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Characterization of the Gray Whale Eschrichtius robustus Genome and a Genotyping Array Based on Single-Nucleotide Polymorphisms in Candidate Genes.

Abstract. Genetic and genomic approaches have much to offer in terms of ecology, evolution, and conservation. To better understand the biology of the gray whale Eschrichtius robustus (Lilljeborg, 1861), we sequenced the genome and produced an assembly that contains ~95% of the genes known to be highly conserved among eukaryotes. From this assembly, we annotated 22,711 genes and identified 2,057,254 single-nucleotide polymorphisms (SNPs). Using this assembly, we generated a curated list of candidate genes potentially subject to strong natural selection, including genes associated with osmoregulation, oxygen binding and delivery, and other aspects of marine life. From these candidate genes, we queried 92 autosomal protein-coding markers with a panel of 96 SNPs that also included 2 sexing and 2 mitochondrial markers. Genotyping error rates, calculated across loci and across 69 intentional replicate samples, were low (0.021%), and observed heterozygosity was 0.33 averaged over all autosomal markers. This level of variability provides substantial discriminatory power across loci (mean probability of identity of 1.6 x [10.sup.-25] and mean probability of exclusion >0.999 with neither parent known), indicating that these markers provide a powerful means to assess parentage and relatedness in gray whales. We found 29 unique multilocus genotypes represented among our 36 biopsies (indicating that we inadvertently sampled 7 whales twice). In total, we compiled an individual data set of 28 western gray whales (WGSs) and 1 presumptive eastern gray whale (EGW). The lone EGW we sampled was no more or less related to the WGWs than expected by chance alone. The gray whale genomes reported here will enable comparative studies of natural selection in cetaceans, and the SNP markers should be highly informative for future studies of gray whale evolution, population structure, demography, and relatedness.


The gray whale Eschrichtius robustus (Lilljeborg, 1861) was once common in both the North Pacific and the North Atlantic Ocean but disappeared from the Atlantic by the early 1700s because of overhunting (Mead and Mitchell, 1984). Contemporary gray whales are found only along the eastern and western coasts of the North Pacific Ocean (Andrews, 1914; Rice and Wolman, 1971; Fig. 1). Here, we follow the convention established by the International Union for Conservation of Nature (IUCN, 2008; Reilly et al., 2008) and refer to an eastern gray whale (EGW) population and a western gray whale (WGW) population. We use WGW to refer to the gray whales that feed in the western North Pacific off the coast of Sakhalin Island, Russia (Fig. 1). The IUCN considers the extant WGW population to be critically endangered (IUCN, 2008; Reilly et al., 2008), as there were only about 140 WGW adults in 2012 (Cooke et al., 2013), and the population was previously believed to be extinct (Bowen, 1974; Weller et al., 2002). The EGW population was reduced to a low of about 2000 individuals and has since made a strong recovery, the contemporary population numbers about 19,000 individuals (Laake et al., 2009; Durban et al., 2015).

Like so many of the great whales, modern gray whale populations declined steeply during the commercial whaling era (Mead and Mitchell, 1984; Alter et al., 2012). On the basis of whaling records, Henderson (1984) estimated the size of the EGW population at 15,000-20,000 individuals prior to commercial hunting. The prewhaling WGW population is thought to have been much smaller (see Berzin and Vladimirov, 1981), but they were apparently not as restricted in geographic range as they are today (Reeves et al., 2008).

Gray whales are occasionally sighted along a historical western migration corridor that includes waters near Russia's Sakhalin Island in the Sea of Okhotsk (Weller et al., 2008; Weller and Brownell, 2012; Fig. 1). Studies based on the maternally inherited mitochrondrial DNA (mtDNA) genome have documented genetic differentiation between the WGWs sampled near Sakhalin and the EGW population (LeDuc et al., 2002; Alter et al., 2012; Meschersky et al., 2015). Geospatial and genetic data (e.g., Alter et al., 2015; Mate et al., 2015) suggest that the extant WGW and EGW populations have the potential to mix, and thus contemporary signals of population differentiation may represent historical patterns. Gray whales have great capacity to travel long distances (>22,000 km; Scheinin et al., 2011; Shpak et al., 2013), and in other great whales (e.g., sperm whales; Alexander et al., 2016) nuclear gene pools are nearly homogenous across vast geographic scales. The development of additional, more powerful genetic assays would be a boon to global gray whale conservation, as the ability to identify individuals and family groups, delineate populations, and track patterns of genetic diversity over space and time would result in more informed management decisions.

Suites of single-nucleotide polymorphisms (SNPs) can be used to delineate breaks in genetic structure, but they can also be used for definitive individual identifications (e.g., from biopsies) and for categorical assignment of parentage or relatedness (e.g., Ruegg et al., 2014; Doyle et al., 2016). Here, we developed a gray whale SNP panel to facilitate genetic studies of population differentiation, parentage, and individual identity. Instead of using anonymous SNP markers, we used SNPs from protein-coding genes thought to be targets of selection in other marine mammals because, in addition to providing a rich source of targets for future evolutionary genetic studies, they also have the potential to reveal subtle genetic differentiation sooner than neutral markers because of selection (Hoban et al., 2016; Schweizer et al., 2016).

To develop our SNP panel, we sequenced the genomes of two Sakhalin Island gray whales (a male and a female WGW) and a female EGW from Barrow, Alaska. Subsequently, multilocus SNP genotypes were generated for the single EGW plus a set of WGW biopsy samples (n = 35) collected near Sakhalin Island to provide a preliminary estimate of genomic diversity in gray whales. Our sample sizes are small despite three years of effort because these whales are rare and, like other baleen whales, difficult to sample because of both the expense involved and their extensive movements. However, the genome we describe here will allow researchers to explore evolutionary aspects of gray whales (e.g., rates of nucleotide substitution) and perhaps identify genes and pathways unique to the species.

Materials and Methods

Sample collection and DNA extraction

Skin biopsies were obtained from WGWs on their summer feeding grounds near Sakhalin Island using methods approved by the Scientific Committee of the International Whaling Commission. These include samples from six calves as well as adults. Biopsy samples were frozen and shipped to Purdue University for processing. Skin samples from putative EGWs (n = 2) were cut from two dead gray whales that beached near Barrow, Alaska, and then frozen until processing at Purdue University. Genomic DNA was extracted using a standard potassium acetate protocol (Sambrook and Russell, 2001).

Genome sequencing, assembly, and annotation

The quality and quantity of DNA were greatest from the two WGWs (male ER-14-168 and female ER-14-0147), but we also sequenced one EGW (female GFD-02). For the male WGW, we constructed one paired-end (PE) library without polymerase chain reaction (PCR) amplification and one mate-paired (MP) no-gel library. For the two female gray whales (WGW and EGW), we constructed both MP and PE libraries. PE libraries were constructed per the instructions from the TruSeq DNA PCR-Free Library Preparation Kit (Illumina, San Diego, CA) using the 550-bp insert size method, whereas MP libraries were constructed per the instructions from the Nextera Mate Pair Library Preparation Kit (Illumina) using the "no gel size selection" method. In total, we sequenced 7 lanes of the PE libraries and 1 lane of the MP libraries using an Illumina HiSeq 2500 sequencing system (2 x 100).

We used FastQC software (ver. 0.11.2; Babraham Bioinformatics, 2016) to generate summary statistics for the sequencing data. Trimmomatic software (ver. 0.32; Bolger et al., 2014) was used to remove adaptor sequences and low-quality bases (Phred scores <20). Multiple genome assemblies were generated using ABySS software (ver. 1.9.0; Simpson et al., 2009) with a variety of k-mer values to produce the most comprehensive assemblies. We used the PE data during the contig-building steps and MP data during the scaffold-building steps.

Genome annotation was based on the male WGW to include genes found on the Y chromosome (Zfy) and the X chromosome (Zfx). We used the MAKER pipeline (ver. 2.28; Cantarel et al., 2008) for annotation, following Doyle et al. (2014). Briefly, RepeatMasker software was used to identify and mask stretches of repetitive DNA. We subsequently used SNAP software (ver. 2013-02-16; Korf, 2004) to generate ab initio gene predictions. SNAP was trained (i.e., gray whale gene models were generated) using cetacean protein sequences from the UniProtKB/Swiss-Prot database. SNAP gene predictions supported by expressed sequence tag (EST), protein, or InterProScan evidence were elevated to gene annotations. Cetacean protein data were downloaded from the UniProt Knowledgebase (Uniprot Consortium, 2017), and the manually annotated sequences were used with EST evidence derived from an assembled transcriptome of an Alaskan bow-head whale (Keane et al., 2015). We used CEGMA software to assess the completeness of our genome assemblies (Parra et al., 2007).

SNP identification and development

From published cetacean data, we generated a manually curated list of candidate genes potentially subject to strong natural selection. These include genes associated with osmoregulation, oxygen binding and delivery, and many other aspects of marine life (hereafter referred to as "nonneutral" or "candidate" genes; see Table S1 [available online], Table S2 [available online], and Kosiol et al., 2008). We targeted SNPs from these genes because they can provide insights into adaptive divergence (Rico et al., 2016), because they can help determine the biological significance of population genetic structure (Miller et al., 2010), and because they can provide more power for population assignments (Freamo et al., 2011; Helyar et al., 2011). Using the MAKER transcripts from our genome annotation, we used BLAST software (ver. 2.2.31 +) to annotate these candidate genes in the gray whale genome. For SNP identification, we used BWA software (ver. 0.7.12; Li and Durbin, 2009) to map all PE reads to the male assembly. We used GATK software (ver. 3.4; DePristo et al., 2011; Van der Auwera et al., 2013) to (1) identify and realign reads around insertions/deletions (indels), (2) identify SNPs with a minimum Phred quality score of 30 and a minimum depth of 10 reads, and (3) disregard SNPs within 20 bp of each other. BEDOPS software (ver. 2.0; Neph et al., 2012) was used to identify variable sites (i.e., SNPs) within nonneutral genes. We used IGV software (ver. 2.3; Thorvaldsdottir et al., 2013) to identify target SNPs with at least 60 nucleotides (nt) of high-quality flanking sequence upstream and downstream and guanine and cytosine (GC) content less than 65%. We deliberately minimized linkage disequilibrium (LD) by choosing only single SNPs from a given scaffold.

Ultimately, we attempted to develop 92 autosomal nuclear markers from protein-coding genes specifically targeted because of evidence for selection in other marine mammals. Following preliminary population surveys via dideoxy sequencing (Alter and Palumbi 2009), we designed two mtDNA markers for haplotyping. For sexing, we designed two redundant SNP assays to assess differences in nucleotide sequence between the Zfx and Zfy genes, as reported in GenBank entries AF260789.1, AF260790.1, and AF260791.1.

SNP genotyping, error rates, and variability

We genotyped 36 gray whale biopsy samples at the 96 candidate SNP markers. Samples were genotyped using a Fluidigm Juno Genotyping System (Fluidigm, South San Francisco, CA), and a specific target amplification (STA) step was incorporated to facilitate genotyping of the low-quantity DNA samples that are often associated with endangered species. The STA step refers to Fluidigm's multiplex PCR, which uses a low molar concentration of each primer and limited thermal cycles to increase template material for downstream amplification. Individual SNP calls were visualized and edited using Fluidigm's genotyping analysis software; data from nuclear loci that did not produce obvious clusters of homozygotes and heterozygotes (see Fig. 2) were excluded from further analyses.

In theory, our 35 WGW biopsies could include whales that were inadvertently sampled more than once (e.g., in subsequent years). We used allelematch in R software (Galpern et al., 2012; R Development Core Team, 2008) to group replicate genotypes into unique records that represent individual whales, allowing for a maximum of two mismatches between replicates. Preliminary analyses conducted with the diagnostic function amUniqueProfile were used to determine the number of mismatches most appropriate for clustering replicate genotypes into groups (Galpern et al., 2012). All genotypes identified as replicates were subsequently confirmed by manually evaluating the SNP calls.

Error rates were calculated using replicate DNA samples (n = 69 in total) from 27 individual gray whales. The plurality principle was used to determine a consensus genotype for each individual whale by using SNP calls across replicate samples (see Doyle et al., 2016). The SNP typing error rate (e) was calculated according to the equation e = m/[d(s)], where m represents the total number of mismatches between each replicate sample and the consensus sequence across multiple samples from the same individual, d represents the total number of loci per replicate sample, and s represents the total number of replicate samples. Both incorrect SNP calls (errors of commission) and instances where no amplification occurred (errors of omission) contributed to m.

We used GENALEX software (ver. 6.5; Peakall and Smouse, 2012) to quantify observed ([H.sub.O]) and expected ([H.sub.E]) heterozygosity, the mean probability of identity ([P.sub.I]), and the mean probability of exclusion with neither parent known ([P.sub.E]). We tested for deviations from Hardy-Weinberg equilibrium (HWE) and for LD after applying a sequential Bonferroni correction to account for multiple tests (Holm, 1979). The inbreeding coefficient/(Weir and Cockerham, 1984) was evaluated in GENODIVE software (ver. 2.0b27; Meirmans and Van Tienderen, 2004) using [10.sup.3] permutations.

Molecular sexing and haplotyping

For validation purposes, all samples were sexed using a traditional PCR/gel method (Berube and Palsboll, 1996) as well as by using our novel sexing SNPs. Similarly, mtDNA haplotypes were generated using both traditional dideoxy methods (Alter and Palumbi, 2009) and novel SNP markers. We then compared the concordance among assays to validate our novel markers.

Relatedness and effective size

Relatedness among individuals was first estimated using RELATED software (ver. 1.0; Wang, 2011; Pew et al., 2015), which implements five widely used moment-based relatedness estimators. To identify the estimator that performed best with our data, we used our empirical allele frequencies to simulate 100 data sets for 4 types of dyads: parent-offspring pairs (expected r = 0.5), full siblings (r = 0.5), half siblings (r = 0.25), and unrelated individuals (r = 0.0). Point estimates and 95% confidence intervals (CIs) were estimated on the basis of 100 bootstrap replicates, and the best estimator was identified using Pearson's correlation coefficient. This estimator was used to compare the observed mean pairwise relatedness of each individual whale ([]) to the mean for the population ([r.sub.pop]). Thus, [] represents the mean relatedness of a single individual whale to every other whale in the population, whereas [r.sub.pop] represents the mean relatedness of the population as a whole. By comparing [] to [r.sub.pop], individual whales were identified that were more or less related to the population than expected by chance alone.

Because several commonly used relatedness estimators can be problematic when used with biallelic loci (Oliehoek et al., 2006), we also calculated a pairwise genotypic similarity index following Blouin et al. (1996). The [M.sub.xy] statistic quantifies the mean number of shared alleles ([M.sub.xy]) among pairs of samples (in our case, 630 pairs). For each locus, the number of matching allelic positions (0, 1, or 2) between pairs of individuals was determined using only loci with no missing data among a given pair of whales. For each individual sample (n = 36), we calculated mean observed [M.sub.xy] and 95% CI, which was compared with the population mean and 95% CI.

Conventional relatedness and allele-sharing approaches like those described above do not differentiate between relationship categories. For example, both parent-offspring pairs and full-sibling pairs are expected to have r = 0.5. CERVUS software (ver. 3.0.7; Marshall et al., 1998; Kalinowski et al., 2007) was used to assign parentage to calves. All adults identified in the field were considered candidate parents. Simulations included 100,000 replicate cycles with the number of candidate males and females set to 200 and the proportion of candidate males and females sampled set to 0.1. The proportion of loci mistyped was set to 0.0002, and the minimum allowable confidence level at which a parent assignment was accepted was 95%.

To provide preliminary estimates of contemporary effective population size ([n.sub.e]) and effective number of breeders ([n.sub.eb]) of the critically endangered WGW population, we used NeESTI-MATOR software (ver. 2.01; Do et al., 2014). The software implements two different methods for estimating [n.sub.e] from a single sample, based on (1) LD (Waples and Do, 2008) and (2) heterozygosity excess (Zhdanova and Pudovkin, 2008). A molecular coancestry approach (Nomura, 2008)--also implemented in NeESTIMATOR--was used for estimating [n.sub.eb]. Note that these methods assume selectively neutral markers and closed populations, so these [n.sub.e] and [n.sub.eb] estimates may be biased. Moreover, the influence of selection and migration on such estimates is unknown, as it has not been investigated in a thorough, systematic manner (Waples, 2006).


Genome sequencing, assembly, and annotation

Our WGW sequencing results are compiled in Tables 1 and 2; the EGW genome yielded a poor-quality sequence and was not assembled. We generated >2.5 billion reads (~2 billion high-quality reads after quality control) that collectively span ~200 billion bases and contain nearly all of the core genes common to eukaryotes (Tables 1, 2). The scaffold N50, which represents the value where more than half of the assembly is contained in larger contiguous regions, was ~180,000 bp for the best assembly. We annotated roughly 22,700 genes, a number similar to other cetacean genome studies (Tables S1, S6, available online).

SNP identification and development

We identified 2,057,254 candidate SNPs from the WGWs, of which 1,474,749 passed quality-filtering criteria. Of this high-quality subset, 8413 SNPs were located in exons that were identified in the genome annotation. From these exonic SNPs, we designed and tested 96 SNPs for our genotyping assay. Four were ultimately excluded because they were monomorphic, clustered poorly, or were otherwise of insufficient quality. These novel SNP loci and flanking sequences are described in Table S5, available online. The 92 informative loci include 88 gene-associated nuclear markers, 2 mitochondrial markers, and 2 nuclear sexing markers (Table S2, available online).

SNP genotyping, variability, and error rates

The vast majority of markers amplified in each individual DNA sample. By genotyping 27 of the 36 biopsy samples multiple times, we calculated an overall genotyping error rate (e) of 0.021%. The mean probability of identity ([P.sub.I]; Waits et al., 2001) was 1.6 x [10.sup.-25], and the mean probability of exclusion ([P.sub.E], Jamieson and Taylor, 1997) with neither parent known was >0.999. According to allelematch, the 36 biopsies represented a total of 29 unique multilocus genotypes that correspond to 29 individual gray whales (i.e., we inadvertently sampled 7 whales twice). Observed and expected heterozygosities at autosomal SNPs were 0.32 [+ or -] 0.19 (mean [+ or -] SD) and 0.31 [+ or -] 0.17, respectively, for all WGWs sampled (n = 28). All autosomal SNP loci were in HWE following sequential Bonferroni correction (Table S3, available online). The [H.sub.O] and [H.sub.E] of male whales (n = 11) averaged 0.31 [+ or -] 0.02 and 0.29 [+ or -] 0.02, respectively, across all autosomal markers, whereas the [H.sub.O] and [H.sub.E] of female whales (n = 17) averaged 0.33 [+ or -] 0.02 and 0.31 [+ or -] 0.02, respectively. The mean inbreeding coefficient (f) was -0.05.

Molecular sexing and haplotyping

Samples from the 29 individual gray whales were sexed using both our novel SNP assays and the traditional method with PCR and gel electrophoresis (Table 3). The results of the 2 methods were in complete concordance with one another (see Fig. 3), indicating that our samples were derived from 11 males and 18 females. These same samples were haplotyped using both our mtDNA SNP assays as well as traditional Sanger sequencing. Although our marker set queries only two mtDNA sites, this marker panel could easily be extended to other SNP sites known to be variable in gray whales; we provide here the proof-of-concept data.

Relatedness and effective size

Mean pairwise relatedness ([r.sub.pop]) observed among all 29 individual gray whales (including one EGW individual) was -0.032, and the 95% CI ranged from -0.055 to -0.009. In general, relatedness estimates based on r and on [M.sub.xy]. were qualitatively similar, so we focused on [M.sub.xy] for reasons discussed in Oliehoek et al. (2006). Our analysis of pairwise relatedness confirmed the allelematch results in that 7 of our biopsies were duplicates. For example, the mean number of shared alleles ([M.sub.xy]) ranged from 0.667 to 1.0 for all 630 pair-wise comparisons (Fig. 4), including the 7 duplicate biopsy pairs identified by allelematch, all of which had [M.sub.xy] = 1.0. Many dyads were likely first-degree relatives (e.g., full siblings or parent-offspring pairs). The mean number of shared alleles for each of the 29 individual whales ranged from 0.768 to 0.851 (Fig. 5). The population mean [M.sub.xy(pop)] was 0.834 (95% CI: 0.831-0.838; Fig. 5).

The CERVUS analyses were used to investigate suspected relationships among two ostensible cow-calf pairs. CERVUS confirmed one such relationship (ER14-0159/ER14-0I73) on the basis of field observations but not the other (ER14-0152/ER14-0172). No sires were identified in our sample, but our relatedness analyses identified two potential full sisters (ER14-0162 and ER14-0173; pairwise r = 0.483). These two adult females apparently share the same mother (ER14-0174/ER14-0159).

The [n.sub.e] estimates of contemporary WGW were similar based on LD (mean [n.sub.e] = 14.1; 95% CI: 12.1-16.7) and heterozygosity excess (mean [n.sub.e] = 14.4; 95% CI: 7.6-254.1). The effective number of breeders, based on individual co-ancestry, was 1.3 (95% CI: 1.0-1.7).


Genome sequencing, assembly, and annotation

The gray whale genome assemblies we describe (Tables 1, 2) consist of ~22,700 genes and contain ~95% of the genes known to be highly conserved among eukaryotes (Parra et al., 2007). These assemblies are relatively complete and reveal that the gray whale genome appears to be fairly typical of cetaceans in terms of genome size and gene complement (Table S1, available online). Foote et al. (2015) have argued that the genomes of marine mammals have evolved in a convergent fashion, and future research will determine what proportion of the gray whale genes we annotated (Table S1) are orthologues of the 16,878 genes shared by the killer whale, manatee, walrus, and bottlenose dolphin (Foote et al., 2015) and what proportion are paralogues resulting from gene duplication. We expect that many of these species have faced similar selection pressures with regard to genes involved in processes such as osmoregulation (e.g., adaptation to a saline environment), thermoregulation (e.g., adipose deposition), and oxygen binding.

SNP identification and development

From the genomic sequence data, we identified hundreds of thousands of SNPs that could potentially be used in population genetic surveys. Of these, we evaluated 96 and found that 92 were polymorphic, had low error rates, and could be amplified in a single assay. This panel of markers should be sufficient for investigations of genetic parentage, relatedness, individuality (i.e., DNA fingerprinting), demographic turnover, and population structure. However, the much larger set of SNPs we identified could inform future genome scans based on restriction-site-associated DNA sequencing (Miller et al., 2007) or other queries of anonymous SNPs that might also be informative for studies of natural selection and/or demographic history. We note that our markers are useful in the critically endangered WGWs, so we expect them to be variable in the more plentiful EGWs. However, robust allele frequency estimates will be needed to assess their utility in EGWs (and to search for potential ascertainment biases).

SNP genotyping, variability, and error rates

The oligonucleotides we developed and the SNPs they query were assessed with the Fluidigm platform because it requires very little DNA. We note, however, that our markers could instead be assayed with alternative genotyping technologies. Because these markers are polymorphic and have low error rates (~0.02%), we expect laboratory-to-laboratory variability to be low, as 99.98% of our replicate data were identical across independent genotyping runs. For example, a whale from the western Pacific that is genotyped with these SNPs could be identified as one also sampled in the eastern Pacific provided the genotypes are stored in a common database that could easily be queried by independent research groups (e.g., in a public database on the cloud).

The power available for individual identification and parentage is captured by [P.sub.I] and [P.sub.E], respectively. In theory (based on allele frequencies in our small sample of WGWs), mean [P.sub.I] = 1.6 x [10.sup.-25]. In practice, our limited sampling revealed multiple biopsies from the same donor whales (see below). Similarly, a calf's unknown parentage could be determined among hundreds of candidate parents with virtual certainty if all were genotyped with this SNP panel ([P.sub.E] > 0.999). For applications that require more power, our genome data include a large pool of additional SNPs that could serve as supplemental markers.

Relatedness and effective size

The WGW is critically endangered, and although our sample consisted of only 28 WGW individuals, this is ~20% of the estimated population (Cooke et al., 2013). Our relatedness analysis indicated that a number of our biopsies came from the same whales; seven pairs of samples were found to have identical SNP genotypes across all loci. Given the paucity of WGWs, it is not entirely surprising that over multiple field seasons we (inadvertently) sampled seven individuals twice. Furthermore, a number of individual pairs were apparently derived from close relatives (Figs. 4, 5) that may represent parent-offspring pairs, full siblings, half siblings, and other close relatives. We identified two distinct cow/calf pairs and one ostensible full-sibling pair. Finally, the mean inbreeding coefficient is generally consistent with random mating within the WGW population. Overall, these results illustrate the power of genetic analyses for corroborating or overturning relationships suspected on the basis of fieldwork.

All of our population inferences are necessarily preliminary due to small sample sizes, but we include them here because of the keen interest in the conservation of the WGW population. Whether the population near Sakhalin Island is composed of WGWs, EGWs, or a mixed aggregation of the two stocks is under study by the International Whaling Commission (IWC, 2015). The putative EGW we sampled was no more or less related to the WGW population than expected by chance alone (Fig. 5). If the EGW was derived from an independent gene pool, it should have been more distantly related to the WGW population.

Populations with small effective sizes diverge rapidly due to drift and inbreeding (Wang and Caballero, 1999). Our small estimates of gray whale [n.sub.e] ([n.sub.e] [approximately equal to] 14 by both LD and heterozygosity excess) are consistent with each other, with the idea that the extant WGW population is very small, and with published data indicating that the WGW population is genetically differentiated from the EGW population (e.g., LeDuc et al., 2002; Alter et al., 2012; Meschersky et al., 2015). Such genetic differentiation may be due to population structure associated with divergent selection, genetic drift, and/or a lack of migration (gene flow) among gray whale populations. Additional sampling across the range of the gray whale will be required to differentiate among these possibilities, but the genome sequence and the genotyping platform we describe here should enable those efforts.


Here, we describe the gray whale genome and the development of a gray whale genotyping assay that queries 92 autosomal nuclear SNPs (88 gene-associated autosomal markers, 2 mitochondrial markers, and 2 sex-chromosome markers). We validated these markers by repeated genotyping of 36 gray whale samples and determined that the error rates were low and the markers were polymorphic despite small effective population sizes. The single whale we sampled from the eastern population could not be genetically distinguished from the 28 gray whales we sampled near Sakhalin Island, but our markers provide a powerful platform for distinguishing among individuals and kin (e.g., identifying close relatives). Ultimately, these markers should prove a useful resource for biologists and for the broader conservation community given the difficulty and expense associated with sampling and identifying baleen whales. Furthermore, the genome sequence will serve as a resource for basic studies across a diversity of disciplines.


We thank the International Whaling Commission (IWC) and Aimee Lang for curating the 2011 western gray whale samples provided via the IWC. This work was conducted under the National Marine Fisheries Service (NMFS) Office of Protected Resources' Marine Mammal Health and Stranding Response Program permits 932-1905-MA-009526 and 18786 and Convention on the International Trade in Endangered Species of Wild Fauna and Flora permit 13US082589/9. This work was supported by Exxon Neftegas Limited and the Sakhalin Energy Investment Company. Both collaborative agencies and funding parties received annual progress reports for the past several years. The content presented here is solely the responsibility of the authors and does not necessarily represent the official views of the funding parties. We also thank Teri Rowles (NMFS) for assistance with the project. A. Aziz, J. Dupont, M. Scott, and M. Swindoll provided support in obtaining the biopsies and associated metadata. The Institute of Ecology and Evolution of the Russian Academy of Sciences, the A. V. Zhirmunsky Institute of Marine Biology Far Eastern Branch, and Oregon State University provided invaluable support for the collection of the western gray whale samples. We thank E. Srour and A. Cardoso (Indiana University School of Medicine) for laboratory assistance and support. C. George and R. Suydam (Department of Wildlife Management, North Slope Borough of Alaska) provided the putative eastern gray whale samples. Finally, we thank S. Fears, Y. Ji, M. Sundaram, and J. Willoughby for constructive comments.

Data Accessibility

We have deposited the primary data underlying these analyses into the National Center for Biotechnology Information's Short Read Archive (study accession SRP105779; BioProject PRJNA384396), in the Dryad Digital Repository (DeWoody et al., 2017), and in the associated online supplementary material (Tables S1-S6).

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(1) Department of Forestry and Natural Resources, Purdue University, West Lafayette, Indiana 47907;

(2) Department of Biological Sciences, Purdue University, West Lafayette, Indiana 47907; (3) Department of Biological Sciences, Towson University, Towson, Maryland 21252; (4) Department of Horticulture and Landscape Architecture, Purdue University, West Lafayette, Indiana 47907; (5) Kronotsky State Nature Biosphere Reserve, Elizovo, Kamchatka 684100, Russia; (6) Institute of Environmental and Human Health, Texas Tech University (TTU) and TTU Health Sciences Center, Box 41163, Lubbock, Texas 79409-1163; and (7) Department of Wildlife and Fisheries Sciences, Texas A &M University, College Station, Texas 77845

Received 27 September 2016; Accepted 30 May 2017; Published online 1 September 2017.

* To whom correspondence should be addressed. E-mail:

Abbreviations: EGW, eastern gray whale; EST, expressed sequence tag; HWE, Hardy-Weinberg equilibrium; IUCN, International Union for Conservation of Nature; IWC, International Whaling Commission; LD. linkage disequilibrium; MP, mate paired; PCR, polymerase chain reaction; PE, paired end; SNP, single-nucleotide polymorphism; STA, specific target amplification; WGW, western gray whale.
Table 1
Sequencing statistics associated with the western gray whale genome

                       Mean insert  Mode insert
Sample name            size (bp)    size (bp)    Total reads

Paired-end libraries
  ER-14-0147            471          481         1,331,820,280
  ER-14-0168            497          506         1,254,676,990
Mate-paired libraries
  ER-14-0147           2223         1463           184,404,728
  ER-14-0168           2363         1426           213,408,466

Sample name            Total bases      Quality reads  Quality bases

Paired-end libraries
  ER-14-0147           134,513,848,280  988,439,854    97,099,230,094
  ER-14-0168           126,722,375,990  974,126,824    95,896,562,771
Mate-paired libraries
  ER-14-0147            18,624,877,528  146,095,780    13,350,014,231
  ER-14-0168            21,554,255,066  168,844,292    15,387,314,171

Table 2
Summary statistics generated by ABySS software for the western gray
whale genome assemblies

Sample      Contig N50  Scaffolds  Assembly size  Min. length

ER-14-0147  8336        60,534     3,082,450,012  500
ER-14-0168  8690        57,219     2,849,466,389  500

Sample      Max. length  Scaffold N50  n : N50   Comp%

ER-14-0147  1,943,192    180,882       4539      95
ER-14-0168  1,944,941    187,455       4089      96

Table 3
Haplotype and sex data

                        mtDNA            Sex chromosomes
Sample      Population  CR_82   CR_104   ZFY_288   ZFY_342   Sex

ER-14-0147  Western     G : G   G : G    C : C     G : G     Female
ER-14-0148  Western     A : A   G : G    C : C     G : G     Female
ER-14-0149  Western     A : A   G : G    C : C     G : G     Female
ER-14-0150  Western     G : G   A : A    C : T     A : G     Male
ER-14-0153  Western     G : G   G : G    C : C     G : G     Female
ER-14-0155  Western     A : A   G : G    C : C     G : G     Female
ER-14-0156  Western     A : A   G : G    C : T     A : G     Male
ER-14-0160  Western     G : G   A : A    C : T     A : G     Male
ER-14-0161  Western     G : G   A : A    C : C     G : G     Female
ER-14-0162  Western     G : G   G : G    C : C     G : G     Female
ER-14-0163  Western     G : G   G : G    C : C     G : G     Female
ER-14-0164  Western     A : A   G : G    C : T     A : G     Male
ER-14-0165  Western     A : A   G : G    C : T     A : G     Male
ER-14-0167  Western     G : G   G : G    C : T     A : G     Male
ER-14-0168  Western     G : G   G : G    C : T     A : G     Male
ER-14-0169  Western     A : A   G : G    C : T     A : G     Male
ER-14-0170  Western     G : G   A : A    C : T     A : G     Male
ER-14-0171  Western     G : G   A : A    C : C     G : G     Female
ER-14-0172  Western     G : G   G : G    C : C     G : G     Female
ER-14-0173  Western     G : G   G : G    C : C     G : G     Female
ER-14-0174  Western     G : G   G : G    C : C     G : G     Female
ER-14-0175  Western     A : A   G : G    C : T     ...       Male
Zl12743     Western     G : G   G : G    C : T     A : G     Male
Zl12744     Western     G : G   G : G    C : C     G : G     Female
Zl12745     Western     G : G   G : G    C : C     G : G     Female
Zl12746     Western     A : A   G : G    C : C     G : G     Female
Zl12747     Western     A : A   G : G    C : C     G : G     Female
Zl12748     Western     A : A   G : G    C : C     G : G     Female
GFD02       Eastern     G : G   A : A    C : C     G : G     Female

Shown are genotypes for two mitochondrial (CR_82 and CR_104) and two
sexing (ZFY_288 and ZFY_342) markers. A missing genotype is represented
by an ellipsis.
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Author:Dewoody, J. Andrew; Fernandez, Nadia B.; Bruniche-Olsen, Anna; Antonides, Jennifer D.; Doyle, Jacque
Publication:The Biological Bulletin
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Date:Jun 1, 2017
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