Evaluating mutation intolerance and natural selection in schizophrenia GWAS data
Antonio F. Pardiñas The CLOZUK project
▪ Genetics of treatment-resistance in SCZ.
▪ Initiated with CLOZUK1 (N=7,000). ▪ Expanded with CLOZUK2 (N=8,000). ▪ Collaboration with Novartis & Leyden Delta.
▪ Anonymised blood samples genotyped. ▪ Controls obtained through collaboration. The CLOZUK project
Dataset Status Samples in GWAS Reference
CLOZUK1 Cases 5,528 Hamshere et al. 2013 CLOZUK2 Cases 4,973 Pardiñas et al. BioRxiv CardiffCOGS1 Cases 512 Rees et al. 2014 CardiffCOGS2 Cases 247 Pardiñas et al. BioRxiv WTCCC2 Controls 4,641 WTCCC, 2007 Cardiff Controls Controls 1,078 Green et al. 2010 Generation Scotland Controls 6,480 Amador et al. 2015 T1DGC Controls 2,532 Hilner et al. 2010 POBI Controls 2,516 Leslie et al. 2015 TWINSUK Controls 2,426 Moayyeri et al. 2013 QIMR Controls 2,339 Wright et al. 2004 TEDS Controls 1,752 Haworth et al. 2013 GERAD Controls 778 Harold et al. 2009 Meta-analysis (CLOZUK + PGC)
40,675 cases and 64,643 controls.
143 independent GWS loci (50 novel) Gene-set analyses
Set name Source N (genes) P-value P-value (FWER- corrected)
LoF-intolerant genes ExAC 2921 4.07 x 10-16 2.02 x 10-13
* MGI gene sets curated by Pocklington et al. 2015. Gene-set analyses
Set name Source N (genes) P-value P-value (FWER- corrected)
LoF-intolerant genes ExAC 2921 4.07 x 10-16 2.02 x 10-13
FMRP targets Darnell et al. 2011 798 1.92 x 10-8 0.00001
Abnormal behaviour MGI database* 1939 1.20 x 10-6 0.00018
Abnormal nervious system MGI database* 201 2.27 x 10-5 0.00303 electrophysiology Voltage-gated Ca++ Müller et al. 2010 196 8.01 x 10-5 0.01144 channel complexes 5HT-2C receptor Becamel et al. 2002 16 2.26 x 10-4 0.02924 Abnornal long term MGI database* 142 2.32 x 10-4 0.02982 potentiation * MGI gene sets curated by Pocklington et al. 2015. How much signal explained by gene sets? (Gusev et al. 2016, de Leeuw et al. 2016)
Genic SNPs accounted 2 for 64% h SNP SNPs in CNS-related 2 genes: 39% h SNP SNPs in LoF-intolerant 2 genes : 30% h SNP
Summary (1)
Gene set analysis Partitioned heritability LoF-intolerant genes are LoF-intolerant genes explain 2 more enriched than any set 30% h SNP and account for from a curated collection. enrichment in other sets.
▪ Robust enrichment of FMRP targets.
▪ Utility of LoF-intolerance (Lek et al. 2016) for highlighting genes with common risk alleles. Risk alleles of large effect recurrently eliminated from the population (Rees 2011; Kirov 2012, 2014)
Risk alleles of small effect persist at common frequencies (ISC 2009, PGC 2014). Why do risk alleles persist?
▪ Usually diagnosed in young adults. ▪ Reduces life expectancy by 10-20 years. ▪ Patients have 30% of the fecundity of the general population (Power 2013)
▪ Effects too small to be selected against. ▪ Potential (past) beneficial effects caused these alleles to be selected for. Positive selection in psychiatric disorders
Schizophrenia ASD Depression Risk alleles might Risk alleles linked Risk allelles linked be advantageous to cognition might to sun tolerance for mating (Crow be favoured by might have been 1993, Shaner 2004). assortative mating benefitial for Positive effects on (Crespi 2016) or human populations creativity (Kyoga ancient selection outside of Africa. 2011, Power 2015). (Polimanti 2017). (Simonti 2016) Comparing GWAS results with selection signals
▪ CLOZUK+PGC summary statistics. ▪ LDSR partitioned heritability.
▪ SNP-based selection metrics: ▫ iHS, CMS (~30,000 yrs BP; Grossman et al. 2013) ▫ XP-EEH (~50,000 yrs BP; Sabeti et al. 2007) ▫ CLR (>60,000 yrs BP; Huber et al. 2016) ▫ B (Background selection; McVicker et al. 2009) Results
Enrichment Enrichment P-value P-value Annotation (genomic top 2%) (genomic top 1%)
Background -4 selection 1.801 0.001 2.341 9.9×10
iHS 0.973 0.946 0.980 0.974
CMS 0.053 0.001 0.037 0.006
XP-EEH 0.621 0.034 0.383 0.303
CLR 0.401 6.5×10-5 0.173 5.8×10-7 Follow-up analyses
▪ Is the result confounded by thresholding? ▫ No, supported by quantitative LDSR.
▪ Is the result confounded by functionality? ▫ No, supported by significant LDSR tau-C.
▪ Is there any mechanistic explanation? ▪ Is it feasible for schizophrenia? Background selection (BGS) occurs in genomic regions with low recombination and reduces genetic diversity:
Fn FN
Neutral alleles: Deleterious allele: Nearly-neutral model (Ohta 1973)
λ = Fixation probability.
Ne = Effective population size. s = Selection coefficient.
Selection is more effective in large, diverse populations.
Neutrality limit: abs(2×Ne×s) > 1
Mutations in loci with reduced effective size (i.e. by BGS) can be under the effect of genetic drift. Reduced genetic diversity allows weakly deleterious alleles to drift to high frequencies in the population:
Fn FN
Neutral alleles: Deleterious alleles: Liability-threshold model (Dempster 1950, Wray 2010)
k = Population prevalence.
Relates effect size (OR), fecundity and selection (s). k OR=1.05; s=-2.26×10-4
Ne = 4,500 (Gravel et al. 2011) No BGS: 2×Ne×s = -2.032 With BGS: 2×Ne×s×B < -0.238
No effect for CNVs (s=-0.2) Feasibility in schizophrenia (E. Santiago & A. Caballero)
Simulations Trait reduces fecundity. Causal genetic locus. Range of effective sizes. Mutations appear with small effect sizes (OR: 1.05-1.60). Individuals are sampled from the population using actual case-control frequency.
Ne decreases SNP heritability. Feasibility in schizophrenia (E. Santiago & A. Caballero)
Simulations Trait does not affect fecundity. Causal genetic locus. Range of effective sizes. Mutations appear with small effect sizes (OR: 1.05-1.60). Individuals are sampled from the population using actual case-control frequency.
Ne increases SNP heritability. Summary (2)
Positive selection Background selection All metrics “depleted” in LDSR Enriched in LDSR analysis. analysis. No effect in human Consistent with follow-up and evolutionary timescales. mechanistic hypothesis.
▪ Drift, not selection, explains the findings.
▪ Compatible with reduction in fecundity and enrichment in LoF-intolerant genes. Acknowledgements
Psychosis team Core team Other collaborators
James Walters Lucinda Hopkins Stephan Ripke Peter Holmans Naomi Wray Andrew Pocklington Enrique Santiago Valentina Escott-Price HPC team Armando Caballero Michael O’Donovan Michael Owen Mark Einon CRESTAR EU-FP7 THANKS! Any questions?
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“Brain” icon by Creative Stall from The Noun Project. “Human Mind” icons by Thomas Helbig from The Noun Project.