Speeding Genetic Discovery in Autism through the iHART Information - - PowerPoint PPT Presentation

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Feb 04, 2024 516 likes •697 views

Speeding Genetic Discovery in Autism through the iHART Information Commons J. Jung, N. Stockham, K. Paskov, A. Gupta, M. Sun, D. Wall Stanford University March 2018 Exponential Growth of Sequenced Genomes Stephens ZD et al., Big Data:

SLIDE 1

Speeding Genetic Discovery in Autism through the iHART Information Commons

J. Jung, N. Stockham, K. Paskov, A. Gupta, M. Sun, D. Wall

Stanford University March 2018

SLIDE 2

Exponential Growth of Sequenced Genomes

Stephens ZD et al., “Big Data: Astronomical or Genomical?”, PLoS 2015, https://doi.org/10.1371/journal.pbio.1002195

SLIDE 3

Computing Capacity & Network Bandwidth

Core2 Duo Zeon 7400 AMD Epyc 100000000 200000000 300000000 400000000 500000000 600000000 700000000 2004 2006 2008 2010 2012 2014 2016 2018

Number of Transistors per Core

Source: https://en.wikipedia.org/wiki/Transistor_count

Network Bandwidth is still 10 Gbps max, just like 10 years ago
But cloud computing enables us to use as many cores as needed

SLIDE 4

Impact of Cloud Computing in Data Sharing

Past: Everything was packed in one bottle
Data, data storage, access tool, and computing facility
Had to use the fixed platform, or
Had to copy everything and to build a new one
Now: loosely coupled in cloud
Can access remote data with a group of different tools locally
Almost no need for local premises

SLIDE 5

Precision Medicine and Cloud Computing

Genetic profiling needs 5K >

cases for low-OR disorders

Larger sample size makes

better precision

Data integration needed for

accelerated discovery

Cloud computing provides

cost-effective solutions

Source: “Quantifying realistic sample size requirements for human genome epidemiology”, Int J Epidemiol 2009

SLIDE 6

iHART: Autism Research & Technology Initiative

Whole genome sequencing of families with Autistic kids
Illumina HiSeqX, targeting coverage 30x
Phase I Data Set: about to submit a main paper
2,308 individuals in 493 (multiplex) families
94 MZ twins (89 ASD), 445 female autistic children
750 TB of BAMs and 1 TB of VCFs
Phase II Data Set
Another 2,254 individuals in 567 (multiplex) families

SLIDE 7

Genetic Findings in Autism Spectrum Disorder

Many syndromic genes
No strong GWAS signals
Rare CNVs & de novos

in multiple pathways

Source: “Convergence of genes and cellular pathways dysregulated in autism spectrum disorders“, Am J Hum Genet, 2014

SLIDE 8

Preliminary Findings with iHART Cohort

Phase I Data set has been analyzed
Novel ASD-risk genes with inherited protein-truncating mutations
Novel structural variants and confirmation of known variants
Phase II Dataset Analysis is ongoing

SLIDE 9

What Data should be Shared?

Annotated VCF for the whole cohort
Primary source for the variants and their annotation
Per-sample gVCF calls
Required to do joint genotyping collectively with other data
Pedigree and phenotype traits
For trio/quad analyses and association studies
Raw read BAM files
For confirming calls, de novo analysis, and realigning

SLIDE 10

iHART Data Sharing Scheme

Data and access platforms are on Cloud
Accessing variant data and their annotations
Virtual machines and shared data disk with VCF
SQL-like search engine for VCF data access
Data overview with Web UI: Illumina Caselog
Accessing raw reads
Direct Visualization of BAMs with IGV
Traditional data copy via AWS S3 and Globus

SLIDE 11

iHART Website for Data Access Request

Secure and traceable data access using cloud account

SLIDE 12

Virtual Machine & Shared Data Disk on Cloud

Tailored Virtual Machine Image (AMI)
Open to Access-granted users
Maintains popular genomic processing tools
3rd-party AMI and Docker will be available
Share Data Snapshot (Volume)
Contains main genome reference files and iHART VCF
Multiple users can use the same data individually
Can attach as a local disk when an instance is launched

SLIDE 13

SQL-like Engine on VCF Data: Amazon Athena

Can query on variants, annotation, pedigree, and phenotypes

SLIDE 14

Data Overview with Web UI: Illumina CaseLog

SLIDE 15

Direct Visualization of BAMs with IGV

With presigned URL of BAMs, IGV can show reads without BAM file downloading

SLIDE 16

Traditional Data Copy

Amazon S3
Multiple, concurrent download possible
Popular files will be on CDN for low latency / high transfer speed
Globus (planned)
Reliable and faster than other traditional methods (e.g., ftp or scp)
Online monitoring feature available
Works on different platforms, including clouds and HPC clusters