Biology subjects

Dodds, K. G.

Publications and source records attributed to Dodds, K. G..

6 recordsLinked to original sources

bioRxiv2022

Low-cost sample preservation methods for high-throughput processing of rumen microbiomes

BackgroundThe use of rumen microbial community (RMC) profiles to predict methane emissions has driven interest in ruminal DNA preservation and extraction protocols that can be processed cheaply while also maintaining or improving DNA quality for RMC profiling. Our standard approach for preserving rumen samples, as defined in the Global Rumen Census (GRC), requires time-consuming pre-processing steps of freeze drying and grinding prior to international transportation and DNA extraction. This impedes researchers unable to access sufficient funding or infrastructure. To circumvent these pre-processing steps, we investigated three methods of preserving rumen samples for subsequent DNA extraction, based on existing lysis buffers Tris-NaCl-EDTA-SDS (TNx2) and guanidine hydrochloride (GHx2), or 100% ethanol. ResultsRumen samples were collected via stomach intubation from 151 sheep at two time-points two weeks apart. Each sample was separated into four subsamples and preserved using the three preservation methods and the GRC method (n = 4x302). DNA was extracted and sequenced using Restriction Enzyme-Reduced Representation Sequencing to generate RMC profiles. Differences in DNA yield, quality and integrity, and sequencing metrics were observed across the methods (p < 0.0001). Ethanol exhibited poorer quality DNA (A260/A230 < 2) and more failed samples compared to the other methods. Samples preserved using the GRC method had smaller relative abundances in gram-negative genera Anaerovibrio, Bacteroides, Prevotella, Selenomonas, and Succiniclasticum, but larger relative abundances in the majority of 56 additional genera compared to TNx2 and GHx2. However, log10 relative abundances across all genera and time-points for TNx2 and GHx2 were on average consistent (R2 > 0.99) but slightly more variable compared to the GRC method. Relative abundances were moderately to highly correlated (0.68 {+/-} 0.13) between methods for samples collected within a time-point, which was greater than the average correlation (0.17 {+/-} 0.11) between time-points within a preservation method. ConclusionsThe two modified lysis buffers solutions (TNx2 and GHx2) proposed in this study were shown to be viable alternatives to the GRC method for RMC profiling in sheep. Use of these preservative solutions reduces cost and improves throughput associated with processing and sequencing ruminal samples. This development could significantly advance implementation of RMC profiles as a tool for breeding ruminant livestock.

microbiology↗

bioRxiv2021

Development of epigenetic clocks for New Zealand livestock

Robust biological 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 correlation 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 whereby we have used the custom mammalian methylation array "HorvathMammalMethyl40"1 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 New Zealand livestock farm animal clock for all animals included in the study, which will enable robust predictions to be extended to various breeds. The farm animal clock shows similarly high accuracies to the individual species clocks (r>0.97), utilising only 217 CpG sites to estimate age (relative to the maximum lifespan of the species) with a single mathematical model. We envision 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 utilise livestock clocks in breeding programmes as a predictor for age-related, production traits.

genomics↗

bioRxiv2020

Inherent population structure determines the importance of filtering parameters for reduced representation sequencing analyses

As technological advancements enhance our ability to study population genetics, we must understand how the intrinsic properties of our datasets influence the decisions we make when designing experiments. Filtering parameter thresholds, such as call rate and minimum minor allele frequency (MAF), are known to affect inferences of population structure in reduced representation sequencing (RRS) studies. However, it is unclear to what extent the impacts of these parameter choices vary across datasets. Here, we reviewed literature on filtering choices and levels of genetic differentiation across RRS studies on wild populations to highlight the diverse approaches that have been used. Next, we hypothesized that choices in filtering thresholds would have the greatest impact when analyzing datasets with low levels of genetic differentiation between populations. To test this hypothesis, we produced seven simulated RRS datasets with varying levels of population structure, and analyzed them using four different combinations of call rate and MAF. We performed the same analysis on two empirical RRS datasets (low or high population structure). Our simulated and empirical results suggest that the effects of filtering choices indeed vary based on inherent levels of differentiation: specifically, choosing stringent filtering choices was important to detect distinct populations that were slightly differentiated, but not those that were highly differentiated. As a result, experimental design and analysis choices need to consider attributes of each specific dataset. Based on our literature review and analyses, we recommend testing a range of filtering parameter choices, and presenting all results with clear justification for ultimate filtering decisions used in downstream analyses.

bioinformatics↗

bioRxiv2019

Exclusion and genomic relatedness methods for assignment of parentage using genotyping-by-sequencing data

Genotypes are often used to assign parentage in agricultural and ecological settings. Sequencing can be used to obtain genotypes but does not provide unambiguous genotype calls, especially when sequencing depth is low in order to reduce costs. In that case, standard parentage analysis methods no longer apply. A strategy for using low-depth sequencing data for parentage assignment is developed here. It entails the use of relatedness estimates along with a metric termed excess mismatch rate which, for parent-offspring pairs or trios, is the difference between the observed mismatch rate and the rate expected under a model of inheritance and allele reads without error. When more than one putative parent has similar statistics, bootstrapping can provide a measure of the relatedness similarity. Putative parent-offspring trios can be further checked for consistency by comparing the offsprings estimated inbreeding to half the parent relatedness. Suitable thresholds are required for each metric. These methods were applied to a deer breeding operation consisting of two herds of different breeds. Relatedness estimates were more in line with expectation when the herds were analysed separately than when combined, although this did not alter which parents were the best matches with each offspring. Parentage results were largely consistent with those based on a microsatellite parentage panel with three discordant parent assignments out of 1561. Two models are investigated to allow the parentage metrics to be calculated with non-random selection of alleles. The tools and strategies given here allow parentage to be assigned from low-depth sequencing data.

genetics↗

bioRxiv2018

Accounting for Errors in Low Coverage High-Throughput Sequencing Data when Constructing Genetic Maps using Biparental Outcrossed Populations

Next generation sequencing is an efficient method that allows for substantially more markers than previous technologies, providing opportunities for building high density genetic linkage maps, which facilitate the development of non-model species genomic assemblies and the investigation of their genes. However, constructing genetic maps using data generated via high-throughput sequencing technology (e.g., genotyping-by-sequencing) is complicated by the presence of sequencing errors and genotyping errors resulting from missing parental alleles due to low sequencing depth. If unaccounted for, these errors lead to inflated genetic maps. In addition, map construction in many species is performed using full-sib family populations derived from the outcrossing of two individuals, where unknown parental phase and varying segregation types further complicate construction. We present a new methodology for modeling low coverage sequencing data in the construction of genetic linkage maps using full-sib populations of diploid species, implemented in a package called GUSMap. Our model is based on an extension of the Lander-Green hidden Markov model that accounts for errors present in sequencing data. Results show that GUSMap was able to give accurate estimates of the recombination fractions and overall map distance, while most existing mapping packages produced inflated genetic maps in the presence of errors. Our results demonstrate the feasibility of using low coverage sequencing data to produce genetic maps without requiring extensive filtering of potentially erroneous genotypes, provided that the associated errors are correctly accounted for in the model.

genetics↗

bioRxiv2017

Linkage Disequilibrium Estimation in Low Coverage High-Throughput Sequencing Data

High-throughput sequencing methods that multiplex a large number of individuals have provided a cost-effective approach for discovering genome-wide genetic variation in large populations. These sequencing methods are increasingly being utilized in population genetic studies across a diverse range of species. One side-effect of these methods, however, is that one or more alleles at a particular locus may not be sequenced, particularly when the sequencing depth is low, resulting in some heterozygous genotypes being called as homozygous. Under-called heterozygous genotypes have a profound effect on the estimation of linkage disequilibrium and, if not taken into account, leads to inaccurate estimates. We developed a new likelihood method, GUS-LD, to estimate pairwise linkage disequilibrium using low coverage sequencing data that accounts for under-called heterozygous genotypes. Our findings show that accurate estimates were obtained using GUS-LD on low coverage sequencing data, whereas underestimation of linkage disequilibrium results if no adjustment is made for under-called heterozygotes.

genetics↗