Dermatoglyphic markers based algorithm to select children for sports

E.N. Radchenko1
Dr.Biol., Professor V.N. Kalaev1
PhD, Associate Professor I.E. Popova2
PhD, Associate Professor A.V. Sysoev2
1Voronezh State University, Voronezh
2Voronezh State Institute of Physical Training, Voronezh

Keywords: dermatoglyphics, papillary patterns, delta index, ridge count, sport disciplines, fingerprint phenotype.

Introduction. The increasing social and political prestige of sport achievements dictated the need for determining genetic markers that would allow selecting potentially gifted children for various sports. This is also due to the fact that to become successful in sport you have to start training as early as possible. However, it is almost impossible to visually observe a predisposition to a certain kind of sport among 5-6 year old children as their sport skills are not formed yet at this stage of development. One of the most reliable and inexpensive genetic markers is the dermatoglyphic marker. However, despite extensive use of dermatoglyphics in predicting psychophysiological traits, such research is criticised [9, 14 etc.] as a rule due to the statistical processing methods of research results.

The goal of this paper is to perform an overview of studies conducted by several authors and to create an algorithm that could be used to determine the best kind of sport for children when selected by a sport institution by means of fingerprint dermatoglyphics.

Fingerprint dermatoglyphics as morphogenetic markers Dermatoglyphics are patterns on finger and toe pads, and patterns on palms and soles. Qualitative and quantitative characteristics [11] are used to describe the patterns. Quantitative characteristics include types of patterns. According to the classification by T.D. Gladkova [4], all finger patterns can be described as simple and complex. Simple patterns include arches, loops, and whorls. Complex patterns have central pockets, lateral pockets, twinned loops, and accidental patterns. The following dermatoglyphic phenotypes can be distinguished depending on the combination of simple finger patterns on both hands [1]: 10L – ten loops, AL – arches and loops, ALW – arches, loops, and whorls, LW – loops and whorls with great amount of loops, WL – whorls and loops with great or amount of whorls or equal amount of loops and whorls, and 10W – ten whorls. The quantitative characteristics include ridge count (the number of ridges and dots that are either tangent to or cross the line between the pattern delta and its centre), and delta index (number of deltas on fingers). Additionally, sport genetics can use total ridge count (total number of ridge count values for all fingers for both hands) and D10 (total number of deltas for both hands) [10].

Results This paper summarises the results of research conducted by several authors [1, 2, 3, 5, 6, 7, 8, 12, 13], who revealed interrelations between dermatoglyphic patterns and the specialisations of athletes.The described sports were divided into groups according to Olympic Sports Classification.

1. Cyclic sports (swimming, rowing, speed skiing, and speed skating). Among girls involved inswimming, loops (15%) are 3 times more frequent that in the control group (5.2%), whereas whorls are less frequent (34.1% and 22% respectively). The dominant phenotypes are ALW (30%) and WL. Delta index (D10) is 10.83±0.68 and total ridge count (TRC) is 101.47±7.32 [4]. Whorls are more frequent (34.6 %) and arches are less frequent (4.2 %) among boys rather than girls. D10 equals 13.0 and TRC is 121.2 [2].

Boysengaged in rowing have a lower number of arches (3.8%) and a medium number of loops (65.9%) and whorls (30.5%). High values of total ridge count and D10 are also characteristic for this group. The dominant phenotypes are WL (21.1%) and LW [12]. Moreover, athletes with different role functions tend to have distinguishing dermatoglyphic characteristics. For example, stroke oarsmen have higher D10 (>13 and <13 respectively) and TRC values (>130 and <130 respectively) compared to middle crew. The dominant phenotypes for stroke oarsmen are WL and LW, and for middle crew are 10L and LW [1].

Among male racing skiers, whorls (32%) are more frequent and arches are less frequent (4%) compared to overall population [5]. D10 equals 12.2 [2]. Total ridge count differs depending on the researcher: for example, according to Abramova TRC=115.5 [2], and TRC calculated by Brazda and Nazmutdinova equals 170.86±18.11 [3]. According to Abramova, whorls are more frequent among girls rather than boys, which contradicts the population manifestations of sexual dimorphism [1]. However, results by Oleinik demonstrate the domination of loops (70%) among female racing skiers whereas the frequency of whorls is 20% [4], which is considerably lower than among boys. Such a discrepancy in results shows that this aspect needs further investigation. According to Oleinik, the dominant phenotypes for girls are ALW (30.4%) and LW; 10L, and WL are very rare. D10 equals 10.96±0.61, and TRC is 104.96±7.17 [4].

In speed skating, dermatoglyphics depends on specialisation: sprinting or long-distance speed skating. The dominant phenotypes for sprinters are AL and LW with low values of D10 (<11) and TRC (<110). On the contrary, the dominant phenotypes for long-distance skaters are WL and LW with high values of D10 (>13) and TRC (>130) [1].

2. Precise sports (aerobics, rhythmic gymnastics). Girls who do aerobics demonstrate a statistically significant (Р<0.05) increase in the number of whorls and decrease in the number of arches compared to the control group. Statistically (Р<0.05) higher TRC (128.63±7.69) and D10 values (13.09±0.69) are also characteristic for this group compared to the control group. The dominants phenotypes are WL and LW (Р<0.05) [7].

Female gymnasts tend to have a lower number of arches (5.9%) and a higher number of whorls (30.9%) compared to the control group (difference is significant, Р<0,05). They also have  statistically (Р<0.05) higher D10 (12.65±0.54) and TRC (125.82±5.96) values [6]. The dominant phenotypes are WL [6, 13] and LW [6], 10L and AL representation is also higher compared to the control group [6]. According to the results of correlation analysis, the higher the athlete’s qualification is, the higher is: RC on the right hand (r=0.4 where Р<0.05) and on the left hand (r=0.4 where Р<0.05), TRC (r=0.4 where Р<0.05), RC for 2 and 4 finger on the left hand (r=0.36 and r=0.35 where Р<0.05) and for 3 finger on the right hand (r=0.45 where Р<0.05); and the less frequent are arch patterns [6].

3. Combat sports (fencing, taekwondo). Arches are very rare among girls who do fencing (about 1.7%), and the frequency values for loops and whorls are very close (50% and 48.3% respectively). The dominant phenotypes are WL and LW, which is characteristic for most combat sports. There is even distribution of ALW, 10W, and 10L phenotypes and AL phenotype is not represented. D10 equals 14.63±0.62 and TRC is 156.57±5.87 [8].

Similar to the above sport, arches are less frequent (about 2%) and whorls are more frequent (44%) among girls who do taekwondo. The dominants phenotypes are WL and LW, there is an equal number of AL and 10W phenotypes. The 10L phenotype is not represented. D10 equals 14.4±0.75 and TRC is 139.68±7.64 [8].

4. Sports games (basketball, football, volleyball). In basketball, football, and volleyball dermatoglyphics depends on players’ roles. For example, whorls are dominant among guards in basketball and goalkeepers in football. LW and WL phenotypes are frequent. These players are characterised by high D10 (14.4-15.6 and 15.6-16.4 respectively) and TRC values (127.8-148.8 and 156.2-166.4 respectively). On the contrary, centres in basketball and forwards in football are characterised by the dominance of arches and low D10 (14.4-15.6 and 15.6-16.4 respectively) and TRC values (127.8-148.8 and 156.2-166.4 respectively). The dominant phenotypes for basketball centres are 10L, AL, and ALW, in case of football forwards only the AL phenotype is represented. Among football defenders and midfielders D10 (13.3-14.7 and 13.5-14.9 respectively) and TRC values (143.4-164.2 and 137.8-156.2 respectively) are intermediate, and loops and whorls are more frequent. The dominant phenotypes for midfielders are LW and WL, and for defenders it is LW. The dominant phenotypes for basketball forwards are LW and 10L. D10 (11.5-12.5) and TRC values (99.3-110.9) are also intermediate for this kind of sport. Volleyball hitters are characterised by higher D10 (13.8-14.8 and 11.0-11.6 respectively) and TRC values (142.8-151.2 and 136.5-147.5 respectively) compared to setters. The dominant phenotypes for setters are 10L and LW, and for hitters are LW and WL [1].

Conclusion. Thus, athletes with different specialisations have certain dermatoglyphic characteristics that can be used to select children for sport clubs. We have developed a selection algorithm that is based on the differences between athletes playing different sports. Such differences include dermatoglyphic phenotypes (AL, ALW, 10L, WL, LW, 10W), the number of deltas on fingers of both hands (D10), and the value of total ridge number (figures 1 and 2). Application of the algorithm seems to be relevant for the selection of children for sport institutions as dermatoglyphic characteristics are absolute genetic markers that are quite reliable. Additionally, their analysis does not require any specialised education, expensive equipment, is not time consuming and is cost effective.


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The study summarizes findings of the national study reports with concern to the papillary patterns (fingerprint patterns) of the leading athletes in different sports. The study findings were applied to produce a fingerprint-specific selection algorithm for different sports with application of dermatoglyphic phenotypes, delta indices and fingerprint ridge counts. As demonstrated by the study data and analysis, the most informative phenotypes for selection of female prospects are the WL phenotypes (loops and whorls counts with domination of whorls or equal counts of loops and whorls), LW phenotypes (domination of loops in the fingerprint pattern) and ALW phenotype (arches, loops and whorls); and the most informative phenotypes for selection of male prospects are the WL, LW, ALW  phenotypes (arches, loops and whorls), 10L type (10 loops) and AL type (arches and loops). The fingerprint markers based selection algorithm may be successfully applied to detect the individual predispositions for football, volleyball, basketball, academic rowing and skating sport in the male prospects and for swimming, cross-country skiing, aerobics, rhythmic gymnastics, taekwondo and fencing in the female prospects.

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