Detection of Rare-Alleles and Their Carriers Using Compressed Se(que)nsing Or Zuk Broad Institute of MIT and Harvard In collaboration with: Amnon Amir Dept. of Physics of Complex Systems, Weizmann Inst. of Science Noam Shental Dept. of Computer Science, The Open University of Israel

The Problem Identify genotypes (disease) in a large population AB AA genotypes Specifics: Large populations (hundreds to tens of thousands) Rare alleles Pre-defined genomic regions

Naïve Approach – Targeted selection + Next Gen Seq.: One Test per Individual collect DNA samples Apply 9 independent tests AB AA fraction of B’s out of tested alleles 0 1/ Problem: Rare alleles require profiling a high number of individuals. Still very costly. Multiplexing/barcoding provides partial solution (laborious, expensive, often not enough different barcodes) Targeted selection

Our approach - Targeted Selection + Smart pooling + Next Gen seq. collect DNA samples. Prepare Pools Advantages: Fewer pools Reduced sample preparation and sequencing costs Can still achieve accurate genotypes Apply 3 pooled tests AB AA fraction of B’s out of tested alleles 0 1/ Targeted selection Reconstruct genotypes

Application 1: Rare recessive genetic diseases CarrierHealthy! NormalHealthy GenotypePhenotype AffectedSick Identify carriers of known deleterious mutations

Nationwide carrier screen

Genetic DisorderCarrier rate Tay-Sachs1:25 Cystic Fibrosis1:30 Familial Dysautonomia1:30 Usher Syndrome1:40 Canavan1:40 Glycogen Storage1:71 Fanconi Anemia C1:80 Niemann-Pick1:80 Mucolipidosis type 41:100 Bloom1:102 Nemaline Myopathay1:108 Large scale carrier screen (rates vary across ethnic groups)

Specific mutations - notation “A” “B” Reference genome …AGCGTTCT… …AGTGTTCT… Single-nucleotide polymorphism (SNPs) …AGGTTCT Insertions/Deletions (InDels) Carrier test screen: Amplify a sample of DNA and then test “AA” “AB” fraction of B’s out of tested alleles 1/2 0

Application 2: Genome Wide Association Studies collect DNA samples AB BBABBBAA AB CasesControls AAAB AA ABAA Count: CasesControls AAX AA Y AA ABX AB Y AB BBX BB Y BB Try ~10 5 – 10 6 different SNPs. Significant ones called ‘discoveries’/’associations’ Statistical test, p-value

What Associations are Detected? [T.A. Manolio et al. Nature 2009] Goal: push further Find Novel mutations associated with common disease and their carriers

What Associations are Detected? Find Novel mutations associated with common disease and their carriers Proposed approaches: Profile larger populations. Look at SNPs with lower Minor Allele Frequency Re-sequencing in regions with common SNPs found, and other regions of interest

infer/reconstruct Compressed Sensing Based Group Testing Next Generation Sequencing Technology compressed sensing (CS) a few tests instead of 9 fraction of B’s

Rare Allele Identification in a CS Framework individuals in the pool # rare alleles

The standard CS problem: n variables k << n equations But: x is sparse: Matrix should obey certain properties (Robust Isometry Property) Example: random Gaussian or Bernoulli matrix Then: Can reconstruct x uniquely with k = O(s log(n/s)) equations (a.k.a. ‘measurements’) Can do so efficiently, even for large matrices (L 1 minimization) Compressed Sensing (CS)

NextGenSeq Output output: “reads” Example: Illumina, A few millions reads per lane Read length – a few dozens to a few hundreds line = “read”

NextGenSeq – Targeted Sequencing Measure the number of reads containing B out of total number of reads. Here: 1/16

Parts of this modeling appeared in [P. Prabhu & I. Pe’er, Genome Research July 09] Ideal measurement - the fraction of “B” reads: Model Formulation r is itself a random variable 1.sampling noise: finite number of reads from each site - r NGST measurement: 2. Technical errors: read errors: 0.5-1% DNA preparation errors, Estimated frequency: sparsity-promoting term error term

Results (simulations) arxiv v1 [f = freq. of rare allele] Can reconstruct over 10,000 people with no errors, using only 200 lanes Software Package: Comseq [unique solver for this application noise model, translating to CS, reconstruction..]

Results (real data) 1.Pooled-sequencing experimental data Validate the Pooling part (variation in amount of DNA) genomes data Validate all other technical errors (e.g. read error, sampling error ) in a large-scale experiment

Results (dataset 1) Pooling dataset from: [Out et al., Human Mutation 2009] 88 People in one pool – region length (hyb-selection) sequenced by 5 SNPs identified, of which 9 are ‘rare’ (carrier freq. < 4%): 5 with one carrier, 3 with two carriers, 1 with one carrier. Create ‘in-silico’ pools: Randomize individuals’ identity in each pool Determine number of carriers Sample frequencies based on observed frequencies in the single pool for the same number of carriers

Results (dataset 1) Pooling dataset from: [Out et al., Human Mutation 2009] Cartoon:

Results (dataset 1) One and two carriers: real pooling results match theoretical model Three carriers: real pooling are worse due to one problematic SNP When constructing pools of at most 2 people, results match theoretical model # tests % with perfect reconstruction

Results (dataset 2) 1000 Genomes Data: Pilot 3 data: Exome Sequencing, ~1000 genes, ~700 people Filtered: 633 rare SNP (MAF < 2%), of which 20 contained rar heterozygous 364 individuals sequenced by Illumina Create ‘in-silico’ pools: Randomize individuals’ identity in each pool Determine number of carriers Sample and individual from the pool at random. Then sample a read from the set of reads for this individual.

Results (dataset 2) Results from derived from actual 1000 genomes read match Simulations from our statistical model

Generic approach: puts together sequencing and CS to identify rare allele carriers. Naturally deals with all possible scenarios of multiple carriers and heterozygous or homozygous rare alleles. Much higher efficiency over the naive approach. Can be combined with barcoding Manuscript available on arxiv: arxiv v1 [N. Shental, A. Amir and O. Zuk, in revision] Comseq Package: Code Available at: [simulating, designing experiments, reconstructing genotypes..] Conclusions

Thank You Noam Shental Amnon Amir