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

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Fantastic students and collaborators

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Evan Beiter

Barbara Stranger

Katya Khramtsova

Tony Capra

Celia Van Der MerweEmile ChimusaJim Knowles

Dan Stein

Corinne Simonti

Presentation Overview

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Research questions and study motivation

Signatures of selection Methods and Results

Integrating eQTLs to further understand the biology driving

selection

Future directions

1. 2. 3.

4. 5.

Research question and study motivation

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How do psychiatric traits with reduced fecundity persist in the population and demonstrate such high heritability?

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Early age of onset

Moderate to high prevalence

Reduced fecundity

High heritability

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

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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)

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Sullivan, Daly, O’Donnovan, 2012, Nat. Gen Rev

…meanwhile in evolutionary genomics labs

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Efforts to refine detection of recent (~25,000 years) positive selection

across the genomeIncreased interest in detecting signals

of polygenic selection

Methods developed to test very recent (~2,000 years) selection

Improved sequencing of Neanderthal and Denisovan genomes

Hypothesis: Polygenic selection has acted on neuropsychiatric trait-associated alleles through selection acting on genetically correlated phenotypes

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Signatures of selection

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Hard Sweeps and Soft Sweeps

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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

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., Ceuand Asn)

12John Novembre, and Eunjung Han Phil. Trans. R. Soc. B

2012;367:878-886

Polygenic Adaptation

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Schienfeldt and Tishkov, 2013, Nat Rev Genet

Signatures of Polygenic Selection

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• 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

Methods and Results

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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

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GWAS Summary Statistics

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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.

iHS and Fst Enrichment Analysis Workflow

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Matched by: • minor allele

frequency (± 3%) • gene density (±

50%)• distance to

nearest gene (±50%)

Empirical p-value

Phenotypes Population Differentiation (Fst) Linkage Disequilibrium (iHS)

Neuropsychiatric Traits #SNPs Fst > 0.30 Fst > 0.56 #SNPs |iHS| > 2.0 |iHS| > 2.5

ADHD 1036 0.048 0.376 514 0.448 0.240

Alzheimer’s 3863 0.482 0.362 1613 0.086 0.032

Anorexia 4247 0.132 0.168 1512 0.056 0.286

Anxiety 2504 0.066 0.332 1317 0.026 0.368

Autism 3467 0.208 0.390 1383 0.464 0.120

Bipolar Disorder 1847 <0.002* 0.036 924 0.132 0.090

Extraversion 3316 0.186 0.028 1586 0.286 0.242

MDD 1162 0.146 0.368 595 0.034 0.110

Neuroticism 3306 0.030 0.162 1566 0.138 0.190

OCD 3271 0.490 0.362 1262 0.276 0.204

Schizophrenia 8759 0.378 0.140 3845 0.026 0.028

TS 4246 0.122 0.250 1694 0.406 0.226

Non-neuropsychiatric Traits#

BMI 179 0.260 0.124 107 0.032 0.422

Height 838 0.150 0.394 405 0.480 0.028

Inflammatory Bowel Disease 1250 0.314 0.388 482 0.150 0.452

Type 2 Diabetes 2037 <0.002* 0.030 1040 0.474 0.332

#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

iHS and Fst Enrichment Analysis Results

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

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!

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Polygenic Analysis Pipeline

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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.

Comparison phenotypes for context

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N = 65

N = 140 N = 135

N = 32

N = 4N = 180

Berg and Coop, 2014, Plos Gen.

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 1,777 83.80 0.014 259 46.81 0.688

Anorexia 1196 68.96 0.112 166 51.41 0.556

Anxiety 1420 66.41 0.141 183 59.21 0.235

Autism 1149 58.02 0.334 138 42.69 0.833

Bipolar Disorder 2634 57.26 0.310 449 54.30 0.356

Extraversion 1485 88.04 0.001* 196 49.71 0.545

MDD 1806 63.31 0.146 224 70.11 0.065

Neuroticism 1617 77.52 0.156 205 75.69 0.029

OCD 1126 52.30 0.482 141 54.69 0.565

Schizophrenia 3307 208.36 <0.001* 1,029 101.30 <0.001*

Tourette Syndrome 1441 73.51 0.082 209 42.79 0.828

Non-neuropsychiatric Traits

P < 5.0 x 10-4 P < 5.0 x 10-6

Inflammatory Bowel Disease 423 101.13 <0.001* 81 77.25 0.017

Type 2 Diabetes 478 117.77 <0.001* 42 50.61 0.525

P < 5.0 x 10-6 P < 5.0 x 10-8

BMI 246 78.20 0.010 97 82.61 0.003*

Height 2,002 303.29 <0.001* 1,087 209.27 <0.001*

Results of polygenic adaptation analysis on neuropsychiatric phenotypes

SchizophreniaExtraversion

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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 1,152 61.61 0.203 134 63.86 0.162

Amygdala 1,164 55.05 0.378 137 53.34 0.470

Caudate Nucleus 1,210 48.33 0.626 153 56.18 0.348

Hippocampus 1,237 108.98 <0.001* 177 79.65 0.010

Intracranial Volume 1,249 54.37 0.386 175 50.79 0.526

Pallidum 1,183 52.76 0.559 176 48.78 0.625

Putamen 1,203 115.76 <0.001* 155 72.69 0.029

Thalamus 1,123 70.98 0.088 153 57.51 0.288

Results of polygenic adaptation analysis on brain structure volume phenotypes

Hippocampus Putamen

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Increased stringency of clumping thresholds

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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 1,471 73.11 0.052 223 51.29 0.518

Anorexia 971 57.09 0.372 149 47.70 0.649

Anxiety 1,184 66.30 0.153 171 64.52 0.105

Autism 980 47.11 0.705 126 47.38 0.727

Bipolar Disorder 2,235 55.04 0.369 395 47.10 0.662

Extraversion 1,261 84.67 0.004* 184 54.63 0.367

MDD 1,629 52.10 0.466 209 62.10 0.163

Neuroticism 1,382 66.70 0.442 186 72.90 0.051

OCD 985 49.58 0.572 133 47.36 0.654

Schizophrenia 2,336 172.77 <0.001* 747 80.06 0.012*

Tourette Syndrome 1,215 76.75 0.042 192 45.23 0.781

Brain Regions P < 5.0 x 10-3 P < 5.0 x 10-4

Accumbens 999 55.25 0.355 129 57.94 0.335

Amygdala 1004 56.37 0.317 130 57.08 0.318

Caudate Nucleus 1040 53.79 0.390 140 56.02 0.354

Hippocampus 1043 90.99 <0.001* 161 75.86 0.017

Intracranial Volume 1045 52.12 0.459 157 47.37 0.626

Pallidum 1004 51.03 0.577 162 47.41 0.680

Putamen 1020 106.94 <0.001* 137 64.33 0.125

Thalamus 958 70.24 0.083 144 55.49q 0.348

Controls P < 5.0 x 10-4 P < 5.0 x 10-6

Inflammatory Bowel Disease 310 74.38 0.020 56 62.98 0.146

Type 2 Diabetes 433 110.42 <0.001* 37 52.97 0.422

P < 5.0 x 10-6 P < 5.0 x 10-8

BMI 184 70.77 0.048 72 82.53 0.007*

Height 1,360 245.80 <0.001* 738 159.36 <0.001*

R2 < 0.1, 1000 Kb

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

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Height SCZ Hippocampus

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

• Robust to clumping thresholds

• 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

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Integrating eQTLs to further understand the biology driving selection

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Data integration

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Schienfeldt and Tishkov, 2013, Nat Rev Genet

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

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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

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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

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Future directions

• Developing approaches to test predictions consistent with polygenic stabilizing or directional selection in biobank data

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PRS

Number of disease codes

Den

sity

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

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