特集 SFC が拓く知の方法論［研究論文］

Genome Analysis Workshop: a Personal
Genomics Class at Keio SFC
SFCにおけるパーソナルゲノム時代のリテラシー教育
Kazuharu Arakawa

Project Associate Professor, Graduate School of Media and Governance, Keio University

荒川 和晴
慶應義塾大学大学院政策・メディア研究科特任准教授

Masaru Tomita

Director, Institute for Advanced Biosciences, Keio University /Professor, Graduate School of Media and Governance, Keio University /Professor, Faculty of Environment and Information Studies, Keio University

冨田 勝
慶應義塾大学先端生命科学研究所所長 /慶應義塾大学大学院政策・メディア研究科教授 /慶應義塾大学環境情報学部教授

Abstract:

Because of the development of sequencing technologies, the era of personal
genomics is rapidly approaching, and several Direct-to-Consumer genetic
testing services are already available. However, sociopolitical guidelines for
personal genomics have lagged behind technological advances. Here, we report
our innovative literacy education class on personal genomics in Keio University
Shonan Fujisawa Campus, using an actual personal genome as the course material.
DNA 配列決定における技術革新などにより、近い将来に個別化医療などのパーソナルゲノム時代が到来することは確実であろう。一方で、残念ながら
パーソナルゲノム時代を受け入れるために必要な法律やガイドラインなどの社会システムや、一般市民のリテラシーに関しては、技術に対して大きく遅れをとっているのが現状である。そこで、本論文ではこのような問題に対応できる人材育成のために我々が SFC にて取り組んでいるパーソナルゲノム時
代のリテラシー教育における先進的試みについて報告する。

Keywords: personal genomics, literacy education, molecular biology, bioinformatics,
DNA sequencing

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1 Introduction
Within a decade after the completion of the landmark $3 billion
international Human Genome Project in 2003 (International Human Genome
Sequencing Consortium, 2004), more than 1,000 human genome sequences
are publicly available (1000 Genomes Project Consortium, 2012). A projectto sequence 100,000 human genomes over the next five years is ongoing(Rossolatos and Aitchison, 2014), and another project to sequence a million
genomes began in 2011 (Normile, 2012). This dramatic progress in genome
projects was facilitated by the advent of the next-generation DNA sequencing
technologies, which have decreased the time and cost of sequencing by
approximately 10,000-fold over the last decade, surpassing the predictionof Moore’s law of semiconductors (Mardis, 2011; Shendure, et al., 2004).
Affordability is a prerequisite for personal genome sequencing to beclinically feasible, and the highly anticipated milestone of“$1,000 genomes”
has recently been reached. The Archon Genomics XPRIZE, a $10-million
competition to rapidly and accurately sequence the whole genomes of 100
humans at a cost of $10,000 or less per genome was cancelled before it
began in August 2013 because it was“outpaced by innovation”(Diamandis,2013). In January 2014, Illumina, Inc. launched the HiSeq X Ten Sequencer,
which generated a $1,000 genome at 30x coverage, including the cost of
sample preparation, reagents, and instrument depreciation (Sheridan, 2014).
Although the current cost does not include informatics analysis and necessary
personnel expenses, the projected milestones are likely to be achieved rapidly
considering the current pace of innovation. Therefore, we are entering the eraof personal genomics.
The generation of a reference genome by the Human Genome Project
has significantly contributed to genetics, molecular biology, biochemistry,
and medicine, enabling the identification of disease-related genes and theirfunctions, as well as the physiological and pathological implications of thesystematic interactions of these genes. However, knowledge of the genomes
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and genetic variations of individuals can further such studies and transformcurrent approaches in medicine and healthcare (Khoury, et al., 2009). A highly
significant contribution of personal genomics is in preventive healthcare. Whilecurrent medical practice predominantly comprises curative care and treatment
of disease, understanding of the genetic variations based on personal
genomes provides information on innate susceptibility and risks for various
diseases, enabling proactive control of individual lifestyles to prevent healthconsequences. For example, if an individual has a genetic variation leading
to a higher risk of obesity or type-II diabetes, practicing appropriate calorie
control and limiting a high-fat diet can improve the individual’s chances ofremaining in good health. Disease prevention directly leads to a better qualityof life, and preventive healthcare also has profound sociopolitical implications,especially in nations undergoing population aging, such as Japan. Considering
the decreasing birth rate and overall population aging, the old-age dependency
ratio (number of elderly individuals (65 years and older) as a percentageof the working-age population (20 to 64 years)) is expected to reach 47%
by 2060 in Japan; this is the highest among major industrialized nations,
imposing a fiscal challenge due to increased healthcare costs (Mühleisen andFaruqee, 2001). Therefore, preventive care would reduce healthcare costs,
and personal genomics provides indispensable information for this purpose.
Personal genomics also contributes to the identification and prediction of
the cause and physiology of rare orphan diseases for which resources ortreatments are either unavailable or insufficient. Human nucleotide diversity
is estimated to be approximately 0.1%; therefore, there are approximately
3 million nucleotide differences between any two individuals (Jorde andWooding, 2004). Only a very small number of variations actually account fora specific disease; however, the availability of a large collection of singlenucleotide polymorphisms from healthy individuals and a sufficient amount ofgenetic information from patients with a specific disease would facilitate theelimination of common polymorphisms and the identification of variations that

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are only present in patients. Therefore, with active community participation,
the use of personal genomics has the potential to identify cures for orphan
diseases. Personal genetic variations also lead to differing drug responses
or varying symptoms, and treatment effectiveness requires personalized andtailored medicine. For example, specific populations are either sensitive or
resistant to the anticoagulant warfarin, a drug used to prevent thrombosis,
necessitating careful control of the optimal dose (Daly and Aithal, 2003).
Warfarin response can be partly explained by specific single-nucleotide
variations. Knowledge on drug responses and their relationships with various
genotypes is rapidly accumulating, including for anti-cancer drugs such as
Gefitinib (Cusatis, et al., 2006; Mayo, et al., 2012) and Crizotinib (Cusatis, etal., 2006; Mano, 2008; Ou, et al., 2012), where knowledge of the genotype isextremely useful for avoiding serious side effects and for tailoring the mosteffective drug suited to the specific individual. Therefore, personal genomics
can benefit medicine and healthcare; new healthcare concepts in the era of
personal genomics have been described previously, for instance, the“P4
medicine”discipline, which encompasses the predictive, preventive, personalized,
and participatory aspects described above (Hood and Friend, 2011).
Among the 3 billion base pairs in the human genome, only 1% or
30 million bases comprise the protein-coding exome (Ng, et al., 2009).
Furthermore, exome mutations may not necessarily cause amino acid changes
because of codon degeneracy, and the phenotypic implications of genetic
variations are subtle or not well understood in the majority of genomic
positions. Therefore, it is not always necessary to sequence the entire
human genome to understand an individual’s genotype: the study of exomes
or common sites of single-nucleotide polymorphisms (SNPs) is a sufficientand cost-effective alternative. Although whole-genome sequencing costs
thousands of dollars, targeted exome sequencing or array-based capture ofcommon SNPs can be commercialized at lower costs, approximately hundredsof dollars. Therefore, commercial direct-to-consumer (DTC) personalized
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genetic testing services are increasingly available to the general publicthrough companies such as 23andMe, Navigenics, deCODEme, Pathway
Genomics, and GeneQuest. For example, genetic testing at 23andMe costs
only $99 to analyze approximately 1 million SNPs and includes a reportcomprising the genotyping results for 260 genes including those related
to health conditions and inherited traits as well as the carrier status ofrecessive alleles and genealogy. As of December 2013, 23andMe had morethan 500,000 genotyping customers (Wojcicki, 2013). Therefore, personal
genomics is already highly accessible to the general public using these DTC
services; however, the quality of genotyping, the scientific validity of risk
assessments, and the availability of adequate explanations or counseling tosupport careful interpretations of the results are not always sufficient in these
services, leading to criticisms and concerns (Hudson, et al., 2007; Khoury,
et al., 2009; Prainsack, et al., 2008). Thus, in November 2013, the US Foodand Drug Administration (FDA) ordered 23andMe to stop the sale of healthrelated genotyping due to the insufficiency of scientific evidence to satisfy the
FDA requirements for medical diagnosis (Wojcicki, 2013).
The interpretation of genotyping results is inherently complex anddifficult. Phenotypes or diseases can be highly multi-factorial, and causes aremost often non-genetic, such as lifestyle and other environmental factors.
For example, the allelic profile of 54 genomic loci considered related tohuman height only explains 4-6% of actual measurements (Aulchenko, et al.,2009), and risk behavior such as smoking and high-calorie diet often results
in a higher contribution to lifestyle-related diseases. Furthermore, there
may be yet undiscovered genotypes with higher predictive power or unknown
linkages between genotypes and phenotypes; overall, there is insufficientquantitative data to accurately calculate risk percentages. Therefore, theuse and interpretation of genotyping requires care and caution to avoid
misinterpretation; nevertheless, the rate of technological advances clearlyexceeds the rate of policy making, and personal genomic information will soon

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be ubiquitous, most likely before all the necessary policies and guidelines forsocial and ethical issues (summarized in Table 1) are discussed (Fisher andHarrington McCarthy, 2013). It is therefore crucial for as many citizens as
possible to have sufficient literacy about personal genomics, and this type ofliteracy education should not be limited to scientists and physicians. ShonanFujisawa Campus (SFC) of Keio University is a unique and ideal location forsuch literacy education because of the diverse interdisciplinary nature of thedepartments encompassing all major disciplines, including advanced biosciences
as well as business management, policy making, and others. In the following
sections, we describe the design of a one-semester course on personal genomics
titled the“Genome Analysis Workshop”, which began in 2012 in SFC using
actual personal genome sequence data as a course material.

2 Requirement Analysis
2.1 Related Studies
When the course was developed in 2011, there was limited publicly
available information on educational effort in the field of personal genomics
worldwide because this field and DTC services were in their infancy. One ofthe pioneers in this regard was the Personal Genome Project (PGP) initiatedin 2005, which is dedicated to creating public genome, health, and trait data by
sequencing the genome of thousands of volunteers to identify novel associations
between genotypes and phenotypes and to observe the social implications of
personal genomics (Church, 2005; Jones, 2012). Because PGP predominantly
relies on the participation of volunteers willing to donate their personal
information as a public resource, genetics education and thorough informed
consent were essential prerequisites of the project. Considering the emergenceof DTC services and to target a broader body of citizens, the Personal
Genetics Education Project (pgEd) based at Harvard Medical School provides
a wide range of lesson plans and interactive exercises designed to provide
education on the benefits and ethical implications of personal genetics mainly
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Table 1

Technical, social, political, and ethical issues surrounding personal genetic
testing

Issuesvalidity of the results

limite current knowledge

shortage of genetic
counsellors

genome stays unchanged
for life

data protection

presymptomatic diagnosislack of working treatment

Description
Genotyping may not be entirely accurate. Even with 99.999%
accuracy, analysis of 3 billion nucleotides of genome results in
unnegligble percentage of error. At the current level of precision,
alternative testing is required for validation for actual diagnosis.
Negative results, such as no known genotype to a certain
disease, may not be accurate, since a novel genetic risk
association may be discovered in the future. Likewise, newdiscoveries may elucidate unexptected or undesirable risks evenfor positive results.
Interpretation of genetic test results require expert knowledge
in genetics, medicine, and healthcare, and therefore genetic
counsellors that can appropriately deliver and consult the results
are currently lacking.
Every person’s genome is unique, and remains unchanged
throughout one’s life. A genotype once identified remains
unaltered by aging or other environmental factors. Therefore,
once a genome sequence is made available to others by anymeans, it is impossible to revamp the personal information. Thisis different from other personal data such as name or addressthat can be altered.
Genetic information is the ultimate private information,
containing multitude of information related to the physical traits
and disease risks. Since this information remains unchanged
throught one’s life, handling and protection of the data requiresafe means to prevent information leakage. However, as
living organisms, it is impossible to leave biological traces
such as dropped hair, skin contact by touching, or saliva ontableware. With decreased cost in the near future, it is possible
for strangers to gather such traces and sequence the genome
unnoticed.
Genetic tests predict risks to diseases before any sign ofsymptoms. Risk is given as a percentatge relative to a certain
population group, but such statistics requires suff icientknowledge for appropriate interpretation. Even with relatively
high risk to a certain disease, it is only a matter of likelihood
for he/she to actually develop the illness. Moreover, current
healthcare system in Japan is not generally applicable topreventive care for healthy individuals.
Predicted high-risk factor may not have a working treatment.
For example, several SNPs are identified to significantly elevate
the risk of Alzheimer's disease, but there are no definitive
treatment for this disease at the time being.

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right to "not know"

test for children

family relationships

genetic discrimination

carrier statusprenatal screening

designer’s baby

Since the knowledge of high genetic risk to certain disease may
become mental burden, especially when there is currently noworking cure, such knowledge could possibly lower the qualityof life. Therefore, the right to "not know" one’s own genotype
should be respected. On the other hand, in order for the society
to reduce the medical costs, it is most effective to have a massscreening of genotypes, possibly at the time of birth, andthe means for the delivery of such results must be debatedrespecting the right to "not know".
Genetic tests require thorough informed consent due to themany issues, and the right to "not know" certain genotype
should be respected. Such consideration requires one to be
an adult, and genetic test of children should be handled withcaution. On the other hand, preventive actions are most effective
when the genotypes are tested at the time of birth.
Since mitochondrial sequence is strictly inherited from themother and the Y-chromosome is from the father, genetic testing
identifies the family relathionships and ancestry. In case ofadaptions, genetic testing identifies non-paternity, which could
be undesirable in some cases.
When personal genomic information becomes common in thesociety, employer or insurance companies may choose to treat
people differently based on the genetic risks. Preventive actions
must be taken to prohibit such discrimination based on inheritedconditions. In the United States, the Genetic Information
Nondiscrimination Act has been effective since 2008 toprohibit the use of genetic information in health insurance andemployment, but analogous law is still not available in Japan.
For recessive Mendelian diseases, a person can be a carrier ofthe gene without displaying the symptom of the disease due todiploid nature of the human genome. Therefore, knowledge ofthe carrier status will likely affect the choice of mating partners.
Non-invasive prenatal screening by sequencing of fetal DNA in
mother's blood is already available for Down’s syndrome, andcould be extended to other genetic features. However, prenatal
screening often leads to the decision of abortion, and thus is anarea of intense debate.
While genetic modification of human embryo is banned in mostcountries, preimplantation genetic screening of embryos, or
screening of gametes prior to in vitro fertilization is possible
to certain extent, leaving possibilities in selecting or even"designing" the genotype of a newborn.

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to high school students (Kung and Gelbart, 2012). Personal genomics education
should ideally be coupled with personal genetic testing, but performing suchtesting on a minor is ethically challenging (i.e., high school students); therefore,
personal genetics classes were first attempted at the undergraduate level in2010. One such attempt,“Bring Your Genes to Cal”program at the Universityof California, Berkeley, initially announced plans to offer voluntary genetic
testing to all 5500 incoming undergraduate students to test three genes
related to alcohol metabolism from saliva samples. However, this plan met withimmediate criticism even from the state of California, leading to abandonmentof the plan to deliver individual results (Hendershot, 2011). The ethical issuesof genetic testing in classrooms have proved controversial (Callier, 2012;
Rogers and Taylor, 2011; Taylor and Rogers, 2011). Considering the difficulty
seen in Berkeley, subsequent efforts have incorporated thorough instructions
on the risks before obtaining informed consent and using DTC services forgenotyping. A medical graduate course in Stanford School of Medicine entitled“Genomics and Personalized Medicine”partnered with the DTC companies
23andMe and Navigenics for genotyping and reported that students who
voluntarily participated in personal genetic testing learned better comparedwith students who declined to participate (Salari, et al., 2013). Several
undergraduate and graduate courses are now available, mostly as honors
seminars partnered with 23andMe, for instance, in the University of Iowa,
University of Illinois, University of Texas, and Duke University (Foley, 2013).
2.2 Requirements
Through careful reviewing of related courses and discussions withcolleagues studying personal genomics, we defined the following requirements
for a new personal genetics course at SFC.

●

(R1) Targeting a wide variety of interdisciplinary students. Previous
related courses were predominantly held at biological or medical colleges,

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presumably due to the nature of existing departments as well as limitations
in human resources; however, the social implications of personal genomics
goes well beyond biology and medicine, requiring active participation frombusiness, politics, and many other areas of society. Keio SFC provides aunique and ideal environment for this purpose.
●(R2) Use of personal genomic information. As shown by the Stanford group(Salari, et al., 2013), the use of personal genomic information enhances the
motivation and learning of the students.

●(R3) Avoiding the ethical issues of individual genetic testing. As majorityof the students enrolled in the first two years of undergraduate course
are under-aged, their personal genetic testing may raise significant ethicalconcerns even if we carefully obtain informed consent under the Japanese law.

●(R4) Affordability and accessibility. Comprehensive DTC genetic testing
was not available in Japan at the time of course development, and genetic
testing should be affordable, with price points ideally around those of
expensive textbooks (<$100).

●(R5) Interactive course work. Personal genomics is still in its infancy, andissues surrounding this new discipline are mostly exploratory. Therefore,
traditional one-directional lectures are not sufficient, and a more interactivemeans of learning is essential.

●(R6) Helping students to understand the complicated nature of the genotypephenotype linkage. Phenotypes such as physical traits and disease risks
are the result of a highly complex interaction of multi-factorial genotypes
and environmental factors. Furthermore, current knowledge in genotypephenotype associations is very limited, and new discoveries of unexpectedassociations are highly likely. Therefore, a key lesson of personal genomics
education is that these links are difficult to identify.

●(R7) Reusability. The course will become one of the first attempts for
personal genomics education in Japan and should serve as a model that canbe copied elsewhere to promote such education nation-wide.
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3 Design of the Genome Analysis Workshop
3.1 Course Design
The course“Genome Analysis Workshop”has been available since 2012as a 15-week course in the spring semester aimed at freshman undergraduate
students in Keio SFC. Because the overall curriculum of SFC becomes
specialized over the four-year college period, freshman students presumably
represent the most interdisciplinary population (R1). To achieve highinteractivity and participation within the course, group work was set as thecentral activity of the workshop (R5). The course outline is shown in Figure
1. The 15-week course is divided into two parts: the first 6 weeks comprise
mostly hands-on lectures on the various concepts of personal genomics andits analysis, and the latter 6 weeks comprise predominantly group work inwhich the lecturer and student assistants provide support ad hoc to each groupdepending on the progress. Concurrent with the hands-on lectures, the students
are placed in groups of 5-8 in the 3rd week, and each group discusses anddecides an analysis theme for the second part until the mid-term presentation,
where they present the analysis plans. Due to the space limitation of thecomputer lab required for the hands-on lectures, the number of students was
expected to be around 50, resulting in about eight groups. Student assistants
were required to have the knowledge of computational tools used during thehands-on lectures.
In the Genome Analysis Workshop, the whole-genome sequence ofProfessor Masaru Tomita is provided and used as the course material.
Professor Tomita was the dean of the Department of Environment andInformation Studies, and he frequently appears in news coverage and on
television. Therefore, first-year students have most likely seen him in person
several times during orientations, and we assume that students are fairly
familiar with him; by analyzing his genome throughout the semester and bypresenting the analysis results at the end of semester with his presence in thelast two classes, we hope to provide a sufficient sense of handling personal

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Genome Analysis Workshop: a Personal Genomics Class at Keio SFC:,

Set groups of 5 〜 8Present rough theme of thegroup

Discussions using online
discussion board about thelink between phenotype andcellular biological functions

Final
presentation

Mid-term
presentation

Each group presents:
・ What theme to analyze
・Background information
about the theme
・ Which genes to analyze

Discussions using online discussion board about the link
between cellular biological
functions and genes

Group Work

After the presentation, students are required to write areport (A4-5) summarize thefindings

Figure 1 Outline of the Genome Analysis Workshop course in SFC.

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genomics information (R2), while avoiding the issues related to genetic testingof students (R3, R4). We have deposited Professor Tomita’s genome data in a
public database, which is now freely accessible online (accession:DRA000583),
and we also provide other course materials such as the slides used in thehands-on lectures upon request; therefore, with the wide media recognition ofProfessor Tomita, we believe that it will be relatively easy to host a similarcourse elsewhere (R7).
DTC genetic testing commonly yields genotyping reports based on theallele types of SNPs. This bottom-up report of phenotype-genotype links
can be misleading because it often overlooks or underestimates the multiple
genes related to individual phenotypes. For example, while there are tens ofgenes involved in pathways related to type-II diabetes, only a few SNPs withknown associations to the disease are considered in the report. Therefore, wehave reversed the analysis approach to begin with the phenotype of interestand then examine the related genotypes (R6). For example, if a group works
on assessing the risk of Professor Tomita for lifestyle-related diseases, thegroup must first identify the types of lifestyle-related diseases, such as type-II
diabetes and hypercholesterolemia. Next, the group must identify the biologicalprocesses responsible for such diseases, such as the sensing of blood glucose
levels in the pancreas, secretion of insulin, and differentiation of adipose
tissues in the case of diabetes. Subsequently, tens of genes involved in thesebiological processes can be elucidated, and finally the SNPs within these genes
are considered in the calculation of risk. Although it is currently possible toidentify SNPs directly from a phenotype of interest, through the series ofexperiences in the group work, we attempt to reveal the complexity of genetic
networks that influence individual phenotypes.
3.2 Preparation of Personal Genome Data
Genomic DNA was extracted from the saliva of Professor Masaru
Tomita using Oragene (DNA Genotek) and was purified using the DNeasy

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Blood and Tissue Kit (Qiagen) according to manufacturers’ instructions.
Library preparation and sequencing were performed at the Beijing Genomics
Institute following the manufacturer’s instructions (Illumina). Briefly, 8 μgof genomic DNA was fragmented into approximately 500-bp fragments by
Covaris (Covaris, E210). T4 DNA polymerase and the Klenow enzyme werethen used to convert the overhangs into blunt ends. An 'A' base was added tothe 3' end of the blunt phosphorylated DNA fragments, which was ligated withadapters (Illumina) on both ends. The correctly ligated products were purified
by 2% agarose gel electrophoresis followed by the QIAquick gel extraction kit(Qiagen). DNA fragments with adapter molecules on both ends were selected
and amplified. Polymerase Chain Reactions (PCR) was performed with two
primers that anneal to the ends of the adapters. The number of PCR cycles
was minimized to avoid skewing the representation of the library. The PCR
products were verified and purified by agarose gel electrophoresis. The
fragment size and molar concentration of the library were determined using a
Bioanalyzer (Agilent, 2100) and Real-Time PCR System (ABI, StepOnePlus),
respectively. The qualified library was sequenced on an Illumina HiSeq 2000
platform following the manufacturer’s instructions, as paired-end reads of 90
bp. The sequence reads with adaptor sequence or a high rate of low quality
bases were removed, yielding a total of 1,299,459,974 reads, representing acoverage of approximately 40x. The resulting sequence data were deposited in
the DDBJ Sequence Read Archive (DRA) under accession number DRA000583,
along with health record information in CCD/CCR formats. The study wasapproved by the Faculty’s Ethics Committee (No. 48, 2012).
Sequence reads were mapped to NCBI Build 37, Feb. 2009 Assembly(hg19) using Burrows Wheeler Aligner (BWA) (Li and Durbin, 2009), and
SNPs were called using the SOAPsnp software (Li, et al., 2009) and annotatedusing ANNOVAR (Wang, et al., 2010). A total of 4,036,647 SNPs wereidentified. Two alleles were separately re-inserted into the reference sequence
using a custom Perl script, generating two pseudo-diploid chromosome
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sequences with the called SNPs. These sequences were used as the databasefor the online BLAST server (Altschul, et al., 1997). For computational
efficiency and visualization purposes, the online BLAST parameters weremodified as follows: Expect=1e-25 and Alignment view=master-slave withidentities.

4 Conclusions and Outlook
In 2012, 39 (40 enrolled) students completed all the group work, including
three required presentations and a five-page final report, and the number ofparticipants increased to 58 (67 enrolled) in 2013. Group-work themes covered
highly unique topics, including the following:●

Assessment of Professor Tomita’s driving aptitude based on genetics●

Which Olympic sport is genetically optimal for Professor Tomita?

●

Discovering the ancestry roots of Professor Tomita●

Genetic carrier aptitude test for Professor Tomita●

Professor Tomita’s risk for lifestyle-related diseases●

How sensitive is Professor Tomita to various types of allergies?

●

What type of games is Professor Tomita genetically suited for?
Overall, the students were remarkably motivated to participate in thegroup work, and the use of Professor Tomita’s actual personal genome as acourse material and his presence at the final presentation seemed to enhance
the learning of personal genomics. Figure 2 shows the course survey resultat the end of semester in 2012 and 2013, and the reactions were generally
favorable, mostly scoring over four, with the rating of five being the majority.
It is notable that the questionnaire B, asking if the course was challenging,scored highest in both of the semesters, indicating a certain level of success incommunicating the complexity of the subject of personal genomics. However,
we also experienced difficulty in teaching the concept of multi-factorial risks

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Figure 2

Results of course survey in 2012 and 2013.
All questionnaires A 〜 D were scored with 1 〜 5 rating, 5 being the best.

because the students lacked sufficient statistical knowledge. Knowledge of the
genotype is often perceived in a similar manner to the popular personality
analysis myth based on blood type, which happens to be an example ofgenotype-phenotype association. The tendency to perceive genetic information
in a manner similar to fortune-telling may possibly be unique to Japan due
to the popularity of blood-type tests, and special care may be required when
explaining such aspects. Based on such impression of the course in 2012, wehave revised the lectures to allocate more time in teaching the ethical topics
as well as statistical aspects of genetics. This change may have resulted in
the overall decrease of score in the survey, as well as the increase of score inquestionnaire B. In order to improve the learning experience while maintaining
the necessary content, we will try to use more real-world examples to describe
the ethical and statistical topics in the future courses.
In 2013, we have surveyed whether the students want to know their
genotypes, at the beginning and end of the semester (Figure 3A). Approximately
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Figure 3

Result of a survey asking whether s/he wants to know his/her genotypes in 2013.
A: Survey result before and after the course. B: Composition of answers in the endof semester by the beginning of semesters.

70% of students answered“Yes”and this percentage stayed roughly the same;
however, as many as 37% of the students actually changed their opinion during
the semester (Figure 3B). Although there was a slight tendency to becomefavorable to know his/her own genotype (i.e. change from No to Yes), basicallythe changes were bidirectional. As an undergraduate class, the purpose ofthis course is obviously not in persuading the students about the usefulness of
personal genomics, but in provoking their critical thinking on this controversial
subject. This survey result presumably suggests that the students were
stimulated by the new knowledge, engaged in thinking and discussions, andfinally helped in taking their own positions regarding the subject of personal
genomics.
Because of the rapid technological advances in DNA sequencing andgenotyping, DTC testing is likely to become extremely affordable in the near
future, introducing the possibility of using individual personal genetic data foreducational purposes instead of Professor Tomita’s genome. However, the174

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use of common data also has considerable merits because the availability ofsuch material allows group-based work and can be used without the risk of
disclosing private information. By adopting careful measures for the ethicalissues listed in (R3), we believe that personal genetic testing can be used tosupplement the current course work based on Professor Tomita’s genome.

Acknowledegements
We are grateful to Mr. Masakazu Kohda (Saitama Medical University)for valuable advice in course design. The authors would also like to thank thefollowing student assistants for their enthusiastic contribution, which madethe group work successful: Soh Ishiguro, Emi Umeda, Reina Fukuzawa, Midori
Kawasaki, Kyoko Ishino, Yuki Yoshida, Gembu Maryu, and Hidehito Munakata.
This work was supported in part by funding from the Yamagata Prefectural
Government and Tsuruoka City.

References
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