A Genome-wide association study of Copy number variation in

schizophrenia

Andrés Ingason

CNS Division, deCODE Genetics.

Research Institute of Biological Psychiatry, Mental Health Centre Sct. Hans, Copenhagen University Hospital.

Common variant – common disease

• The development of SNP microarrays providing genotypes for hundreds of thousands of markers at an affordable price has paved the way for the identification of at-risk genes for many common diseases

Nicotine addiction / lung cancer

Schizophrenia– all quiet on the western front

• Many studies using large samples have been conducted in schizophrenia (and other psyciatric disorders for that matter) since the introduction of genome-wide SNP microarrays, without producing unanimous positive results.

Why are psychatric diseases different?Why do we not find susceptibility variants?

• Reduced fecundity, associated with disorders such as autism, schizophrenia and mental retardation, places negative selection pressure on haplotypes carrying risk alleles.

• The genetic risk of diseases under such pressure is less likely to be accounted for by common low penetrant variants.

• Instead rare, more penetrant mutations at many sites across the genome may be involved and account for a proportion of the genetic risk.

Can we find rare high risk variants with current technology?

• In contrast to rare single nucleotide mutations, rare copy number variations (CNVs) can be detected using genome-wide arrays.

• This has led to the identification of CNVs associated with mental retardation and autism.

• Walsh et al. report very recently of CNVs being involved in schizophrenia as well

How can the prevalence of schizophrenia be reconciled with the

existence of rare high risk CNVs under negative selection ?

• Genomic regions flanked by low copy repeats (LCR) prone to non-allelic homologous recombination (NAHR) provide sites for recurrent CNV events

• These CNVs should appear more often as de novo upon inspection when under negative selection pressure.

Study designIdentification of de novo CNVsDosageMiner, software that identifies CNVs from chip data~2200 trios genotyped on Illumina chips~5700 parent-offspring pairs genotyped on Illumina chips~10100 transmissions to identify de novo deletions~4400 transmissions to identify de novo duplicationsQuality Control

Association Study (SGENE sample)~1,500 Schizophrenia patients genotyped on Illumina chips~30,000 controls genotyped on Illumina microarrays

DosageMiner

• Signal intensities from SNPs on Illumina chips are normalized to reduce noise

• Sample effect• SNP/genotype effect• GC content effect

DosageMiner

Black dots: normalized intensity of SNP

Red dots: moving average intensity

Blue line: Deletion, as defined by DosageMiner

DosageMiner

Black dots: normalized intensity of SNP

Red dots: moving average intensity

Green line: Duplication, as defined by DosageMiner

Quality ControlSize matters

Quality ControlSize matters

Quality ControlSample matters

De novo CNVs in trios

• For the 2189 trios, we query the offspring for all deletions and duplications (as reported by DosageMiner) in autosomal chromosomes spanning at least 10 consecutive SNP markers.

• We use the overall variance in SNP intensities for each offspring, to estimate the quality of the intensity data

De novo CNVs in trios

2057 trio-offspring with mean variance <300(arbitrary cutoff value approx. 2SD>mean of all samples)

Chip Trios CNVs 317k 891 17.046DUO 809 15.683370k 357 14.455

De novo CNVs in trios

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370k PNs

De novo CNVs in triosNoise vs. CNVs pr. PN

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De novo CNVs in trios

Chip 317k DUO 370k

PN ratio 0.74 0.77 0.64

CNVs 2028 1589 698

CNV ratio 0.12 0.10 0.05

Using only samples with <10 reported CNVs we retain most of the power (73% of samples) while we get rid of most of the noise.

De novo CNVs in triosPower of detection (n=1500)

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population frequency

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De novo CNVs in trios

Offspring

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Mother

Offspring

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De novo deletion or false calling

CNV length Accuracy N CNVs %

10-14 0.42 326 137 0.55

15-19 0.61 69 42 0.17>19 0.89 80 71 0.28

Data from 317k trios, chr1-4

De novo CNVs in trios

Chip 317k DUO 370k

CNVs >9 SNP 2028 1589 698

CNVs >19 SNP 439 333 122

>19 SNP / >9 SNP 0.22 0.21 0.18

De novo CNVs in trios

De novo CNVs in parent-offspring pairs

• We used LOH analysis combined with DosageMiner data to identify de novo deletions in offspring where only one parent is genotyped.

• When 2 or more inheritence errors with genotyped parent were found in a stretch of homozygous markers in offspring, and DosageMiner revealed deletion in offspring and not in genotyped parent we conclude that the deletion is de novo.

De novo CNVs in parent-offspring pairs

Genotyped parent

Offspring

De novo CNVs - results

• We found 70 de novo CNVs at 66 loci• 55 deletions and 15 duplications• 4 deletions observed de novo twice• 20 deletions and 6 duplications observed in

the 1600 vs. 30000 sample• 8 deletions and 2 duplications observed in the

schizophrenia sample• 3 deletions showed nominal association to

schizophrenia

De novo CNVs - resultsChromosome Deletions Duplications CNVs

Chr1 3 1 4 (3)

Chr2 4 2 6

Chr3 4 1 5

Chr4 1   1

Chr5 2   2

Chr6 2   2

Chr7 6 3 9

Chr8 1   1

Chr9 1   1

Chr10 3   3

Chr11 1 1 2

De novo CNVs - resultsChromosome Deletions Duplications CNVs

Chr12 1 1 2

Chr13      

Chr14      

Chr15 3 2 5 (4)

Chr16 10 (7)   10 (7)

Chr17 4 1 5

Chr18   1 1

Chr19 2 1 3

Chr20 4 (3)   4 (3)

Chr21   1 1

Chr22 3   3

Premutations could provide another mechanism for recurrent CNVs in severe psyciatric disorders.

Father

Mother

Offspring

Complete GW-CNV studies

• Next step is to finish inspecting the 4000 or so candidate CNVs from the 1500 trios to give us a fixed set of confirmed CNVs of >50-100 Kb in >0.001 population frequency.

• This is already well underway, and when finished can be used as a basis for studies in other diseases/traits as well.

Take home message

• Recurrent CNVs may be involved in schizophrenia as well as autism and mental retardation and other severe psychiatric disorders where reduced fecundity is observed

• Analysing CNVdata from GW SNP chips requires much effort to read through the noise.

Thanks to....

• all my coworkers at decode

• the collaborators in SGENE

• EC for funding SGENE

• the participants of the study

• the organisers for inviting me

• you for listening