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. 2020 Jun 16;15(6):e0234803. doi: 10.1371/journal.pone.0234803

Genetic analysis of body weight in wild populations of medaka fish from different latitudes

Tamiris I Yassumoto 1, Mana Nakatsukasa 1,2, Atsushi J Nagano 3, Masaki Yasugi 4,¤a, Takashi Yoshimura 1,2,5,*, Ai Shinomiya 2,¤b,*
Editor: Christos Maravelias6
PMCID: PMC7297337  PMID: 32544202

Abstract

The genetic bases of growth and body weight are of economic and scientific interest, and teleost fish models have proven useful in such investigations. The Oryzias latipes species complex (medaka) is an abundant freshwater fish in Japan and suitable for genetic studies. We compared two wild medaka stocks originating from different latitudes. The Maizuru population from higher latitudes weighed more than the Ginoza population. We investigated the genetic basis of body weight, using quantitative trait locus (QTL) analysis of the F2 offspring of these populations. We detected one statistically significant QTL for body weight on medaka chromosome 4 and identified 12 candidate genes that might be associated with body weight or growth. Nine of these 12 genes had at least one single nucleotide polymorphism that caused amino acid substitutions in protein-coding regions, and we estimated the effects of these substitutions. The present findings might contribute to the marker-assisted selection of economically important aquaculture species.

Introduction

Growth and body weight are economically important traits in the livestock industry and in aquaculture. Such traits involve complex physiological processes that are controlled by various environmental and genetic factors. Quantitative trait locus (QTL) mapping and marker-assisted selection for economic traits, including growth and body weight in aquaculture, have recently been conducted in several studies using molecular markers such as microsatellites and single nucleotide polymorphisms (SNP) [1–7].

Body weight depends not only on growth traits but also on body composition and metabolism. Genome-wide association studies (GWAS) of body mass index (BMI) over the past decade have associated several hundred SNPs with body weight and obesity [8,9].

Animal models play essential roles in most aspects of medicine. Diet-induced obesity in zebrafish and mammalian obesity are pathophysiologically similar; thus, the roles of genes associated with visceral adiposity have been examined in zebrafish models of human obesity [10,11]. Understanding the genetic basis of body weight in teleost fish models could help deepen the medical understanding of obesity.

Ecological profiles of body size are distributed within species according to the Bergmann’s rule, which states that animals living at high latitudes are generally larger than those living in low latitudes [12]. Some exceptions exist, but the findings of several studies are in line with the Bergmann’s rule and its applicability in many types of mammals [13], birds [14,15], and ectothermic vertebrate and invertebrate taxa [16]. However, the underlying genetic mechanisms that result in a body size cline remain obscure.

Medaka are small freshwater fish that are native to Japan, Korea, and China. Japanese wild populations of the Oryzias latipes species complex are widely distributed from high to low latitudes throughout the Japanese archipelago. Previous phylogeographic studies using allozymes, mitochondrial DNA (mtDNA) sequences, and genome-wide SNP analysis revealed that Japanese wild medaka comprise Northern and Southern Japanese populations [17–20]. The average rate of SNPs between two inbred strains derived from the two populations is 3.4% [21]. Inbred strains and wild stocks of medaka originating from Japanese wild populations are currently available through the National BioResource Project (NBRP) (https://shigen.nig.ac.jp/medaka/top/top.jsp). Phenotypic variations between these two populations have been described for several traits, including brain [22] and craniofacial morphology [23], body color and sexual dimorphism [24], vertebral regionalization and number [25], aggressiveness [26], startle behavior [27], and male-specific ossified processes and sex steroid levels [28,29]. Several QTL have been detected by focusing on these phenotypic variations using genetic analyses based on draft [21] and updated medaka genome sequences (http://utgenome.org/medaka_v2/#!Top.md). We investigated two medaka wild stocks originating from different latitudes. Body weight was higher in the Maizuru population than in the Ginoza population from high and low latitudes, respectively. We investigated the genetic basis of body weight via QTL analysis of the F2 offspring of these medaka populations. We detected one statistically significant QTL for body weight on chromosome 4 and assessed candidate genes located within that QTL region.

Materials and methods

Ethics statement

The Animal Experiment Committee at the National Institutes of Natural Sciences, Japan approved the study protocol (14A108, 15A047). The medaka used in these experiments were treated according to the animal experiment guidelines of the National Institutes of Natural Sciences, Japan.

Animals

The NBRP Medaka (https://shigen.nig.ac.jp/medaka/) supplied adult medaka (G0 generations) from stocks originating from wild North and South Japanese populations at Maizuru City (Maizuru stock; strain ID, WS215) located at 35° 28′ N 135° 23′ E, Kyoto Prefecture) and Ginoza Village (Ginoza stock; strain ID, WS255) located at 26° 28′ N 127° 58′ E, Kunigamigun, Okinawa Prefecture), respectively. We then raised several generations of these fish in the laboratory. The Maizuru and Ginoza stocks were crossed to collect the G₁ generations, respectively. Then, Maizuru females were mass-mated with Ginoza males and vice versa to obtain the F₁ offspring, which was used to analyze body weight. Five Maizuru × Ginoza pairs were mated to generate the F₂ offspring for QTL mapping. Three of these pairs comprised Ginoza females and Maizuru males and two comprised Maizuru females and Ginoza males.

Two to four weeks after hatching, all the generations were transferred outdoors between June and July, 2014, and maintained for 1 year under natural temperature and photoperiod at the outdoor experimental field of the National Institute of Basic Biology (34° 57′ N 137° 9′ E) in Okazaki, Aichi, Japan. Between June and July 2015, the fish were transferred to experimental aquariums and maintained in water circulation systems at 26°C ± 1°C for 1 month. The fish were fed with artificial dry food twice daily. Body weight was analyzed in 10 Ginoza G1, 26 Maizuru G1, 15 F₁ hybrid, and 126 F₂ fish euthanized with 0.05% 3-aminobenzoic acid ethyl ester methanesulfonate salt (MS222). Water was removed from the fish by blotting them with paper towels; then, each fish was weighed. Thereafter, the fish were frozen in liquid nitrogen and stored in a deep freezer (-80°C).

Restriction site-associated DNA sequencing (RAD-Seq) and SNP markers

Genomic DNA was extracted from muscle tissue using DNeasy Blood & Tissue Kits (Qiagen GmbH, Hilden, Germany) according to the manufacturer’s instructions. The concentration of DNA was determined using a Qubit 3.0 fluorometer (Thermo Fisher Scientific Waltham, MA, USA). Genomic DNA (40 ng) from each sample was digested using two restriction enzymes, BglII and EcoRI, ligated with a Y-shaped adaptor, and amplified by polymerase chain reaction (PCR) using KAPA HiFi HS ReadyMix (Kapa Biosystems Inc., Wilmington, MA, USA). Fragments (~300–360 bp) were selected using E-Gel Size Select (Life Technologies, Carlsbad, CA, USA). Details of the library preparation method are described elsewhere [30]. The fragments were sequenced on a HiSeq 2500 platform (Illumina Inc., San Diego, CA, USA) in 50-bp single-end mode. We conducted RAD-Seq in 10 parent fish from the 5 Maizuru × Ginoza pairs, 3–4 from the F₁ generation of each Maizuru × Ginoza pair, and 126 of the F2 generation. The reads were quality filtered using Trimmomatic [31] under the following parameters: trimmomatic-0.32.jar SE -threads 8 -phred33 ILLUMINACLIP TruSeq3-PE-2.fa:2:30:10 LEADING:19 TRAILING:19 SLIDINGWINDOW:30:20 AVGQUAL:20 MINLEN:51. The trimmed reads were mapped to the draft genome of the Hd-rR inbred medaka strain (v. 2.2.4, http://utgenome.org/medaka_v2/#!Assembly.md), and SNPs were called using the Stacks pipeline [32]. We identified RAD tags with a homozygous genotype in all Maizuru and all Ginoza and those that had different alleles between all Maizuru and all Ginoza parents for SNP marker selection. Among these markers, we selected those that were heterozygous in all the F₁ individuals and genotyped in > 80% of the 126 F₂ fish. Finally, we selected 371 RAD markers for QTL analysis (S1 Table). Genetic distances (cM) involving each chromosome were calculated using the Kosambi map function [33].

Raw sequence data were deposited in the DDBJ Sequence Read Archive (DRA) (https://www.ddbj.nig.ac.jp/dra/index.html) under the accession numbers DRR226810-DRR226964.

Analysis of QTL

Quantitative trait loci associated with body weight were mapped in the 126 F₂ fish, and simple interval mapping [34] proceeded using R/qtl software [35,36]. Genome-wide significant (5%) and suggestive (10%) thresholds of a single QTL were determined by 1000 permutation tests. Bayesian credible intervals (95% CI) were computed using the R/qtl function. Physical lengths of credible intervals (Mb) were predicted by extending the physical position of the nearest flanking markers.

Analyses of SNPs in the candidate genes

We selected genes within the 95% CI that were positioned according to the NCBI ASM223467v1 (GCF_002234675.1) assembly and associated with growth, body weight, and obesity on the basis of a literature search. We sequenced the selected candidate genes for the Maizuru and Ginoza fish and cataloged the SNPs located on their coding regions (S2 Table). We then analyzed these polymorphisms using GENETYX software (version 13, GENETICS Inc., Tokyo, Japan) to detect variants that could cause amino acid substitutions. We only considered polymorphisms in which all eight sequenced Ginoza and Maizuru individuals (n = 4 each) were homozygous for the allele. We then analyzed the amino acid substitutions using Protein Variation Effect Analyzer (PROVEAN) v1.1 to estimate their functional effects on the encoded protein [37]. A non-synonymous amino acid substitution with a potential functional effect was found on one of the genes (sned1), and its protein sequence (accession number XP_011471983.1) was compared with those of two teleost fishes, Gasterosteus aculeatus (stickleback; ENSGACG00000003698) and Danio rerio (zebrafish; XP_017212114.1), Mus musculus (mice; XP_006529380.1), and Homo sapiens (humans; XP_011509233) using the sequence alignment tool, ClustalW (version 2.1, DNA Data Bank of Japan).

Results

Body weight variation

Fig 1 shows that body weight was significantly higher in the Maizuru than in the Ginoza (p < 0.01, Student t-test), and Fig 2 shows a broader range of body weight distribution in the F₂ generation than in the parental populations.

Fig 1. Mean body weight of Maizuru (n = 26) and Ginoza (n = 10).

Fig 1

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Data are shown as the means and standard errors (SE). Body weight was significantly higher in Maizuru than in Ginoza stock (512.6 vs. 374.7 mg; **p < 0.01; Student t-tests).

Fig 2. Distribution of body weight of the Ginoza, Maizuru, F₁, and F₂ fish.

Fig 2

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The Y axis indicates the frequency; the X axis indicates the body weight categories at 20-mg intervals. Black triangles indicate the means.

Identification of body weight QTL

A genetic map was constructed using 371 SNP markers obtained by RAD-seq (Fig 3). The total length of the genetic map of the 24 chromosomes was 1496.32 cM, and the average calculated interval between each marker was 4.51 cM (Table 1). Physical lengths were determined based on reference medaka genome data. The total physical length was 676.74 Mb, and the average interval between each marker was 1.75 Mb (Table 1).

Fig 3. Genetic map of the single nucleotide polymorphism (SNP) markers.

Fig 3

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(A) Marker distribution across the 24 medaka chromosomes. (B) Genetic map of chromosome 4 show the marker names and locations (cM). Markers with the highest logarithm of odds (LOD) scores in the QTL analysis are shown in red.

Table 1. Summary of markers used in the present study.

Chromosome no. No. of markers Genetic length (cM) Average marker interval (cM) Physical length (Mb) Average marker interval (Mb)
1 16 52.23 3.48 34.38 2.05
2 10 50.5 5.61 22.52 2.1
3 22 73.18 3.48 37.51 1.68
4 11 56.87 5.69 29.86 2.75
5 21 96.32 4.82 32.35 1.61
6 14 47.04 3.62 28.5 1.32
7 15 74.12 5.29 33.17 1.92
8 10 70.61 7.84 26.12 2.79
9 16 66.73 4.45 33.05 1.72
10 21 79.51 3.98 31.09 1.52
11 15 36.82 2.63 23.49 1.65
12 16 58.75 3.92 24.39 1.25
13 10 28.19 3.13 26.89 1.46
14 24 65.83 2.86 28.27 1.01
15 14 66.65 5.13 29.59 1.9
16 19 66.33 3.68 29.84 1.25
17 16 74.15 4.94 31.74 1.92
18 13 60.63 5.05 28.49 1.57
19 14 68.67 5.28 25.31 1.75
20 16 64.06 4.27 24.71 1.63
21 13 61.22 5.1 29.82 2.35
22 22 68.5 3.26 24.06 1.1
23 10 62.08 6.9 21.47 2.38
24 13 47.29 3.94 20.12 1.43
Total 371 1496.28 4.51 676.74 1.75
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A QTL analysis of the 126 F₂ fish identified a statistically significant QTL region on the distal arm of chromosome 4, with a maximum LOD of 4.14 (Figs 3 and 4). The closest SNP marker was 56900. We calculated the mean body weight of the F₂ individuals that were homozygous for the Maizuru and Ginoza alleles of the closest marker and the heterozygous fish. The mean body weight was higher among individuals that carried a homozygous or heterozygous Maizuru allele than among those that carried a homozygous Ginoza allele (Fig 4C). We confirmed a linkage between an amplified fragment length polymorphism marker located on the distal position of chromosome 4 (0.68 Mb, S3 Table) and the SNP marker 56900 (4.7 cM) by genotyping using PCR.

Fig 4. QTL analysis of the F₂ generation.

Fig 4

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(A) Results for all chromosomes. The significant QTL on chromosome 4 (B) has a peak LOD of 4.14. Gray areas indicate 95% Bayesian CI. (C) Body weight for genotypes of SNP marker 56900, which had the highest LOD score among the markers. Points represent the body weight of the F₂ individuals homozygous for the Maizuru alleles (n = 29), heterozygous for the Ginoza and Maizuru alleles (G/M, n = 46), and homozygous for the Ginoza alleles (n = 34). The means and standard errors (SE) are presented for each genotype. Differences between the mean body weight of the Ginoza and G/M genotypes and of the Ginoza and Maizuru fish were statistically significant (*p < 0.01, ANOVA and Tukey’s honestly significance difference (HSD) test) but those between the G/M and Maizuru fish were not.

Differences in the amino acid sequences of the candidate genes between Maizuru and Ginoza

The 95% Bayesian CI of the QTL was 0–7 cM (Fig 4B), and the physical location of the CI estimated from the genetic and physical positions of the markers 56900 (0 cM, 2.38 Mb) and 55681 (17.7 cM, 11.01 Mb) that were the closest to the QTL (Fig 3B) was 0–5.74 Mb. Among the 141 genes encoding proteins within the CI, we identified 12 that are reportedly associated with body weight or growth. Nine of these genes had at least one SNP that caused substitutions of amino acids in the coding regions (Table 2). We estimated, using PROVEAN, that most of these amino acid substitutions exerted neutral protein functional effects. However, one substitution in the protein SNED1 encoded by the gene sned1 at G1013S appeared to have a deleterious (significantly different function) effect with a PROVEAN score of -2.648 (cutoff, -2.5). The amino acid glycine at 1013 in Ginoza SNED1 is conserved across other vertebrates, such as teleost, stickleback and zebrafish, as well mice and humans (Fig 5). In contrast, serine, which substituted for glycine in Maizuru SNED1, was not found in any other analyzed species.

Table 2. SNPs with non-synonymous substitutions in the candidate genes in Maizuru and Ginoza.

Gene symbol (Accession no.)1 Description Position of mRNA (bp)1 SNP Number of amino acids Non-synonymous substitutions
Position (bp)1 Maizuru Ginoza Position (aa)1 Maizuru Ginoza
sgcb (XP_020558516.1) sarcoglycan beta 1356188–1364186 1360124 G C 296 98 Leu Val
cilp2 (XP_011471792.1) cartilage intermediate layer protein 2 2139202–2161322 2161297 T C 1302 3 Lys Arg
2161159 G A 49 Ser Leu
2149453 T A 146 Thr Ser
2149400 A C 163 Asp Glu
2149359 C T 177 Ser Asn
2147355 T C 326 Asp Gly
2147167 C T 389 Val Ile
2146872 C T 420 Gly Asp
2141205 T A 961 Tyr Phe
2141068 T A 1007 Met Leu
2140229 G C 1286 Ile Met
mef2b (XP_011471858.1) myocyte-specific enhancer factor 2B 2306973–2324496 2314502 G A 421 218 Pro Leu
2314431 A C 242 Ser Ala
2313743 C T 266 Gly Ser
2308851 A C 298 Val Gly
2308220 G C 360 Ser Thr
2308128 T C 391 Ile Val
rfxank (XP_011471878.1) DNA-binding protein RFXANK 2358970–2362447 _ _ _ 208 _ _ _
rab6b (XP_004067647.1) ras-related protein Rab-6B 2693698–2739982 _ _ _ 215 _ _ _
sned1 (XP_011471983.1) sushi, nidogen and EGF-like domain-containing protein 1 2824005–2850954 2850480 T C 1349 39 Lys Glu
2843533 T G 207 Gln Pro
2842008 T A 372 Thr Ser
2841981 A G 381 Tyr His
2837261 A G 559 Leu Pro
2837260 C T
2837250 C T 563 Ala Thr
2835940 T G 660 Asn Thr
2835939 G T
2835923 C G 666 Val Leu
2835883 G C 679 Thr Ser
2835439 G C 772 Gln Glu
2835283 A G 792 Tyr His
2833621 T G 912 Lys Gln
2833038 T C 1013 Ser Gly
2830096 T G 1070 Thr Pro
2829960 A G 1115 Val Ala
2829735 C A 1146 Ala Ser
2829731 C T 1147 Arg Lys
2829460 C G 1213 Ser Thr
2825995 T A 1315 Gln Leu
2825713 T A 1346 Asn Ile
scly (XP_023809718.1) selenocysteine lyase 2908002–2933175 2908316 A C 445 411 Ser Ala
cep19 (XP_023809746.1) centrosomal protein 19 kDa 3244247–3245808 3244607 C T 157 128 Asp Asn
dhcr24 (XP_004067663.1) delta(24)-sterol reductase 3287533–3295716 3288635 A C 516 396 Ser Ala
3288401 G A 452 Ala Val
lmo4 (XP_023809796.1) LIM domain transcription factor LMO4 4586958–4596199 _ _ _ 165 _ _ _
sgta (XP_023809802.1) small glutamine-rich tetratricopeptide repeat-containing protein alpha 5201953–5215418 _ _ _ 342 _ _ _
mrpl55 (XP_023809802.1) 39S ribosomal protein L55, mitochondrial 5446241–5450258 5446451 T C 138 16 Thr Met
5446578 C A 21 Pro Thr
5446708 T A 64 Leu Gln
5450099 T A 118 Ser Thr
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1Genes and positions are according to the NCBI ASM223467v1 (GCF_002234675.1) assembly.

The G1013S substitution in SNED1 with a deleterious effect (significantly different function) estimated by PROVEAN is shaded in gray.

Fig 5. SNED1 protein sequence comparison.

Fig 5

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Glycine at position 1013 in Ginoza SNED1 (red square) is conserved among other vertebrate species. Serine (blue square) was found only in Maizuru SNED1.

Discussion

Medaka fish found at various latitudes provide an excellent model for investigating the genetic basis of body weight. Therefore, we compared two wild medaka stocks from Maizuru and Ginoza at different latitudes. Fish from the parental populations and the F₁ and F₂ generations were reared under the same environmental conditions to avoid the effects of plastic responses to temperature and other variables such as food availability during growth. Mean body weight differed significantly between the Maizuru and Ginoza individuals (Fig 1), reflecting the involvement of genetic components in the determination of body size. The Bergmann’s rule is currently defined as a within-species tendency for body size to increase as latitude increases [12]. Our results conformed to Bergmann’s rule, as body weight was greater among the Northern Maizuru population than the Southern Ginoza population.

The identification of genes that regulate complex multigenic traits such as growth and body weight has proven challenging. Over 6000 genes are considered to influence body weight in mice [38]. Multiple QTL regions are associated with body weight in Atlantic salmon (Salmo salar) [2]. Our QTL analysis identified a significant QTL on chromosome 4 (peak LOD, 4.14) (Fig 3), and the effects of the allele of the marker with the highest score supported the phenotype found in the parental strains (the body weight was higher for the Maizuru allele than the Ginoza allele in the F₂ medaka). This explains 14% of the variance.

We identified 12 candidate genes involved in body weight and growth regulation within the significant QTL region. The genes cilp2, expressing cartilage intermediate layer protein 2, and sned1, expressing sushi, nidogen and EGF-like domain-containing protein 1, are associated with BMI in humans [39]; mef2b (myocyte-specific enhancer factor 2B), rfxank (regulatory factor X-associated ankyrin-containing protein), and rab6b (Ras-related protein Rab-6B) are associated with body weight and growth traits in sheep [40–42]; sgcb (sarcoglycan beta) is associated with body weight in broilers [43]; scly (selenocysteine lyase), cep19 (19-kDa centrosomal protein), dhcr24 (24-dehydrocholesterol reductase), and lmo4 (LIM domain transcription factor) are associated with body weight and obesity traits in mammals [44–47]; and mrpl55 (mitochondrial ribosomal protein L55) has a critical role in development and body size in Drosophila [48]. Furthermore, sgta (small glutamine-rich tetratricopeptide repeat-containing protein alpha) is a regulator of growth hormone receptors, which consequently influence body weight, because sgta knockout mice are smaller than wild-type mice [49].

Among the identified candidate genes, nine had amino acid substitutions that distinguished Maizuru from Ginoza (Table 2). The SNED1 amino acid substitution G1013S was predicted to be deleterious (functionally different) according to the PROVEAN score. In addition, the Ginoza amino acid variant, glycine, was prevalent among other vertebrate groups, whereas the serine residue in Maizuru was not found in other investigated species (Fig 5). SNED1 in humans is known as insulin-responsive sequence DNA-binding protein 1 (IRE-BP1); it is a transcription factor involved in the determination of BMI [37] and the activation of insulin-responsive genes and obesity [50]. SNED1 is located at the terminal region of chromosome 2 in humans. Patients with a deletion in that region, with breakpoints at or within cytogenetic band 2q37, have a short stature among other features [51]. Sned1 might be involved in mouse skeletal development, as Sned1 knockout mice have craniofacial malformations and growth defects [52]. Therefore, we speculate that a functional difference in SNED1 protein activity caused by the amino substitution that distinguished Maizuru from Ginoza induced the difference in body weight. However, unidentified genes in the QTL region might also influence body weight through mechanisms other than differences in protein function, such as changes in their expression levels and/or profiles. These speculations await further investigation.

Future studies are necessary to identify the genetic variation(s) responsible for the differences in body weight between the medaka populations studied here. Nevertheless, the present results will contribute to the marker-assisted selection of economically important aquaculture species and provide a better understanding of the genetic mechanisms underlying ecological differences in body weight among populations at different latitudes.

Supporting information

S1 Table. Single nucleotide polymorphism (SNP) markers used for quantitative trait locus (QTL) mapping and genotyping for each SNP in the parental, F1, and F2 generations.

Number of markers used for QTL mapping: 371.

(XLSX)

Click here for additional data file. (251.3KB, xlsx)
S2 Table. Single nucleotide polymorphisms (SNPs) on the coding regions in 12 candidate genes from the Maizuru and Ginoza individuals.

Number of Maizuru and Ginoza fish: n = 4 each.

(XLSX)

Click here for additional data file. (21.8KB, xlsx)
S3 Table. Genotyping amplified fragment length polymorphism marker ch4_0.68M and SNP marker 56900 in the parental and F2 generations.

The forward and reverse primer sequences of ch4_0.68M for genotyping using PCR are 5′-caattgcctgtttgtcagttacac-3′ and 5′-cgcctaatgccactccagcac-3′, respectively. Their locations are 685055–685078 bp and 685136–685156 bp on chromosome 4, respectively. The sizes of the amplified fragments separated using Microchip Electrophoresis System MCE®-202 MultiNA microchip electrophoresis (Shimadzu Corporation, Kyoto, Japan).

(XLSX)

Click here for additional data file. (13.3KB, xlsx)

Acknowledgments

We thank the National Bio-Resource Project Medaka of MEXT, Japan, and the Data Integration and Analysis Facility of NIBB, Japan, for permitting the use of their facilities. We thank Dr. T. Shimmura, D. Adachi, N. Baba, A. Akama, and M. Okubo for technical assistance.

Data Availability

Raw sequence data were deposited in the DDBJ Sequence Read Archive (DRA) (https://www.ddbj.nig.ac.jp/dra/index.html) under the accession numbers DRR226810-DRR226964.

Funding Statement

This work was supported by a JSPS KAKENHI Grant-in-Aid for Scientific Research (C) (15K07163, 19K06785) to AS, Grants-in-Aid for Specially Promoted Research (26000013), and for Scientific Research (S) (19H05643) to TY, and the Human Frontier Science Program (RGP0030/2015) to TY. There was no additional external funding received for this study. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Decision Letter 0

Christos Maravelias

24 Mar 2020

PONE-D-20-01808

Genetic analysis of body weight in wild populations of medaka fish from different latitudes

PLOS ONE

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Reviewer #1: Yes

Reviewer #2: Partly

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: No

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: No

**********

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PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: No

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The articile is well-written, and the topic quite interesting with potential implications for aquaculture breeding programmes.

No major modifications are required prior to publication.

The only minor modification suggested is related to the introduction.

In fact, while the authors recognized that their results could have implications for aquaculture, it seems not strictly related to their research the reference to human diseases and medicine science in the two paragraphs (lines 53-64). This part could be shortned or even omitted.

Reviewer #2: I am unfortunately confused with the way the authors approached this subject.

The content of the paper does not help much the readers to understand how the laboratory experiments and the downstream analyses were performed.

More specifically:

- in the abstract, the authors have at least a paragraph for obesity research. How is this relevant to the manuscript? Using medaka as a model for human health? We already know that most of these traits are polygenic. Therefore, the results of your study pointing on a single gene for growth might need more thorough reanalysis.

- For the RAD approach: did you finally use both or a single enzyme? The number of SNPs you finally used (371) is much fewer than that people usually report for RAD or ddRAD approaches. You do not comment on this.

- Mapping and QTL identification: with the above mentioned number of SNPs it is normal to have a very sparse linkage map with an average of only 15 markers per linkage group or chromosome. To my opinion, this number is very small to have a sound analysis.

- On chromosome 4, there are only 11 markers mapped and the one showing a positive association with growth is located at the extreme; from the literature we know that sometimes it might be erroneous.

- The 12 genes span a region, if I am not mistaken, of approximately 4Mb and the authors have invested a lot of effort and expenses to go for sequencing all these genes for the “source” populations (how many fish exactly?) and identify fixed differences. Very little is reported for the way you performed it; the primers, how many PCR reactions and different fragments per gene etc.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Reviewer #1: No

Reviewer #2: No

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PLoS One. 2020 Jun 16;15(6):e0234803. doi: 10.1371/journal.pone.0234803.r002

Author response to Decision Letter 0

9 May 2020

Response to Reviewer 1

The article is well-written, and the topic quite interesting with potential implications for aquaculture breeding programs. No major modifications are required prior to publication. The only minor modification suggested is related to the introduction. In fact, while the authors recognized that their results could have implications for aquaculture, it seems not strictly related to their research the reference to human diseases and medicine science in the two paragraphs (lines 53-64). This part could be shortened or even omitted.

Response: We have shortened the two paragraphs as suggested (lines 50–57).

Responses to Reviewer 2

Is the manuscript presented in an intelligible fashion and written in standard English?

No

Response: We used the service of an English proofreading company to improve clarity.

I am unfortunately confused with the way the authors approached this subject.

The content of the paper does not help much the readers to understand how the laboratory experiments and the downstream analyses were performed.

Response: We have added more details to facilitate a better understanding of the experimental procedures and downstream analyses.

1.- in the abstract, the authors have at least a paragraph for obesity research. How is this relevant to the manuscript? Using medaka as a model for human health? We already know that most of these traits are polygenic. Therefore, the results of your study pointing on a single gene for growth might need more thorough reanalysis.

Response: We have deleted the word “medical” from the abstract and have diminished the claim in the Introduction section. We have shortened and revised the two paragraphs that described obesity research (lines 50–57). Furthermore, we have added a description of the relevance and relationships among body weight, growth, and animal health, which affect aquaculture economics.

2.- For the RAD approach: did you finally use both or a single enzyme? The number of SNPs you finally used (371) is much fewer than that people usually report for RAD or ddRAD approaches. You do not comment on this.

Response: We used both enzymes; we have added the relevant information in the Materials and Methods section (line 124).

We initially obtained 5,019 RAD-sequencing markers that were genotyped in > 70% of all 155 individuals from the parental, F1, and F2 generations.

In this experiment, F2 individuals originated from five parental Maizuru and Ginoza pairs. To obtain informative and reliable markers for QTL analysis, we selected markers with genotyping datasets in the parental, F1, and F2 generations with homozygous but different genotypes between all Maizuru and all Ginoza parents and a heterozygous genotype in F1. We also selected markers that were genotyped in > 80% of the F2 fish. After these selection processes, we reduced the number of markers to 371 for QTL analysis.

3. - Mapping and QTL identification: with the above mentioned number of SNPs it is normal to have a very sparse linkage map with an average of only 15 markers per linkage group or chromosome. To my opinion, this number is very small to have a sound analysis.

Response: Indeed, more markers would increase the statistical power to detect more QTL and be useful for high-resolution mapping. However, we decided to use only reliable markers to minimize or avoid errors.

4. - On chromosome 4, there are only 11 markers mapped and the one showing a positive association with growth is located at the extreme; from the literature we know that sometimes it might be erroneous.

Response: We designed and genotyped the new marker “ch4_0.68M,” which is located on the distal region of the medaka chromosome 4. The genetic distance between ch4_0.68M and the RAD 56900 marker supported the linkage of these two loci. Therefore, the position of 56900 and the QTL on chromosome 4 is reliable. Genotyping data on the new marker can be found in the supplemental S3 Table and in the revised manuscript (lines 209–211).

5. - The 12 genes span a region, if I am not mistaken, of approximately 4Mb and the authors have invested a lot of effort and expenses to go for sequencing all these genes for the “source” populations (how many fish exactly?) and identify fixed differences. Very little is reported for the way you performed it; the primers, how many PCR reactions and different fragments per gene etc.

Response: We sequenced the whole genomes of four Maizuru and four Ginoza individuals using HiSeq 4000 (Illumina). Since these whole genome data will be published elsewhere (in preparation), the data cannot be submitted to a public database at this time. However, we showed all the SNP in the coding regions of the 12 genes in all Maizuru and Ginoza individuals included in the variant analyses in the supplemental S2 Table, lines 158–160. After reanalyzing the SNP of these Maizuru and Ginoza individuals, we added more data to Table 2.

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file. (27KB, docx)

Decision Letter 1

Christos Maravelias

3 Jun 2020

Genetic analysis of body weight in wild populations of medaka fish from different latitudes

PONE-D-20-01808R1

Dear Dr. Yoshimura,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

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Kind regards,

Christos Maravelias, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #2: the authors have adequately addressed the comments raised in the previous round of review and I feel that this manuscript is now acceptable for publication

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #2: No

Acceptance letter

Christos Maravelias

5 Jun 2020

PONE-D-20-01808R1

Genetic analysis of body weight in wild populations of medaka fish from different latitudes

Dear Dr. Yoshimura:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Christos Maravelias

Academic Editor

PLOS ONE

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Table. Single nucleotide polymorphism (SNP) markers used for quantitative trait locus (QTL) mapping and genotyping for each SNP in the parental, F1, and F2 generations.

    Number of markers used for QTL mapping: 371.

    (XLSX)

    Click here for additional data file. (251.3KB, xlsx)
    S2 Table. Single nucleotide polymorphisms (SNPs) on the coding regions in 12 candidate genes from the Maizuru and Ginoza individuals.

    Number of Maizuru and Ginoza fish: n = 4 each.

    (XLSX)

    Click here for additional data file. (21.8KB, xlsx)
    S3 Table. Genotyping amplified fragment length polymorphism marker ch4_0.68M and SNP marker 56900 in the parental and F2 generations.

    The forward and reverse primer sequences of ch4_0.68M for genotyping using PCR are 5′-caattgcctgtttgtcagttacac-3′ and 5′-cgcctaatgccactccagcac-3′, respectively. Their locations are 685055–685078 bp and 685136–685156 bp on chromosome 4, respectively. The sizes of the amplified fragments separated using Microchip Electrophoresis System MCE®-202 MultiNA microchip electrophoresis (Shimadzu Corporation, Kyoto, Japan).

    (XLSX)

    Click here for additional data file. (13.3KB, xlsx)
    Attachment

    Submitted filename: Response to Reviewers.docx

    Click here for additional data file. (27KB, docx)

    Data Availability Statement

    Raw sequence data were deposited in the DDBJ Sequence Read Archive (DRA) (https://www.ddbj.nig.ac.jp/dra/index.html) under the accession numbers DRR226810-DRR226964.

    Articles from PLoS ONE are provided here courtesy of PLOS

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