Taking a closer look at trio designs and unscreened controls in the GWAS era

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1 Taking a closer look at trio designs and unscreened controls in the GWAS era PGC Sta8s8cal Analysis Call, November 4th 015 Wouter Peyrot, MD, Psychiatrist in training, PhD candidate Professors Brenda Penninx, Dorret Boomsma, Naomi Wray w.peyrot@ggzingeest.nl (Manuscript s current status: minor revision AJHG) 1

2 Psychiatric Gene8cs: variety of disorders ASD = au8sm spectrum disorder SCZ = schizophrenia BIP = bipolar disorder AN = anorexia nervosa MDD = major depressive disorder AD = Alzheimer's disease NIC = nico8ne dependence ALC = alcohol dependence CD = Crohn's disease TDM = type II diabetes BRCA = breast cancer

3 Psychiatric Genomics Consor8um (PGC) Great successes, but also challenges PGC combines heterogeneous samples Some controls MDD poorly screened Proband-parent trio design for some childhood onset disorder samples (ASD, ADHD) Excess mul8plex ASD families (Anney. Hum Mol Gen 01) All on the background of assorta8ve ma8ng 3

4 Background trio design O_en applied in candidate gene studies Advantages De novo muta8ons Accurately phased haplotypes Parental imprin8ng studies Protect popula8on stra8fica8on pre-gwas " Proband(case ( Pseudocontrol ( Several designs with trio data, but one pseudocontrol most o_en applied in GWAS 4

5 5 Trio design PGC: lower SNP-heritability

6 6 Pseudocontrols: mul8plex and simplex

7 Literature trio design Rich literature on power to detect single risk variant Mul8plex families: pseudocontrols less power then screened controls (Teng 1999) Pseudocontrol mul8plex family less power then pseudocontrol simplex family (Ferreira 007) Case-control more power when case from mul8plex family (Risch 001) However, almost no literature SNP-heritability 7

8 Research ques8ons 1. What is the impact of the trio design on the SNPheritability of psychiatric disorders?. What is the impact of unscreened (unrelated) controls? 3. How does this relate to power to detect a single risk variant? 8

9 Methods: 3 scenarios A)"Random"proband"families " B)"Addi4onal"sibling"affected " C)"Parental"assortave"mang " Proband"case " Pseudocontrol " Proband"case " Pseudocontrol " Proband"case " Pseudocontrol " Pseudocontrols (A) equivalent unscreened controls Theory validated with simula8on 9

10 SNP-heritability: observed to liability scale SNP-heritability = part of phenotypic varia8on captured by genome-wide SNPs First assessed observed scale (control=0; case=1), then transformed to liability scale 10 Affected

11 "#$%&'()* "##$%&' "#: (1 ) (1 ) ℎ = ℎ"" (1 )(1 ) A)"Random"proband"families" Equa8on unscreened controls (A) K = population disease prevalence P = proportion cases in study u = proportion unscreened controls F = proportion false controls F K u z = height standard normal at threshold defining K cases 11 Proband"case" Pseudocontrol" " : "#$%&'"( "#$%& ( = ) ℎ = ℎ (1 ) " : "#$ "#$%"& "#$%& ( > ) (1 ) ℎ = ℎ"" (1 ) " : "#$% "#$%& ( > ) (1 ) ℎ = ℎ"" (1 )(1 ) "#$%&'()* "##$%&' "#: (1 ) (1 ) ℎ = ℎ"" (1 )(1 )

12 "#$%&'()* "##$%&' "#: (1 ) (1 ) ℎ = ℎ"" (1 )(1 ) A)"Random"proband"families" Equa8on unscreened controls (A) P = proportion cases in study u = proportion unscreened controls F = proportion false controls F K u z = height standard normal at threshold defining K cases 1 Proband"case" K = population disease prevalence Dempster et al 1950; Lee et al 011 Pseudocontrol" " : "#$%&'"( "#$%& ( = ) ℎ = ℎ (1 ) " : "#$ "#$%"& "#$%& ( > ) (1 ) ℎ = ℎ"" (1 ) " : "#$% "#$%& ( > ) (1 ) ℎ = ℎ"" (1 )(1 ) "#$%&'()* "##$%&' "#: (1 ) (1 ) ℎ = ℎ"" (1 )(1 )

13 "#$%&'()* "##$%&' "#: (1 ) (1 ) ℎ = ℎ"" (1 )(1 ) A)"Random"proband"families" Equa8on unscreened controls (A) K = population disease prevalence P = proportion cases in study u = proportion unscreened controls F = proportion false controls F K u z = height standard normal at threshold defining K cases 13 Proband"case" Pseudocontrol" " : "#$%&'"( "#$%& ( = ) ℎ = ℎ (1 ) " : "#$ "#$%"& "#$%& ( > ) (1 ) ℎ = ℎ"" (1 ) " : "#$% "#$%& ( > ) (1 ) ℎ = ℎ"" (1 )(1 ) "#$%&'()* "##$%&' "#: (1 ) (1 ) ℎ = ℎ"" (1 )(1 )

14 "#$%&'()* "##$%&' "#: (1 ) (1 ) ℎ = ℎ"" (1 )(1 ) A)"Random"proband"families" Equa8on unscreened controls (A) K = population disease prevalence P = proportion cases in study u = proportion unscreened controls F = proportion false controls F K u z = height standard normal at threshold defining K cases 14 Proband"case" Pseudocontrol" " : "#$%&'"( "#$%& ( = ) ℎ = ℎ (1 ) " : "#$ "#$%"& "#$%& ( > ) (1 ) ℎ = ℎ"" (1 ) " : "#$% "#$%& ( > ) (1 ) ℎ = ℎ"" (1 )(1 ) "#$%&'()* "##$%&' "#: (1 ) (1 ) ℎ = ℎ"" (1 )(1 )

15 A)"Random"proband"families " Example mistakenly applying Eq Proband"case " Pseudocontrol " 15

16 B)"Addi4onal"sibling"affected " C)"Parental"assortave"mang " Scenarios B & C Proband"case " Pseudocontrol " Proband"case " Pseudocontrol " Theory requires many subsequent steps: not captured in single equa8on Validated with simula8on 10,000 SNPs and Haseman-Elston regression in line Golan et al (PNAS 014) 16

17 K = 0.01 K = 0.05 K = 0.15 A)"Random"proband"families " h^l assessed Screened control Unscreened control Pseudocontrol Proband"case " Pseudocontrol " B)"Addi4onal"sibling"affected " h^l assessed Screened control Unscreened control Pseudocontrol Proband"case " Pseudocontrol " C)"Parental"assortave"mang " h^l assessed Screened control Unscreened control Pseudocontrol Proband"case " Pseudocontrol "

18 K = 0.01 K = 0.05 K = 0.15 A)"Random"proband"families " h^l assessed Screened control Unscreened control Pseudocontrol Proband"case " Pseudocontrol " h^l assessed Screened control Unscreened control Pseudocontrol (A) Mean Genetic Liability K=0.01 K=0.05 K=0.15 Proband case Screened control Unscreened control Pseudocontrol h^l assessed Screened control Unscreened control Pseudocontrol

19 K = 0.01 K = 0.05 K = 0.15 A)"Random"proband"families " h^l assessed Screened control Unscreened control Pseudocontrol Proband"case " Pseudocontrol " B)"Addi4onal"sibling"affected " h^l assessed Screened control Unscreened control Pseudocontrol Proband"case " Pseudocontrol " h^l assessed Screened control Unscreened control Pseudocontrol (B) Mean Genetic Liability K=0.01 K=0.05 K=0.15 Proband case Screened control Unscreened control Pseudocontrol

20 K = 0.01 K = 0.05 K = 0.15 A)"Random"proband"families " h^l assessed Screened control Unscreened control Pseudocontrol Proband"case " Pseudocontrol " h^l assessed Screened control Unscreened control Pseudocontrol (C) Mean Genetic Liability K=0.01 K=0.05 K=0.15 Proband case Screened control Unscreened control Pseudocontrol C)"Parental"assortave"mang " h^l assessed Screened control Unscreened control Pseudocontrol Proband"case " Pseudocontrol "

21 Power single risk variant Assorta8ve ma8ng no impact, because of small propor8on of varia8on explained by individual loci Example: Locus explains 1% varia8on in phenotype Spouse-correla8on phenotype 0.3 Spouse-correla8on locus 0.3*0.01=

22 Power single risk variant with 10,000 cases K=0.01 K=0.05 K=0.15 A)"Random"proband"families " Proband"case " Pseudocontrol " Power Number and type controls: 10,000 screened 10,000 unscreened 10,000 pseudo B)"Addi4onal"sibling"affected " Proband"case " Pseudocontrol " Power RR Bb RR Bb RR Bb (%) h locus (%) h locus (%) h locus

23 Power single risk variant with 10,000 cases K=0.01 K=0.05 K=0.15 A)"Random"proband"families " Proband"case " Pseudocontrol " Power Number and type controls: 10,000 screened 10,000 unscreened 10,000 pseudo ,400 1,400 1, B)"Addi4onal"sibling"affected " Proband"case " Pseudocontrol " Power ,300 11,500 16, RR Bb RR Bb RR Bb 3 In green: number of unscreened controls required to obtain similar power as for 10,000 screened controls (N cases constant 10,000)

24 Discussion trio/unscreened Trio: genotype 3 persons for two genotypes Decreased SNP-heritability for (1) trio data or unscreened controls for common disease () trio data with addi8onal sibling affected (3) trio data with assorta8ve ma8ng Similar impact design on power to detect single risk variant, but not for assorta8ve ma8ng 4

25 Discussion trio/unscreened Advice psychiatric gene8cs Avoid trio design when aiming to detect polygenic architecture Carefully screen controls MDD and ADHD, or When unscreened controls much easier to collect, apply updated Equa8on to transform hocc to hl: 5

26 Assorta8ve ma8ng in more detail: Defining boundaries for the gene8c consequences of assorta8ve ma8ng for psychiatric traits 6

27 Heterogeneous Literature - Various instruments to assess psychiatric traits (e.g. disease classifica8on vs symptoms) - Different measures/scales (e.g. OR, RR, correla8on) 7

28 SCZ%Lichtenstein%006% MDD%Maes%1998% MDD%Maes%1998% MDD%Mathews%001% MDD%Grootheest%008% BIP%Mathews%001% ASD%Hoekstra%007% ASD%Van%Steijn%01% ASD%De%la%Marche%015% ASD%Virkud%009% ASD%ConstanKno%005% ANX%Low%007% ANX%Distel%007% SAD%Isomura%015% PD%Maes%1998% PD%Maes%1998% OCD%MataixCols%013% OCD%Grootheest%008% GAD%Maes%1998% GAD%Maes%1998% ANX%Grootheest%008% ADHD%Boomsma%010% ADHD%Van%Steijn%01% 0.1% 0% 0.1% 0.% 0.3% 0.4% 8 Spouse%correlaKon%

29 Research ques8ons 1. What are the boundaries for the gene8c consequences of assorta8ve ma8ng for psychiatric traits?. How does this relate to reduced fecundity for psychiatric traits? 9

30 Causes spouse-correla8on 1. Direct assorta8ve ma8ng. Secondary assortment 3. Social homogamy 4. Marital interac8on 30

31 Reduced Fecundity or Natural Selec8on Fecundity ra8o = #kids affected/#kids unaffected 31

32 Methods: assump8ons required Literature, theory and simula8on Assump8ons: 1. Liability threshold model. Spouse-correla8on due to direct assortment 3. Environmental effects & threshold constant 4. Assortment and reduced fecundity constant 3

33 Consequences assorta8ve ma8ng Assortment results in gene8c changes via heritability Only few effec8ve loci: increase homozygotes Many loci small effect: No effect genotype frequencies LD between effec8ve loci: à popula8on variance ñ à popula8on prevalence ñ (NB. No change popula8on mean) Frequency No assortative mating After 3 generations 33 Bulmer 1985; Lynch & Walsh Liability

34 Concequences reduced fecundity Results in gene8c changes via heritability Only few effec8ve loci: risk allele frequency decrease Many loci small effect: No effect genotype frequencies Nega8ve LD between effec8ve loci: à popula8on variance decreases & popula8on mean liability decreases à popula8on prevalence decreases 34 Walsh & Lynch (to be published, available at hpp://nitro.biosci.arizona.edu/zbook/newvolume_/newvol.html#b)

35 SCZ & ASD - heritability 0.8 A. Assortative Mating only B. Natural Selection only C. Assortment & Natural Selection Liability Liability Liability Gener. Variance Mean h K Variance Mean h K Variance Mean h K : : : : : : : : : : : : : K=0.01 ; Heritability =0.8 ; spouse-correlation = 0.3 ; Fecundity-ratio=

36 SCZ & ASD - heritability 0.8 A. Assortative Mating only B. Natural Selection only C. Assortment & Natural Selection Liability Liability Liability Gener. Variance Mean h K Variance Mean h K Variance Mean h K : : : : : : : : : : : : : K=0.01 ; Heritability =0.8 ; spouse-correlation = 0.3 ; Fecundity-ratio=

37 SCZ & ASD - heritability 0.8 A. Assortative Mating only B. Natural Selection only C. Assortment & Natural Selection Liability Liability Liability Gener. Variance Mean h K Variance Mean h K Variance Mean h K : : : : : : : : : : : : : K=0.01 ; Heritability =0.8 ; spouse-correlation = 0.3 ; Fecundity-ratio=

38 SCZ & ASD - heritability 0.8 A. Assortative Mating only B. Natural Selection only C. Assortment & Natural Selection Liability Liability Liability Gener. Variance Mean h K Variance Mean h K Variance Mean h K : : : : : : : : : : : : : K=0.01 ; Heritability =0.8 ; spouse-correlation = 0.3 ; Fecundity-ratio=0.35 SCZ & ASD - heritability 0.5 A. Assortative Mating only B. Natural Selection only C. Assortment & Natural Selection Liability Liability Liability Gener. Variance Mean h K Variance Mean h K Variance Mean h K : : : : : : : : : : : : : K=0.01 ; Heritability =0.5 ; spouse-correlation = 0.3 ; Fecundity-ratio=

39 Discussion - Assorta8ve ma8ng Limita8on of models Not modeled: de novo muta8on, increased fecundity, balancing selec8on Strong assump8ons required Nevertheless, modeling helps to compare and interpret results, provide upper boundaries, and evaluate GWAS design 39

40 Acknowedgements Naomi Wray, Queensland Brain Ins8tute Brenda Penninx, Netherlands Study of Depression and Anxiety Dorret Boomsma, Netherlands Twin Registry 40

41 Literature Bulmer M: The mathema8cal theory of quan8ta8ve gene8cs. Oxford, Clarendon press, 1985 Lynch M, Walsh B: Gene8cs and analysis of quan8ta8ve traits. Sunderland, Sinauer, 1998 Walsh B, Lynch M: Volume : Evolu8on and Selec8on of Quan8ta8ve Traits. Not published yet, available at hpp://nitro.biosci.arizona.edu/zbook/ NewVolume_/newvol.html#B 41