Huijuan Feng, Shining Ma,Chao Ye & Zhixing Feng

Transcription

1 Huijuan Feng, Shining Ma,Chao Ye & Zhixing Feng

2 Background-Author introduction Research interest: Methods for gene mapping of complex traits Inference of population structure from genetic data Genome variation and evolution Computational modeling of the controls of gene expression Jonathan K. Pritchard Dept. of Human Genetics Univ. of Chicago Yoav Gilad Dept. of Human Genetics Univ. of Chicago Research interest: Gene regulatory mechanism eqtls mapping and fuctional analysis Gregory E. Crawford Duke University School of Medicine Research interest: Identify gene regulatory elements to find out how chromatin structure affect cell variation Page 2

3 eqtls Important contributors to phenotypic variation Function QTLs:quantitative trait loci eqtls:expression QTLsgenomic loci that regulate expression levels Definition eqtls Mechanism Methods for mapping Using genetic markers like SNP Statistical methods to find significantly genotype-gene expression level correlation An important tool for linking genetic variation to changes in gene regulation Page 3

4 eqtls Previous hypothesis: Play roles in three levels: Transcriptional: TF binding efficiency Cotranscriptioanl: mrna degradation Posttranscriptional: Alternative splicing Previous study(examples) SNP in NFκB binding region affectstf binding efficiency by ChIP-seq A SNP in the promoter of CHI3L2 implicated in recruiting variable amounts of RNA polymerase. Hypothesis: SNP affects TF binding or nucleosome occupancy at regulatory sites and may lead to gene expression differences Page 4

5 DNase-seq Chromatin accessibility Page 5 Song & Crawford, Cold Spring Harb Protoc 2010

6 Previous research results about DNase-seq 10% of the DNase HS sites are lymphocyte specific Approximately 80% of these DNase HS sites uniquely map within one or more annotated regions of the genome believed to contain regulatory elements. Mapping DNase I hypersensitive (HS) sites is an accurate method of identifying the location of genetic regulatory elements. Page 6

7 Close to the question Question eqtls affect TF binding or nucleosome occupancy at regulatory sites and may lead to gene expression differences Method Mapping for eqtls is an important tool for linking genetic variation to changes in gene regulation Technique Mapping DNase I hypersensitive (HS) sites is an accurate method of identifying the location of genetic regulatory elements. Page 7

8 Background-Question Genotype Gene expression DNase-seq eqtls eqtls How? TF binding Chromatin accessibility modification Gene regulation Gene expression differences Page 8

9 Which genotypes affect chromatin accessibility? Genotypes of 70 YRI SNP(14.9M) HapMap & 1000 Genomes indel(0.6m) Chromatin accessibility DNase I sensitivity Page 9

10 DNase I-hypersensitive sites(dhss) Page 10

11 Quantitative markers Page 11

12 Pipeline of dsqtls identification Z = β + β G + ε i, j 0, j 1, jk, ik, i H : β = 0 t test 0 1, jk, Page 12

13 Page 13

14 Correlation coefficient r = 0.72, P << Page 14

15 An example Page 15

16 Page 16

17 Properties of dsqtls Now we know how to get dsqtl and obtain many dsqtls. Let s turn to this question: what is the consequence that these dsqtls lead to, or in other words, what is the biological meaning of dsqtl? Do dsqtls effect TF binding? What about gene expression? If so, how do they do? Page 17

18 Aggregate properties of dsqtls They observed that dsqtls typically affected chromatin accessibility for about bp. Of the DHSs affected by dsqtls, 77% lie in chromatin regions previously predicted to be functional in lymphoblastoid cell lines: 41% in predicted enhancers 26% in promoters 10% in insulators Page 18

19 dsqtls density with distance 56% of the dsqtls were due to variants that lay within the same DHSs. Page 19

20 What is a DNaseI footprint? Operationally defined as small regions of low DNaseI cutting surrounded by two flanks with high cutting rates. Page 20 Weake & Workman, Nat. Rev. Genet., (2010)

21 dsqtls frequency in regulatory annotations Page 21

22 Conclusion From these three figures, we know that dsqtls found here are likely to impact locally. Taking relationship between Dnase I sensitivity and TF into account, they consider whether dsqtls influence the TF binding. Two approaches: Position Weight Matrix ChIP-seq data Page 22

23 Effects of PWM changes on DNaseI sensitivity Page 23

24 Characterizing the effect of dsqtls on TF binding with ChIP-seq in LCLs Page 24

25 Summary dsqtls tend to impact locally. dsqtls contribute to TF binding event. Since dsqtls are related to TF binding, what is the relationship between dsqtl and gene expression? Page 25

26 How dsqtls affect gene expression? Correlation between dsqtls and gene expression. Cis-acting or trans-acting? Page 26

27 Correlation between dsqtls and gene expression 100kb 100kb Gene 1 Gene 2 Gene n dsqtl Linearly regress gene expression by dsqtl (8902 dsqtls), so we got 8092 p-values. FDR was estimated by pfdr approach (John Storey et al 2001). ~39% of 8902 dsqtls were detected as eqtls (FDR=10%) Page 27

28 Correlation between dsqtls and gene expression (cont d) Page 28

29 Correlation between dsqtls and gene expression 100kb 100kb DHS 1 DHS2 DHS m eqtl Linearly regress DHS sensitivity by eqtl (1271 eqtls), so we got 1271 p-values. FDR was estimated by pfdr approach (John Storey et al 2001). ~55% of 1271 eqtls were detected as dsqtls (FDR=10%) Page 29

30 Correlation between dsqtls and gene expression (cont d) Page 30

31 An example of dsqtl/eqtl pair Page 31

32 Features affecting dsqtls behavior Page 32

33 Features affecting dsqtls behavior (cont d) Page 33

34 Further investigation on features affecting dsqtls behavior(cont d) Page 34

35 Further investigation on features affecting dsqtls behavior Page 35

36 Trans-acting of dsqtls? Test association between detected dsqtl/eqtl pairs and DHS genome-widely and DHSs that significantly correlated with gene expression, but did not have cis-variants themselves. Very weak signal Page 36

37 Trans-acting of dsqtls? (cont d) Page 37

38 Comments Large scale data 70 HapMap Yoruba lymphoblastoid cell lines Multiple type integration ChIP-seq, RNA-seq, SNP, DNaseI footprint The first work to investigate Relationship between DNase I sensitivity and SNP Rrelationship between DNase I sensitivity and gene expression Page 38

39 Comments They found only weak evidence of trans-acting dsqtls, probably because their experiment was underpowered for detecting these. Creative to develop new methods and concepts, but relation between chromatin accessibility and gene expression is not surprising. Page 39

40 Thanks! Q&A Page 40