Statistical Genetics 統計遺伝学

Presentation on theme: "Statistical Genetics 統計遺伝学"— Presentation transcript:

1 Statistical Genetics 統計遺伝学
2011/06/06 Ryo Yamada Unit of Statistical Genetics Center for Genomic Medicine Graduate School of Medicine Kyoto University

2 太陽 おけら てのひら みみにみみず あめんぼ みつばち とんぼ かえる 私が両手をひろげても、 お空はちっとも飛べないが、
　　　私が両手をひろげても、 　　　お空はちっとも飛べないが、 　　　飛べる小鳥は私のやうに、 　　　地面（じべた）を速くは走れない。 　　　私がからだをゆすっても、 　　　きれいな音は出ないけど、 　　　あの鳴る鈴は私のやうに、 　　　たくさんな唄は知らないよ。 　　　鈴と、小鳥と、それから私、 　　　みんなちがって、みんないい。 みつばち とんぼ かえる 2

3 瓜二つ 瓜の蔓に茄子はならぬ 鳶が鷹を生む カエルの子はカエル 3

4 What to study? How to study?
What do you want to know? How do you want to know it?

5 Genetics Genotype Phenotype

6 Genetics Genotype Phenotype Identity Variation
How to grab “Genotype” and “Phenotype” with their “Identity” and “Variation”. One way is to make a catalogue of facts among “them”. The other way is to give a strategy to make the catalogue.

7 Genotype Phenotype Intermediate phenotype Terminal phenotype

8 Graph (Theory) グラフ(理論)

9 Pedigree 家系図

10 Pedigree 家系図 phylogenetic tree 系統樹

11 Phylogety, a tree

12 Distance 距離

13 Distance: More than one definition
距離にもいろいろな定義がある

14 Make graph from distance info 距離情報からグラフを作る
Distance is a way to quantitate relation. Three items that make a triangle can be connected as a star without changing their pathway-distance.

15 Difference among graphs グラフ間の違い
Same or different as a tree?

16 Difference among graphs グラフ間の違い
Topology (Shape) and length of edges 位相(形)と辺の距離 Same or different as a tree?

17 Items can be expressed as a tree when distance among them are given.
Different “distances” give different trees.

18 Graphs for data-analyses グラフによるデータ解析

19 Heatmap Data-mining approach also uses trees.

20 Tree needs “distance” with its definition.
Clustering methods also need definition to make tree structure..

21 Data give trees and change the order in items.
When the order in items are not changed and when relation among the items are displayed, it is correlation matrix.

22 Relations among columns
Original data Relations among lines Relations among columns and lines

23 Pedigrees are NOT graphs 家系図はグラフでは ない

24 Trees in classical genetics: Pedigrees

25 Relation between humans. Relation between chromosomes.

26 A human relation can be multiple chromosomal relations.

27 Chromosomes have two parental chromosomes.
But a base has only one parental base.

28 Sexual and Asexual Reproduction Graph 有性生殖と無性生殖、とグラフ

29 Genotype Phenotype Intermediate phenotype Terminal phenotype

30 Types of Data データ タイプ

31 Categorical data and sets.
カテゴリ型データと集合

32 Ordered and Non-ordered.

33 High-dimensional data and graph

34 Genotype Phenotype Intermediate phenotype Terminal phenotype

35 Networks ネットワーク

36 ? ? ? ? ? ? DNA配列 次世代 シークエンス eQTL GWAS E1 E2 E3 E4 E5 D1 D1a D2 D1b
エピゲノム修飾 ? 次世代 シークエンス eQTL ? ネットワーク (転写物・翻訳物) ? ? ? GWAS ? E1 E2 E3 E4 E5 疾患に共通する因子 D1 疾患とその亜分類 D1a D2 D1b D2b D3 D2a D2c D4 D5

37 Components of graphs グラフの部品 Concepts of regulations/interactions 制御/相互関係の概念 Graphs are being used for biology

38 Networks are complex ネットワークは複雑

39 We can not grab the graph as a whole at once グラフ全体を一発で了解することは無理

40 Transition of Stata 状態推移
step-by-step 順番に Markov-chain マルコフ連鎖 Bayesian networks ベイズネットワーク

41 Genetic Heterogeneity and Transition of Stata 遺伝的多様性と状態推移
Three components to make genetic heterogeneity 遺伝的多様性を作る３要素 Mutations Recombinations Genetic drifts 変異 組換え 遺伝的浮動 Variants will drift out from the world.

42 疾患原因・薬剤応答性遺伝因子探索 GWAS

43 Complementary strand, for what?

44 Cross-overs and recombinatios

45 What is the relation between crossovers and identity of origin?

46 What is the distribution of segment-length between crossovers?
Exponential...

47 Some chromosomes leave many copies but others none.

48 Variants will drift out from the world.

49 Chromosomal relations in generations.

50 Population. Chronological changes. Spatial changes.

51 Time,Space 時間、空間 Dimensions 次元 Dimensions of data

52 Data analyses and space of data

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57 Alleles and haplotypes and their relation.

58 RNA codon table

59 RNA codon table can be drawn as a tree.

60 Space may not be Euclidean 空間はユークリッド的でないかも

61 Closed space 閉じた空間 Populations in “Space” and “Time”.
Finite space vs. Infinite Space

62 Non-linear 非線形

63 Stable Equilibrium 安定 定常
Models how to handle space and time.

64 ? in Statistical genetics?
Welcome Any questions on how to handle bio-medical data ? Also welcome Ryo Yamada, M.D., Ph.D. Unit of Statisical Genetics 4F Kaibou-center building phone: (+81) fax: (+81) 統計遺伝学分野 山田　亮 医学部解剖センター棟４階