Disease and Polygenic Architecture: Avoid Trio Design and Appropriately Account for Unscreened Control Subjects for Common Disease  Wouter J. Peyrot,

Slides:



Advertisements
Similar presentations
Previous Estimates of Mitochondrial DNA Mutation Level Variance Did Not Account for Sampling Error: Comparing the mtDNA Genetic Bottleneck in Mice and.
Advertisements

Functional Analysis of the Neurofibromatosis Type 2 Protein by Means of Disease- Causing Point Mutations Renee P. Stokowski, David R. Cox The American.
Marc A. Coram, Huaying Fang, Sophie I. Candille, Themistocles L
Robustness and Power of the Maximum-Likelihood–Binomial and Maximum-Likelihood– Score Methods, in Multipoint Linkage Analysis of Affected-Sibship Data 
Michael H. Duyzend, Xander Nuttle, Bradley P
Model-free Estimation of Recent Genetic Relatedness
Genetic Linkage Analysis of a Dichotomous Trait Incorporating a Tightly Linked Quantitative Trait in Affected Sib Pairs  Jian Huang, Yanming Jiang  The.
2016 Curt Stern Award Address: From Rare to Common Diseases: Translating Genetic Discovery to Therapy1  Brendan Lee  The American Journal of Human Genetics 
Integrating Gene Expression with Summary Association Statistics to Identify Genes Associated with 30 Complex Traits  Nicholas Mancuso, Huwenbo Shi, Pagé.
Comparing Algorithms for Genotype Imputation
Yu Jiang, Glen A. Satten, Yujun Han, Michael P. Epstein, Erin L
Huwenbo Shi, Nicholas Mancuso, Sarah Spendlove, Bogdan Pasaniuc 
Haplotype Estimation Using Sequencing Reads
Caroline Durrant, Krina T. Zondervan, Lon R
Miao-Xin Li, Hong-Sheng Gui, Johnny S.H. Kwan, Pak C. Sham 
Improved Heritability Estimation from Genome-wide SNPs
Alternative Splicing QTLs in European and African Populations
10 Years of GWAS Discovery: Biology, Function, and Translation
Arpita Ghosh, Fei Zou, Fred A. Wright 
Relationship between Deleterious Variation, Genomic Autozygosity, and Disease Risk: Insights from The 1000 Genomes Project  Trevor J. Pemberton, Zachary.
XMCPDT Does Have Correct Type I Error Rates
A Flexible Bayesian Framework for Modeling Haplotype Association with Disease, Allowing for Dominance Effects of the Underlying Causative Variants  Andrew.
A Selection Operator for Summary Association Statistics Reveals Allelic Heterogeneity of Complex Traits  Zheng Ning, Youngjo Lee, Peter K. Joshi, James.
Transethnic Genetic-Correlation Estimates from Summary Statistics
Xiangqing Sun, Robert Elston, Nathan Morris, Xiaofeng Zhu 
Maximizing the Power of Principal-Component Analysis of Correlated Phenotypes in Genome-wide Association Studies  Hugues Aschard, Bjarni J. Vilhjálmsson,
An Excess of Risk-Increasing Low-Frequency Variants Can Be a Signal of Polygenic Inheritance in Complex Diseases  Yingleong Chan, Elaine T. Lim, Niina.
Family-Based Association Studies for Next-Generation Sequencing
Sang Hong Lee, Naomi R. Wray, Michael E. Goddard, Peter M. Visscher 
The Genetics of Major Depression
Alkes L. Price, Gregory V. Kryukov, Paul I. W. de Bakker, Shaun M
Are Rare Variants Responsible for Susceptibility to Complex Diseases?
Structural Architecture of SNP Effects on Complex Traits
Studying Gene and Gene-Environment Effects of Uncommon and Common Variants on Continuous Traits: A Marker-Set Approach Using Gene-Trait Similarity Regression 
Christoph Lange, Nan M. Laird  The American Journal of Human Genetics 
10 Years of GWAS Discovery: Biology, Function, and Translation
Jon Wakefield  The American Journal of Human Genetics 
Five Years of GWAS Discovery
Pier Francesco Palamara, Laurent C. Francioli, Peter R
Hugues Aschard, Bjarni J. Vilhjálmsson, Amit D. Joshi, Alkes L
Dan-Yu Lin, Zheng-Zheng Tang  The American Journal of Human Genetics 
Erratum The American Journal of Human Genetics
Benjamin A. Rybicki, Robert C. Elston 
A Unified Approach to Genotype Imputation and Haplotype-Phase Inference for Large Data Sets of Trios and Unrelated Individuals  Brian L. Browning, Sharon.
Huwenbo Shi, Gleb Kichaev, Bogdan Pasaniuc 
A Fast, Powerful Method for Detecting Identity by Descent
Nonpaternity in Linkage Studies of Extremely Discordant Sib Pairs
David B. Allison, Bonnie Thiel, Pamela St. Jean, Robert C
Suzanne M. Leal, Jurg Ott  The American Journal of Human Genetics 
Wei Pan, Il-Youp Kwak, Peng Wei  The American Journal of Human Genetics 
Jared R. Kohler, David J. Cutler 
Epistasis and Its Implications for Personal Genetics
The Power of Genomic Control
Unified Sequence-Based Association Tests Allowing for Multiple Functional Annotations and Meta-analysis of Noncoding Variation in Metabochip Data  Zihuai.
A Perspective on Epistasis: Limits of Models Displaying No Main Effect
Are Variants in the CAPN10 Gene Related to Risk of Type 2 Diabetes
Inclusion of Gene-Gene and Gene-Environment Interactions Unlikely to Dramatically Improve Risk Prediction for Complex Diseases  Hugues Aschard, Jinbo.
Tao Wang, Robert C. Elston  The American Journal of Human Genetics 
Iuliana Ionita-Laza, Seunggeun Lee, Vlad Makarov, Joseph D
Data Mining Applied to Linkage Disequilibrium Mapping
Evaluating the Effects of Imputation on the Power, Coverage, and Cost Efficiency of Genome-wide SNP Platforms  Carl A. Anderson, Fredrik H. Pettersson,
Harold A. Nieuwboer, René Pool, Conor V. Dolan, Dorret I
Alice S. Whittemore, Jerry Halpern 
Leveraging Multi-ethnic Evidence for Mapping Complex Traits in Minority Populations: An Empirical Bayes Approach  Marc A. Coram, Sophie I. Candille, Qing.
Quanhe Yang, W. Dana Flanders, Ramal Moonesinghe, John P. A
Genetic Linkage Analysis of a Dichotomous Trait Incorporating a Tightly Linked Quantitative Trait in Affected Sib Pairs  Jian Huang, Yanming Jiang  The.
Risk Prediction of Complex Diseases from Family History and Known Susceptibility Loci, with Applications for Cancer Screening  Hon-Cheong So, Johnny S.H.
Estimating SNP-Based Heritability and Genetic Correlation in Case-Control Studies Directly and with Summary Statistics  Omer Weissbrod, Jonathan Flint,
Jung-Ying Tzeng, Daowen Zhang  The American Journal of Human Genetics 
Kung-Yee Liang, Fang-Chi Hsu, Terri H. Beaty, Kathleen C. Barnes 
Presentation transcript:

Disease and Polygenic Architecture: Avoid Trio Design and Appropriately Account for Unscreened Control Subjects for Common Disease  Wouter J. Peyrot, Dorret I. Boomsma, Brenda W.J.H. Penninx, Naomi R. Wray  The American Journal of Human Genetics  Volume 98, Issue 2, Pages 382-391 (February 2016) DOI: 10.1016/j.ajhg.2015.12.017 Copyright © 2016 The American Society of Human Genetics Terms and Conditions

Figure 1 Relationship between the True SNP Heritability and Its Estimates Based on the Standard Transformation with Equation 2 from Trio Data, Screened Controls, and Unscreened Controls The SNP heritability hˆl2 that would be estimated based on the standard liability transformation equation (Equation 2) for GWASs using pseudocontrol subjects (dotted lines), unscreened control subjects (dashed lines), and screened control subjects (solid lines) compared to the true parental SNP heritability hl2 for designs based on randomly ascertained proband families (A), families with an additional affected sibling (B), in the context of parental assortative mating with a correlation on the liability scale of ρl = 0.3 (C), and families with an additional affected sibling in the context of parental assortative mating (D) for disorders with lifetime risk K = 0.01, 0.05, and 0.15. The pseudocontrol subjects of random proband families are equivalent to unscreened control subjects (dashed and dotted lines overlap in A), and the slope of these lines are defined by (1 − K)2, i.e., the underestimation of hˆl2 when mistakenly applying Equation 2 rather than Equation 3 to transform the heritability on the observed scale to the liability scale when none of the control subjects are screened. The American Journal of Human Genetics 2016 98, 382-391DOI: (10.1016/j.ajhg.2015.12.017) Copyright © 2016 The American Society of Human Genetics Terms and Conditions

Figure 2 Power to Detect a Single Risk Variant in Association Studies of 10,000 Case Subjects that Use a Trio Design, Screened Control Subjects, or Unscreened Control Subjects Power of association analysis comparing 10,000 probands to 10,000 screened control subjects (solid line), 10,000 unscreened control subjects (dashed), 20,000 unscreened control subjects (dot-dashed), and 10,000 pseudocontrol subjects (dotted) to detect a single associated risk variant for a risk allele with frequency p = 0.2, for a baseline disease risk K = 0.01, 0.05, and 0.15. Power was estimated for risk variants with underlying additive effect (RRBB=RRBb2) for random ascertainment of probands (A) and probands from families with an additional affected sibling (B). Note that pseudocontrol subjects from random families are equivalent to unscreened control subjects and that the dotted and dashed lines in (A) overlap. The variation explained on the liability scale was approximated by hlocus2≈2p(1−p)(RRBb−1)2/i2, where i equals z/K the mean liability of probands, and z the height of the standard normal density function at the threshold corresponding with disease of lifetime risk K. The American Journal of Human Genetics 2016 98, 382-391DOI: (10.1016/j.ajhg.2015.12.017) Copyright © 2016 The American Society of Human Genetics Terms and Conditions

Figure 3 Power to Detect an Associated Locus by the Proportion of Variation It Explains The power to detect an associated locus depends on the proportion of variation it explains on the liability scale hlocus2, the baseline disease risk K, and is displayed for random case versus screened control. For a locus with the same hlocus2, larger sample sizes are required for larger K. hlocus2 can be approximated by 2p(1 − p)(RRBb − 1)2/i2, where i equals z/K the mean liability of probands, and z the height of the standard normal density function at the threshold corresponding with disease of lifetime risk K. The (complex) relation between allele frequency p, RRBb, and the non-centrality parameter NCP given hlocus2 results in an identical relation between power and hlocus2 for varying p. The American Journal of Human Genetics 2016 98, 382-391DOI: (10.1016/j.ajhg.2015.12.017) Copyright © 2016 The American Society of Human Genetics Terms and Conditions