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CORSO INTEGRATO DI GENETICA E BIOLOGIA MOLECOLARE GENETICA A.A.2016/2017 prof. Giovanni Malerba [email protected] Scansioni Genomiche II

GENETICA - Università degli Studi di Veronamedgen.univr.it/didattica/genetica16/diapositive2016/Lez_35_36... · GENETICA A.A.2016/2017 prof. Giovanni Malerba [email protected]

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CORSO INTEGRATO DI GENETICA E BIOLOGIA MOLECOLARE

GENETICAA.A.2016/2017

prof. Giovanni Malerba [email protected]

Scansioni Genomiche II

GWAS: Bone-mineral-density loci

Nature Genetics 41, 1199 - 1206 (2009)

- 75,891 inds of European ancestry (30 studies) - 67,786 i ndis of European ancestry from the UK Biobank; - 10,104 individuals: African American, Chinese, Filipino, Surinamese,Turkish and Moroccan (6 six studies)

IMPUTATION using 1000G + UK10K

60 loci (53 novel)

Associations between fetal genotype and BW could result from indirect effects of the maternal genotype influencing BW via the intrauterine environment, given the correlation (R ≈ 0.5) between maternal and fetal genotype. However, two lines of evidence indicatedthat variation in the fetal genome was the predominant driver of BW associations.

Genotyping, quality control, pre-phasing,imputation, and association analysis

Hierarchical clustering of BW loci based on similarity of overlap with adult diseases, metabolic and anthropometric trait

Z scores

GWAS – BMD at 2 body sites

BMD: known and new loci

Nature Genetics 41, 1199 - 1206 (2009)

Meta-analisi dei loci associati

Nature Genetics 41, 1199 - 1206 (2009)

BMD e numero di alleli di rischio

La distribuzione del numero di alleli di rischio portati da un individuo si avvicina alla curva a campana

Il valore di BMD (espresso in Z-score), in media, si riduce all'aumentare del numero di alleli di rischio!! (ricorda il modello a soglia)

45% of variance (human height) can be explained by considering all SNPs (~300000) simultaneously

423 loci (697 variants) explain ~20% of the heritability~2000 SNPs → ~42% ~3700 SNPs → ~48%~9500 SNPs → ~58%

~253,000 individuals

Heritability of Height

Number of height SNPs on each chromosome against chromosome length

Dimensione dell'effetto e frequenza allelica

P < 1 x 10-10

P < 1 x 10-8

P < 1 x 10-7

P < 1 x 10-6

FINRISK97 panel with complete genotype at the 12 SNPs with P <5 X 10-7 (7566 individuals),

Combined effects of 12 SNPs

~2% of the population variation in height

Difference : 3.5 cm

Combined impact of the 20 SNPs with a P < 5 x 10-7.

Combined effects of 20 SNPs

13,665 individuals(+ 16,482 samples)

~3% of height variation, with a ~5 cm difference

https://www.ebi.ac.uk/gwas/docs/downloads

GWAS Catalog

https://www.genome.gov/26525384

Autoimmune Disease Classification by Inverse Association with SNP Alleles

Marina Sirota Marc A. Schaub Serafim Batzoglou William H. Robinson Atul J. Butte

Disease heatmap based on genetic variation profile

● 8% of the general population carry a large deletion or duplication

(>500 kb) at low allele frequency of (<0.05%)● Several hundred genes that map to regions of copy-number

polymorphic (CNP) duplications● True associations are hidden in the fog of random associations● Interazioni di varianti in cis● Rare variants of large effect (?)● Gene–environment (G×E) interactions● Parent of origin effect (es. asthma, T2D)● Epigenome

Giovanni Malerba, [email protected] -

PROGETTO ENCODE

Slide 22/23

Overall, the project provides new insights into the organization and regulation of our genes and genome, and is an expansive resource of functional annotations for biomedical research.

Giovanni Malerba, [email protected] -

ENCODE – la variabilità

Slide 23/23

● TRANSCRIPTION FACTOR (TP) MOTIFS

● CHROMATIN PATTERNS AT TP BINDING SITES

● INTERGENIC REGIONS AND GENE DEFINITION

● RNA AND CHROMATIN MODIFICATION PATTERNS AROUND PROMOTERS

● EPIGENETIC REGULATION OF RNA PROCESSING

● NON-CODING RNA CHARACTERIZATION

● DNA METHYLATION

● ENHANCER DISCOVERY AND CHARACTERIZATION

● 3D CONNECTIONS ACROSS GENOME

● CHARACTERIZATION OF NETWORK TOPOLOGY

● MACHINE LEARNING APPROACHES TO GENOMICS

● IMPACT OF FUNCTIONAL INFORMATION ON UNDERSTANDING VARIATIONS

● IMPACT OF EVOLUTIONARY SELECTION ON FUNCTIONAL REGIONS