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Василий

РаменскийUCLA Center for Neurobehavioral Genetics

2 октября

2014 г.

UCLA Center for Neurobehavioral Genetics, Los Angeles USA

University of California Los AngelesCenter for Neurobehavioral Genetics

Introduction

(a) Contribution of genetic factors// Genetic ≠

inherited: de novo mutations

(b) Non-Mendelian inheritance

What is a complex phenotype?

(a) Contribution of genetic factors// Genetic ≠

inherited: de novo mutations

(b) Non-Mendelian inheritance

SCZ, schizophrenia; ASD, autistic spectrum disorders; BP, bipolar disorder; AD, Alzheimer’s disease, ADHD, attention deficit hyperactivity disorder; TS, Tourette syndrome; OCD, obsessive compulsive disorder; ID, intellectual disability

What is a complex phenotype?

-- Heritable quantitative traits Examples: working memory, executive function, sociability,

attention, temperament, brain measures

-- Hypothesis: individuals diagnosed with conditions like ASD or SCZ may be at the extreme end of distribution for some endophenotypes; risk prediction

-- Hope: simpler genetic architectures than clinical diagnoses, easier to dissect

Endophenotypes: intermediate layer

Tactical-- Loci involved (in an individual and in the population)-- Causal allele spectrum at each loci: rare, common…-- Loci interaction: common allele as a modifier of rare ones

Strategical-- Risk prediction-- Identification of disease pathways treatment

Goals of genetic analysis

Experiment-- Genome-wide association analysis (GWAS)-- Sequencing: DNA-Seq, RNA-Seq, ChIP-Seq, …

Data analysis-- Bioinformatics: variant calling and quality control-- Bioinformatics: variant annotation and functionality prediction-- Statistical genetics: single variant or gene level association

analysis

Validation-- Followup genotyping-- Model organisms, in vitro experiments

Methods

Genetic architecture of disease

Sullivan et al. 2012; Mitchell 2014

-- AD, BP, CSZ: allelic spectrum and aetiological role for both rare and common variation

-- ASD, SCZ: variation at hundreds of different genes involved; organized in pathways

-- AD: unexpected cholesterol metabolism and the innate immune response pathways

-- ASD: de novos-- The same SNVs in ASD, SCZ, epilepsy, ADHD, ID and other (Mitchell

2014)-- SCZ: GWAS points to verified and predicted targets of non-coding

RNA miR-137

Genetic architecture of disease

Genetic architecture of disease

1) High risk rare alleles causing Mendelian disease-- Mostly coding: nonsense, missense, splice site, indels-- Examples: APP or PS mutations in AD; LRRK2 mutations in

Parkinson’s disease2) Moderate risk low frequency alleles-- Example: GBA mutations in Parkinson’s disease-- Most difficult to detect earlier3) Low risk common alleles-- Detectable by GWAS-- Examples: SNCA or MAPT inParkinson’s; CLU, PICALM , CR1: AD

-- Rarely coding; gene regulation?

Genetic architecture of disease

4) High risk common alleles-- Examples: APOE mutations in AD; complement H factor in

macular degeneration-- Easily identifiable by GWAS-- Late onset diseases

5) De novo mutations-- Example: autism-- Diseases which affect reproductive fitness-- Requires trio sequencing

Genetic architecture of disease

6) Low risk rare variants-- Expected to affect gene regulation, splicing etc.-- Most difficult to identify, require:

-- large number of cases and controls, -- reliable bioinformatic and statistical genetics methods; -- functional followup

“Auxiliary” alleles:

7) Alleles in phenotype modifier genes-- Example: modifier genes in cystic fibrosis8) Alleles in epistasis with the disease one-- Example: Bardet-Biedl syndrome

Genetic architecture of disease

Published GWA at p≤5X10-8 for 18 trait categories (07/2012)

NHGRI GWA Catalogwww.genome.gov/GWAStudieswww.ebi.ac.uk/fgpt/gwas/ 

Stories

I. Allelic Spectrum of Metabolic Syndrome (ASMS) in the Northern Finland Birth Cohort 1966 (NFBC66)

Genetically homogenous Finnish population

-- Finns descend from small number of founders 4000- 2000 years ago

-- Internal migration in the 17th century created small subisolates

-- Grew rapidly with little further migration

-- Genetically homogenous sub-populations

Sabatti et al., 2009

NFBC66:-- genetic isolate that is relatively homogeneous in genetic background

(extensive LD) and environmental exposures;-- quantitative traits: no biases characteristic of case-control studies;-- birth cohort: no age as a potential confounder; longitudinal data;-- founder population: potential enrichment in damaging variants (not

pertinent for GWAS, though)-- genotypes on ~329K SNPs in 4,763 individuals (out of 12,058 live births)

Nine heritable traits (risk factors for cardiovascular disease or T2D):-- body mass index (BMI, 1); fasting serum concentrations of lipids:

triglycerides (TG), HDL and LDL (2-4); indicators of glucose homeostasis (glucose (GLU), and insulin (INS)) and inflammation (CRP) (5-7); systolic (SBP) and diastolic (DBP) blood pressure (8-9);

-- Extreme values of these traits, in combination, identify a metabolic syndrome, hypothesized to increase risks for both CVD and T2D

NFBC66 and metabolic traits

-- 31 associations to 6 traits passing a 5x10-7 threshold after correction, mostly replicating earlier findings;

-- 9 previously unreported associations

-- “Five of these associations—HDL with NR1H3 (LXRA), LDL with AR and FADS1-FADS2, glucose with MTNR1B and insulin with PANK1— implicate genes with known or postulated roles in metabolism”;

-- the currently identified loci, singly and cumulatively, explain littleof the trait variability in NFBC1966 (at most ~6% based on multivariateregression);

-- contribution of rare variants?

GWAS results in NFBC66

Sabatti et al., 2009

Sabatti et al., 2009

GWAS in Finnish population cohorts: known genes and environment explained little of trait variance

Sabatti et al., 2009

ASMS: preliminary evidence

ASMS sequencing: overview-- Samples: 6,121 persons: 4,447 NFBC + 835 FUSION controls + 839

FUSION cases (Finland-United States Investigation of NIDDM Genetics)

-- Regions of interest: 78 genes from 17 loci on 10 chromosomes, UTRs+coding, ~270Kbp

-- Sequencing: pools of barcoded libraries per lane; 12 for Illumina GAIIx and 18 for Illumina HiSeq 2000; mean coverage depth 31-285x

-- Data processing: BWA, single sample BAMs, independent variant calling by three centers (UMich, WashU, UCLA); extensive QC

-- Consensus sites: 2,234 consensus sites, overall concordance rate between centers was 99.96%; 1,072 singletons or doubletons; 1,697 with MAF<=0.5%

-- Annotation/prediction: MapSNPs/PolyPhen-2

Summary of variant allele frequency

Service et al., 2014

Distribution of variant types

Service et al., 2014

Service et al., 2014

Association analysis strategyPhenotypes:

-- low-density lipoprotein (LDL), high-density lipoprotein (HDL), total cholesterol (TC), triglycerides (TG), fasting glucose (FG), fasting insuline (FI);

-- residuals regressed on age, age^2, sex, oral contraceptive use, pregnancy status;

-- excluded T2D cases from fusion excluded for GLU and INS analysis

Single-variant analysis: variants with MAF>0.1% in additive genetic model; first 5 PCs as covariates; method: PLINK

Gene-level tests: non-synonymous variants with MAF<1% (from 2 to 33 per gene); methods: CMC, SKAT (with direction)

Goal: new single variant signals independent from GWAS or association at the gene level (group tests)

Association resultsInitially: 17 loci X 6 metabolic phenotypes => 39 unique locus-phenotype

combinations ( 32 for lipid measures + 6 for GLU + 1 for INS)

Results:

-- For 27 of the 39 locus-phenotype combinations, the re-sequencing analysis essentially recapitulated the results from the GWAS

-- Remaining 12 locus-phenotype associations (7 loci): new signals independent from GWAS

-- ABCA1, gene-level: 23 rare variants implicated in TC and HDL-C

-- CETP, gene-level : 4 and 4 rare NS variants assoc. with increased and decreased HDL-C

-- Protective variant His177Tyr in G6PC2 (lowering FG), FinnMAF=1.4% (vs. 0.23% in Europe);

-- Damaging rs28933094 in LIPC (hepatic lipase deficiency), FinnMAF=1.5%

Service et al., 2014

Service et al., 2014

Service et al., 2014

Why?!

-- Incomplete coverage for some loci-- Causal non-coding variants?-- Indels, CNVs etc (complicated architecture)?-- Epistatic interactions?-- Compound heterozygotes?

-- Extensive rare variation in the human population

-- GWAS DNA-seq transition: knowing full coding SNV spectrum may not give immediate answers

Lessons from ASMS story

Harvard Medical School: Jeremiah Scharf, Dongmei Yu UCLA: Giovanni Coppola, Nelson Freimer, Alden Huang, Jae-Hoon Sul, Renee Sears, Vasily Ramenskiy; U.Chicago: Nancy Cox, Vasa Trubetskoy, Lea Davis

II.Tourette

syndrome in large pedigrees and independent samples

Tourette

syndrome (TS)

-- an inherited neuropsychiatric disorder with onset in childhood, characterized by multiple physical (motor) tics and at least one vocal (phonic) tic

-- ~0.4%-3.8% of children ages 5 to 18 may have TS

-- extreme TS in adulthood is a rarity, and TS does not adversely affect intelligence or life expectancy

TS/CT chr

2p linkage region in pedigrees

Dongmei Yu, Jeremiah Scharf

Tourette

syndrome Large Family sequencing by CIDR (2011)Samples: 15 pedigrees, 109 samples: 66 affected, 35 not affected, 8 unknown

Exome sequencing: Agilent HumanExon 50Mb Kit, >100 K SNVs

Custom targeted sequencing: 5.7 Mbp from chr2 (1-91 Mbp): ~22K SNVs-- known and predicted exons not on the Agilent exome kit; -- additional, brain-specific transcripts and AS exons (derived from UCLA fetal and adult brain RNA-sequencing libraries); -- alternative brain-specific TSS tags using a brain cap-analysis gene expression (CAGE) library; -- putative promoter regions;-- predicted splice sites; -- conserved sequences derived from alignments with 44 vertebrate species

•

Single-Variant

Analysis‣

EMMAX

‣

EIGENSTRAT‣

PLINK-TDT

•

Gene-Based Tests ‣

PLINK/SEQ methods

‣

VAAST‣

Zhu-Xiong

method (?)

•

Imputed Data•

CNV

Analysis

Analysis Plan Global Local

•

Perfect

Cosegregation•

Whole Dataset

•

Under Linkage Peaks•

Regions from Literature

•

Multiple-Hit Analysis•

Family-based VAAST

•

De novo

Analysis

Data in Web-Based Database

Manhattan plot of GWAS meta-analysis (Dongmei Yu)

-- Genome-wide significant result in the linkage region

-- Significant SNPs are located in the lncRNA gene

Expression correlation with top hit gene

-- BrainSpan database: expression values for 48,582 genes in 237 experiments, prenatal states only (total: ~53K in 524 exp.); gene should have >0 expression in at least one experiment

-- Pearson correlation coefficient calculated for all gene pairs in prenatal samples

-- List of genes with expression in developing brain correlated with the query gene

-- Compares a gene list against background of ~49K genes

-- Check 1-tail p<0.01 positive correlation: 476 genes

-- Check 1-tail p<0.001 negative correlation: 259 genes

GO terms in 476 genes (positive, p<0.01)

GO terms in 259 genes (neg. corr., p<0.001)

-- “Wnt1 has also been shown to antagonize neural differentiation and is a major factor in self-renewal of neural stem cells. This allows for regeneration of nervous system cells, which is further evidence of a role in promoting neural stem cell proliferation”

-- Sample sizes

-- GWAS is not dead

-- Non-coding RNA genes

Lessons from TS story: what matters?

III. Analysis of WGS variation in the genomic region associated with amygdala

volume in bipolar family individuals

UCLA Bipolar projectNelson FreimerSusan ServiceScott FearsCarrie Bearden+ many others

Bipolar disorder

-- A severe psychiatric illness, characterized by alternating episodes of depression and mania, -- Ranks among the top ten causes of morbidity and life- long disability world-wide-- Prevalence: 1-2% of the population

Sequencing and variant calling1) Initial WGS and variant calling: Illumina

-- 450 individuals from 27 large families (67 trios, 78 married-ins)2) Genotype recalling at high-quality segregating sites: Samtools

-- 24.6 mln variants in 450 individuals-- Average genotype concordance with genotyped SNPs per individual: 99.78%; Mendelian inconsistency rate in trios: 1.78%3) Pedigree-based genotype refinement: TrioCaller (Jae-Hoon Sul)-- 23 mln variants in 450 individuals-- Genotype concordance: 99.86%; Mendelian inconsistencies: 0.18%; 4) Imputation on chr6: PLINK, FamLDCaller (Jae-Hoon Sul)

-- 977K variants on chr6 after QC in 839 individuals

-- No singletons, no sites with >=5 discordant genotypes, threshold r^2=0.1

Multisystem component phenotypes of bipolar disorder (Fears et al, 2014)

-- 169 quantitative neurocognitive, temperament-related, and neuroanatomical phenotypes that appear heritable and associated with severe BP, measured in 738 adults (181 affected);-- About 25% of the phenotypes, including measures from each phenotype domain, were both heritable and associated with BP-I

// Susan Service

Amygdala

volume associated regionRegion: chr6 144-155 Mbp, table1.022814.bed:

Intergenic 23,353Noncoding 8,279Protein-coding32,090Total 63,722-------------------------------------------

Protein-coding (62 genes):Benign 169Damaging 86Exon 1062Flank 1104Intron 29473Nonsense 3Splice-site 1Synon 191-------------------------------------------

-- Burden test with effect direction (over-dispersion)

-- Earlier method SKAT (Sequence Kernel Association Test) modified to work with family samples

-- OK for quantitative phenotypes

Bioinformatics: potential functional variants

Coding variants: 3 nonsense, splice site2 damaging1 benign

Non-coding variants (accumulated):+1if conserved or accelerated in any available lineage+0.5 if Active/Strong chromatin in 10 brain tissues+0.25 if disrupts TF binding site

-- Protein coding AND “my rank”>0: ~16 K variants-- MAF<10%

Gene Pvalue Nvar Pos,Mbp----------------------------------LATS1 0.001005 138 150.01RAET1G 0.003231 14 150.24CNKSR3 0.004171 30 154.73UST 0.004190 494 149.23PPIL4 0.005438 97 149.85

famSKAT

results: take 1 (Susan Service)Variants:-- Protein coding genes-- MAF<10% in married ins-- Rank>0

Bioinformatics: functional variants for take 2

-- Priority: nonsense >> splice-site >> damaging >> benign >> synonymous >> UTR exon >> flank >> enhancer >> intron

-- New: “Enhancer”, FANTOM5 enhancers associated with a gene

-- New: GWAVA scores (0..1)

-- Components for “my rank”: conservation, TFBS overlap, active chromatin

Gene Pvalue Nvar Pos,Mbp----------------------------------LATS1 0.000074 57 150.01PPIL4 0.001620 51 149.85TFL 0.003353 8 149.79UST 0.004600 367 149.23SYNE1 0.013834 667 152.66

Take 2 + indels vs. Take 2: top 5 genesGene Pvalue Nvar Pos,Mbp----------------------------------LATS1 0.000164 45 150.01PPIL4 0.000606 46 149.85TFL 0.003353 8 149.79UST 0.004549 364 149.23SYNE1 0.012377 640 152.66

-- New statistical genetics methods needed

-- Non-coding variants and indels in protein-coding genes?

Lesson from BP story: what matters