Alces alces is the largest member of the Cervidae family, and ranges throughout the circumpolar boreal forests of Eurasia and North America ^{1, 2} . The species diverged from the ancestors of domestic cattle and sheep approximately 27 million years ago ³ . Moose are important ecologically, as a large ungulate with strong ecosystem impacts; economically, due to their value for tourism and hunting; and culturally, as a prominent symbol in many regions ⁴ . Consequently, there is active management of moose populations by wildlife agencies throughout their range in North America. However, management is hampered by a lack of genetic tools for monitoring moose, assessing the genetic health of populations, and even detecting illegal harvesting. Moose populations appear to be declining in some regions, including parts of the Upper Midwest of the United States ^{5, 6} , and effective management is often dependent on data that are logistically challenging and/or costly to collect.

Identifying individual animals and measuring relatedness among and within populations are important for effective wildlife management and conservation efforts ^{7– 9} . Identifying individuals can be as simple as observing their unique color patterns, for example in wild dogs ( Lycaon pictus) ¹⁰ . However, this is not practical in species such as moose that have few features with obvious variation between individuals. Moreover, coat color patterns provide little information about genetic relatedness. Association of younger and older animals has been used to infer relationships, for example in swift foxes ( Vulpes velox) where pups at a den are presumed to be offspring of the attending parents based on the monogamous behaviors they exhibit. However, detailed parentage studies have revealed multiple paternity within swift fox litters ¹¹ and other fox species ¹² . Generally, genetic testing provides a more accurate assignment of parentage and supports unique identification of individuals in the vast majority of instances, as well as an estimation of intra- and inter-population genetic variability.

Genetic testing using DNA markers has been applied to human, livestock, and wildlife studies for many years ^{13– 16} . This form of testing first gained popularity with the development of microsatellite short tandem repeat (STR), and mitochondrial genome markers, concurrent with the development of DNA amplification and sequencing technologies. Approximately 5 to 11 microsatellite markers from cattle, sheep, and caribou ( Rangifer tarandus) have been adapted for moose studies ^{17– 22} . These studies form the basis of our current understanding of North American moose population structure and genetic diversity. DNA technology developments in the past decade have led to the replacement of microsatellite and mitochondrial genome markers with SNP markers because SNPs are more abundant, have greater stability over generations, are more accurately genotyped, and are amenable to automating the genotyping processes ²³ . Moreover, panels of SNPs broaden the use of genotyping for management and conservation efforts, because they can provide not only identification of individuals and parentage, but also estimation of inbreeding and relatedness, and detection of admixture between populations of wildlife. For example, an SNP-based approach has been used for conservation efforts in endangered species such as the Iberian lynx ( Lynx pardinus) ²⁴ and Tasmanian devil ( Sarcophilus harrisii) ²⁵ , as well as more common but wide-ranging species like the brown bear ( Ursus arctos) ²⁶ . The application of SNP-based approaches, however, requires first the identification of polymorphisms segregating in the populations being studied, and developing assays that support accurate genotyping. Accordingly, a SNP panel spanning the genome of North American moose would be useful for addressing fundamental questions about population genetics in this species.

Low genetic diversity among North American moose populations has been previously reported ^{17, 18} , making development of SNP panels challenging. The low diversity has been attributed in the prevailing theory, to a relatively recent (ca. 11,000–14,000 years ago) colonization from Asia and subsequent founder effect induced by extended range expansion from an original small group of animals ²⁷ . However, no definitive evidence has been presented that refutes an alternative hypothesis, that North American moose experienced a severe population bottleneck at some time in the past ²⁰ , as occurred for North American bison ( Bison bison) populations ²⁸ . Whether a founder effect, bottleneck, or both, the small effective population size simultaneously increases the need for developing genetic tools for management and the challenge of creating SNP marker panels.

Discovery of SNPs in a species has generally been preceded by development of its reference genome assembly. Using this assembly, whole genome sequence (WGS) reads from individual animals can be aligned, and differences between segregating alleles identified. However, creation of a reference assembly still represents a significant barrier for most research communities interested in wildlife species. Fortunately, an alternative approach that uses the reference genomes of related species has been developed ²⁹ , and shown to effectively identify high-confidence SNPs likely to be segregating within the target species. Here we report the whole genome sequencing of four moose genomes, each obtained from distant geographic regions of North America, and the use of the cattle and sheep reference genomes to align the sequence data and identify SNPs likely to be segregating among moose populations. A set of criteria was developed to select the potentially most useful set of moose SNPs, and to identify 317 autosomal variants meeting these criteria. The associated sequence information was made freely available, and represents a resource for developing genotyping assays to support moose genetic research.

An average of 63.5 Gb total genome sequence was collected for four moose. Based on similar estimates in cattle and sheep, this would correspond to an average read depth of 19-fold coverage if aligned to a moose reference genome of similar quality ( Table 1). However, when cattle and sheep reference genomes were used, an average of 11.0 and 8.7% of the moose reads were aligned, respectively. For comparison, the same alignment method was performed with sets of bovine and ovine genomic sequences and resulted in 88.4% and 83.8% reads aligned to their respective genome assemblies ( Table 1). For cross-species comparison, 22.2% of the ovine set of genomic sequence reads were aligned to the bovine assembly. Although the moose read depth was low when averaged across the entire genome of cattle or sheep, at conserved genome regions it was consistent with the expected average read depth of 19-fold. Thus, the moose read depth in conserved genomic regions appeared to be sufficient for identifying polymorphic sites and accurately assigning variant alleles.

Alignment of moose reads to the cattle and sheep genomes identified approximately 48.3 million and 39.7 million sites that differed from the reference assemblies, respectively. These included SNPs, insertions and deletions, and sites where moose-associated nucleotide differences occurred. The latter sites were defined as having homozygous genotypes in the four moose, with alleles differing from those in cattle or sheep ( Figure 2). After stringent filtering for read depth and alignment quality, there were 1,095,371 and 813,006 moose variants identified with the respective cattle and sheep genome assemblies ( Table 2). Approximately 96% of these were homozygous moose-associated nucleotide differences ( Supplementary file S1 and Supplementary file S2). The remaining 46,005 and 36,934 variants were moose SNPs identified by the respective cattle and sheep alignments ( Supplementary file S3 and Supplementary file S4). The MAF distribution of the moose SNPs was similar for both sets with the large group having a 0.125 MAF (approximately 37%, Table 2). The most informative moose SNPs (i.e., “highly informative”) were defined as those with a 0.5 MAF and both homozygous genotypes present among any of the four moose, and are candidate SNPs that may have arisen to a high MAF prior to the species arrival in North America ( Figure 3). There were 1,341 and 1,014 of these moose SNPs identified with the cattle and sheep alignments, respectively ( Table 2).

Candidate moose SNPs were further excluded when the flanking sequences in one reference genome were not uniquely identified in the other. This left 773 and 552 highly informative moose SNPs identified in conserved regions of the cattle and sheep genomes, respectively ( Supplementary Table S1 and Supplementary Table S2). Of these 1,325 highly informative SNPs, 1,008 were unique between the two sets, while 317 were common to both sets. The latter represents the most informative moose SNPs, with the highest flanking sequence conservation, due to their independent alignment to both reference genomes ( Table 2).

The alignment coordinates of the 1,325 highly informative SNPs were analyzed for genome-wide distribution patterns that may indicate ascertainment biases caused by the variant selection. Overall, the distribution of SNP sites in the sets with 773, 552, and the 317 intersecting markers, appeared to be widespread in the cattle and sheep genomes and generally appropriate for genome-wide estimates ( Figure 4). However, some SNP clustering was observed as the set of 317 had a mean and median spacing of 5.3 and 2.1 Mb, respectively ( Supplementary Figure S1A). To facilitate SNP genotype assay design, the clustered SNPs were manually grouped into 216 bins with a mean size of approximately 8.1 Mb (median 5.9 Mb, Supplementary Figure S1B). Thus, SNP assay designs could be directed to each bin, with the option to use any SNP from that bin for multiplex assay design ( Table S3).

Genotype analysis for the 773 and 552 moose SNPs derived from the cattle and sheep alignments, respectively, showed that each moose had approximately the same proportion of opposing homozygous genotypes ( Figure 5A, Supplementary Table S4 and Supplementary Table S5). However, there were significant differences in the ratio of heterozygous genotypes to homozygous genotypes ( Figure 5B). The Alaskan moose had the most favorable average heterozygosity ratio (1.26), followed by the moose from Wyoming and Idaho (1.10 and 1.06, respectively), and the Vermont moose (0.68). Note that the numerical value of the ratios calculated from these SNP is likely an underestimate of the within-animal genome-wide heterozygosity, because there may be ascertainment bias resulting from targeting of SNP discovery to genomic regions conserved between three species. The SNP allele sharing between each of the four moose was analyzed with the sets of 773 and 552 markers to obtain a genome-wide measurement of their relatedness. This was possible because the method for selecting each of these SNPs was not dependent on which two of the four moose were heterozygous. The pair of moose from Alaska and Idaho had the highest proportion of shared alleles (0.430 and 0.397), while the Alaska and Vermont pair had the lowest (0.255 and 0.279, Table 3). Together, the genotype results with these sets of 773 and 552 SNPs indicate that there was a west-to-east pattern of decreasing genetic diversity in the four moose used in this study.

The combined set of 1,008 highly informative moose SNPs were also evaluated for their relative proximity to genes in the annotated reference assemblies of cattle and sheep. In the sets of 773 and 552 moose SNPs, 256 and 181 were present within genes, respectively ( Table S6 and Table S7). Some genes contained more than one polymorphism, and thus, there were 221 and 178 total cattle and sheep genes, respectively, with highly informative SNPs. Of these genes with moose SNPs, 84 were identified in both cattle and sheep alignments. In addition, there were a number of informative SNPs in noteworthy genes that did not pass the read depth and quality score filters. For example, the prion gene ( PRNP) affects susceptibility to spongiform encephalopathies such as chronic wasting disease in cervids. By manually viewing the PRNP coding sequence with IGV software, a coding SNP with 0.5 MAF and both homozygotes present was identified (M217I, Table 4). Thus, the publicly searchable and viewable moose WGS presented here represents a novel genomics resource that may facilitate candidate gene-based research in this species.

We sequenced four moose from regions that span the United States, to approximately 19-fold genome coverage, and aligned them to the cattle and sheep reference genomes. Approximately 10% of moose sequences were aligned and used to identify more than 40 k moose SNPs in this cross-species approach. The relatively low alignment rate may be a reflection of the 27 million year average molecular divergence time between moose and non-cervid members of the Pecora infraorder ³ . In spite of the alignment rate, 1,008 highly informative moose SNPs were identified for future use in developing DNA-based genetic tests to support forensic and wildlife conservation activities. These 1,008 moose SNPs were derived from the intersection of two overlapping sets aligned to cattle (773 SNPs) and sheep (552 SNPs) reference genome assemblies. The 1,008 moose SNPs were refined to a minimal subset of 317 moose SNPs found in the most highly conserved genome regions. All of these markers are publicly available and ready for validation on a variety of SNP genotyping technology platforms. An important first step in evaluating these SNPs will be characterizing their MAFs in wild populations of North American moose. The online whole genome moose sequences, together with reference genotypes ( Supplementary Table S4 and Supplementary Table S5) and DNA from these four moose, provide the opportunity for immediate design, testing, and validation of these candidate parentage SNPs.

Genotype information from the 1,008 moose SNPs was useful for measuring genome-wide differences in DNA sequence diversity among the four individuals. Measurements of heterozygosity and allele sharing showed that the Alaskan moose was the most diverse, the Vermont moose was the least, with the moose from Idaho and Wyoming being intermediate. This is consistent with a species that crossed the Bering Land Bridge into Alaska and radiated outward from west to east across North America. SNPs have been previously used to estimate genome diversity in other species with low genetic diversity like the European bison ( B. bonasus) ⁴¹ and the Tasmanian devil ( S. harrisii) ²⁵ . A caveat with our results is the overall heterozygosity of each moose may be underestimated due to ascertainment bias for highly informative SNPs in highly conserved genomic regions. In other words, variation in conserved moose genome regions may occur at a lower rate than that in non-conserved regions. In spite of this potential ascertainment bias, the results suggest that combinations of these markers may be useful in detecting population structure.

An important unanswered question is: how informative will these SNPs be in moose populations? Population-wide data to address this question will require development and application of genotyping assays, and assembly of pertinent samples for testing, which was beyond the scope and resources of the present report. The data presented here, which identify polymorphisms with alternate homozygous genotypes in a limited sample of only four individuals, suggest that the SNP selected represent variation that existed prior to arrival of moose in North America.

These moose SNPs and associated sequence information are available for use without restriction, and provide a basis for developing commercial SNP-based “parentage” SNP DNA tests for validation in North American moose populations.

FASTQ files for the four moose combined are available in NCBI SRA, with contiguous accession numbers SRX3218250 - SRX3218281.

The SRA accession numbers for each individual are:

SRX3218264 - SRX3218271, Alaska moose HM2013;

SRX3218254 - SRX3218259 and SRX3218262 - SRX3218263, Wyoming moose JC2001; SRX3218250 - SRX3218253 and SRX3218272 - SRX3218275, Vermont moose R199; and SRX3218260 - SRX3218261 and SRX3218276 - SRX3218281, Idaho moose Clearwater06.

The data are part of NCBI BioProject Accession PRJNA325061.

In addition, access to the aligned sequences is available via the USDA: http://www.ars.usda.gov/Research/docs.htm?docid=25590 (moose aligned to cattle), and http://www.ars.usda.gov/Research/docs.htm?docid=25712 (moose aligned to sheep).

Download access to the BAM files is available at the Intrepid Bioinformatics sites: http://server1.intrepidbio.com/FeatureBrowser/customlist/record?listid=7919250313 (moose aligned to cattle), and http://server1.intrepidbio.com/FeatureBrowser/customlist/record?listid=7919250315 (moose aligned to sheep).