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Transactions on Machine Learning and Artificial Intelligence - Vol. 9, No. 6

Publication Date: December 25, 2021

DOI:10.14738/tmlai.96.11290. Kanazawa, M. (2021). AI-based Analysis on Relationship between Genes and Personality: Evaluation Results with the ABO Blood

Type. Transactions on Machine Learning and Artificial Intelligence, 9(6). 26-37.

Services for Science and Education – United Kingdom

AI-based Analysis on Relationship between Genes and

Personality: Evaluation Results with the ABO Blood Type

Masayuki Kanazawa

Human Sciences ABO Center, Tokyo, Japan

ABSTRACT

AI can be applied in various ways to the measurement of personality in psychology.

Measuring the impact of a single gene on personality can be handled by AI

technologies, at least technically, i.e., using supervised learning models of machine

learning. The ABO blood type is a relatively easy biological marker to examine;

therefore, people in many countries know their type, and its impact on the

relationship with personality has been the subject of a large amount of research. In

this study, we selected the ABO blood type as the target gene, examined its

association with personality, and cross-checked the results with previous works.

Two scales were used to measure personality: a) blood type personality traits

extracted from previous studies, and b) the TIPI-J, a simplified version of the Big

Five personality test. In the former, the AI was able to predict the respondents’

blood types with a higher probability than chance, while in the latter, the accuracy

was within the range of chance. These obtained results were also discussed.

Keywords: ABO; Blood Type; Gene; Big Five; Personality.

INTRODUCTION

Aims of This Study

The use of artificial intelligence (AI) for personality measurement has a variety of possible

applications in psychology. Many of them are conceptual, but some studies have yielded

concrete results [1-2].

One possible application of AI is to investigate the relationship between genes and personality.

In general, traditional studies examined the similarity of twins' personalities [3], or the

relationship between personality tests and genes [4]. However, no clear consensus has been

reached on the relationship between a single gene and personality so far. The most typical

method for these studies is ANOVA, and traditional ANOVA can only handle linear relationships,

not non-linear ones. Meanwhile, these issues are typical examples of supervised learning in AI,

and since the models are relatively simple, they can be tested on currently available “ready- made” platforms, i.e., AWS or Microsoft Azure, at least technically. The aim of this study is to

provide a practical and pioneering application of using AI to check these non-linear

relationships.

Naturally, the most important issue is which gene to select. To examine multiple genes, DNA

testing is necessary, but this can be time-consuming and expensive. Since blood type is a

biological marker that is relatively easy to test, many people in many countries, especially in

East Asian countries like Japan, know their own ABO blood type. More than 90% of Japanese

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Kanazawa, M. (2021). AI-based Analysis on Relationship between Genes and Personality: Evaluation Results with the ABO Blood Type. Transactions

on Machine Learning and Artificial Intelligence, 9(6). 26-37.

URL: http://dx.doi.org/10.14738/tmlai.96.11290.

people know their types, and a lot of research has been conducted on its relationship with

personality, not only in Japan but all over the world. As mentioned above, DNA testing is

necessary to accurately determine genes, which requires considerable time and cost. However,

in the case of the ABO blood type, self-reporting is not a problem as long as the sample is

Japanese, making it suitable for a pioneering study like this one. Furthermore, it is easy to

compare and cross-check the results with those of many previous studies. Therefore, in this

study, we selected ABO blood type as the target gene, examined its association with personality,

and compared it with studies that are representative of the previous research in this field.

Big Five Personality Test

The “Big Five” personality test is the most commonly used personality test in current

psychology. As the name suggests, the Big Five test is a comprehensive description of

personality by five factors called the Big Five [5-6]. These five factors are usually referred to as

Neuroticism, Extraversion, Openness, Agreeableness, and Conscientiousness. In this model,

question items are collected from dictionaries and traditional personality tests. Personality

scales are re-analyzed, and the five factors are extracted through factor analysis. In this way,

the Big Five are characterized by a bottom-up process without assuming any background

theory, and personality is comprehensively captured by the five factors [7].

As mentioned earlier, the Big Five is a questionnaire-based personality assessment that

consists of answering a series of questions about multiple self-reported personality traits.

These traits are then integrated into five personality factors through statistical processing.

Theoretically, the self-reported answers will appear directly or indirectly in the results.

Recently, a variety of efforts have been made to measure psychological factors with a small

number of question items. Typical examples are subjective well-being [8] and self-esteem [9],

which have been used in many studies. In 2003, for the Big Five personality tests, Gosling,

Rentfrow and Swann developed a 10-item inventory with a seven-point scale, the Ten-Item

Personality Inventory (TIPI) [10]. A Japanese version of this personality test was developed by

Oshio, Abe and Cutrone in 2012 [11]. The test-retest reliabilities of these were 0.62-0.77 and

0.64-0.84, and Cronbach's alphas were 0.40-0.73 and 0.72-0.91 [10-11], which largely

supported the validity of these two tests.

Blood Type Personality Theory

In 1927, Furukawa, a Japanese educational psychologist, published the world’s first academic

study of blood type and personality based on statistics [12-13]. Later, Nomi, an independent

Japanese researcher, further developed Furukawa’s study and published his work in 1971 [14],

which has had the greatest impact on current research [15-16]. Nomi utilized various methods,

including questionnaires, observations and statistical analyses of human behaviors, and

surveys of the distribution of blood types among various occupations and special groups (Table

1). He claimed that the sample size of his studies was tens of thousands in total.

In Japan, South Korea, and Taiwan, half or more of the population believed in the relationship

between blood type and personality [15, 17-20].

Sato, Miyazaki, and Watanabe, a group of Japanese psychologists, conducted a free-response

survey of 197 college students [21]. Another Japanese group, led by Sakamoto and Yamazaki,

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has surveyed 177 college students on 24 personality traits in annual opinion polls conducted

by company affiliated with one of Japan's major television stations [22]. These results are

shown in Table 2, and in general, these personality traits studied by Japanese academic

researchers matched with those studied by Nomi (Table 1).

Table 1. Blood Type, Personality, and High Distribution Rate

Blood

Type

Personality High Distribution Rate

A Have a sense of duty for “someone.” Suppress

emotions and desires. Value gentle compassion and

perceptions. Observe rules, custom and order;

avoid extremes. On the other hand, a bit

conventional. Very consistent, drawing a clear line.

Most stubborn and short-tempered inside.

Education minister

High-ranking sumo

wrestler

B Self-paced behavior. Reluctant to be argued with or

restrained. Cognitively flexible, highly innovative

and accepting of new people. Least influenced by

their surroundings, unconcerned; most careless. In

general, not particular about things.

Baseball hitter

O Strong desire to live and full of vitality. Strongly

purpose-oriented. Once target is set, go straight

ahead without hesitation. Physical intimacy for

friends, family-oriented. Naked openness and

parental love for best friends. On the other hand,

very wary of “outsiders” or the unknown.

Prime minister

Foreign minister

Baseball hitter

AB Rational, businesslike. Duality with a calm, cool

stable side and an easily disturbed side with

sentimental fragility. Very friendly and tidy; treat

others with kindness when asked; smiling. Keep a

certain distance from others.

Foreign minister

Education minister

Many academic studies have used multi-item personality tests, i.e., the Big Five test, but the

results have been considered as inconsistent [23-35]. Thus, there remains an endless academic

controversy on whether the relationship has been scientifically confirmed or not. Furthermore,

there was little statistical difference among respondents who knew little about blood type

personality traits [36]. A 2014 study re-analyzed data from 10,000 respondents to a survey on

consumer life in Japan and the United States, but no meaningful differences were found [37].

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Kanazawa, M. (2021). AI-based Analysis on Relationship between Genes and Personality: Evaluation Results with the ABO Blood Type. Transactions

on Machine Learning and Artificial Intelligence, 9(6). 26-37.

URL: http://dx.doi.org/10.14738/tmlai.96.11290.

Table 2. Blood Type and Its Associated Stereotypes (#1: [21]; #2: [22])

Blood

Type

# Personality

A 1 Meticulous, Nervous, Serious

2 I am careful when doing something.

I am serious when I should be so.

I am often worried about things.

I make an effort toward my goal.

B 1 Self-paced, Individual, Lukewarm, Egoistic, Self-centered, Optimistic,

Pleasant

2 I often change my mind.

I am not particular about things.

I cannot be quiet and I make merry when I am delighted.

I am ready to keep company with everyone.

O 1 Big-hearted, Laid back, Unassuming

2 I have a lot of friends.

I am ready to keep company with everyone.

I cannot be quiet and I make merry when I am delighted.

I am not particular about things.

AB 1 Dual personality, Two-faced, Oddball, Hard to understand

2 I do not change my idea after I present it.

I make an effort toward my goal.

I sometimes burst into a rage.

I often change my mind.

Contemporary Trends and Issues

Self-fulfilling prophecy is a phenomenon in which a person who believes in a certain prophecy

begins to behave accordingly, and thereby the prophecy becomes reality. Psychologists have

examined a classic example: astrology [38-39]. As stated above, half or more of the people in

Japan, South Korea, and Taiwan believe in the relationship between blood type and personality

[15, 17-20]. Therefore, the self-fulfilling knowledge of astrology suggests that a person's

personality would changes in the direction of personality traits associated with their blood

type, at least theoretically.

Based on this hypothesis of self-fulfilling prophecy, several large-scale Japanese academic

surveys clearly validated the phenomenon. Archetypical sample sizes for these were several

thousands or more [40-42]. However, respondents without knowledge of astrology or blood

type traits, showed no consistent statistical differences [40]. Therefore, the current scientific

consensus is that these differences are caused by “contamination by knowledge,” or self- fulfilling prophecy [15, 17-18, 21-22, 36, 38-39, ,41-42].

Some studies have focused on biological factors: in 2015, a Japanese study using DNA testing

methods and the Temperament and Character Inventory (TCI) personality test, which assumes

the influence of neurotransmitters, found that among 1,427 participants, those with blood type

A tended to be “persistent,”, as predicted [43]. Another researcher in the United States assessed

both the ABO phenotype and its genotype and investigated the association of disequilibrium

between the ABO genes and the DBH genes [44-45].

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Personality is influenced by genetic factors, i.e., gender or age, and these interactions are

considered as non-linear [46-49]. However, the majority of previous studies have assumed that

the effect of blood type was linear, regardless of the nature of these factors [12-13, 15-19, 21-

37]. Statistical methods used in the design of questionnaire-based personality tests, i.e.,

correlation or factor analysis, basically assume a linear relationship between variables.

Therefore, there is no theoretical guarantee that traditional personality tests based on these

statistical methods could properly handle real non-linear data.

PARTICIPANTS AND METHODS

Participants

The data for the survey was collected through an Internet research company, using the method

of crowdsourcing. The survey was conducted in 2021 with a sample of 3,000 Japanese

respondents between the ages of 20 and 59. The participants were asked to rate a total of eight

items corresponding to four blood types (A, B, O, AB), using a score from one to five to indicate

how closely each item matched their own personality traits (the larger the number, the more

fitting the trait). The participants were also asked to indicate which blood type they thought

these eight items corresponded to. In addition, we provided them with a score from one to three

indicating the strength of the relationship (the higher the number, the stronger the

relationship) and a score from one to four indicating their knowledge of blood type personality

theory (the higher the number, the more knowledgeable they were).

In line with the methods of the aforementioned psychological personality tests, we deliberately

selected the traits that would certainly make a difference, i.e., the common stereotype of the

traits consistent with prior academic studies, showing a large difference and a mean value close

to 50%, not showing extreme values, and being consistent with prior research in other studies.

The results are as follows:

Type A: Meticulous, nervous

Type B: Self-paced, self-centered

Type O: Big-hearted, laid-back

Type AB: Easily misunderstood, dual personality

A simplified Big Five personality test, the Ten-Item Personality Inventory (TIPI) [10], Japanese

version (TIPI-J) [11], was also conducted simultaneously with these same participants.

Since this study was not a medical study involving human subjects, the research ethics review

committee determined that there were no ethical issues with using only information that was

already unlinked and anonymized.

The questions regarding blood type traits for this study were extracted from the reviewed

scientific papers (Table 2). These items were again checked by a crowdsourcing company that

had passed the Japanese privacy review (JIS Q 15001) mentioned earlier. The company

confirmed that there were no problems with the questionnaire items. The company provides

anonymized data to its clients and obtains informed consent from respondents prior to its

surveys. In this survey, the blood type of the participants was determined by self-reporting, as

most Japanese people know their own blood type; it was common practice in Japan until

recently to test the blood type of newborns. As the result, only 166 out of 3,000 respondents

(5.5% of the total) did not know their own blood type, so we used data from the remaining

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Kanazawa, M. (2021). AI-based Analysis on Relationship between Genes and Personality: Evaluation Results with the ABO Blood Type. Transactions

on Machine Learning and Artificial Intelligence, 9(6). 26-37.

URL: http://dx.doi.org/10.14738/tmlai.96.11290.

2,834 individuals whose blood types were known. All samples were evenly distributed by age

and gender in 10-year increments (Appendix). The distributions of participants’ blood types

were almost equal to the Japanese average [50].

ANALYTICAL METHODS

First, we tested whether AI predicts participants' blood types more than chance. Second, to

focus on whether the self-fulfilling prophecy phenomenon was occurring, we conducted an

analysis using data from participants who either “do not know their blood type traits” or “do

not believe in the relationship” (hereafter referred to as the “no knowledge” group).

The analysis methods for personality are as follows:

Analysis 1: Blood type prediction using AI with blood type traits

Analysis 2: Blood type prediction using AI with TIPI-J

Analysis 3: ANOVA for blood type and its traits for all participants

Analysis 4: ANOVA for blood type and its traits for the "no knowledge" group

Analysis 5: ANOVA for blood type and TIPI-J for all participants

All the data was stored in Microsoft Azure, and Microsoft Azure Machine Learning Studio was

used for predictions. The algorithms we used were Multiclass Logistic Regression, Multiclass

Neural Network, Multiclass Decision Jungle, and Multiclass Decision Forest. Predictions were

run four times each (random seeds from one to four), and the average values were used as the

accuracy rates.

We conducted an analysis of variance (ANOVA) using self-reported ABO phenotypes (O, A, B,

AB) with personality trait scores as the dependent variable. The alpha ratio was set at 0.05.

Prior to the ANOVA analysis, the normality of the distribution was checked for each personality

trait score, and it was normally distributed. The data were analyzed using jamovi software

version 1.2.27. The effect sizes [51] were also calculated.

RESULTS

Analysis 1: Blood Type Predictions with Blood Type Trait

The results for the Multiclass Logistic Regression, Multiclass Neural Network, Multiclass

Decision Jungle, and Multiclass Decision Forest algorithms showed accuracies of 0.434, 0.418,

0.395, and 0.356, respectively (Chart 1).

Chart 1. Accuracy of Blood Type Predictions with Blood Type Traits

0.434

0.418

0.395

0.356

0.384

0.34 0.36 0.38 0.40 0.42 0.44 0.46

Multiclass Logisitic Regression

Multiclass Neural Network

Multiclass Decision Jungle

Multiclass Decision Forest

Chance (Type A)

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Analysis 2: Blood type prediction using AI with TIPI-J

The results for Multiclass Logistic Regression, Multiclass Neural Network, Multiclass Decision

Jungle, and Multiclass Decision Forest algorithms showed accuracies of 0.376, 0.375, 0.344, and

0.323, respectively (Chart 2).

Chart 2. Accuracy of Blood Type Predictions with TIPI-J

As mentioned above, type A is the most common blood type among Japanese people, accounting

for 0.384 (1,089 persons) of the total number of participants in this study. Therefore, if AI

simply predicted each participant to be type A, the accuracy would have been 0.384. Microsoft

Azure Machine Learning Studio predicted the blood type with a lower accuracy than this value

in all cases.

Analysis 3: ANOVA for Blood Type and Its Traits for All Participants

In the survey’s ANOVA result (Table 3), all four types showed the same results as those shown

for blood type traits in the preceding psychology papers (Table 2). The score for each blood

type was the sum of the two question items that corresponded to that blood type. All types were

statistically significant at p < 0.05, and after the Bonferroni’s correction. The magnitude of the

difference: η2 = 0.035 at the maximum, thus all the effect sizes were small. All of the responses

were consistent with the most common stereotypes of blood types that respondents expected.

Table 3. Result of ANOVA for Traits Scores and Blood Type

Blood Type

Traits

Average Scores by Blood Type

F η2 p

A

(N =

1,089)

B

(N = 616)

O

(N = 844)

AB

(N = 285)

A 6.73 5.98 6.05 6.31 34.10 0.035 <0.001 *

B 6.35 6.92 6.46 6.43 16.52 0.017 <0.001 *

O 6.17 6.36 6.88 6.15 34.68 0.035 <0.001 *

AB 5.55 5.55 5.51 6.51 30.08 0.031 <0.001 *

Note. Highest scores that match with blood type traits, and p < 0.05 are highlighted in bold. * p <

0.05 after the Bonferroni’s correction

On the question item “Do you think blood type and personality are related?”, 8.2% of the

respondents answered “related,” 51.7% answered “somewhat related,” 26.3% answered “not

related,” and 13.8% answered “I don't know.” On the question item “Do you know the traits and

0.376

0.375

0.344

0.323

0.384

0.28 0.30 0.32 0.34 0.36 0.38 0.40

Multiclass Logisitic Regression

Multiclass Neural Network

Multiclass Decision Jungle

Multiclass Decision Forest

Chance (Type A)

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Kanazawa, M. (2021). AI-based Analysis on Relationship between Genes and Personality: Evaluation Results with the ABO Blood Type. Transactions

on Machine Learning and Artificial Intelligence, 9(6). 26-37.

URL: http://dx.doi.org/10.14738/tmlai.96.11290.

compatibilities of blood types?”, 4.0% answered “I know a lot,” 37.8% answered “I know some,”

40.5% answered “I know a little,” and 17.7% answered “I don't know at all.”

Analysis 4: ANOVA for Blood Type and Its Traits of the "No Knowledge" Group

In the survey’s ANOVA result (Table 4), all four types showed the same results as those shown

for blood type traits in the preceding psychology papers (Table 2). The score for each blood

type was the sum of the two question items that corresponded to that blood type. Three types

were statistically significant at p < 0.05, and after the Bonferroni’s correction. The magnitude

of the difference: η2 = 0.039 at the maximum, thus all the effect sizes were small.

Table 4. Result of ANOVA for Traits Scores and Blood Type

Blood Type

Traits

Average Scores by Blood Type

A F η2 p

(N = 207)

B

(N = 152)

O

(N = 182)

AB

(N = 54)

A 6.73 5.98 6.05 6.31 A 6.73 5.98

B 6.35 6.92 6.46 6.43 B 6.35 6.92

O 6.17 6.36 6.88 6.15 O 6.17 6.36

AB 5.55 5.55 5.51 6.51 AB 5.55 5.55

Note. Highest scores that match with blood type traits, and p < 0.05 are highlighted in bold. * p <

0.05 after the Bonferroni’s correction

Analysis 5: ANOVA for TIPI-J by Blood Type

In the survey’s ANOVA result (Table 5), two factors were statistically significant at p < 0.05, and

one after the Bonferroni’s correction. The magnitude of the difference: η2 = 0.005 at the

maximum, thus all the effect sizes were considerably small.

Table 5. Result of ANOVA for Traits Scores and Blood Type

Factors

Average Scores by Blood Type

F η2 p

A

(N =

1,089)

B

(N = 616)

O

(N = 844)

AB

(N = 285)

Extraversion 3.71 3.78 3.74 3.78 0.52 0.001 0.669

Agreeableness 4.72 4.64 4.71 4.65 0.88 0.001 0.451

Conscientiousness 3.69 3.55 3.52 3.56 2.98 0.003 0.030

Neuroticisms 4.33 4.26 4.29 4.31 0.54 0.001 0.653

Openness 3.76 3.93 3.85 3.99 4.40 0.005 0.004 *

Note. Highest scores that match with blood type traits, and p < 0.05 are highlighted in bold. * p <

0.05 after the Bonferroni’s correction

DISCUSSION AND CONCLUSION

Using blood type personality traits, AI predicted the participants' blood types more accurately

than chance with the exception of one algorithm. We observed a clear and consistent

relationship between blood type and self-reported personality among all four types. The same

traits were also observed in the groups that did not know their blood type, although the

differences were smaller. On the contrary, when using the simplified Big Five personality test

(TIPI-J), AI could not predict the participants' blood type more accurately than chance, and

there was little difference among blood types, which corresponds to the typical past results.

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Previous studies by psychologists found no differences in personality by blood type, and even

if there were differences predicted by blood type, they were thought to be the result of a self- fulfilling prophecy phenomenon [14, 16-17, 20, 28, 35-36, 37-41]. However, when using the

blood type personality traits, the same differences in scores were found in the “no knowledge”

group, although the values were smaller. Therefore, the differences by blood type were most

likely not due to the self-fulfilling prophecy phenomenon, but actually existed, and the

differences in the “no knowledge” group were not due to Type II errors.

In this study, the following four algorithms were used: Multiclass Logistic Regression,

Multiclass Neural Network, Multiclass Decision Jungle, and Multiclass Decision Forest. The first

one showed the highest accuracy for both blood type personality traits and the TIPI-J, which

may indicate that the personality test data are closer to linear than expected, since this

algorithm is mainly used for linear predictions.

These results provide a new, albeit hypothetical, framework for examining how genes influence

human personality. On the other hand, the sample in this study was limited to Japanese

populations, the sample size of the AI training data was small, and the study was used on an

experimental basis. Future studies with larger, more global datasets are needed to clarify the

true impact and to improve algorithms and methodologies.

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