Inheritance of finger pattern types in MZ and DZ twins

HOMO - Journal of Comparative Human Biology 62 (2011) 298–306

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Inheritance of finger pattern types in MZ and DZ twins B. Karmakar a,∗, I. Malkin b, E. Kobyliansky b a

Biological Anthropology Unit, Indian Statistical Institute, 203 B.T. Road, Kolkata 700 108, India Human Population Biology Unit, Department of Anatomy and Anthropology, Sackler Faculty of Medicine, Tel Aviv University, Ramat Aviv, Tel Aviv 69978, Israel b

a r t i c l e

i n f o

Article history: Received 4 February 2010 Accepted 15 February 2011

a b s t r a c t Digital patterns of a sample on twins were analyzed to estimate the resemblance between monozygotic (MZ) and dizygotic (DZ) twins and to evaluate the mode of inheritance by the use of maximum likelihood based variance decomposition analysis. MZ twin resemblance of finger pattern types appears to be more pronounced than in DZ twins, which suggests the presence of genetic factors in the forming of fingertip patterns. The most parsimonious model shows twin resemblance in count of all three basic finger patterns on 10 fingers. It has significant dominant genetic variance component across all fingers. In the general model, the dominant genetic variance component proportion is similar for all fingertips (about 60%) and the sibling environmental variance is significantly nonzero, but the proportion between additive and dominant variance components was different. Application of genetic model fitting technique of segregation analyses clearly shows mode of inheritance. A dominant genetic variance component or a specific genetic system modifies the phenotypic expression of the fingertip patterns. The present study provided evidence of strong genetic component in finger pattern types and seems more informative compared to the earlier traditional method of correlation analysis. © 2011 Published by Elsevier GmbH.

Introduction Studies of dermatoglyphic traits are particularly valuable with respect to developmental stability. The unique quality of these traits is that once formed at the end of the first and beginning of the

∗ Corresponding author. E-mail address: [email protected] (B. Karmakar). 0018-442X/$ – see front matter © 2011 Published by Elsevier GmbH. doi:10.1016/j.jchb.2011.02.002

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second trimester of intrauterine development, they remain unchanged through the individual’s life (Cummins and Midlo, 1961; Penrose and Ohara, 1973; Gooseva, 1986). Therefore, dermatoglyphic traits are phylogenetically more stable than other biological traits (Rothhammer et al., 1977; Froehlich and Giles, 1981). Familial studies now have established that hereditary factors are very important in the phenotypic expression of dermatoglyphic traits (Darlu and Iagolnitzer, 1984; Gilligan et al., 1985, 1987; Sengupta and Karmakar, 2004; Karmakar et al., 2005, 2006). Heritability values close to 1.0 have been found for the total finger ridge count, total pattern intensity on fingers, the number of whorls and ulnar loops on fingers, the total pattern intensity on palms, and the a–b palmar ridge count (Loesch, 1971, 1974, 1982). In addition, Malhotra et al. (1981) found values of 0.60 for the total pattern ridge count, and heritability values close to 0.5 have been found for the thenar loops, hypothenar loops, interdigital loop III, and triradius t. All studies indicate that there is a predominant genetic component in dermatoglyphic traits (Reed et al., 1975; Loesch, 1979). Twin studies have played a central role in sorting out genetic from environmental variation (Holt, 1952; Reed et al., 1975; Reed and Christian, 1979; Jantz et al., 1984; Arrieta et al., 1991; Pechenkina et al., 2000; Karmakar et al., 2011). Familial studies on twins have been very valuable in human genetics. Using twin data, estimates of heritability and genetic variance, obtained by means of the traditional indices, have shown results similar to those found in family samples (Arrieta et al., 1991). Majority of the above studies are based on intra-class correlation analysis, which is very poor to detect the mode of inheritance of a trait. Recently, a number of program packages have become available, which are very useful in performing complex segregation analysis to determine the effect of genes. Thus, a model of inheritance has yet to be established to resolve the existing inconsistencies in the literature to understand the nature of genetic and environmental bases of dermatoglyphic trait. Twin data from Moscow have been used in the present report, because the high genetic diversity of the people living in Moscow is expected to produce high heritability estimates. In the present study we have applied model-fitting methods of analysis, using the maximum likelihood ratio test and evaluating both putative genetic effects and common familial environmental influences. The main goal of the present communication is not only to estimate the resemblance between MZ and DZ twins (females only) in finger pattern types, but also to evaluate the mode of inheritance by the use of maximum likelihood variance decomposition analysis. Our objectives are of two kinds. In order to accomplish these goals, first we obtained frequency of coincidence for four basic types of finger patterns (UL, RL, A, and W) on each finger in MZ and DZ female twins separately. Second, we analyzed the genetic and environmental components in the distribution of the patterns on 10 fingers for three basic types of finger patterns (A, L, and W) in the whole sample, including males and females. The variance decomposition model accounted for sex and zygosity.

Materials and methods Samples and traits We analyzed the sample of monozygotic (MZ) twins of two sexes (102 male pairs, 140 and 138 female pairs) and 120 pairs of female dizygotic (DZ) twins. Finger prints of these individuals were kindly provided by the Anuchin Anthropological Museum, Moscow State University, Russia. All digital prints were collected from genetically healthy people, living in Moscow. Finger prints were obtained with the traditional ink method described by Cummins and Midlo (1961). The analyzed variables are digital pattern types. They were classified into three major categories namely ‘Whorls’, ‘Loops’ and ‘Arches’ according to Galton (1892). All types of true whorls such as concentric, single spiral, double spiral, accidental, etc. and also all types of composite whorls such as twin loops, central pocket loops, lateral pocket loops, crested and knot-crested loops are grouped under the broad category of ‘Whorls’. On the other hand radial and ulnar loops are categorized into ‘Loops’; both simple and tented arches are grouped into the category ‘Arches’. Thus these three groups of finger patterns according to Galton (1892) represent a general picture of the pattern distribution in fingers. However, in the present report loops were classified into ulnar loops and radial loops separately, based on Cummins and Midlo (1961) and thus four digital patterns were considered in the present analysis.

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Statistical analyses Frequency distribution To estimate the presence of inheritance of digital pattern types, we measured the difference in resemblance between the two types (DZ and MZ) of female twins as follows: for both female subsamples we established frequencies with which the same pattern occurred on the same finger (F1–F5 for left and right hand) in both twins of the pair. In the null hypothesis, if this event occurs randomly, its probability should be square of the frequency of a given pattern type on the appropriate finger. A 2 goodness-of-fit test was used to determine if the observed pattern of shared finger patterns deviated from the null expectation. We also calculated hypothetical probabilities of random events with their frequencies in the sample of independent observations, which enables us to estimate the significance of described type of resemblance in twin pair as a deviation from random probability. To characterize this resemblance quantitatively we introduce the ratio of observed event co-occurrence frequency to its hypothetical probability of random occurrence. Variance decomposition analysis This method allows to distinguish between different independent components that form the variation of the trait, including additive and dominant genetic effects and several potential common family environment components. The primary difficulty we encountered in this analysis is that our pattern score data are qualitative and discrete while the genetic model of inheritance requires interval-ratio data. Therefore, for this analysis we used the count of each pattern type on 10 fingers of each individual as an approximation of continuous trait. Initially we distinguished four patterns: arch, ulnar loop, radial loop, and whorl. We combined loops (ulnar and radial) to obtain three quantitative traits in order to facilitate the use of these statistical analyses. We analyzed inheritance of these traits, supposing polygenic nature of main three patterns arch, loop (ulnar and radial) and whorl. The variance decomposition analysis for quantitative trait inheritance assumes that the individual trait value is a function of covariates plus a random trait residual partially correlated between relatives, xi = ϕ(yi1 , yi2 , . . ., yiK ) +  i , where xi is the individual trait value, for the ith individual, yi1 , yi2 , . . ., yiK are the individual’s covariate values and  i is the n residual variance. The trait residual  in a pedigree is assumed to have n variable normal distribution, where n is a number of pedigree members. The components of variance–covariance matrix determining this distribution are parameterised as follows: (1) We assume that the trait residual is a result of the simultaneous influence of a number of orthogonal genetic or environmental factors. Genetic variance is assumed to be a result of joint effect of a number of genes, each having small input. (2) We should distinguish additive genetic variance 2 shared by two relatives which is approximately proportional to the proportion of coinciding gene AD 2 is shared only by monozygotic twins, with 0.5 2 for parent–offspring and siblings alleles: the full AD AD 2 for half-sibs and uncle–nephew and so on. The dominant genetic variance or di-zygotic twins, 0.25AD 2 shared by two relatives is approximately proportional to the proportion of coinciding genotypes DO 2 ) and for (combinations of two alleles); it is assumed to be nonzero only for monozygotic twins (DO 2 siblings or dizygotic twins (0.25DO ). For each environmental factor we define the group of pedigree 2 household component, shared members, sharing it. The possible environmental components are: HS 2 additional environmental component shared by by the members of the same nuclear family; SP 2 additional component shared by siblings;  2 additional environmental factors shared spouses; SB TW 2 residual individual variance. Because we assume that all factors are orthogonal to by twins; and RS each other, the covariance matrix component for each pair of individuals is a sum of the genetic and environmental effect variances shared by the relatives i and k (Thompson, 1973). Variance and covariance parameters were estimated by maximum likelihood (Malkin and Ginsburg, 2007) procedure. For constrained models any of described above variance components can be fixed to zero. Other model parameters, for example mean values for different sexes, can be constrained to be fixed or equal to another. The purpose of analysis is to find the most parsimonious model by means of likelihood ratio test (LRT), comparing different models. Then, we can estimate the input of significant covariates and significant components of residual variance. Using these estimates, we are able to use

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the most parsimonious model to characterize the familial resemblance of observed individual trait values as, for example, polygenic additive or dominant. It also allows us to determine whether genetic components are significant, and to determine the magnitude of purely environmental effects. The best fitting and most parsimonious model was obtained after excluding all non-significant parameters from the general model. We used the program package MAN for pedigree analysis (Malkin and Ginsburg, 2007). Results Frequency distribution Table 1 shows frequencies of each of the four pattern types on all five fingers combined and on each fingertip (F1–F5) individually (column “freq”), the frequency of findings of co-occurrence of the same pattern types on the same finger for twin pair (column “freq.du”), the ratio between observed frequency of coincidence of patterns and hypothetical random probability of this event, coincidence of patterns and the null expectation of chance co-occurrence (column “ratio” = freq.du/freq2 ) for each type of digital pattern in both MZ and DZ female twin pairs. Combined fingers Frequencies of ulnar loop (UL) and radial loop (RL) in DZ twins (0.611; 0.043) were greater than in MZ twins (0.577; 0.032) while of arches (A) and whorls (W) were higher in MZ twins (0.198; 0.346) than in DZ twins (0.163; 0.268), but this difference was not significant for our sub-sample sizes. However, ratios were significantly different for UL (ratio = 1.463; p = 0.0001) and W (ratio = 2.268; p = 5 × 10−8 ) only for MZ twins; for DZ twins W ratio = 1.632 was only marginally significant (p = 0.055) (Table 1). Coincidence of fingerprint patterns for appropriate fingers was much greater than random in MZ twins and only slightly greater in DZ twins at least in UL and W patterns. Different fingers Patterns similar to those in combined 10 fingers were obtained in all digits: frequencies of patterns UL and RL were higher while A and W were lower in DZ twins compared to MZ twins. MZ twins showed lower frequency of patterns UL and RL, while greater for patterns A and W compared to DZ twins. However, the number of significant (p < 0.05) ratios in MZ twins was 11 (see Table 1, bold items in column ratio) including 6 ratios with p < 10−4 , whereas in DZ twins the ratio was significant only in one case of W (ratio = 2.315; p = 0.03) for 3rd finger (F3). In the case of MZ twins the UL and W ratio was significant across all fingers. Thus coincidence in fingerprint patterns for appropriate fingers was much greater than random in MZ twins and only slightly greater in DZ twins, at least in UL and W patterns. Fig. 1 shows that in the MZ sample for W and UL (having greater frequencies), ratios were significant for all 10 fingers, whereas in DZ twins the ratio was significant only in two cases (FF25 and FF27). For A in MZ twins all ratios excluding only two: FF20 and FF26 (left-right symmetry) were significant, whereas in DZ twins four fingers (FF21, FF23, FF25, FF27) had statistically significant ratios. For RL only MZ twins had significant ratios for FF21 and FF28. Patterns having greater values of significant ratios are A (4.5 to 136) and W (1.6 to 5.1). Fig. 2 shows the proportion of coinciding patterns of all types (for twin pairs) for different fingers in MZ and DZ samples. The ratio of this proportion is minimal for more polymorphic patterns on fingers FF22 and FF28 for both MZ and DZ samples. The ratio of this proportion to random probability of coincidence is significant for all fingers in MZ twins (ratio about 2) and only for FF19 in DZ twins. Variance decomposition analysis Table 2 presents the results of variance decomposition analysis of particular fingerprint pattern counts for the whole sample including DZ and MZ twins and for both sexes – males and females.

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Table 1 Pattern type fraction and ratio between random frequency of coincidences of finger patterns and hypothetical random probability of coincidence in combined and single fingers. Left hand

Pat.

DZ freq

FF19 UL RL A W FF20 UL RL A W FF21 UL RL A W FF22 UL RL A W FF23 UL RL A W

MZ freq.du

Ratio

p

freq

freq.du

Ratio

Digit

Right hand

Pat.

DZ

p

0.609 0.004 0.048 0.339

0.400 0.000 0.009 0.157

1.1 0.0 3.8 1.4

0.513 0.963 0.150 0.163

0.571 0.000 0.037 0.392

0.470 0.000 0.030 0.299

1.4 – 21.4 1.9

<0.001 – <0.001 <0.001

0.591 0.000 0.047 0.362

0.371 0.000 0.000 0.155

1.1 – 0.0 1.2

0.619 – 0.609 0.442

0.541 0.000 0.007 0.452

0.444 0.000 0.000 0.363

1.5 – 0.0 1.8

<0.001 – 0.931 <0.001

0.687 0.017 0.130 0.165

0.530 0.000 0.052 0.052

1.1 0.0 3.1 1.9

0.209 0.852 0.004 0.102

0.615 0.022 0.078 0.285

0.467 0.000 0.044 0.193

1.2 0.0 7.3 2.4

0.034 0.796 <0.001 <0.001

0.309 0.222 0.161 0.309

0.104 0.078 0.043 0.139

1.1 1.6 1.7 1.5

0.741 0.149 0.235 0.109

0.270 0.159 0.107 0.463

0.163 0.052 0.052 0.363

2.2 2.0 4.5 1.7

<0.001 0.050 <0.001 <0.001

0.621 0.009 0.060 0.310

0.422 0.000 0.017 0.112

1.1 0.0 4.7 1.2

0.411 0.926 0.015 0.565

0.609 0.000 0.068 0.323

0.526 0.000 0.053 0.256

1.4 – 11.5 2.4

<0.001 – <0.001 <0.001

FF25 UL RL A W FF26 UL RL A W FF27 UL RL A W FF28 UL RL A W FF29 UL RL A W

MZ

freq

freq.du

Ratio

p

freq

freq.du

Ratio

p

0.823 0.000 0.034 0.142

0.716 0.000 0.009 0.060

1.1 – 7.3 3.0

0.384 – 0.020 0.002

0.831 0.000 0.007 0.162

0.801 0.000 0.007 0.132

1.2 – 136.0 5.1

0.005 – <0.001 <0.001

0.543 0.009 0.034 0.414

0.328 0.000 0.000 0.216

1.1 0.0 0.0 1.3

0.441 0.926 0.710 0.205

0.471 0.004 0.004 0.522

0.390 0.000 0.000 0.441

1.8 0.0 0.0 1.6

<0.001 0.966 0.966 <0.001

0.772 0.009 0.073 0.147

0.647 0.000 0.026 0.060

1.1 0.0 4.8 2.8

0.261 0.926 0.003 0.004

0.721 0.015 0.029 0.235

0.625 0.000 0.007 0.169

1.2 0.0 8.5 3.1

0.014 0.864 0.010 <0.001

0.341 0.159 0.151 0.349

0.138 0.034 0.043 0.155

1.2 1.4 1.9 1.3

0.460 0.536 0.142 0.273

0.316 0.118 0.107 0.460

0.199 0.044 0.059 0.368

2.0 3.2 5.2 1.7

<0.001 0.003 <0.001 <0.001

0.609 0.004 0.048 0.339

0.400 0.000 0.009 0.157

1.1 0.0 3.8 1.4

0.513 0.963 0.150 0.163

0.571 0.000 0.037 0.392

0.470 0.000 0.030 0.299

1.4 – 21.4 1.9

<0.001 – <0.001 <0.001

Freq: frequency of the given fingerprint; freq.du: proportion of cases in which both twins have the same fingerprint pattern on the same finger; ratio: ratio between observed and expected random coincidence (ratio = freq.du/freq2 ). Bold are ratios significantly greater than 1 (p < 0.05), FF19–FF23 left hand, FF25–FF29 right hand.

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Fig. 1. Frequencies of different fingerprint patterns for each finger in MZ (a) and DZ (b) female samples (FF19–FF23 left hand; FF25–FF29 right hand).

The general variance component model accounted for sex. We united UL and RL patterns in one trait, because the fraction of RL in the sample was too low to form a separate quantitative trait. The most parsimonious model shows all significant parameters. The mean count of Arches on individual fingers has a significant sex difference in mean counts. The mean count of arches is twice greater in males (1.29) than in females (0.63). The type of inheritance appears to be genetic dominant (about 80% of trait variance); additive genetic and sibling environmen1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 FF19 FF20 FF21 FF22 FF23 FF25 FF26 FF27 FF28 FF29 DZ

MZ

Fig. 2. Proportion of coinciding patterns for each finger in MZ and DZ female samples (FF19–FF23 left hand; FF25–FF29 right hand).

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Table 2 Variance decomposition analysis of similar fingertip patterns on 10 fingers. Fingertip

Parameter

Model General

Arch

Whorl

Loop

m f 2 AD 2 DO 2 SB 2 RS LH 2 (p) m f 2 AD 2 DO 2 SB 2 RS LH 2 (p) m f 2 AD 2 DO 2 SB 2 RS LH 2 (p)

1.291 0.628 0.393 (18.1%) 1.289 (59.3%) 0.018 (0.8%) 0.475 (21.8%) −1108.83 3.038 3.062 0.056 (0.7%) 5.198 (62.6%) 2.170 (26.1%) 0.886 (10.7%) −1479.67 5.670 6.323 1.534 (20.6%) 4.491 (60.2%) 0.384 (5.2%) 1.052 (14.1%) −1478.18

Most parsimonious 1.291 ± 0.146 0.631 ± 0.080 [0] 1.697 ± 0.141 (78.2% ± 6.5%) [0] 0.472 ± 0.045 (21.8% ± 2.1%) −1109.08 0.50 (0.78) 3.056 ± 0.145 3.056! [0] 5.237 ± 0.885 (63.0% ± 10.6%) 2.187 ± 0.936 (26.3% ± 11.3%) 0.886 ± 0.083 (10.7% ± 1.0%) −1479.67 0.00 (1) 5.670 ± 0.275 6.331 ± 0.150 [0] 6.354 ± 0.479 (85.8% ± 6.5%) [0] 1.048 ± 0.098 (14.2% ± 1.3%) −1478.71 1.06 (0.59)

2 2 2 m , f – mean values for male and female; AD – additive genetic variance; DO – dominant genetic variance; SB – sibling 2 – residual variance; [0] – parameter was constrained to zero; ! – parameter was constrained to be equal to the variance; RS upper one; in parentheses ( ) percent of the total variance is given. For most parsimonious model parameters with standard errors are given.

tal components were not significant. There is no significant difference between sexes in mean counts of Whorls. The type of inheritance is also dominant (63% of trait variance), but also, a significant sibling environmental component (26% of trait variance) exists; additive genetic component was not significant. There is a significant difference between males and females in mean counts of Loops but the difference is not as impressive, as it is for arches. The type of inheritance in the most parsimonious model is additive (89.4% of trait variance). It is also dominant (63% of the trait variance) but also has significant constrained additive variance. The 26% of sibling environment variance is also accepted in comparison with the general model, but it has more degrees of freedom. Hence based on the most parsimonious principle we should prefer additive inheritance model for Whorls. Discussion The obtained results show the highest rate of concordance for monozygotic twins in the frequency distribution of four basic pattern types (Table 1). We suppose that dermatoglyphes may be affected by the presence of genetic factors in forming of fingertip patterns, because for MZ female twins, resemblance in sibs finger-tip patterns appears to be more pronounced than in DZ female twins, while shared intrauterine environments should influence both types of twins similarly. This result fully supports earlier studies of twin dermatoglyphes (Pena et al., 1973; Plato et al., 1976; Reed et al., 1975, 1978a,b; Loesch and Swiatkowska, 1978; Loesch, 1979; Lin et al., 1982; Arrieta et al., 1991). Several other studies of dermatoglyphic traits other than digital patterns also found similar results in twins. Reed and Christian (1979) obtained highest concordance in MZ twins for ulnar and radial finger ridge counts; Voitenko and Poliukhov (1984) for quantitative dermatoglyphic traits; Okajima and Usukura (1984) for epidermal ridge minutiae; Arrieta et al. (1992) for palmar interdigital areas.

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Comparison of our heritability results for basic three finger patterns namely Arch, Loop and Whorl, obtained by the maximum likelihood method is presented in Table 2. The most parsimonious model shows that all three traits have significant dominant genetic variance component of about 90% for Arches and 80% for Whorls. In the general model its proportion is similar for all fingertips (about 60%) and the sibling environmental variance is significantly nonzero. But for A the most parsimonious model shows the advantage of dominant genetic variance, whereas for W that of additive variance. Arches are more likely appear on fingers where the mechanical pressure due to muscular tone is significant (Fig. 1). Thus inheritance of this pattern (A) can include genes responsible for different muscular tone, whereas inheritance of W may not. In this way, the inheritance type of these two patterns can be significantly different. Thus the results obtained here demonstrate the way in which the analysis of finger pattern types may be usefully applied in studies of genetic properties of dermatoglyphics. We cannot discuss our results in comparison to earlier studies due to lack of such application of genetic model fitting analysis in the latter. However, we shall mention here some earlier studies on twins, based on a traditional method for estimation of heritability. The study of palmar C-triradius absence in 478 pairs of twins, by Kloepfer and Parisi (1976) strongly indicates that familial transmission was compatible with an autosomal dominant gene. Loesch (1979) while studying palmar and sole patterns in twins, found highest values of genetic parameters for pattern intensities and ridge counts on fingertips, and indicated that some pattern elements or their combinations may be each influenced by a specific genetic system which modifies their phenotypic expression. Lin et al. (1982) stated that the phenotypic expression of dermatoglyphic features was controlled by distinct genetic entities in different digital patterns. Jantz et al. (1984) applied multivariate analyses to twin data for estimating heritable components of ridge counts and suggested that (i) each of the dermatoglyphic areas yielded several independent genetic components, ranging from general to specific; and (ii) environmental variation was found to be local and frequently involved reciprocal interaction between twin pairs. Arrieta et al. (1991) obtained the highest heritability values on the first digit for the four basic finger patterns in a sample of twin data. Reed et al. (2006) used fingerprints of 2484 twin pairs to estimate heritability only for the Arch pattern and found a heritability of 91%. They concluded that with such high heritability, some specific genes influencing the occurrence of fingertip arch patterns may exist. Medland et al. (2007) applied linkage analysis of absolute (a sum of all the ridge counts on all 10 fingers) and individual finger ridge counts in 922 nuclear families. However, they obtained significant linkage only in individual fingers. All the above earlier studies show that digital pattern or finger ridge counts (a measure of pattern size) is one of the most heritable traits in humans. The predominant genetic component or a specific genetic system in the fingertip patterns modifies their phenotypic expression. Our present results are also consistent with this suggestion. In one of our recent studies (Karmakar et al., 2011) on the same sample, inheritance patterns were studied based on factors extracted from 18 quantitative dermatoglyphic traits. The result of variance decomposition analysis, the most parsimonious model, indicates that all factors have a significant proportion of additive genetic variance. The relationship between twins is due to common genes that affect dermatoglyphic traits. Similarly, our present results indicate a strong genetic component in finger pattern types through variance decomposition analysis (genetic model-fitting methods): a significant dominant genetic variance component (about 80–90%) across all fingers; while the sibling environmental variance is significantly nonzero; but the proportion between additive and dominant variance components differred. However, some studies on twins assume only additive genetic variance, no genotype–environment interaction and, that MZ and DZ twins share relevant aspects of their environment. The genetic model does provide a straightforward multivariate solution to the problem of genetic components. Thus, our result of genetic model-fitting techniques provides clearer evidence of a strong genetic component in the finger pattern types, than earlier traditional statistical analyses. Acknowledgements We thank the Anuchin Anthropological Museum in Moscow, Russia for providing us with access to the fingerprints. The authors are grateful to the two anonymous reviewers for their valuable comments on an earlier version of the paper.

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