Slide for Arithmer Seminar given by Dr. Jeffrey Fawcett (RIKEN) at Arithmer inc. The topic is how data science is used in genetics, especially in analyzing thoroughbred gene pool. "Arithmer Seminar" is weekly held, where professionals from within and outside our company give lectures on their respective expertise. The slides are made by the lecturer from outside our company, and shared here with his/her permission. Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。 Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.

1. Computational approaches
to study Genetics
2020/03/09
RIKEN
Dr. Jeffrey Fawcett
遺伝学を研究するための計算アプローチ

2. Computational approaches
to study Genetics
Jeffrey Fawcett
ロゴタイプ＋フルネーム一行組 白バック
(31 Oct 2019 @Arithmer)

3. 梅⾥雪⼭（6,740m）
Mt. Meili Xueshan

6. Tibet
Yunnan
Sichuan
Yangtze
River
Mekong
River
Salween
River
Fagopyrum esculentum
ssp. ancestrale
Fagopyrum esculentum
ssp. esculentum
Origin of common buckwheat
Domestication

7. Jeffrey Fawcett
(RIKEN iTHEMS)
Chengyun Li
(Yunnan Agricultural Uni)
Yasuo Yasui
(Kyoto Uni)
Takanori Ohsako
(Kyoto Pref Uni)
Diane Lister
(Cambridge Uni)

8. 中国雲南省の野⽣ソバ遺伝資源を活⽤した栽培化関連遺伝⼦の網羅的同定
科研費・ 国際共同研究強化B 研究代表者：安井康夫（京都⼤）
“Extensive characterization of domestication-related genes in buckwheat by
utilizing the genetic resource of Yunnan province, China”
KAKENHI Fostering Joint International Research B, PI: Yasuo Yasui (Kyoto U)
Aim of Buckwheat Project
q Collect & maintain genetic resources of buckwheat-related species
q Understand domestication process of buckwheat
q Identify genes important for breeding of buckwheat
(many wild species are facing extinction due to development)

9. Why am I involved? lots of DNA data to be generated
• Huge advance in technology to generate large-scale DNA data
• Need for people with computational skills and knowledge of genetics
(very few such people that work on buckwheat, horses, etc)
Ø Process of genetics/evolution responsible for creating the diversity of life
Ø Apply existing knowledge to (e.g. agronomically important) species
Main Research Interest

10. q What is written in the DNA?
q How can we know what’s written in the DNA?
q Basic concepts of genetics and evolution
Outline
q How can we associate “genotype” with “phenotype”?
§ Research on Thoroughbred horses

11. AA aa
Aa
Aa Aa
AA Aa Aa aa
Evolution: “Tree of Life” Mendelian Genetics DNA
Branching process
“Difference” increases with time
Traits as symbols (AA, Aa, aa)
Change of frequency as
a probabilistic process
History of Abstraction in Biology
Forms of life as digital information,
“sequence” of letters
Biology is… complex, diverse, changes over time

12. • Parent and offspring are similar – genetic information is passed on
What is passed on and how?
Basic concepts of genetics
AA aa
Aa
Aa Aa
AA Aa Aa aa
• Information of A and a is transmitted
across generations without changing
• The “phenotype” (appearance) of AA
and Aa are the same

13. www.twmu.ac.jp/IMG/about-gene/about-gene.html
Genome: entire set of genetic information
Every human has 2 genomes (1 from each parent)
Mechanism of Heredity

14. or or or or or or…
A a
B b
C c
2.5x108
2.4x108
2x108
33x108
Chromosome 1
Chromosome 2
Chromosome 3
Mechanism of Heredity
human: 23x2 chromosomes (22x2 + XX or XY),
~33x108 (3 billion) (x2) nucleotides
A
B
a
b
a
b
A
b
A
B
A
b

15. Species #Nucleotides (x108)
Budding yeast 0.121
Fruit fly 1.75
Fugu 3.9
Rice 4.5
Maize 25
Mice 27
Human 33
Onion 150
Grasshopper 650
Lungfish 1300
Canopy plant
（キヌガサソウ）
1490 en.wikipedia.org
Large variation in genome size

16. parent
offspring
mutation
AGCCTGCC
AGTCTGCC
AGCCTGCC
AGGCC
AGCCTGCC
AGCCTGCCTCC
point
mutation
deletion insertion
(duplication)
1 nucleotide
changes at a time
A few to thousands of
nucleotide change at once
~100 mutations per generation in humans
~1/108 point mutations per nucleotide per generation
Mutation
“Replication” cannot generate diversity, genetic information changes

17. Evolution
evolutionnews.org
Many individuals do not produce any offspring
We can’t observe mutations in those individuals
• Heredity
• Mutation => some of them change the “phenotype”
• “Population process” (competition, “struggle for survival”)
Do all individuals have equal chance to produce offspring?
Did the mutation change the chance to produce offspring?

18. q What is written in the DNA?
q How can we know what’s written in the DNA?
q Basic concepts of genetics and evolution
Outline
q How can we associate “genotype” with “phenotype”?
§ Research on Thoroughbred horses

19. DNA
RNA
Protein
Gene Gene Gene
transcription
translation
What is written in the genome?
All cells contain the same set of genes
The amount and timing of RNA/proteins produced differ
(very complicated process)

20. A (Adenine) -> A (Adenine)
G (Guanine) -> G (Guanine)
C (Cytosine) -> C (Cytosine)
T (Thymine) -> U (Uracil)
DNA -> RNA
(transcription)
RNA -> Amino Acid
(translation)
redundancy in genetic code
• 20 kinds of amino acids

21. www.123rf.com
Proteins as amino acid sequences
Protein Amino Acid sequence of Histone H4 gene
• The “same” genes are similar across species
• Similar sequence -> similar function
Ø Many genes share high similarity across distantly related species

22. DNA
pre-mRNA
mRNA
タンパク (protein)
exon exon exon exon
intron intronintron
coding region (CDS) coding region (CDS)
UTR UTR
タンパクを生成する遺伝子 (protein-coding gene)
regulatory
elements
regulatory
elements
1 2 3 4
1 2 3 4
Gene structure

23. Species #Nucleotides (x108) #Genes
Budding yeast 0.121 6,000
Fruit fly 1.75 17,000
Fugu 3.9 28,000
Rice 4.5 40,000
Maize 25 32,000
Mice 27 23,000
Human 33 21,000
Onion 150 ?
Grasshopper 650 ?
Lungfish 1300 ?
Canopy plant
（キヌガサソウ）
1490 ?
Number of genes are not so different across species

24. Evolution by Gene Duplication
• most genes were created by duplication
of another existing gene
• mutation can create a new function while
keeping the original function

25. What is written in the genome?
protein-coding sequences: 1-2%
q Human genome
regulatory sequences: ~5-10%
Transposable Elements: >60%
http://cubocube.com
Retrotransposon DNA transposon
• major reason for genome size variation
• virus-like parasites that amplify
• have their own “genes”
• often harmful, sometimes beneficial

26. A lot of unnecessary information in the genome!?
“So much Junk DNA in our Genome”
(Susumu Ohno, 1972)
We all acquire ~100 new mutations but most of
us are fine (i.e. most mutations are harmless)
“Junk DNA” should be removed during evolution!?!?
- each “junk” is removed but lots of “junk” are being generated
- they are parasites that try to survive themselves

27. q What is written in the DNA?
q How can we know what’s written in the DNA?
q Basic concepts of genetics and evolution
Outline
q How can we associate “genotype” with “phenotype”?
§ Research on Thoroughbred horses

28. http://knowgenetics.org
• Genome sequencing and assembly
How do we know the sequence of the genome?
Can only sequence a few
hundred nucleotides at once
Difficult because of many
similar sequences

29. DNA
RNA
Protein
Gene Gene Gene
transcription
translation
Extract RNA, sequence, and “map” them to the genome
Not so simple because of many similar genes and the presence of introns
How can we “find” the genes?

30. Most genes in 1 species have similar genes in other species
Search for similar sequences in the genome
How can we “find” the genes?
• Histone H4 gene
Similar genes are likely to have similar functions

31. DNA
ATG TAG or TAA or TGAGT AG GT AG GT AG
タンパクを生成する遺伝子 (protein-coding gene)
How can we “find” the genes?
Predict genes based on their features
Nucleotide composition is statistically different
protein-coding sequence vs non-coding sequences, introns
GT-AG of intron vs GT-AG not associated with introns

32. q What is written in the DNA?
q How can we know what’s written in the DNA?
q Basic concepts of genetics and evolution
Outline
q How can we associate “genotype” with “phenotype”?
§ Research on Thoroughbred horses

33. Domestication of Horses (~5500 yrs ago)
Fages et al. Cell 2019

34. www.tigermoon.co.uk
q Thoroughbreds
• Originated in 18th Century
(3 “founder” stallions)
• Horses that win races are selected to
breed in many different countries
Many diverse breeds established after domestication

35. Selective breeding of Thoroughbreds in Japan
birth racing career breed breed
successful successful
• ~7000 horses are born and registered at JRA (Japan Racing Association)
• Only 10-20 males per generation (>50% of females) are selected for breeding
Ø Thoroughbreds are much faster than other horse breeds due to
selective breeding for 20-30 generations
=> but, genetic information has not been utilized yet

36. Many differences between wild and cultivated Buckwheat
Fagopyrum esculentum
ssp. ancestrale
Fagopyrum esculentum
ssp. esculentum
Domestication
Loss of seed shattering
Loss of seed dormancy
Erect growth
Larger seeds

37. Process of domestication
evolutionnews.org
• Mutations occur that result in a desirable trait
(might already exist in wild, or occur afterwards)
• Humans preferentially select and breed
those individuals
• Can we identify the mutations selected by humans?
• Can we identify other useful mutations/genes
that could be useful for breeding?
(should speed up the selective breeding)

38. ATGTACGTGAGTCAGTACGATGCAGATCTATGAAGACATCCGA…
ATGTACGTGAGTCAGTACGATGCAGATGTATGAAGAAATCCGA…
ATGTACGTGAGTCAGGACGATGCAGATGTATGAAGACATCCGA…
ATGTACGCGAGTCAGGACGATGCAGATGTATGAAGACATCCGA…
ATGTACGCGAGTCAGTACGATGCAGATGTATGAAGAAATCCGA…
ATGTACGCGAGTCAGGACGATGCAGATCTATGAAGAAATCCGA…
１
２
３
TT: Long distance
CT: Middle distance
CC: Short distance
SNP of Myostatin gene
SNP of Gene X related
to racing ability
0
1000
2000
3000
4000
AA AC CC
Expected
earnings
(x10,000 yen)
Variation in the DNA of each individual
Single Nucleotide Polymorphism (SNP)

39. 出典：サラBLOOD Vol. 1&2
Example of variation (SNP) associated with racing ability
SNP in myostatin gene affects optimum racing distance

40. Example of variation (SNP) associated with racing ability

41. Using genetic information for Thoroughbred breeding
Ø Can evaluate the genetic potential of each horse
=> more informed choice of which horse to keep and which to discard
Ø Can identify the best breeding (male x female) combination
=> more informed choice of which male and female to mate

42. Kit to survey 600,000
SNPs available for horses
• ~400 Thoroughbred horses from JRA
• Collect blood samples, extract DNA, identify SNPs
• Can we identify genetic variation associated
with variation in traits?
• Can we identify genetic variation associated
with variation in racing ability?
(Currently extended to ~1000 samples)

43. A:40 T:20 A:20 T:10
G:31 C:29 G:14 C:16
G:30 C:30 G:2 C:28
T:29 A:31 T:1 A:29
A:32 T:28 A:3 T:27
T:5 G:55 T:4 G:26
Horses with trait A (30) Horses without trait A (15)
Genetic factor responsible for
difference in trait A should be present
Strong association between
nucleotide frequency and
difference in trait
Genome-wide Association Study (GWAS)
SNPs in neighboring region are “linked”

44. GWAS works with coat color genes (proof of concept)
Chestnut (94) vs Non-chestnut (258)
Chromosome
Gray (18) vs Non-gray (358)

45. x20
x20x2x10x5
X
Nucleotidediversity
Genomic signatures of selection
• Region containing the mutation
has reduced diversity

46. 10 12 14 16 18 20
Chromosome 28
Conditionalnucleotidediversity
Genetic Position (Mb)
Non-thoroughbred (Schaefer et al. 2017)
Thoroughbred(This study)
Thoroughbred(Schaefer et al. 2017)
42 44 46 48 50
Chromosome 1
0.000.100.200.30
25 30 35 40 45 50 55 60
Chromosome 7
0.000.100.200.30
34 35 36 37 38 39
Chromosome 3
0.000.050.100.150.200.250.30
Conditionalnucleotidediversity
Genetic Position (Mb)
Non-thoroughbred (Schaefer et al. 2017)
Chestnut Thoroughbred (This study)
Non-chestnut Thoroughbred (This study)
MC1R
Chromosome 3
250.30
ersity
Non-thoroughbred (Schaefer et al. 2017)
Chestnut Thoroughbred (This study)
Non-chestnut Thoroughbred (This study)
MC1R
34 35 36 37 38 39
Chromosome 3
0.000.050.100.150.200.250.30
Conditionalnucleotidediversity
Genetic Position (Mb)
Non-thoroughbred (Schaefer et al. 2017)
Chestnut Thoroughbred (This study)
Non-chestnut Thoroughbred (This study)
MC1R
Regions of reduced diversity regions in Thoroughbreds
PCDH15
(hearing and
balance functions)
KITLG
(fertility, neural cell
development)

47. Computational approaches to identify important regions
q Genome-wide association studies
q Genomic signature of artificial/natural selection
• Difficult to pinpoint actual causative mutation/gene
• Can identify candidate regions/genes that can be tested experimentally
very powerful when there is a targeted, simple trait
can use also for complex traits and where there is no specific trait

48. Summary
• We can explain the process creating the diversity of life in a
common mathematical/computational framework
• We can apply the same approaches to study many different
species (e.g. horses, buckwheat)
• Computational/mathematical approaches are becoming
more important with the increase of data
• More advanced mathematics are needed to go from
“sequences” to complex networks, shapes, 3D structures etc?

49. Acknowledgements
q Thoroughbred project
q Buckwheat project
Yasuo Yasui (Kyoto U), Chengyun Li (Yunnan Agri U), Takanori
Ohsako (Kyoto Pref U), Hideki Hirakawa (Kazusa DNA Res), etc
Hideki Innan, Takahiro Sakamoto, Watal M Iwasaki (SOKENDAI),
Fumio Sato (JRA Hidaka), Teruaki Tozaki (Lab of Racing Chemistry),
Takao Suda (Journalist), etc