Genetic Analysis of Craniofacial Traits in the Medaka · 2007. 12. 13. · two inbred medaka (Oryzias latipes) strains, HNI and Hd-rR. Relative distances between deﬁned landmarks

Copyright � 2007 by the Genetics Society of AmericaDOI: 10.1534/genetics.106.068460

Genetic Analysis of Craniofacial Traits in the Medaka

Tetsuaki Kimura,* Atsuko Shimada,† Noriyoshi Sakai,‡ Hiroshi Mitani,§ Kiyoshi Naruse,†

Hiroyuki Takeda,† Hidetoshi Inoko,* Gen Tamiya* and Minori Shinya*,‡,1

*Department of Molecular Life Science, Course of Basic Medical Science and Molecular Medicine, Tokai University School of Medicine,Kanagawa 259-1193, Japan, †Department of Biological Sciences, Graduate School of Science, University of Tokyo, Bunkyo-ku,

Tokyo 113-0033, Japan, ‡Genetic Strains Research Center, National Institute of Genetics, Mishima 411-8540 Japanand §Department of Integrated Biosciences, Graduate School of Frontier Sciences, University of Tokyo,

Kashiwa, Chiba 277-8562, Japan

Manuscript received February 5, 2007Accepted for publication October 8, 2007

ABSTRACT

Family and twin studies suggest that a substantial genetic component underlies individual differences incraniofacial morphology. In the current study, we quantified 444 craniofacial traits in 100 individuals fromtwo inbred medaka (Oryzias latipes) strains, HNI and Hd-rR. Relative distances between defined landmarkswere measured in digital images of the medaka head region. A total of 379 traits differed significantlybetween the two strains, indicating that many craniofacial traits are controlled by genetic factors. Of these,89 traits were analyzed via interval mapping of 184 F2 progeny from an intercross between HNI and Hd-rR.We identified quantitative trait loci for 66 craniofacial traits. The highest logarithm of the odds score was6.2 for linkage group (LG) 9 and 11. Trait L33, which corresponds to the ratio of head length to headheight at eye level, mapped to LG9; trait V15, which corresponds to the ratio of snout length to headwidth measured behind the eyes, mapped to LG11. Our initial results confirm the potential of the medakaas a model system for the genetic analysis of complex traits such as craniofacial morphology.

CRANIOFACIAL morphology is a complex but in-teresting trait that reveals individual phenotypic

differences within a species as well as morphologicaldivergence among species. Multiple genetic factors andenvironmental variables account for the large degree ofvariability in human craniofacial morphology. The her-itability of human craniofacial morphology has beenthoroughly investigated in twins and families. A geneticcomponent has been reported for 60–90% of cranio-facial traits, including facial height, position of thelower jaw, and cranial base dimensions (Savoye et al.1998; Johannsdottir et al. 2005). Further analysis ofhuman linkages is difficult, due in part to sample het-erogeneity, limited sample numbers, and a significantimpact of environmental factors on craniofacial phe-notypes. To control for these effects, animal models areoften used to genetically dissect the developmental path-ways that operate in concert to govern multifactorialtraits.

Single gene mutation analyses in mice and zebrafishhave shown that several common signaling cascadesfunction during vertebrate craniofacial development,highlighting the potential of these animals as models. A

loss of Sonic hedgehog (Shh) signaling severely reducesthe size of the craniofacial skeleton and producesmarked craniofacial defects, including complete orpartial cyclopia, in both mice and zebrafish (Brand

et al. 1996; Chiang et al. 1996; Chen et al. 2001). Thesedata suggest that Shh signaling is crucial for thedevelopment of craniofacial components. Similarly,mouse embryos lacking Endothelin-1 (Et-1) show severedefects in Meckel’s cartilage in the mandibular arch andthe ventral cartilage in the hyoid arch (Kurihara et al.1994). The mandibular and hyoid arch ventral carti-lages are also affected by mutation of sucker (suc), thezebrafish Et-1 ortholog (Miller et al. 2000). Thus, thecraniofacial phenotypes of homozygous mouse Et-1 mu-tants and zebrafish suc mutants are essentially identical.These findings suggest that the molecular mechanismsunderlying craniofacial development are largely con-served in vertebrate species.

Quantitative trait locus (QTL) analysis has been usedto successfully identify chromosomal regions affectingthe quantitative traits, including obesity, bone density,and cerebellum size (Beamer et al. 1999; Brockmann

et al. 2000; Airey et al. 2001). The jaw apparatus, one ofthe most thoroughly investigated craniofacial compo-nents, has recently been analyzed as a quantitative trait,and QTL analysis has identified several genomic regionsresponsible for the size and shape of the mandible inmice (Klingenberg et al. 2001, 2004; Dohmoto et al.

1Corresponding author: Model Fish Genomics Resource, Genetic StrainsResearch Center, National Institute of Genetics, Yata 1111, Mishima, 411-8540, Japan. E-mail: [email protected]

Genetics 177: 2379–2388 (December 2007)

2002). Furthermore, the shape of the oral jaw in the eastAfrican cichlid fish has been analyzed using quantitativegenetics, and associated QTL have been identified(Albertson et al. 2003); however, there are few quan-titative studies on gross craniofacial morphology.

The medaka is a small, egg-laying freshwater fishfound in eastern Asia. It is suitable for genetic studies ofboth Mendelian and complex traits because it has ashort life cycle and is very fertile. In addition, severalinbred strains are available (reviewed in Naruse et al.2004a). Inbred strains of medaka differ in terms ofvarious quantitative traits, including body shape, behav-ior, and susceptibility to chemical or radiation-inducedtumorigenesis (Hyodo-Taguchi 1990). Furthermore,gross brain morphology varies greatly in five inbredstrains with different genotypes (Ishikawa et al. 1999).The availability of a dense and accurate genetic linkagemap (Naruse et al. 2004b; Kimura et al. 2005), in com-bination with a number of inbred strains that displaydiverse quantitative traits, makes it possible to identifyQTL for complex traits in the medaka.

Here, we demonstrate that craniofacial variation has agenetic basis in the medaka and estimate the heritabilityfor many of the traits. QTL mapping performed using F2

analysis from two inbred medaka strains, HNI and Hd-rR, demonstrates the potential of this species for geneticanalysis of craniofacial morphology. This is the firststudy using the medaka to dissect complex traits by QTLanalysis.

MATERIALS AND METHODS

Strains, pedigree design, and fish maintenance: HNI andHd-rR strains (Hyodo-Taguchi 1996) are inbred strains ofmedaka established from a northern Japanese population anda southern Japanese population, respectively. Three pairs ofHNI males and Hd-rR females and three pairs of Hd-rR malesand HNI females were crossed to generate F1 progeny. A totalof 184 F2 progeny were obtained by intercrossing eight pairs ofthe F1 fish. Fish were maintained in an in-house facility at 26�in a constant recirculating system on a 14 hr light/10 hr darkcycle.

Phenotypic analysis: We analyzed 100 HNI fish, 100 Hd-rRfish, 50 F1 progeny (HNI 3 Hd-rR), and 184 F2 progenyobtained by intercrossing F1 fish. The F2 progeny were fromthe same set we used in a previous study (Kimura et al. 2005).Adult fish (4 months old) were anesthetized and mounted in2% methylcellulose. Lateral, dorsal, and ventral head imagesof each medaka were then captured with a CCD camera (Fujidigital camera HC-2500) mounted on a Leica MZ12.5 stereo-microscope. Images were collected at a constant magnificationwith a resolution of 1280 3 968 pixels. Craniofacial landmarkswere identified on each image as shown in Figure 1. We cal-culated the distances between different landmark combina-tions from their coordinates. Ratios of different craniofacialdistances were then calculated to generate phenotypic values.The quantified traits we obtained are listed in supplementalTable 1 (http://www.genetics.org/supplemental/). The t-testwas used to determine whether males and females withinthe same strain differed significantly and whether the twostrains differed significantly. Step-down Holm-Sidak correc-

tion (Ludbrook 1998) was applied when testing for signifi-cance between the two strains. Sex-linked traits should bemapped using sorted samples by gender. Therefore, no cor-rection was carried out when testing for gender differences soas to reduce the errors of analyzing sex-dependent traits in agender-mixed sample.

Genetic analysis: The broad-sense heritability of eachquantified trait was estimated from the phenotypic varianceof F1 and F2 progeny (Falconer 1989). The genetic correla-tion was estimated from the phenotypic variance and co-variance of F1 and F2 progeny (Ukai 2002). A total of 304markers, composed of 48 M-marker 2003 (Kimura et al. 2004)and 256 newly mapped markers (Kimura et al. 2005), wereused to map the quantified craniofacial traits. The genotypedata are those described in Kimura et al. (2005). Both geno-type and phenotype data were analyzed by interval mappingusing a MAPMAKER/QTL 1.1 program (Lander et al. 1987,DOS version, used in Windows 2000). The algorithms uponwhich MAPMAKER/QTL are based assume that the quantita-tive trait values are normally distributed across the population.We examined whether the phenotype data were normallydistributed by using a chi-square test for goodness of fit. Whenthe data did not fit well with a normal distribution, it wascorrected using the renouncement method in the programSTSS/EXCEL 5.5. The significance threshold of the logarithmof the odds (LOD) score was 2.8, based on the average in-termarker distance in this study and the genome size of themedaka. This indicates that the chance of a false positiveoccurring anywhere in the medaka genome is at most 5%(Lander and Botstein 1989). Traits that were mapped with amaximum LOD score of 5.0 or greater were analyzed by thecomposite interval mapping (Zeng 1993, 1994) using the Win-dows program QTL Cartographer 2.0 (in Windows 2000).

RESULTS

Gender differences in craniofacial traits: We firstexamined the craniofacial traits of 100 medaka (50males and 50 females) from HNI and Hd-rR, the twomajor inbred strains from northern and southernJapanese populations, respectively. These two medakapopulations differ in terms of both their genetic(Sakaizumi et al. 1983; Takehana et al. 2003) andmorphological traits (Ishikawa 2000). These differ-ences were likely generated by their geographic separa-tion. In this study, 444 traits were quantified: 158 lateralview traits, 157 dorsal view traits, and 129 ventral viewtraits. Gender differences in teeth size—one of the cra-niofacial phenotypes—have previously been reported inthe medaka (Egami 1956). We tested the two strains forthe presence of other gender-dependent morphologi-cal differences (supplemental Table 1 at http://www.genetics.org/supplemental/). Gender differences wereobserved in 304 of 444 traits (68.5%, Table 1). Inter-estingly, the fraction of gender-specific trait differenceswas extremely large in ventral view (82.2%) and was thesmallest in dorsal view (57.3%, Table 1). The landmarksin the ventral view were mostly on the pharyngealstructures, such as the lower jaw (Figure 1C), indicatingthat many of these structures may develop under thecontrol of sex-dependent factors.

2380 T. Kimura et al.

Strain differences in craniofacial traits: We nextexamined whether craniofacial traits differed be-tween the two inbred medaka strains, HNI and Hd-rR.Gender-independent traits were examined in a gender-mixed population of 100 individuals from each strain,whereas gender-dependent traits were tested in maleand female subpopulations. Most of the craniofacialtraits quantified in this study differed significantlybetween HNI and Hd-rR (Table 1). Of the 140 gender-independent traits, 125 (89.3%) differed between thesetwo strains, whereas 254 of the 304 gender-dependenttraits (83.6%) differed in either male or female medaka(Table 1). Of these, 145 (47.7%) differed in both sexes,59 (19.4%) differed only in males, and 50 (16.4%) dif-fered only in females (supplemental Figure 1 at http://www.genetics.org/supplemental/). Thus, craniofacialphenotypes in this organism are controlled genetically,at least to a certain extent.

Heritability of craniofacial traits in the medaka:Craniofacial traits that differed significantly betweenstrains HNI and Hd-rR were further quantified in 50 F1

and 184 F2 progeny. The broad-sense heritability of eachtrait was estimated from the variance in the isogenic F1

population and heterogeneous F2 samples. Supplemen-tal Table 2 (http://www.genetics.org/supplemental/)shows the mean values for gender-independent cranio-facial traits in HNI, Hd-rR, F1, and F2 populations, as wellas the heritability of these traits in medaka. Many of thecraniofacial traits were highly heritable. The greatestheritability for a lateral view trait was 0.768 for L37, for adorsal view trait was 0.870 for D34, and for a ventral traitwas 0.893 for both V1 and V4. A high degree of herita-bility was evident in many of the lateral view traits; 67.6%showed a heritability value . 0.5. Only 43.2% (38 of 88traits) of the dorsal and ventral view traits showed such ahigh degree of heritability. The mean values and herita-bility of gender-dependent traits are shown in supplemen-tal Table 3 (http://www.genetics.org/supplemental/).

QTL mapping: Craniofacial traits that fulfilled thefollowing conditions were analyzed by QTL mapping:(1) no gender difference; (2) differed between strainsHNI and Hd-rR; and (3) the F2 population was normally

distributed, indicating polygenic regulation of the traits.F2 samples should be analyzed by each gender in orderto map gender-dependent traits. Because the resultingsample size (,100) was too small to detect QTL, gender-dependent traits were not dealt with in this study but willbe analyzed in the near future as we collect additional F2

medaka. Eighty-nine of the target traits met the criteriadescribed above: 31 of the lateral view traits, 35 of thedorsal view traits, and 23 of the ventral view traits. Six ofthe lateral view traits and 30 of the dorsal view traits werenot normally distributed; 4 of these lateral view traitsinvolved the length between landmarks 13 and 47(L13:47), and most of these dorsal view traits containedeither D19:29 or D29:31 (non-normally distributedtraits are marked by asterisks in supplemental Table 2).An interval mapping genomic screen was performed bygenotyping all 184 F2 medaka using 304 primers thatcover the entire genome with an average intermarkerdistance of 5.9 cM (supplemental Table 4 at http://www.genetics.org/supplemental/, Figures 2–4). We de-tected QTL for a total of 66 traits (66/89; 74.2%): 17lateral view traits (17/31; 54.8%), 34 dorsal view traits(34/35; 97.1%), and 15 ventral view traits (15/23;65.2%).

Several traits were sometimes mapped to the sameregion of a linkage group (LG). Five lateral view traits(L3, L5, L7, L9, and L28) were mapped to LG4 (Figure2). Four of these traits (L3, L5, L7, and L9) exhibitedmoderate to high phenotypic and genetic correlationsin F2 medaka. L28 was not well correlated with theothers (supplemental Table 5 at http://www.genetics.org/supplemental/). QTL for another 5 traits (L2, L3,L11, L13, and L17) were detected on LG8, and a similar,but different, combination of 5 traits (L2, L4, L11, L13,and L17) were found on LG23 (Figure 2). Most of thesetraits were highly correlated (supplemental Table 6 athttp://www.genetics.org/supplemental/), and as manyas 4 traits (L2, L11, L13, and L17) shared regions onboth LG8 and LG23. All of these traits were associatedwith the position of the eye along the anteroposterioraxis in the head region. The genetic factors for eyepositioning along the anteroposterior axis within the

TABLE 1

Characteristics of craniofacial traits quantified in medaka

Lateral Dorsal Ventral Total

No. of traits % No. of traits % No. of traits % No. of traits %

Quantified traits 158 — 157 — 129 — 444 —Gender independent 50 31.6 67 42.7 23 17.8 140 31.5

With strain difference 37 74.0 65 97.0 23 100.0 125 89.3Without strain difference 13 26.0 2 3.0 0 0.0 15 10.7

Gender dependent 108 68.4 90 57.3 106 82.2 304 68.5With strain difference 81 75.0 78 86.7 95 89.6 254 83.6Without strain difference 27 25.0 12 13.3 11 10.4 50 16.4

Craniofacial Traits in the Medaka 2381

head region are therefore likely located in the sharedregions on LG8 and LG23. Overlapping QTL were alsodetected for dorsal traits. Fifteen traits (D1–10, D17,D21, D25, D32, and D35) were mapped to LG3, another7 (D23, D24, D26, D27, D31, D33, and D34) weremapped to the upper distal region of LG4, and 9 (D6,D13, D14, D16, D20, D24, D27, D30, and D31) werelocated on LG12 (Figure 3). The 15 traits that weremapped to LG3 were highly correlated except for D6,which was only moderately correlated with the other14 traits (supplemental Table 7 at http://www.genetics.org/supplemental/). High phenotypic correlations wereobserved between D23, D24, D26, and D27 and betweenD31 and D33, but correlations were low to moderatebetween the two groups of traits. D34 showed littlephenotypic correlation and relatively low genetic corre-lation with the others (supplemental Table 8 at http://www.genetics.org/supplemental/). The traits that were

mapped to LG12 could be roughly classified into fourgroups on the basis of the phenotypic correlations: (1)D6, D13, and D14; (2) D16 and D20; (3) D24 and D27;and (4) D30 and D31. Moderate to high correlationswere observed between the traits within a group, but lowor few correlations were found between traits fromdifferent groups (supplemental Table 9 at http://www.genetics.org/supplemental/). In contrast to the dorsalview traits, only one region was overlapped by 5 or moreventral view traits. Traits V1, V4, V5, V12, V13, and V17were mapped to LG5 (Figure 4). High correlations wereobserved between V1, V4, and V5. V12 correlatedwell with V13 (supplemental Table 10 at http://www.genetics.org/supplemental/); however, V17 did notcorrelate well with the other traits from these groups.

Eight traits were mapped with a maximum LOD scoreof $5.0 (supplemental Table 4). Three of these were thelateral view traits, L23, L29, and L33. L23 was mapped toLG14 with a maximum LOD score of 5.2. L29 and L33were mapped to LG9 with maximum LOD scores of 5.4and 6.2, respectively. The QTL for L23 accounted for13.0% of the phenotypic variation, and two Hd-rRalleles in the nearest marker of the QTL had longersnout-to-eye height than the other genotypes (Table 2).For L29 and L33, 13.1% and 15.9%, respectively, of thephenotypic variation could be explained by the QTL onLG9. The ratio of the head length to height was greaterin homozygotes with the HNI allele near the given QTL(Table 2). The QTL for the dorsal view traits, D13 andD14, both localize to the same region of LG12, with thesame maximum LOD score (5.3). Both of them accountfor 12.6% of the variance. Both D13 and D14 corre-spond to the ratio of snout length to premaxillary lengthin the dorsal view, and measurements are smaller inmedaka that are homozygous for the Hd-rR allele in thenearest marker of the QTL (Table 2). Another dorsaltrait, D29, was mapped to LG22 with a maximum LODscore of 5.0, accounting for 12.5% of the total variance.Fish homozygous for HNI alleles near the mapped locushad the shortest head-to-snout width; those homozy-gous for Hd-rR alleles had the largest head length tosnout width. Heterozygotes were intermediate, suggest-ing no dominance between the alleles (Table 2). Aregion within LG11 was also highly associated with V14and V15 with maximum LOD scores of 6.0 and 6.2,accounting for 15.8% and 16.2% of variance, respec-tively. An HNI allele in the nearest marker of the QTLenlarged the proportion of the head width to snoutlength (Table 2). The eight traits described above werefurther analyzed with composite interval mapping toconfirm these results. Some QTL with low LOD scoreson L29 and L33 could not be detected by compositeinterval mapping; however, all QTL that were identifiedwith LOD $ 5.0 with interval mapping were confirmed(supplemental Table 11 at http://www.genetics.org/supplemental/). We did not find any traits with anextremely high phenotypic variance that could account

Figure 1.—Landmarks in the digital images of the medakahead region. Dots indicate odd-numbered landmarks, whichwere denoted this way for convenience. (A) Lateral view, (B)dorsal view, and (C) ventral view. Bar, 3 mm.


Figure 2.—QTL locations for the craniofacial traits in the lateral view. Thick lines in the chromosomes represent the chromo-some parts constructed by Kimura et al. (2005). The thin lines were added following analysis of M-marker 2003 (Kimura et al.2004). The LGs are listed according to the numbering used by Naruse et al. (2004b). The numbers on the left side of each chro-mosome show the genetic distances in Kosambi centimorgan (cM). Markers are indicated on the right side of each chromosome.Colored bars to the left of the genetic distances indicate the regions of 1-LOD support interval of linkage.


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Figure 4.—QTL locations for the craniofacial traits in the ventral view. See details in the legend for Figure 2.


for the total variance. The highest that we detected was16.2% in V15, which was mapped to LG11.

DISCUSSION

We have analyzed the craniofacial traits of two inbredmedaka strains, HNI and Hd-rR, and their F1 and F2

progeny. Our phenotypic survey shows that 304 of 444quantified traits are gender dependent and 379 traitsdiffer significantly between the two inbred strains. Thus,many craniofacial traits are highly heritable in medaka.Furthermore, 66 of 89 traits were localized in the me-daka genome with interval mapping. This is the firstdemonstration that the QTL of craniofacial traits in me-daka can be identified by genetic analysis of F2 progeny.

To measure the craniofacial traits, we collected mag-nified digital images and extracted the quantitative traitsfrom the images using a computer. Direct measure-ments are difficult when the object is as small as medaka.The use of magnified digital images reduces measure-ment errors. Furthermore, individual traits can be quan-tified repeatedly using the images, thereby reducinghuman measurement errors. Finally, the same set ofimages can be used to test quantification methods, in-cluding length or ratio calculations with different land-mark combinations, identification of novel landmarks,area determinations, and mathematical computationsof various indices. To successfully detect a QTL, it is vitalto extract trait data that are highly heritable and withoutmuch environmental influence. There is currently nostandard efficient method for measuring craniofacialtraits, however. Different laboratories utilize varioustechniques and refine these methods as needed (forexample, Klingenberg et al. 2001, 2004; Albertson

et al. 2003; Nishimura et al. 2003). The present methodfor determining craniofacial traits is effective andfacilitates repeated trials on a two-dimensional display.

We calculated the ratios of two linear distances togenerate phenotypic values and identified at least aQTL for 74.2% of traits, with the highest LOD score of6.2. This is a simple, classic method that quantifiesproportions of a shape independent from absolute size.There is no guarantee that variations in shape arecaptured completely, but quantification of ratios makesit possible to directly quantify differences that a re-searcher finds through careful observation. This simpletechnique has been utilized for genetic studies of cra-niofacial morphology in humans (Savoye et al. 1998;Johannsdottir et al. 2005) as well as quantification andmapping of wing shape traits in Drosophila (Zimmerman

et al. 2000). Another more sophisticated approach isknown as geometric morphometrics, which reflects theentire diversity of spatial patterns, thus allowing identi-fication of QTL affecting all features in a single analysis(Klingenberg et al. 2001, 2004; Albertson et al. 2003).Some QTL in these studies have higher LOD scoresthan ours; however, it is difficult to directly compare theresults. There are many factors that affect the power todetect QTL, including the type of samples, sample size,and statistical procedures. To clarify whether geometricmorphometrics is a better method for detecting QTLin medaka, we plan to quantify our samples using thistechnique and directly compare the results with thecurrent mapping results. Consistent mapping result ofwing shape traits have been obtained by either geometricmorphometrics or ratios (Zimmerman et al. 2000). Thus,at the very least, the ratio method we used is unlikely tobe inferior to geometric morphometrics.

In this study, several craniofacial traits were frequentlymapped to an overlapping region of the medaka genome(Figures 2–4). Shared QTL for several craniofacial traitshave been reported for cichlids, East African fish speciesknown for their divergent morphology (Albertson et al.2003). In cichlids, the shape of the oral jaw apparatus,

TABLE 2

Traits mapped with an LOD $ 5 for each of the three genotypes of the nearest marker to the QTL

Trait LG Nearest marker N/Na mean 6 SD N/Rb mean 6 SD R/Rc mean 6 SD Pd

L23 14 MM04G02K 1.018 6 0.056 1.040 6 0.067 1.078 6 0.051e,f 0.0000078L29 9 MF01SSA025F06 1.827 6 0.066 1.763 6 0.067e 1.784 6 0.077e 0.0000054L33 9 MF01SSA025F06 1.931 6 0.068 1.862 6 0.073e 1.873 6 0.075e 0.0000013D13 12 MF01SSA068F11 2.48 6 0.42 2.31 6 0.46 2.04 6 0.35e,f 0.0000061D14 12 MF01SSA068F11 1.48 6 0.42 1.31 6 0.46 1.04 6 0.35e,f 0.0000060D29 22 OLa3003a 1.945 6 0.068 1.987 6 0.077e 2.023 6 0.072e,f 0.000016V14 11 MF01SSA045F05 2.92 6 0.21 2.78 6 0.18e 2.70 6 0.21e 0.0000014V15 11 MF01SSA045F05 3.00 6 0.22 2.87 6 0.18e 2.78 6 0.20e,f 0.0000010

a Homozygous for the HNI allele.b Heterozygous for HNI and Hd-rR alleles.c Homozygous for the Hd-rR allele.d P-value of one-way analysis of variance.e Significantly different from N/N mean (P , 0.05).f Significantly different from N/R mean (P , 0.05).


the craniofacial skeleton, and the teeth were analyzed.Statistically significant QTL clusters were found in thecichlid genome. Thus, craniofacial development may bepleiotropically regulated or controlled by several closelylinked genes. Alternatively, traits on a common locusmay share a common feature. This latter possibility ismore likely in this study, because a feature can be rep-resented by several ratios and because a given distance isused to calculate several ratios. For example, the traitsmapped on both LG8 and LG23 were all associated withthe position of the eye along the anteroposterior axis inthe head region. Another clear example is the region onLG3 that contains 15 dorsal view traits (Figure 3). Withthe exception of D6, all of these traits are proportionalmeasurements calculated with the same denominator,D7:13 (supplemental Table 1). The values were highlycorrelated in F2 medaka (supplemental Table 7). Thenumerators used to calculate these 14 traits are variouslengths along the anteroposterior and left-right axes.Thus, these mapping results probably reflect character-istics specific to the D7:13 length. The D7:13 measure-ment, not each ratio, is the trait controlled by the regionon LG3. When D7:13 was subjected to interval mapping,it was mapped to the same region on LG3 (data notshown). This example suggests that analysis of each dis-tance may provide additional insight into the geneticstructure of craniofacial morphology. We are preparingsuch an analysis of the distances corrected for the size ofthe entire head.

The ventral view traits, V14 and V15, were mapped toa shared region of LG11 with high maximum LODscores (supplemental Table 4, Figure 4). These traits areassociated with the ratio of head width to snout length,measured behind the eyes. Interval mapping of dorsalview traits localized two additional traits within the sameregion, D18 and D22 (see Figures 3 and 4). These traitsalso reflect the ratio of head width to snout lengthbehind the eyes. Although there was little phenotypiccorrelation between the ratios in the ventral view andthose in the dorsal view (r 2 , 0.1, data not shown),medaka homozygous for the HNI allele at the nearestmarker of the QTL had wider head-to-snout lengthratios in all related traits (Table 2, data not shown). Thesefindings strongly suggest that these ratios are regulatedby a genetic factor(s) within a shared region of LG11.Future fine mapping studies are necessary to preciselyidentify the gene(s) involved in this regulation.

Non-normally distributed traits were excluded fromthis study. These traits might be controlled by a fewmajor genes, however, making it relatively easy to detectQTL. Length of the pelvic spine in F2 progeny ofthreespine sticklebacks is non-normally distributed intwo peaks and has been successfully mapped near thegene Pitx1 with an extremely high LOD score (82.8,Shapiro et al. 2004). We carefully examined the distri-butions of F2 progeny that were not normally distrib-uted for such patterns, but no traits were distributed

across a few clear peaks. We also tried to map a trait fromeach group classified by phenotypic correlations. NoQTL with higher LOD scores were identified, however,and the percentage of phenotypic variance explained bythe QTL did not increase (data not shown), indicatingthat we did not overlook traits controlled by a few majorgenes.

A number of studies have analyzed morphologicalphenotypes as quantitative traits in mice (Cheverud

et al. 1997; Dohmoto et al. 2002; Nishimura et al. 2003;Lang et al. 2005). The LOD scores in our study of me-daka are similar to those obtained in mice. When cra-niofacial traits were analyzed in .100 mice, a few QTLwere identified for each trait with highest LOD scores of�5 (Dohmoto et al. 2002; Nishimura et al. 2003). Whenskeletal morphology was analyzed in 200 F2 mice, 7.3 wasthe highest LOD score and most traits were mapped tobetween 1 and 5 genomic regions (Lang et al. 2005).The ability to identify QTL is affected by a sample size.Increasing the samples size increases the probability offinding QTL with high LOD scores. When mandibleshape was analyzed in 480 F2 mice, 21 traits weremapped to between 2 and 10 genomic regions. Thehighest LOD score in that study was 9.8 (Cheverud et al.1997). We analyzed 184 F2 samples in this study, andmapped, at most, 5 genomic regions per trait (supple-mental Table 4). The highest LOD score in our studywas 6.2, just slightly lower than the LOD score of 7.3obtained in the study of 200 F2 mice (Lang et al. 2005).Thus, the medaka can be an alternative animal modelfor the genetic analysis of morphological traits, which isless expensive and easier to prepare a large number ofsamples.

In conclusion, this study demonstrates that many ofcraniofacial traits in medaka are quantitative traits con-trolled by both environmental and genetic factors. Itis possible to identify the genomic regions responsiblefor these traits using QTL analysis of F2 progeny frominbred medaka strains. Our genomewide study hasidentified many QTL in the medaka genome. Furtheranalysis of these regions is necessary to identify thegenes. We plan to utilize congenic strains carrying anindividual QTL to resolve the genes. These congenicstrains will take several years to construct, but these linesand the subcongenics derived from them will be verypowerful tools for studying the effects of these genesand the interactions among them. By comparing ourresults with those from future genetic studies of cranio-facial traits in other animals, common molecular mech-anisms underlying craniofacial development may beidentified.

We thank Keiko Yoshida and Yasuko Ozawa for their valuableassistance with this study, and Shuhei Mano, Tsuyoshi Koide, JuzohUmemori, and Akinori Nishi for helpful discussions. The workpresented here was supported in part by the Research and StudyProgram of Tokai University Educational System General ResearchOrganization, by the Center of Excellence 21st Century Program, and


by a Grant-in-Aid for Scientific Research on Priority Areas ‘‘SystemsGenomics’’ and ‘‘Comparative Genomics’’ from the Ministry of Educa-tion, Culture, Sports, Science and Technology in Japan.

LITERATURE CITED

Airey, D. C., L. Lu and R. W. Williams, 2001 Genetic control of themouse cerebellum: identification of quantitative trait loci modu-lating size and architecture. J. Neurosci. 21: 5099–5109.

Albertson, R. C., J. T. Streelman and T. D. Kocher, 2003 Direc-tional selection has shaped the oral jaws of Lake Malawi cichlidfishes. Proc. Nat. Acad. Sci. USA 100: 5252–5257.

Beamer, W. G., K. L. Shultz, G. A. Churchill, W. N. Frankel, D. J.Baylink et al., 1999 Quantitative trait loci for bone density inC57BL/6J and CAST/EiJ inbred mice. Mamm. Genome 10:1043–1049.

Brand, M., C. P. Heisenberg, R. M. Warga, F. Pelegri, R. O.Karlstrom et al., 1996 Mutations affecting development ofthe midline and general body shape during zebrafish embryo-genesis. Development 123: 129–142.

Brockmann, G. A., J. Kratzsch, C. S. Haley, U. Renne, M. Schwerin

et al., 2000 Single QTL effects, epistasis, and pleiotropy accountfor two-thirds of the phenotypic F2 variance of growth and obesityin DU6i x DBA/2 mice. Genome Res. 10: 1941–1957.

Chen, W., S. Burgess and N. Hopkins, 2001 Analysis of the zebra-fish smoothened mutant reveals conserved and divergent functionsof hedgehog activity. Development 128: 2385–2396.

Cheverud, J. M., E. J. Routman and D. J. Irschick, 1997 Pleio-tropic effects of individual gene loci on mandibular morphology.Evolution 51: 2006–2016.

Chiang, C., Y. Litingtung, E. Lee, K. E. Young, J. L. Corden et al.,1996 Cyclopia and defective axial patterning in mice lackingSonic hedgehog gene function. Nature 383: 407–413.

Dohmoto, A., K. Shimizu, Y. Asada and T. Maeda, 2002 Quan-titative trait loci on chromosomes 10 and 11 influencing mandi-ble size of SMXA RI mouse strains. J. Dent. Res. 81: 501–504.

Egami, N., 1956 Notes on sexual difference in size of teeth of thefish, Oryzias latipes. Jap. J. Zool. 12: 65–69.

Falconer, D. S., 1989 Introduction to Quantitative Genetics. Longman,Essex, UK.

Hyodo-Taguchi, Y., 1990 Inbred strains of the medaka and theircharacteristics, pp. 129–142 in Biology of the Medaka, edited byN. Egami, K. Yamagami and A. Shima. Tokyo University Press,Tokyo.

Hyodo-Taguchi, Y., 1996 Inbred strains of the medaka, Oryzias lat-ipes. Fish Biol. J. Medaka 8: 11–14.

Ishikawa, Y., 2000 Medakafish as a model system for vertebrate de-velopmental genetics. BioEssays 22: 487–495.

Ishikawa, Y., M. Yoshimoto, N. Yamamoto and H. Ito, 1999 Dif-ferent brain morphologies from different genotypes in a singleteleost species, the medaka (Oryzias latipes). Brain Behav. Evol.53: 2–9.

Johannsdottir, B., F. Thorarinsson, A. Thordarson and T. E.Magnusson, 2005 Heritability of craniofacial characteristicsbetween parents and offspring estimated from lateral cephalo-grams. Am. J. Orthod. Dentofacial Orthop. 127: 200–207.

Kimura, T., T. Jindo, T. Narita, K. Naruse, D. Kobayashi et al.,2004 Large-scale isolation of ESTs from medaka embryos andits application to medaka developmental genetics. Mech. Dev.121: 915–932.

Kimura, T., K. Yoshida, A. Shimada, T. Jindo, M. Sakaizumi et al.,2005 Genetic linkage map of medaka with polymerase chain re-action length polymorphisms. Gene 363: 24–31.

Klingenberg, C. P., L. J. Leamy, E. J. Routman and J. M. Cheverud,2001 Genetic architecture of mandible shape in mice: effectsof quantitative trait loci analyzed by geometric morphometrics.Genetics 157: 785–802.

Klingenberg, C. P., L. J. Leamy and J. M. Cheverud, 2004 Inte-gration and modularity of quantitative trait locus effects on geo-metric shape in the mouse mandible. Genetics 166: 1909–1921.

Kurihara, Y., H. Kurihara, H. Suzuki, T. Kodama, K. Maemura

et al., 1994 Elevated blood pressure and craniofacial abnor-malities in mice deficient in endothelin-1. Nature 368: 703–710.

Lander, E. S., and D. Botstein, 1989 Mapping Mendelian factorsunderlying quantitative traits using RFLP linkage maps. Genetics121: 185–199.

Lander, E. S., P. Green, J. Abrahamson, A. Barlow, M. J. Daly et al.,1987 MAPMAKER: an interactive computer package for con-structing primary genetic linkage maps of experimental and nat-ural populations. Genomics 1: 174–181.

Lang, D. H., N. A. Sharkey, H. A. Mack, G. P. Vogler, D. J.Vandenbergh et al., 2005 Quantitative trait loci analysis ofstructural and material skeletal phenotypes in C57BL/6J andDBA/2 second-generation and recombinant inbred mice.J. Bone Miner. Res. 20: 88–99.

Ludbrook, J., 1998 Multiple comparison procedures updated. Clin.Exp. Pharmacol. Physiol. 25: 1032–1037.

Miller, C. T., T. F. Schilling, K. Lee, J. Parker and C. B. Kimmel,2000 sucker encodes a zebrafish Endothelin-1 required for ven-tral pharyngeal arch development. Development 127: 3815–3828.

Naruse, K., H. Hori, N. Shimizu, Y. Kohara and H. Takeda,2004a Medaka genomics: a bridge between mutant phenotypeand gene function. Mech. Dev. 121: 619–628.

Naruse, K., M. Tanaka, K. Mita, A. Shima, J. Postlethwait et al.,2004b A medaka gene map: the trace of ancestral vertebrateproto-chromosomes revealed by comparative gene mapping.Genome Res. 14: 820–828.

Nishimura, I., T. A. Drake, A. J. Lusis, K. M. Lyons, J. H. Nadeau

et al., 2003 ENU large-scale mutagenesis and quantitative traitlinkage (QTL) analysis in mice: novel technologies for searchingpolygenetic determinants of craniofacial abnormalities. Crit. Rev.Oral Biol. Med. 14: 320–330.

Sakaizumi, M., K. Moriwaki and N. Egami, 1983 Allozymic varia-tion and regional differentiation in wild populations of the fish,Oryzias latipes. Copeia 1983: 311–318.

Savoye, I., R. Loos, C. Carels, C. Derom and R. Vlietinck, 1998 Agenetic study of anteroposterior and vertical facial proportionsusing model-fitting. Angle Orthod. 68: 467–470.

Shapiro, M. D., M. E. Marks, C. L. Peichel, B. K. Blackman, K. S.Nereng et al., 2004 Genetic and developmental basis of evolu-tionary pelvic reduction in threespine sticklebacks. Nature 428:717–723.

Takehana, Y., N. Nagai, M. Matsuda, K. Tsuchiyaand M. Sakaizumi,2003 Geographic variation and diversity of the cytochrome bgene in Japanese wild populations of medaka, Oryzias latipes.Zoolog. Sci. 20: 1279–1291.

Ukai, Y., 2002 Genetic Analysis of Quantitative Traits. Igaku-Shuppan,Tokyo.

Zeng, Z. B., 1993 Theoretical basis for separation of multiple linkedgene effects in mapping quantitative trait loci. Proc. Natl. Acad.Sci. USA 90: 10972–10976.

Zeng, Z. B., 1994 Precision mapping of quantitative trait loci. Genetics136: 1457–1468.

Zimmerman, E., A. Palsson and G. Gibson, 2000 Quantitative traitloci affecting components of wing shape in Drosophila melanogaster.Genetics 155: 671–683.

Communicating editor: N. Takahata


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Genetic Analysis of Craniofacial Traits in the Medaka · 2007. 12. 13. · two inbred medaka (Oryzias latipes) strains, HNI and Hd-rR. Relative distances between deﬁned landmarks