Plasma proteomics of SARS-CoV-2 infection and severity reveals impact on Alzheimer's and coronary disease pathways.

Identification of proteins dysregulated by COVID-19 infection is critically important for better understanding of its pathophysiology, building prognostic models, and identifying new targets. Plasma proteomic profiling of 4,301 proteins was performed in two independent datasets and tested for the association for three COVID-19 outcomes (infection, ventilation, and death). We identified 1,449 proteins consistently associated in both datasets with any of these three outcomes. We subsequently created highly accurate models that distinctively predict infection, ventilation, and death. These proteins were enriched in specific biological processes including cytokine signaling, Alzheimer's disease, and coronary artery disease. Mendelian randomization and gene network analyses identified eight causal proteins and 141 highly connected hub proteins including 35 with known drug targets. Our findings provide distinctive prognostic biomarkers for two severe COVID-19 outcomes, reveal their relationship to Alzheimer's disease and coronary artery disease, and identify potential therapeutic targets for COVID-19 outcomes.

Structural variants identified using non-Mendelian inheritance patterns advance the mechanistic understanding of autism spectrum disorder.

The heritability of autism spectrum disorder (ASD), based on 680,000 families and five countries, is estimated to be nearly 80%, yet heritability reported from SNP-based studies are consistently lower, and few significant loci have been identified with genome-wide association studies. This gap in genomic information may reside in rare variants, interaction among variants (epistasis), or cryptic structural variation (SV) and may provide mechanisms that underlie ASD. Here we use a method to identify potential SVs based on non-Mendelian inheritance patterns in pedigrees using parent-child genotypes from ASD families and demonstrate that they are enriched in ASD-risk genes. Most are in non-coding genic space and are over-represented in expression quantitative trait loci, suggesting that they affect gene regulation, which we confirm with their overlap of differentially expressed genes in postmortem brain tissue of ASD individuals. We then identify an SV in the GRIK2 gene that alters RNA splicing and a regulatory region of the ACMSD gene in the kynurenine pathway as significantly associated with a non-verbal ASD phenotype, supporting our hypothesis that these currently excluded loci can provide a clearer mechanistic understanding of ASD. Finally, we use an explainable artificial intelligence approach to define subgroups demonstrating their use in the context of precision medicine.

Plasma proteomics of SARS-CoV-2 infection and severity reveals impact on Alzheimer and coronary disease pathways.

Identification of the plasma proteomic changes of Coronavirus disease 2019 (COVID-19) is essential to understanding the pathophysiology of the disease and developing predictive models and novel therapeutics. We performed plasma deep proteomic profiling from 332 COVID-19 patients and 150 controls and pursued replication in an independent cohort (297 cases and 76 controls) to find potential biomarkers and causal proteins for three COVID-19 outcomes (infection, ventilation, and death). We identified and replicated 1,449 proteins associated with any of the three outcomes (841 for infection, 833 for ventilation, and 253 for death) that can be query on a web portal ( https://covid.proteomics.wustl.edu/ ). Using those proteins and machine learning approached we created and validated specific prediction models for ventilation (AUC>0.91), death (AUC>0.95) and either outcome (AUC>0.80). These proteins were also enriched in specific biological processes, including immune and cytokine signaling (FDR ≤ 3.72×10 -14 ), Alzheimer's disease (FDR ≤ 5.46×10 -10 ) and coronary artery disease (FDR ≤ 4.64×10 -2 ). Mendelian randomization using pQTL as instrumental variants nominated BCAT2 and GOLM1 as a causal proteins for COVID-19. Causal gene network analyses identified 141 highly connected key proteins, of which 35 have known drug targets with FDA-approved compounds. Our findings provide distinctive prognostic biomarkers for two severe COVID-19 outcomes (ventilation and death), reveal their relationship to Alzheimer's disease and coronary artery disease, and identify potential therapeutic targets for COVID-19 outcomes.

What Determines Paternity in Wild Lizards? A Spatiotemporal Analysis of Behavior and Morphology.

Mating behavior in animals can be understood as a sequence of events that begins with individuals encountering one another and ends with the production of offspring. Behavioral descriptions of animal interactions characterize early elements of this sequence, and genetic descriptions use offspring parentage to characterize the final outcome, with behavioral and physiological assessments of mates and mechanisms of copulation and fertilization comprising intermediate steps. However, behavioral and genetic descriptions of mating systems are often inconsistent with one another, complicating expectations for crucial aspects of mating biology, such as the presence of multiple mating. Here, we use behavioral and genetic data from a wild population of the lizard Anolis cristatellus to characterize female multiple mating and the potential for sexual selection through female mate choice in this species. We find that 48% of sampled females bore offspring sired by multiple males. Moreover, spatiotemporal proximity between males and females was associated with whether a male sired a female's offspring, and if yes, how many offspring he sired. Additionally, male body size, but not display behavior, was associated with reproductive outcomes for male-female pairs. While much remains to be learned about the mechanisms of mating and targets of sexual selection in A. cristatellus, it is clear that female multiple mating is a substantial component of this species' mating system in nature.

Subspecies hybridization as a potential conservation tool in species reintroductions.

Reintroductions are a powerful tool for the recovery of endangered species. However, their long-term success is strongly influenced by the genetic diversity of the reintroduced population. The chances of population persistence can be improved by enhancing the population's adaptive ability through the mixing of individuals from different sources. However, where source populations are too diverse the reintroduced population could also suffer from outbreeding depression or unsuccessful admixture due to behavioural or genetic barriers. For the reintroduction of Asiatic wild ass Equus hemionus ssp. in Israel, a breeding core was created from individuals of two different subspecies (E. h. onager & E. h. kulan). Today the population comprises approximately 300 individuals and displays no signs of outbreeding depression. The aim of this study was a population genomic evaluation of this conservation reintroduction protocol. We used maximum likelihood methods and genetic clustering analyses to investigate subspecies admixture and test for spatial autocorrelation based on subspecies ancestry. Further, we analysed heterozygosity and effective population sizes in the breeding core prior to release and the current wild population. We discovered high levels of subspecies admixture in the breeding core and wild population, consistent with a significant heterozygote excess in the breeding core. Furthermore, we found no signs of spatial autocorrelation associated with subspecies ancestry in the wild population. Inbreeding and variance effective population size estimates were low. Our results indicate no genetic or behavioural barriers to admixture between the subspecies and suggest that their hybridization has led to greater genetic diversity in the reintroduced population. The study provides rare empirical evidence of the successful application of subspecies hybridization in a reintroduction. It supports use of intraspecific hybridization as a tool to increase genetic diversity in conservation translocations.

Network-based hierarchical population structure analysis for large genomic data sets.

Analysis of population structure in natural populations using genetic data is a common practice in ecological and evolutionary studies. With large genomic data sets of populations now appearing more frequently across the taxonomic spectrum, it is becoming increasingly possible to reveal many hierarchical levels of structure, including fine-scale genetic clusters. To analyze these data sets, methods need to be appropriately suited to the challenges of extracting multilevel structure from whole-genome data. Here, we present a network-based approach for constructing population structure representations from genetic data. The use of community-detection algorithms from network theory generates a natural hierarchical perspective on the representation that the method produces. The method is computationally efficient, and it requires relatively few assumptions regarding the biological processes that underlie the data. We show the approach by analyzing population structure in the model plant species Arabidopsis thaliana and in human populations. These examples illustrate how network-based approaches for population structure analysis are well-suited to extracting valuable ecological and evolutionary information in the era of large genomic data sets.

Phenotypic plasticity and local adaptations to dissolved oxygen in larvae fire salamander (Salamandra infraimmaculata).

A key environmental factor that varies both spatially and temporally in surface waters is dissolved oxygen (DO). In stagnant ephemeral freshwater ponds, DO can fluctuate diurnally and seasonally, while the constant mixing of water in streams typically maintain DO levels close to saturation with only minor fluctuations. Larvae of the Near Eastern fire salamander (Salamandra infraimmaculata) develop in a range of waterbodies that vary in flow and permanence. To study inter-population variation in larval response to environmental change, we translocated larvae between stream and pond habitats and exposed larvae sampled from different habitat types to hypoxic and normoxic conditions in the laboratory. Larvae transferred from stream to pond retain gill size, while larvae transferred from pond to stream show a reduction in gill size. Larvae that were caged within their native habitat, either stream or pond, display a decrease in gill size similar to larvae transferred from pond to stream. When exposed to experimentally manipulated levels of DO in the laboratory larvae, respectively, increase and decrease gill size under hypoxic and normoxic conditions. Habitat-type origin had a significant effect on the degree of change in gill size with larvae from permanent streams demonstrating the lowest absolute variation in gill size. There was no interaction between DO level (hypoxic/normoxic) and the larvae habitat-type origin. These results suggest that S. infraimmaculata larvae are locally adapted to their aquatic breeding habitat through the plastic ability to respond to the prevailing respiratory conditions by rapidly decreasing or increasing gill size.

Revealing life-history traits by contrasting genetic estimations with predictions of effective population size.

Effective population size, a central concept in conservation biology, is now routinely estimated from genetic surveys and can also be theoretically predicted from demographic, life-history, and mating-system data. By evaluating the consistency of theoretical predictions with empirically estimated effective size, insights can be gained regarding life-history characteristics and the relative impact of different life-history traits on genetic drift. These insights can be used to design and inform management strategies aimed at increasing effective population size. We demonstrated this approach by addressing the conservation of a reintroduced population of Asiatic wild ass (Equus hemionus). We estimated the variance effective size (Nev ) from genetic data (N ev =24.3) and formulated predictions for the impacts on Nev of demography, polygyny, female variance in lifetime reproductive success (RS), and heritability of female RS. By contrasting the genetic estimation with theoretical predictions, we found that polygyny was the strongest factor affecting genetic drift because only when accounting for polygyny were predictions consistent with the genetically measured Nev . The comparison of effective-size estimation and predictions indicated that 10.6% of the males mated per generation when heritability of female RS was unaccounted for (polygyny responsible for 81% decrease in Nev ) and 19.5% mated when female RS was accounted for (polygyny responsible for 67% decrease in Nev ). Heritability of female RS also affected Nev ; hf2=0.91 (heritability responsible for 41% decrease in Nev ). The low effective size is of concern, and we suggest that management actions focus on factors identified as strongly affecting Nev, namely, increasing the availability of artificial water sources to increase number of dominant males contributing to the gene pool. This approach, evaluating life-history hypotheses in light of their impact on effective population size, and contrasting predictions with genetic measurements, is a general, applicable strategy that can be used to inform conservation practice.

Latitudinal Clines of the Human Vitamin D Receptor and Skin Color Genes.

The well-documented latitudinal clines of genes affecting human skin color presumably arise from the need for protection from intense ultraviolet radiation (UVR) vs. the need to use UVR for vitamin D synthesis. Sampling 751 subjects from a broad range of latitudes and skin colors, we investigated possible multilocus correlated adaptation of skin color genes with the vitamin D receptor gene (VDR), using a vector correlation metric and network method called BlocBuster. We discovered two multilocus networks involving VDR promoter and skin color genes that display strong latitudinal clines as multilocus networks, even though many of their single gene components do not. Considered one by one, the VDR components of these networks show diverse patterns: no cline, a weak declining latitudinal cline outside of Africa, and a strong in- vs. out-of-Africa frequency pattern. We confirmed these results with independent data from HapMap. Standard linkage disequilibrium analyses did not detect these networks. We applied BlocBuster across the entire genome, showing that our networks are significant outliers for interchromosomal disequilibrium that overlap with environmental variation relevant to the genes' functions. These results suggest that these multilocus correlations most likely arose from a combination of parallel selective responses to a common environmental variable and coadaptation, given the known Mendelian epistasis among VDR and the skin color genes.

Inference and Analysis of Population Structure Using Genetic Data and Network Theory.

Clustering individuals to subpopulations based on genetic data has become commonplace in many genetic studies. Inference about population structure is most often done by applying model-based approaches, aided by visualization using distance-based approaches such as multidimensional scaling. While existing distance-based approaches suffer from a lack of statistical rigor, model-based approaches entail assumptions of prior conditions such as that the subpopulations are at Hardy-Weinberg equilibria. Here we present a distance-based approach for inference about population structure using genetic data by defining population structure using network theory terminology and methods. A network is constructed from a pairwise genetic-similarity matrix of all sampled individuals. The community partition, a partition of a network to dense subgraphs, is equated with population structure, a partition of the population to genetically related groups. Community-detection algorithms are used to partition the network into communities, interpreted as a partition of the population to subpopulations. The statistical significance of the structure can be estimated by using permutation tests to evaluate the significance of the partition's modularity, a network theory measure indicating the quality of community partitions. To further characterize population structure, a new measure of the strength of association (SA) for an individual to its assigned community is presented. The strength of association distribution (SAD) of the communities is analyzed to provide additional population structure characteristics, such as the relative amount of gene flow experienced by the different subpopulations and identification of hybrid individuals. Human genetic data and simulations are used to demonstrate the applicability of the analyses. The approach presented here provides a novel, computationally efficient model-free method for inference about population structure that does not entail assumption of prior conditions. The method is implemented in the software NetStruct (available at https://giligreenbaum.wordpress.com/software/).

Human gephyrin is encompassed within giant functional noncoding yin-yang sequences.

Gephyrin is a highly conserved gene that is vital for the organization of proteins at inhibitory receptors, molybdenum cofactor biosynthesis and other diverse functions. Its specific function is intricately regulated and its aberrant activities have been observed for a number of human diseases. Here we report a remarkable yin-yang haplotype pattern encompassing gephyrin. Yin-yang haplotypes arise when a stretch of DNA evolves to present two disparate forms that bear differing states for nucleotide variations along their lengths. The gephyrin yin-yang pair consists of 284 divergent nucleotide states and both variants vary drastically from their mutual ancestral haplotype, suggesting rapid evolution. Several independent lines of evidence indicate strong positive selection on the region and suggest these high-frequency haplotypes represent two distinct functional mechanisms. This discovery holds potential to deepen our understanding of variable human-specific regulation of gephyrin while providing clues for rapid evolutionary events and allelic migrations buried within human history.

Stochastic modelling of shifts in allele frequencies reveals a strongly polygynous mating system in the re-introduced Asiatic wild ass.

Small populations are prone to loss of genetic variation and hence to a reduction in their evolutionary potential. Therefore, studying the mating system of small populations and its potential effects on genetic drift and genetic diversity is of high importance for their viability assessments. The traditional method for studying genetic mating systems is paternity analysis. Yet, as small populations are often rare and elusive, the genetic data required for paternity analysis are frequently unavailable. The endangered Asiatic wild ass (Equus hemionus), like all equids, displays a behaviourally polygynous mating system; however, the level of polygyny has never been measured genetically in wild equids. Combining noninvasive genetic data with stochastic modelling of shifts in allele frequencies, we developed an alternative approach to paternity analysis for studying the genetic mating system of the re-introduced Asiatic wild ass in the Negev Desert, Israel. We compared the shifts in allele frequencies (as a measure of genetic drift) that have occurred in the wild ass population since re-introduction onset to simulated scenarios under different proportions of mating males. We revealed a strongly polygynous mating system in which less than 25% of all males participate in the mating process each generation. This strongly polygynous mating system and its potential effect on the re-introduced population's genetic diversity could have significant consequences for the long-term persistence of the population in the Negev. The stochastic modelling approach and the use of allele-frequency shifts can be further applied to systems that are affected by genetic drift and for which genetic data are limited.

Selection of models for the analysis of risk-factor trees: leveraging biological knowledge to mine large sets of risk factors with application to microbiome data.

MOTIVATION: Establishment of a statistical association between microbiome features and clinical outcomes is of growing interest because of the potential for yielding insights into biological mechanisms and pathogenesis. Extracting microbiome features that are relevant for a disease is challenging and existing variable selection methods are limited due to large number of risk factor variables from microbiome sequence data and their complex biological structure. RESULTS: We propose a tree-based scanning method, Selection of Models for the Analysis of Risk factor Trees (referred to as SMART-scan), for identifying taxonomic groups that are associated with a disease or trait. SMART-scan is a model selection technique that uses a predefined taxonomy to organize the large pool of possible predictors into optimized groups, and hierarchically searches and determines variable groups for association test. We investigate the statistical properties of SMART-scan through simulations, in comparison to a regular single-variable analysis and three commonly-used variable selection methods, stepwise regression, least absolute shrinkage and selection operator (LASSO) and classification and regression tree (CART). When there are taxonomic group effects in the data, SMART-scan can significantly increase power by using bacterial taxonomic information to split large numbers of variables into groups. Through an application to microbiome data from a vervet monkey diet experiment, we demonstrate that SMART-scan can identify important phenotype-associated taxonomic features missed by single-variable analysis, stepwise regression, LASSO and CART.

Allelic richness following population founding events--a stochastic modeling framework incorporating gene flow and genetic drift.

Allelic richness (number of alleles) is a measure of genetic diversity indicative of a population's long-term potential for adaptability and persistence. It is used less commonly than heterozygosity as a genetic diversity measure, partially because it is more mathematically difficult to take into account the stochastic process of genetic drift for allelic richness. This paper presents a stochastic model for the allelic richness of a newly founded population experiencing genetic drift and gene flow. The model follows the dynamics of alleles lost during the founder event and simulates the effect of gene flow on maintenance and recovery of allelic richness. The probability of an allele's presence in the population was identified as the relevant statistical property for a meaningful interpretation of allelic richness. A method is discussed that combines the probability of allele presence with a population's allele frequency spectrum to provide predictions for allele recovery. The model's analysis provides insights into the dynamics of allelic richness following a founder event, taking into account gene flow and the allele frequency spectrum. Furthermore, the model indicates that the "One Migrant per Generation" rule, a commonly used conservation guideline related to heterozygosity, may be inadequate for addressing preservation of diversity at the allelic level. This highlights the importance of distinguishing between heterozygosity and allelic richness as measures of genetic diversity, since focusing merely on the preservation of heterozygosity might not be enough to adequately preserve allelic richness, which is crucial for species persistence and evolution.

Allele-specific network reveals combinatorial interaction that transcends small effects in psoriasis GWAS.

Hundreds of genetic markers have shown associations with various complex diseases, yet the "missing heritability" remains alarmingly elusive. Combinatorial interactions may account for a substantial portion of this missing heritability, but their discoveries have been impeded by computational complexity and genetic heterogeneity. We present BlocBuster, a novel systems-level approach that efficiently constructs genome-wide, allele-specific networks that accurately segregate homogenous combinations of genetic factors, tests the associations of these combinations with the given phenotype, and rigorously validates the results using a series of unbiased validation methods. BlocBuster employs a correlation measure that is customized for single nucleotide polymorphisms and returns a multi-faceted collection of values that captures genetic heterogeneity. We applied BlocBuster to analyze psoriasis, discovering a combinatorial pattern with an odds ratio of 3.64 and Bonferroni-corrected p-value of 5.01×10(-16). This pattern was replicated in independent data, reflecting robustness of the method. In addition to improving prediction of disease susceptibility and broadening our understanding of the pathogenesis underlying psoriasis, these results demonstrate BlocBuster's potential for discovering combinatorial genetic associations within heterogeneous genome-wide data, thereby transcending the limiting "small effects" produced by individual markers examined in isolation.

Landscape influences on dispersal behaviour: a theoretical model and empirical test using the fire salamander, Salamandra infraimmaculata.

When populations reside within a heterogeneous landscape, isolation by distance may not be a good predictor of genetic divergence if dispersal behaviour and therefore gene flow depend on landscape features. Commonly used approaches linking landscape features to gene flow include the least cost path (LCP), random walk (RW), and isolation by resistance (IBR) models. However, none of these models is likely to be the most appropriate for all species and in all environments. We compared the performance of LCP, RW and IBR models of dispersal with the aid of simulations conducted on artificially generated landscapes. We also applied each model to empirical data on the landscape genetics of the endangered fire salamander, Salamandra infraimmaculata, in northern Israel, where conservation planning requires an understanding of the dispersal corridors. Our simulations demonstrate that wide dispersal corridors of the low-cost environment facilitate dispersal in the IBR model, but inhibit dispersal in the RW model. In our empirical study, IBR explained the genetic divergence better than the LCP and RW models (partial Mantel correlation 0.413 for IBR, compared to 0.212 for LCP, and 0.340 for RW). Overall dispersal cost in salamanders was also well predicted by landscape feature slope steepness (76%), and elevation (24%). We conclude that fire salamander dispersal is well characterised by IBR predictions. Together with our simulation findings, these results indicate that wide dispersal corridors facilitate, rather than hinder, salamander dispersal. Comparison of genetic data to dispersal model outputs can be a useful technique in inferring dispersal behaviour from population genetic data.

Genetic restoration in the eastern collared lizard under prescribed woodland burning.

Eastern collared lizards of the Ozarks live in glades--open, rocky habitats embedded in a woodland matrix. Past fire suppression had made the woodlands a barrier to dispersal, leading to habitat destruction, fragmentation and local extinction. Reintroduced populations of lizards were subjected to 10 years of habitat fragmentation under continued fire suppression followed by twelve years of landscape restoration with prescribed burns. Prior to prescribed burning, genetic diversity decreased within glades and differentiation increased among glades. With woodland burning, genetic diversity within glades first decreased during an expanding colonization phase, but then increased as a dynamically stable metapopulation was established. Population differentiation among glades also stabilized in the metapopulation under weak isolation-by-distance. This study is one of the first to examine the genetic changes in a species of conservation concern throughout all the stages of decline and recovery and shows the importance of landscape-level restoration for maintaining the genetic integrity of populations. This study also demonstrates how mark-recapture and genetic data together can yield detailed insight into metapopulation dynamics that would be impossible from just one type of data alone.

Biological races in humans.

Races may exist in humans in a cultural sense, but biological concepts of race are needed to access their reality in a non-species-specific manner and to see if cultural categories correspond to biological categories within humans. Modern biological concepts of race can be implemented objectively with molecular genetic data through hypothesis-testing. Genetic data sets are used to see if biological races exist in humans and in our closest evolutionary relative, the chimpanzee. Using the two most commonly used biological concepts of race, chimpanzees are indeed subdivided into races but humans are not. Adaptive traits, such as skin color, have frequently been used to define races in humans, but such adaptive traits reflect the underlying environmental factor to which they are adaptive and not overall genetic differentiation, and different adaptive traits define discordant groups. There are no objective criteria for choosing one adaptive trait over another to define race. As a consequence, adaptive traits do not define races in humans. Much of the recent scientific literature on human evolution portrays human populations as separate branches on an evolutionary tree. A tree-like structure among humans has been falsified whenever tested, so this practice is scientifically indefensible. It is also socially irresponsible as these pictorial representations of human evolution have more impact on the general public than nuanced phrases in the text of a scientific paper. Humans have much genetic diversity, but the vast majority of this diversity reflects individual uniqueness and not race.

Evolution of the human gastrokine locus and confounding factors regarding the pseudogenicity of GKN3.

In a screen for genes expressed specifically in gastric mucous neck cells, we identified GKN3, the recently discovered third member of the gastrokine family. We present confirmatory mouse data and novel porcine data showing that mouse GKN3 expression is confined to mucous cells of the corpus neck and antrum base and is prominently expressed in metaplastic lesions. GKN3 was proposed originally to be expressed in some human populations and a pseudogene in others. To investigate that hypothesis, we studied human GKN3 evolution in the context of its paralogous genomic neighbors, GKN1 and GKN2. Haplotype analysis revealed that GKN3 mimics GKN2 in patterns of exonic SNP allocation, whereas GKN1 appeared to be more stringently selected. GKN3 showed signatures of both directional selection and population based selective sweeps in humans. One such selective sweep includes SNP rs10187256, originally identified as an ancestral tryptophan to premature STOP codon mutation. The derived (nonancestral) allele went to fixation in Asia. We show that another SNP, rs75578132, identified 5 bp downstream of rs10187256, exhibits a second selective sweep in almost all Europeans, some Latinos, and some Africans, possibly resulting from a reintroduction of European genes during African colonization. Finally, we identify a mutation that would destroy the splice donor site in the putative exon3-intron3 boundary, which occurs in all human genomes examined to date. Our results highlight a stomach-specific human genetic locus, which has undergone various selective sweeps across European, Asian, and African populations and thus reflects geographic and ethnic patterns in genome evolution.

A factorial design experiment as a pilot study for noninvasive genetic sampling.

Noninvasive genetic sampling has increasingly been used in ecological and conservation studies during the last decade. A major part of the noninvasive genetic literature is dedicated to the search for optimal protocols, by comparing different methods of collection, preservation and extraction of DNA from noninvasive materials. However, the lack of quantitative comparisons among these studies and the possibility that different methods are optimal for different systems make it difficult to decide which protocol to use. Moreover, most studies that have compared different methods focused on a single factor - collection, preservation or extraction - while there could be interactions between these factors. We designed a factorial experiment, as a pilot study, aimed at exploring the effect of several collection, preservation and extraction methods, and the interactions between them, on the quality and amplification success of DNA obtained from Asiatic wild ass (Equus hemionus) faeces in Israel. The amplification success rates of one mitochondrial DNA and four microsatellite markers differed substantially as a function of collection, preservation and extraction methods and their interactions. The most efficient combination for our system integrated the use of swabs as a collection method with preservation at -20 °C and with the Qiagen DNA Stool Kit with modifications as the DNA extraction method. The significant interaction found between the collection, preservation methods and the extraction methods reinforces the importance of conducting a factorial design experiment, rather than examining each factor separately, as a pilot study before initiating a full-scale noninvasive research project.