A systematic assessment of the population genetic evidence for selection across twenty brain related phenotypes

Transcription

1 A systematic assessment of the population genetic evidence for selection across twenty brain related phenotypes Lea K. Davis, PhD Assistant Professor of Medicine Vanderbilt Genetic Institute Vanderbilt University Medical Center 1

2 Fantastic students and collaborators Katya Khramtsova Evan Beiter Tony Capra Barbara Stranger Dan Stein Corinne Simonti Jim Knowles Emile Chimusa Celia Van Der Merwe 2

3 Presentation Overview Research questions and study motivation Signatures of selection Methods and Results Integrating eqtls to further understand the biology driving selection Future directions 3

4 Research question and study motivation 4

5 How do psychiatric traits with reduced fecundity persist in the population and demonstrate such high heritability? Early age of onset Reduced fecundity Moderate to high prevalence High heritability 5

6 An old question Several explanations have been offered Ancestral neutrality Perhaps reduced fecundity is a modern phenomenon? Khalifeh et al., 2015, Psych. Medicine Balancing selection Heterozygote advantage (i.e., sickle-cell anemia and malaria resistance) Pleiotropy Negative selection of a negatively correlated trait Positive selection of a positively correlated trait Stabilizing selection Polygenic mutation-selection balance Vg = Vm/s 6

7 Viewing the paradox through the lens of genetic architecture Highly polygenic Consistent with liability threshold model High level of genetic correlation Large effect variants very rare Majority of SNP-based heritability for OCD accounted for by SNPs with high MAF Replicated this finding in a subsequent OCD sample (under review) Sullivan, Daly, O Donnovan, 2012, Nat. Gen Rev 7

8 meanwhile in evolutionary genomics labs Efforts to refine detection of recent (~25,000 years) positive selection across the genome Increased interest in detecting signals of polygenic selection Methods developed to test very recent (~2,000 years) selection Improved sequencing of Neanderthal and Denisovan genomes 8

9 Hypothesis: Polygenic selection has acted on neuropsychiatric traitassociated alleles through selection acting on genetically correlated phenotypes 9

10 Signatures of selection 10

11 Hard Sweeps and Soft Sweeps Novembre and Han (2012) 1. Integrated Haplotype Score (ihs) utilizes the haplotype length as a signature of positive selection (Voight et al., 2013) 2. Large negative values indicate unusually long haplotypes carrying the derived allele; large positive values indicate long haplotypes carrying the ancestral allele 11

12 Fixation index measuring population differentiation F statistics describe the deviation in heterozygosity compared to expectation based on Hardy-Weinberg equilibrium F = 1- (observed number of heterozygotes/expected number of heterozygotes) Fst compares rate of heterozygosity between two subpopulations (i.e., Ceu and Asn) John Novembre, and Eunjung Han Phil. Trans. R. Soc. B 2012;367:

13 Polygenic Adaptation 13 Schienfeldt and Tishkov, 2013, Nat Rev Genet

14 Signatures of Polygenic Selection Coordinated shifts in frequency across many trait-associated variants Tests over-dispersion of risk variants compared to models of drift that account for population structure Reduction in density of singleton events at trait-associated loci 14

15 Methods and Results 15

16 Summary of Analyses 1. Test for enrichment of hard sweeps among trait-associated SNPs compared to a null distribution of matched SNPs 1. SNPs with extreme haplotype score (ihs) 2. SNPs with extreme population differentiation (Fst) 2. Test for signature of polygenic selection among trait-associated SNPs compared to model of neutral genetic drift 3. Test for enrichment and direction of SDS 4. Test for enrichment of trait-associated SNPs in regions of the genome depleted of Neanderthal alleles 5. In silico functional analyses to derive potential biological drivers of selection 16

17 GWAS Summary Statistics The complete set of GWAS summary statistics for twenty four phenotypes were obtained from consortium websites (i.e., PGC, IAGP, ENIGMA, T2D, GIANT, IBD). Psychiatric Disorders (10): Attention deficit and hyperactivity disorder, anorexia nervosa, autism spectrum disorders, bipolar disorder, major depression, schizophrenia, anxiety disorder, Alzheimer s disease, Tourette Syndrome, obsessive-compulsive disorder Personality Traits (2): Extraversion, neuroticism Brain Structure Volumes (8): Putamen, nucleus accumbens, amygdala, caudate nucleus, hippocampus, pallidum, thalamus, intracranial volume. Non-psychiatric complex traits (4): Type 2 diabetes, inflammatory bowel disease, height, body mass index We selected SNPs modestly associated with each trait at multiple nominal p-value thresholds (p< 10-3 and p<10-4 ) for subsequent analysis. 17

18 ihs and Fst Enrichment Analysis Workflow Matched by: minor allele frequency (± 3%) gene density (± 50%) distance to nearest gene (± 50%) Empirical p-value 18

19 ihs and Fst Enrichment Analysis Results Phenotypes Population Differentiation (Fst) Linkage Disequilibrium (ihs) Neuropsychiatric Traits #SNPs F st > 0.30 F st > 0.56 #SNPs ihs > 2.0 ihs > 2.5 ADHD Alzheimer s Anorexia Anxiety Autism Bipolar Disorder 1847 <0.002* Extraversion MDD Neuroticism OCD Schizophrenia TS Non-neuropsychiatric Traits # BMI Height Inflammatory Bowel Disease Type 2 Diabetes 2037 <0.002* # Multiple p-value thresholds imposed to roughly equal the number of SNPs included in analysis of neuropsychiatric phenotypes and determine how results change with number of SNPs

20 ihs and Fst Analysis Summary Expectations of non-neuropsych phenotypes: Amato et al., 2011 (very modest Fst differences in height-associated alleles) Lohmueller et al., 2006 (no differences in Fst in height-associated alleles) Polimanti et al., 2016 (functional networks instead of GWAS results) Consistent with expectations for polygenic phenotypes, no trait exhibited significant enrichment of recent strong positive selection (i.e., hard sweeps) as measured by the integrated haplotype score Significant evidence of population differentiation for bipolar disorder and type 2 diabetes at Fst > 0.30, trend at Fst > 0.56 Residual population stratification unlikely cause LD-score regression intercept low for both bipolar and T2D Type 2 Diabetes (1.0088) Bipolar disorder (1.027) 20

21 Important caveats for ihs and Fst Recently introgressed haplotypes (i.e., Neanderthal or Denisovan) also introduce unusually large haplotypes SNPs with high ihs Can be mistaken for positive selection *Potential pitfall for enrichment analysis* Recommended to remove SNPs falling in known regions of introgression Made a big difference in our results! 21

22 Polygenic Analysis Pipeline Null SNPs matched on MAF and B-value* Empirical p-value *McVicker G, Gordon D, Davis C, Green P (2009) Widespread Genomic Signatures of Natural Selection in Hominid Evolution. PLoS Genetics. 22

23 Comparison phenotypes for context N = 180 N = 4 N = 32 N = 65 N = 140 N = 135 Berg and Coop, 2014, Plos Gen. 23

24 Results of polygenic adaptation analysis on neuropsychiatric phenotypes Phenotypes #SNPs Qx P(Qx) #SNPs Qx P(Qx) Neuropsychiatric Traits P < 5.0 x 10-3 P < 5.0 x 10-4 Alzheimer s 1, Anorexia Anxiety Autism Bipolar Disorder Extraversion * MDD Neuroticism OCD Schizophrenia <0.001* 1, <0.001* Tourette Syndrome P < 5.0 x 10-4 P < 5.0 x 10-6 Non-neuropsychiatric Traits Inflammatory Bowel Disease <0.001* Type 2 Diabetes <0.001* P < 5.0 x 10-6 P < 5.0 x 10-8 BMI * Height 2, <0.001* 1, <0.001* Extraversion Schizophrenia 25

25 Results of polygenic adaptation analysis on brain structure volume phenotypes Phenotypes #SNPs Qx P(Qx) #SNPs Qx P(Qx) Brain Structure Volumes P < 5.0 x 10-3 P < 5.0 x 10-4 Accumbens 1, Amygdala 1, Caudate Nucleus 1, Hippocampus 1, <0.001* Intracranial Volume 1, Pallidum 1, Putamen 1, <0.001* Thalamus 1, Hippocampus Putamen 26

26 Increased stringency of clumping thresholds Phenotypes #SNPs Qx P(Qx) #SNPs Qx P(Qx) Neuropsychiatric Traits P < 5.0 x 10-3 P < 5.0 x 10-4 Alzheimer s 1, Anorexia Anxiety 1, Autism Bipolar Disorder 2, Extraversion 1, * MDD 1, Neuroticism 1, OCD Schizophrenia 2, <0.001* * Tourette Syndrome 1, Brain Regions P < 5.0 x 10-3 P < 5.0 x 10-4 Accumbens Amygdala Caudate Nucleus Hippocampus <0.001* Intracranial Volume Pallidum Putamen <0.001* Thalamus q Controls P < 5.0 x 10-4 P < 5.0 x 10-6 Inflammatory Bowel Disease Type 2 Diabetes <0.001* P < 5.0 x 10-6 P < 5.0 x 10-8 BMI * Height 1, <0.001* <0.001* R 2 < 0.1, 1000 Kb 26

27 Singleton Density Score Analysis Results Compared mean SDS of trait associated alleles against distribution of mean SDS of 500 sets of matched null SNPs Empirical p-value Height (p=0.008), schizophrenia (p=0.004), hippocampus (p=0.002) tsds analysis demonstrates direction of effect Replicated height finding Height SCZ Hippocampus 27

28 Summary of Polygenic Adaptation Results Over-dispersion of risk alleles Initial evidence of polygenic adaptation in: Schizophrenia Extraversion Hippocampus volume Putamen volume T2D IBD BMI Height Single density score Secondary evidence of polygenic adaptation in Schizophrenia Hippocampus Height BMI Direction of effect SCZ protective (hippocampus volume reducing) alleles demonstrate evidence of very recent selection Robust to clumping thresholds 28

29 Integrating eqtls to further understand the biology driving selection 29

30 Data integration 30 Schienfeldt and Tishkov, 2013, Nat Rev Genet

31 Enrichment of eqtls from brain and immune tissues among trait-associated SNPs (excluding HLA) 32

32 Conclusions future testable hypotheses 1. We find no evidence of strong, sweeping selection driving allele frequencies of neuropsychiatric-associated alleles 2. Convergent evidence of polygenic selection in schizophrenia, extraversion, hippocampus, and putamen volume 3. Immune adaptation may be driving findings in brain structure volumes 4. Brain-specific adaptation may be driving selection in schizophrenia 32

33 Study Limitations and Caveats 1. Residual population stratification 2. Differential power between traits makes comparisons difficult 3. Pleiotropy both a feature and a bug 4. One piece of a complex story 33

34 Density Future directions Developing approaches to test predictions consistent with polygenic stabilizing or directional selection in biobank data Number of disease codes PRS 34

35 Acknowledgements The individuals who have selflessly participated in genetic research The investigators and consortia who have graciously shared their data The students and collaborators who have worked with us! Dr. Jeremy Berg 35