Lecture 4: Missing Heritability

Transcription

1 Machine Learning in Computational Biology CSC 2431 Lecture 4: Missing Heritability Instructor: Anna Goldenberg

2 Heritability (of a trait) Fraction of phenotypic variability attributable to genetic variation NOT: how much genetics influences trait in one person Relative to specific population in a particular environment (since contribution of genetic factors is relative to contribution of other factors such as environment)

3 Heritability Phenotype P, Genotype G, Environment E G: Additive A, Dominant D, Epistatic J Var(P) = Var(G)+Var(E)+2Cov(G,E) Var(G) = Var(A) + Var(D) + Var(J) Broad Sense Heritability: (includes additive, epistatic, dominant, maternal, paternal) Narrow Sense Heritability (only additive effects) H 2 = Var(G) Var(P ) h 2 = Var(A) Var(P )

4 Regress offspring value on midparent value Example of a height trait Slope = h 2

5 Parent-offspring regression Heritability = slope Problem: Parents and children share other factors besides genome

6 Heritability estimates from other regression analyses Comparison Slope Midparent-offspring h 2 Parent-offspring 1/2h 2 Half-sibs 1/4h 2 First cousins 1/8h 2 as the groups become less related, the precision of the h 2 estimate is reduced.

7 Estimating Heritability Falconer s formula: Twin studies heritability = 2(r(MZ)-r(DZ))

8 Tenesa, Albert, and Chris S. Haley. "The heritability of human disease: estimation, uses and abuses." Nature Reviews Genetics 14.2 (2013): Estimating Heritability Tetrachoric correlation: correlation of disease among relatives of particular type vs random pair from population Twin Method: resemblance between MZ twins vs DZ twins Falconer s method Mixed Linear Models uses Bayesian method or MLE to estimate variances from families and pedigrees

9 Examples of estimated heritability Trait/Disease Estimated heritability Alcoholism 50-60% Alzheimers 58-79% Asthma 30% Bipolar Disorder 70% Depression 50% Hair Curliness 85-95% Lung Cancer 8% Height 81% Obesity 70% Longetivity 26% Sexual Orientation 60% Schizophrenia 81% Type 1 diabetes 88% Type 2 diabetes 26%

10 Genetically Explained Heritability Disease # of Loci Heritability Explained Heritability Estimated Measure of Heritability Age related macular degeneration 5 50% 46-71% Sibling recurrent risk Crohn s Disease 32 20% 50-60% Genetic risk (liability) Systemic Lupus Erithematosus 6 15% 44-66% Sibling recurrent risk Type 2 diabetes 18 6% 26% HDL Cholesterol 7 5.2% Height 40 5% 81% Phenotypic Variance Fasting glucose 4 1.5% Manolio, Teri A., et al. "Finding the missing heritability of complex diseases." Nature (2009):

11 Important question: how is the genetic heritability estimated from GWAS? Disease # of Loci Heritability Explained Heritability Estimated Type 2 diabetes 18 6% 26% Measure of Heritability Height 40 5% 81% Phenotypic Variance Typically: add up the estimated heritability contributed by each of the genetic variants that have achieved clear genome-wide statistical significance Problem: this is just a lower bound Solution: estimate common variant heritability without identifying the exact loci

12 Miscalculated heritability estimates 1 Yang Visscher, Nature Genetics, 2010: Problem: Given SNPs do not account for rare variants, so genetic heritability is under computed Method: linear mixed models, REML Fix: model the extent to which the phenotypic similarity across pairs of individuals in a sample is explained by their genotypic similarity at common variants. Results: using all SNPs found genetic estimate of heritability of height to be 45% (compared to 5% before)

13 Miscalculated heritability estimates 2 Golan, Lander and Rosset, PNAS, 2014: Problem: small sample size, small effect size, true heritability, number of genotyped SNPs Method: Phenotype-correlation genotypecorrelation (PCGC) Fix: regress pairs of phenotypes to pairs of genotypes

14 Miscalculated heritability estimates

15 Missing heritability continued Much larger numbers of variants of smaller effects Rarer variants not present on arrays Structural variants Low power to detect gene-gene interactions Inadequate accounting for shared environment by twins Manolio, Teri A., et al. "Finding the missing heritability of complex diseases." Nature (2009):

16 Missing heritability continued Much larger numbers of variants of smaller effects Rarer variants not present on arrays Structural variants Low power to detect gene-gene interactions Inadequate accounting for shared environment by twins Manolio, Teri A., et al. "Finding the missing heritability of complex diseases." Nature (2009): Heterogeneity of phenotype in complex diseases (our inability to distinguish between multiple less common but similarly manifesting diseases)

17 Missing heritability continued Problem: Much larger numbers of variants of smaller effects Solution: Bigger cohorts (number of people)

18 Rare variants Low: 0.5% < MAF < 5% of the population Rare: MAF < 0.5% Example: 20 variants with MAF < 1% and risk of 3 would account for most variation in Type 2 diabetes! But they were not found yet. Reason: Small sample sizes or insufficiently large arrays Solution: pooling (collapsing)

19 Rare variants CAST - cohort allelic sum test, collapses information on all rare variants within a region (e.g., the exons of a gene) into a single dichotomous variable for each subject by indicating whether or not the subject has any rare variants within the region and then applies a univariate test (Morris and Zeggini, Gen Epi, 2010) Calpha - non-burden-based test, robust to the direction and magnitude of effect. For case-control data, it compares the expected variance to the actual variance of the distribution of allele frequencies (Neale et al, PloS Genetics, 2011) RWAS - Rare variant Weighted Aggregate Statistic groups variants and computes a weighted sum of differences in mutation counts between case and control individuals. Weights of RWAS are estimated from data to achieve nearly optimal power under a disease model in which all variants make an equally small contribution to population disease risk (Sul et al, Genetics) SKAT sequence kernel association test: supervised, flexible, computationally efficient regression method to test for association between genetic variants (common and rare) in a region and a continuous or dichotomous trait while easily adjusting for covariates (Wu et al, AJHG, 2011) SKAT+dmGWAS SKAT + network aggregation (Jia, Bioinformatics, 2011)

20 Rare variants

21 Packages and meta-packages! Lee, Seunggeung, et al. "Rare-variant association analysis: study designs and statistical tests." The American Journal of Human Genetics 95.1 (2014): 5-23.

22 Structural Variations Copy number variants (CNVs) insertions and deletions Copy neutral variations inversions and translocations largely unstudied with respect to complex diseases Common CNVs are large 600kb-3Mb Disease associated CNPs 20-40kb de novo CNVs are shown to be important in neuropsychiatric and developmental conditions

23 Examples of identified CNVs Similar to SNPs rare variants have large effects common variants have small effects

24 Problem with studying structure Technical several hundred genes that map to commonly duplicated regions are considered inaccessible by most existing genotyping and sequencing technologies due to multicopy nature Need characterize sequence content in highly variable regions Evan Eichler et al. Nat. Rev. Gen Missing Heritability and strategies for finding underlying causes of complex disease

25 Epistasis The departure from the independence of the effects of different genetic loci AB = Ab + ab ab no epistasis AB > Ab + ab ab synergistic epistasis (SE) AB < Ab + ab ab antagonistic epistasis E.g. Synthetic lethality synergistic epistasis of harmful mutations (combined together they kill the organism)

26 Parent of origin effect Example an allele if inherited from father hurts, from mother helps (T2D) Variants can increase a recombination rate for fathers and reduce for mothers

27 Epistasis method review Wei et al, Detecting epistasis in human complex traits, Nature Genetics, 2014

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29 Interesting findings Hemani et al. Nature 508, (2014) Found: found 501 significant pairwise interactions between common SNPs influencing the expression of 238 genes (P < ). Replication of these interactions in two independent data sets11, 12 showed both concordance of direction of epistatic effects (P = ) Wood et al: Another explanation for apparent epistasis Found: Using whole-genome sequencing data from 450 individuals we strongly replicated many of the reported interactions but, in each case, a single third variant captured by our sequencing data could explain all of the apparent epistasis.

30 Phenotypic heterogeneity

31 Phenotypic heterogeneity CASES CONTROL

32 Phenotypic heterogeneity Age of onset CASES CONTROL Warde-Farley, David, et al. Mixture model for subphenotyping in GWAS." Pac. Symp. Biocomput. Vol

33 Phenotypic heterogeneity

34 Next class presentations Methods for Rare Variants - Liu, Dajiang J., and Suzanne M. Leal. "Estimating genetic effects and quantifying missing heritability explained by identified rare-variant associations." The American Journal of Human Genetics 91.4 (2012): Methods for Epistasis Schwarz, Daniel F., Inke R. König, and Andreas Ziegler. "On safari to Random Jungle: a fast implementation of Random Forests for high-dimensional data." Bioinformatics (2010): REMINDER: Project Proposals are due by the end of the week