November 2015

Querying the Whole Genome to Elucidate the Roleof HLA Genetics in Autoimmune Disease
Researchers combine human genetics with computational biology to identify novel pathogenic
risk loci.
Introduction
When he was training as a rheumatologist, Dr. Soumya Raychaudhuri
was treating many patients struggling with immune-mediatedconditions. Fascinated and frustrated by the disease complexity,
Dr. Raychaudhuri began developing an interest in studying immunediseases. As a postdoctoral fellow, he conducted rheumatoid arthritis(RA) research, applying large-scale single nucleotide polymorphism(SNP) genotyping to his earliest genetic studies.
Now an Associate Professor of Medicine at Harvard Medical School,
Dr. Raychaudhuri and his team are focused on understanding genetic
susceptibility for complex, immune-mediated diseases such as RA,
type 1 diabetes (T1D), and tuberculosis (TB). Genetic susceptibility
to autoimmune diseases, such as RA, is associated with variants inhuman leucocyte antigen (HLA) genes in the major histocompatibility
complex (MHC) region on chromosome 6. The encoded HLA protein
molecules form a peptide-binding pocket that presents antigenic
peptides to CD4+ T-cells. Amino acid variations in exons encoding theHLA antigen-binding domains are thought to increase susceptibility
to specific diseases.1 Dr. Raychaudhuri integrates computational
approaches, high-density array-based genotyping, and nextgeneration sequencing (NGS) to infer the precise variation in HLA thatfully explains risk. His goal is to identify genetic variations that increasea person’s likelihood of developing an immune-mediated disease.
“To understand the genetic factors that make you susceptible toimmune-mediated diseases, you need to query the whole genome
using NGS or high-throughput genotyping,” said Dr. Raychaudhuri.
Through this approach we’ve identified common factors acrossdiseases such as RA, T1D, and even TB.” iCommunity talked with
Dr. Raychaudhuri to learn more about how he’s using NGS to identify
novel pathogenic risk loci.
Q: What is your approach to studying complex diseases?
Soumya Raychaudhuri (SR): In traditional Mendelian genetic diseases,a mutation in one causal gene increases disease risk. For complexdiseases, it’s a spectrum of alleles rather than just one. For example,
we’ve collaborated in RA research where we defined over 100
alleles that play some role in contributing to RA risk. We are trying tounderstand how these alleles work together to cause disease. This
has led us to investigate how these alleles influence gene expression,
which has developed our interest in transcriptomics. We think thatmany of these alleles influence enhancers or promoters, which hassteered us toward epigenetic assays, ChIP-Seq, and most recently
ATAC-seq (assay for transposase-accessible chromatin sequencing)
to define parts of the genome playing a role in active gene regulation.
Ultimately it’s a systems biology approach we employ to link this data
together to determine how many alleles influence disease risk.

For Research Use Only. Not for use in diagnostic procedures.

Q: Why are you focusing your research on RA, T1D, and TB?
SR: The more we dug into it, the more we began to understandthat there was genetic overlap between these diseases. The geneticfactors that make someone susceptible to RA are closely related topathways that are relevant to CD4+ T-cells. We became interested
in T1D because it has tremendous genetic overlap with RA. We
studied TB because of the importance of the disease and the numberof people that are affected by it, and because CD4+ T-cell biology
plays an important role in TB susceptibility. There is a functional link
between these 3 diseases, and they are all important in terms of theirworldwide morbidity and mortality.
Q: What role does the MHC play in immune-mediated disease?
SR: In RA and T1D, the MHC plays an important role in susceptibility.
In humans, the MHC complex harbors over 200 genes. HLA genes
have many possible variations and are involved in recognizing foreign
antigens, as well as ensuring a match for transplanted tissue. In mostinstances, HLA alleles encode polymorphic amino acid residues that
sit in the binding group of HLA receptors. They likely influence theefficiency with which antigens are found, because specific pathogenic
antigens trigger immune-mediated diseases.
Q: How are HLA genes involved in T1D?
SR: Our group has invested a significant amount of time trying toidentify the individual alleles within particular HLA genes that drive
the risk of T1D. It has long been known that T1D is associated withSoumya Raychaudhuri, MD, PhD, is Associate Professor ofMedicine, Harvard Medical School Divisions of Genetics &Rheumatology; Department of Medicine, Brigham and Women’s
Hospital; Member of Partners Center for Personalized Genomics;
Professor in Genetics, University of Manchester; and Associate
Member, Broad Institute.

November 2015
the MHC and its HLA genes. In 1987, John Todd and his group
identified the 57th amino acid site in HLA-DQß1, a peptide-presenting
protein, and the important role it played in T1D.2 It’s become apparent
that position alone doesn’t explain all the risks attributed to HLA
genes in T1D. We wanted to identify the specific driving alleles. We
took advantage of recent published data from Dr. Suna Onengut,collaborators, and the Type 1 Diabetes Genetics Consortium, where
they typed tens of thousands of cases and controls using theImmunochip platform.3 The MHC was extensively queried and wewere able to apply that statistically to localize the precise association.
By analyzing these samples, we determined that half the risk of T1D
comes from position 57 in HLA-DQß1, and most of the other halfcomes from positions 13 and 71 in HLA-DRß1. The DRß1 alleles andthe DQß1 alleles interact with each other to confer differential risk ofT1D. Not only does this study give us a sense of how to approach risk
prediction using HLA and T1D, but it also identifies specific residues
to pursue in functional follow-up studies.4

data sets produced by the Immunochip and accurately infer what theHLA alleles should be. The HLA alleles themselves are challenging
to sequence. They are always a blind spot. Most clinical labs today
still use PCR-based protocols to do HLA typing. We’re hungry fora technology to sequence the HLA gene effectively.

Q: What is the mechanism in which variation in DRß1 and DQß1
increases T1D risk?
SR: There’s probably multiple pathogenic peptides in T1D that interact
with HLA molecules, DRß1 and DQß1, in different ways. Dependingon which version of DRß1 or DQß1 a person has, their system iseither more or less effective in interacting with the different pathogenic
antigens. This impacts who gets T1D and who doesn’t. We hope that
this study will inspire people to look into what the specific antigens
are and how they’re interacting. There’s a tremendous amount ofbiochemistry that needs to be pursued.

Q: How were you involved in developing the Immunochip?
SR: Illumina collaborated with the autoimmune disease community
to select the set of SNPs for the Immunochip. We were involved inpicking RA susceptibility SNPs. It was a fantastic collaboration withIllumina. We all benefitted from the input of others across different
immunological diseases, resulting in a product that we could all use.
What’s been fascinating is just how successful it’s been in its broad
application to different immune diseases. A wide range of immune
diseases have been queried with the exact same platform. We canlearn quite a lot by continuing to mine those data sets without everhaving to recollect DNA. That’s powerful.

“We determined that half the riskof T1D comes from position 57 in
HLA-DQß1, and most of the otherhalf comes from positions 13 and71 in HLA-DRß1.”

Q: What other Illumina BeadChips are you using?
SR: We used the HumanExome BeadChip to study age-related
macular degeneration (AMD), identifying several rare coding variants
that are associated with AMD susceptibility. The initial discovery wasdone by targeted sequencing of various genes. We validated thevariants found to be associated, and identified susceptibility-related
variants in C9 and C3.5 Those variants are on the HumanExome
BeadChip. It is a useful technology.

Q: What are some approaches to assess loci functions and theirimpact on disease?
SR: It’s important to have some type of physiological response tomeasure. We’ll stimulate T-cells and look at the response, which varies
from person to person. We’ll try to understand how the disease alleles
alter that physiological response. There are many ways to stimulate
T-cells and many subtypes of different T-cells. While it is important tounderstand the genetics of any individual physiologic response, it isequally important to identify what the right response is and what theright cell type is for a given disease.

Q: How are you using NGS in your research?
SR: We are sequencing many genes and use different Illumina
sequencing systems for different studies. We work through the BroadInstitute or Partners Sequencing Center to access the NextSeq® 500and HiSeq® Systems, in rapid and high-output modes respectively.
If we need longer read lengths, we use the NextSeq System. If wehave many samples and flow cells to fill, we’ll use the HiSeq System.
Illumina sequencing systems are widely used and well supported.
We’ve been happy with the results from both sequencing platforms.

Q: How has the Immunochip enabled your studies?
SR: Immunochip is widely used in T1D, RA, psoriasis, ankylosing
spondylitis, and a range of other diseases and is a good technology
to perform gene-based discovery. It facilitates low-cost genotypingof a huge number of samples. The ImmunoChip densely types SNPs
across the MHC, providing a high degree of accuracy. Dense SNP
typing allows us to use HLA imputation to infer HLA alleles within theMHC. The advantage of HLA imputation is that we can take existing

For Research Use Only. Not for use in diagnostic procedures.

“Immunochip is widely used in
T1D, RA, psoriasis, ankylosing
spondylitis, and a range ofother diseases and is a goodtechnology to perform genebased discovery.”

Recently, we’ve been performing transcriptome studies using SMARTseq2 and Illumina TruSeq® Targeted RNA Expression Kits on theNextSeq and HiSeq Systems. The SMART-Seq2 enables us to create
libraries out of low RNA quantities, down to the single-cell level. That’s
been an important feature for us.

November 2015
Q: How important is the sequencing speed of the NextSeq andHiSeq Systems?
SR: Sequencing on the NextSeq and HiSeq Systems is fast.
The sequencing speed is rarely a rate-limiting step for us. In fact,
we spend more time analyzing the data than actually sequencing.
Q: What software tools are you using to integrate various
genomic data?
SR: We develop our own software tools to perform data analysis.
Over the last year, our focus has been integrating genetic data with
epigenetic data. We’ve written a series of methods, including GoShifterand epiGWAS, to help geneticists take their genetic data, put it in thecontext of epigenetic data, and prioritize individual candidate alleles
that might be driving susceptibility. Epigenetic markers identifying gene
regulatory sites and alleles that play a role in gene regulation are most
likely to be causal in a locus. We also have undertaken efforts to lookat transcriptional data in the context of genetic data.

“Sequencing on theNextSeq 500 and HiSeq Systems
is fast... In fact, we spend moretime analyzing the data thanactually sequencing.”
Q: How do you see genomics impacting patient care forautoimmune diseases?
SR: I think it’s clear that genomics will play a huge role in solving
Mendelian and familial diseases in the next decade. What role
genomics plays in complex diseases is an open research question.
We and many others are interested in finding out the best ways
to understand the genomics underlying complex diseases. It’s anexciting area.
Q: What are the next steps in your research?
SR: In the last 5 years, there’s been a tremendous amount of
discovery in terms of identifying alleles and the role they play inimmune-mediated diseases. We are trying to localize thoseassociations to find what the causal alleles are and what they do.
In HLA, we’ve had some success in RA and T1D, but we’d liketo extend that success outside the HLA, to other loci and figureout what the driving alleles are across the genome. We wantto understand how those driver alleles are changing the functionof specific cell types and specific physiologic responses.

For Research Use Only. Not for use in diagnostic procedures.

References:1.

Santamaria P, Yang Y. Autoimmune Disease: Genetics. John Wiley & Sons
Ltd.; 2005.

Todd JA, Bell JI, and McDevitt HO. HLA-DQß gene contributes tosusceptibility and resistance to insulin-dependent diabetes mellitus. Nature.
1987; 329: 599–604.
3. Onengut-Gumuscu S, Chen WM, Burren O, et al. Fine mapping of type 1
diabetes susceptibility loci and evidence for colocalization of causal variants
with lymphoid gene enhancers. Nat. Genet. 2015; 47: 381–386 (2015).
4. Hu X, Deutsch AJ , Lenz TL, et al. Additive and interaction effects atthree amino acid position in HLA-DQ and HLA-DR molecules drive type 1
diabetes risk. Nat. Genet. 2015; 47:898–905.
5. Seddon JM, Yu Y, Miller EC, et al. Rare variants in CFI, C3 and C9 areassociated with high risk of advanced age-related macular degeneration.
Nat. Genet. 2013; 45: 1366–1370.
2.

Learn more about the Illumina products and systems
mentioned in this article:
HiSeq System, www.illumina.com/systems/hiseq_2500_1500.html
NextSeq 500 System, www.illumina.com/systems/nextseq-sequencer.
html
TruSeq Targeted RNA Expression Kits, www.illumina.com/products/
truseq-targeted-rna-expression-kits.html
Infinium HumanExome BeadChip Kit, www.illumina.com/products/
infinium_humanexome_beadchip_kit.html
Infinium ImmunoArray-24 v2 BeadChip Kit, www.illumina.com/
products/infinium-human-immunoarray-24-beadchip.html

November 2015

Illumina • 1.800.809.4566 toll-free (US) • +1.858.202.4566 tel • techsupport@illumina.com • www.illumina.com
For Research Use Only. Not for use in diagnostic procedures.
© 2015 Illumina, Inc. All rights reserved. Illumina, HiSeq, NextSeq, TruSeq, and the pumpkin orange color are trademarks of Illumina, Inc.
and/or its affiliate(s) in the U.S. and/or other countries. Pub. No. 1070-2015-004 Current as of 04 November 2015