONCLUSIONS: Imputation accuracy in sheep breeds can be improved by following a set of recommendations on SNP panels, software, strategies of imputation (one- or two-step imputation), and choice of the animals to be genotyped using both high- and low-density SNP panels. We present a method that predicts imputation accuracy for individual animals at the low-density level, before running imputation, which can be used to restrict genomic prediction only to the animals that can be imputed with sufficient accuracy.

Construction of relatedness matrices using genotyping-by-sequencing data.

BACKGROUND: Genotyping-by-sequencing (GBS) is becoming an attractive alternative to array-based methods for genotyping individuals for a large number of single nucleotide polymorphisms (SNPs). Costs can be lowered by reducing the mean sequencing depth, but this results in genotype calls of lower quality. A common analysis strategy is to filter SNPs to just those with sufficient depth, thereby greatly reducing the number of SNPs available. We investigate methods for estimating relatedness using GBS data, including results of low depth, using theoretical calculation, simulation and application to a real data set. RESULTS: We show that unbiased estimates of relatedness can be obtained by using only those SNPs with genotype calls in both individuals. The expected value of this estimator is independent of the SNP depth in each individual, under a model of genotype calling that includes the special case of the two alleles being read at random. In contrast, the estimator of self-relatedness does depend on the SNP depth, and we provide a modification to provide unbiased estimates of self-relatedness. We refer to these methods of estimation as kinship using GBS with depth adjustment (KGD). The estimators can be calculated using matrix methods, which allow efficient computation. Simulation results were consistent with the methods being unbiased, and suggest that the optimal sequencing depth is around 2-4 for relatedness between individuals and 5-10 for self-relatedness. Application to a real data set revealed that some SNP filtering may still be necessary, for the exclusion of SNPs which did not behave in a Mendelian fashion. A simple graphical method (a 'fin plot') is given to illustrate this issue and to guide filtering parameters. CONCLUSION: We provide a method which gives unbiased estimates of relatedness, based on SNPs assayed by GBS, which accounts for the depth (including zero depth) of the genotype calls. This allows GBS to be applied at read depths which can be chosen to optimise the information obtained. SNPs with excess heterozygosity, often due to (partial) polyploidy or other duplications can be filtered based on a simple graphical method.

The iFat1 transgene permits conditional endogenous n-3 PUFA enrichment both in vitro and in vivo.

Fat-1 transgenic mice, which endogenously convert n-6 PUFA to n-3 PUFA, are a useful tool in health research; however with this model timing of n-3 PUFA enrichment cannot be directly controlled. To add such capability, the novel Cre-recombinase inducible fat-1 (iFat1) transgenic mouse has been developed. The aim of this study was to characterize the utility of the iFat1 transgene as a model of Cre-inducible endogenous n-3 PUFA enrichment. Functionality of the iFat1 transgene was screened both in vitro and in vivo. In the presence of Cre, the iFat1 transgene resulted in a balancing (p < 0.01) of the n-6/n-3 PUFA ratio within phospholipids in the human embryonic kidney 293T cell line. For in vivo analysis, iFat1 transgenic mice were crossed with the R26-Cre-ER(T2) (Tam-Cre) mouse line, a tamoxifen inducible Cre-expression model. Tam-Cre/iFat1 double hybrids were transiently treated with tamoxifen at 6-7 weeks, then terminated 3 weeks later. Tamoxifen treated mice had increased (p < 0.05) tissue n-3 PUFA and ≥two-fold reduction (p < 0.05) in the n-6/n-3 PUFA ratio of liver, kidney and muscle phospholipids relative to vehicle treated controls. Collectively these findings suggest that the iFat1 transgenic mouse may be a promising tool to help elucidate the temporal effects through which n-3 PUFA impacts health related outcomes.

DNA methylation networks underlying mammalian traits.

Using DNA methylation profiles (n = 15,456) from 348 mammalian species, we constructed phyloepigenetic trees that bear marked similarities to traditional phylogenetic ones. Using unsupervised clustering across all samples, we identified 55 distinct cytosine modules, of which 30 are related to traits such as maximum life span, adult weight, age, sex, and human mortality risk. Maximum life span is associated with methylation levels in HOXL subclass homeobox genes and developmental processes and is potentially regulated by pluripotency transcription factors. The methylation state of some modules responds to perturbations such as caloric restriction, ablation of growth hormone receptors, consumption of high-fat diets, and expression of Yamanaka factors. This study reveals an intertwined evolution of the genome and epigenome that mediates the biological characteristics and traits of different mammalian species.

Enzymatic activity and genetic variation in SCD1 modulate the relationship between fatty acids and inflammation.

Fatty acids (FA) represent a diverse class of molecules known to regulate inflammatory pathways. Therefore enzymes that regulate FA metabolism are attractive candidates to better understand the relationship between FA and inflammation. Stearoyl-CoA desaturase 1 (SCD1) is rate limiting for the conversion of saturated FA (SFA) to monounsaturated FA (MUFA). Evidence suggests that SCD1 activity may be positively associated with inflammation. Moreover, genetic variation in SCD1 may alter enzyme activity; however, it is unknown whether this affects inflammatory status. The goal of this study was to examine the relationships between plasma FA, SCD1 activity, and SCD1 polymorphisms with C-reactive protein (CRP) levels in young adults. SFA, MUFA, and CRP were measured in fasted plasma samples from European (n=279, 198 female and 81 male) and Asian (n=249, 179 female and 70 male) subjects, 20-29 years old. Circulating levels of palmitic (16:0), palmitoleic (16:1), stearic (18:0), and oleic acids (18:1) were measured by gas chromatography and SCD1 activity was estimated by the ratio of product to precursor (16:1/16:0; 18:1/18:0). Positive associations were identified between CRP levels and 16:0 (p<2.0×10(-4)), 16:1 (p<0.05), and the SCD1 index (18:1/18:0; p<6.0×10(-3)) in European and Asian females, while 18:0 was inversely associated with CRP (p<2.0×10(-4)) in both groups. Ten single nucleotide polymorphisms (SNPs) in SCD1 were genotyped in all subjects. One SNP (rs2060792) was associated (p<0.05) with 16:0 and 18:0 levels in females of European descent. This same SNP was also associated with CRP levels in both groups of females (p<0.05). Overall, SCD1 activity and genetic variation have an important role in modulating the relationship between FA and inflammation in young adults.

Demographic resilience of brook trout populations subjected to experimental size-selective harvesting.

Sustainable management of exploited populations benefits from integrating demographic and genetic considerations into assessments, as both play a role in determining harvest yields and population persistence. This is especially important in populations subject to size-selective harvest, because size selective harvesting has the potential to result in significant demographic, life-history, and genetic changes. We investigated harvest-induced changes in the effective number of breeders ( N ^ b ) for introduced brook trout populations (Salvelinus fontinalis) in alpine lakes from western Canada. Three populations were subject to 3 years of size-selective harvesting, while three control populations experienced no harvest. The N ^ c decreased consistently across all harvested populations (on average 60.8%) but fluctuated in control populations. There were no consistent changes in N ^ b between control or harvest populations, but one harvest population experienced a decrease in N ^ b of 63.2%. The N ^ b / N ^ c ratio increased consistently across harvest lakes; however we found no evidence of genetic compensation (where variance in reproductive success decreases at lower abundance) based on changes in family evenness ( FE ^ ) and the number of full-sibling families ( N ^ fam ). We found no relationship between FE ^ and N ^ c or between N ^ fam / N ^ c and FE ^ . We posit that change in N ^ b was buffered by constraints on breeding habitat prior to harvest, such that the same number of breeding sites were occupied before and after harvest. These results suggest that effective size in harvested populations may be resilient to considerable changes in Nc in the short-term, but it is still important to monitor exploited populations to assess the risk of inbreeding and ensure their long-term survival.

Polymorphisms in FADS1 and FADS2 alter desaturase activity in young Caucasian and Asian adults.

Recent evidence indicates that genetic variation in fatty acid desaturases 1 and 2 (FADS1 and FADS2) is associated with changes in plasma fatty acid profiles; however, the association with altered desaturase activity has not been examined in different ethnic populations. The present study examined whether genetic variation in the FADS gene cluster regulates desaturase activity in two populations of young Canadian adults (Caucasian and Asian) and whether altered desaturase activity was reflected in both n-3 and n-6 fatty acid profiles. FADS1 and FADS2 were genotyped in a random subset of participants (Caucasian, n=78; Asian, n=69) from the Toronto Nutrigenomics and Health study using MALDI-TOF mass spectrometry, and plasma fatty acids were measured by gas chromatography. Desaturase activities were estimated using the following fatty acid ratios: γ-linoleic acid to linoleic acid (GLA:LA), arachidonic acid to linoleic acid (AA:LA), arachidonic acid to dihomo-γ-linoleic acid (AA:DGLA), and eicosapentaneoic acid to α-linolenic acid (EPA:ALA). Nineteen single nucleotide polymorphisms (SNPs) were examined, and several SNPs (9 in Caucasians and 8 in Asians) were associated with various desaturase activities. The most significant association detected was between the FADS1 rs174547 SNP and AA:LA in both Caucasians (p=4.0 × 10(-8)) and Asians (p=5.0 × 10(-5)). Although the minor allele for this SNP differed between Caucasians (T) and Asians (C), carriers of the C allele had a lower desaturase activity than carriers of the T allele in both groups. To determine whether rs174547 was a dominant SNP in the FADS gene cluster, we constructed an additional model which included this SNP as a covariate. Only one SNP (rs498793 in FADS2) remained associated with the EPA:ALA ratio (p=1.1 × 10(-5)) in Asians. This study shows that genetic variation in the FADS gene cluster (in particular rs174547) can alter desaturase activity in subjects of Caucasians and Asian descent.

Development of Epigenetic Clocks for Key Ruminant Species.

Robust biomarkers of chronological age have been developed in humans and model mammalian species such as rats and mice using DNA methylation data. The concept of these so-called "epigenetic clocks" has emerged from a large body of literature describing the relationship between genome-wide methylation levels and age. Epigenetic clocks exploit this phenomenon and use small panels of differentially methylated cytosine (CpG) sites to make robust predictions of chronological age, independent of tissue type. Here, we present highly accurate livestock epigenetic clocks for which we have used the custom mammalian methylation array "HorvathMammalMethyl40" to construct the first epigenetic clock for domesticated goat (Capra hircus), cattle (Bos taurus), Red (Cervus elaphus) and Wapiti deer (Cervus canadensis) and composite-breed sheep (Ovis aries). Additionally, we have constructed a 'farm animal clock' for all species included in the study, which will allow for robust predictions to be extended to various breeds/strains. The farm animal clock shows similarly high accuracy to the individual species' clocks (r > 0.97), utilizing only 217 CpG sites to estimate age (relative to the maximum lifespan of the species) with a single mathematical model. We hypothesise that the applications of this livestock clock could extend well beyond the scope of chronological age estimates. Many independent studies have demonstrated that a deviation between true age and clock derived molecular age is indicative of past and/or present health (including stress) status. There is, therefore, untapped potential to utilize livestock clocks in breeding programs as a predictor for age-related production traits.

Genomic Tools for the Identification of Loci Associated with Facial Eczema in New Zealand Sheep.

Facial eczema (FE) is a significant metabolic disease that affects New Zealand ruminants. Ingestion of the mycotoxin sporidesmin leads to liver and bile duct damage, which can result in photosensitisation, reduced productivity and death. Strategies used to manage the incidence and severity of the disease include breeding. In sheep, there is considerable genetic variation in the response to FE. A commercial testing program is available for ram breeders who aim to increase tolerance, determined by the concentration of the serum enzyme, gamma-glutamyltransferase 21 days after a measured sporidesmin challenge (GGT21). Genome-wide association studies were carried out to determine regions of the genome associated with GGT21. Two regions on chromosomes 15 and 24 are reported, which explain 5% and 1% of the phenotypic variance in the response to FE, respectively. The region on chromosome 15 contains the ß-globin locus. Of the significant SNPs in the region, one is a missense variant within the haemoglobin subunit ß (HBB) gene. Mass spectrometry of haemoglobin from animals with differing genotypes at this locus indicated that genotypes are associated with different forms of adult ß-globin. Haemoglobin haplotypes have previously been associated with variation in several health-related traits in sheep and warrant further investigation regarding their role in tolerance to FE in sheep. We show a strategic approach to the identification of regions of importance for commercial breeding programs with a combination of discovery, statistical and biological validation. This study highlights the power of using increased density genotyping for the identification of influential genomic regions, combined with subsequent inclusion on lower density genotyping platforms.