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Statistical Genetics 統計遺伝学

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. What to study? How to study?. What do you want to know? How do you want to know it?. Genetics. Genotype Phenotype. Identity

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Statistical Genetics 統計遺伝学

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

  3. 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.

  4. Genotype Phenotype Intermediate phenotype Terminal phenotype

  5. Graph (Theory)グラフ(理論)

  6. Pedigree 家系図 phylogenetic tree 系統樹

  7. Phylogety, a tree

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

  9. 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?

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

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

  12. 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.

  13. Relations among columns Original data Relations among lines Relations among columns and lines

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

  15. Trees in classical genetics: Pedigrees

  16. Relation between humans. Relation between chromosomes.

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

  18. Sexual and Asexual ReproductionGraph有性生殖と無性生殖、とグラフ

  19. Types of Dataデータ タイプ

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

  21. Ordered and Non-ordered.

  22. High-dimensional data and graph

  23. Networksネットワーク

  24. DNA塩基配列バリアント DNA配列 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 疾患に共通する因子 E4 E5 D1 疾患とその亜分類 D2 D1a D1b D2b D3 D5 D4 D2a D2c

  25. 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

  26. 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

  27. 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

  28. 疾患原因・薬剤応答性遺伝因子探索GWAS

  29. Complementary strand, for what?

  30. Cross-overs and recombinatios

  31. What is the relation between crossovers and identity of origin?

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

  33. Some chromosomes leave many copies but others none.

  34. Variants will drift out from the world.

  35. Chromosomal relations in generations.

  36. Population. Chronological changes. Spatial changes.

  37. Time,Space時間、空間 • Dimensions 次元 • Dimensions of data

  38. Alleles and haplotypes and their relation.

  39. RNA codon table

  40. RNA codon table can be drawn as a tree.

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

  42. Non-linear非線形

  43. StableEquilibrium安定定常 Models how to handle space and time.

  44. ? 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)75-753-9470 fax: (+81)75-753-9284 ryamada@genome.med.kyoto-u.ac.jp 統計遺伝学分野 山田　亮 医学部解剖センター棟４階 http://www.med.kyoto-u.ac.jp/E/grad_school/introduction/1525/

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