A dynamic model for genome-wide association studies

Although genome-wide association studies (GWAS) are widely used to identify the genetic and environmental etiology of a trait, several key issues related to their statistical power and biological relevance have remained unexplored. Here, we describe a novel statistical approach, called functional GWAS or fGWAS, to analyze the genetic control of traits by integrating biological principles of trait formation into the GWAS framework through mathematical and statistical bridges. fGWAS can address many fundamental questions, such as the patterns of genetic control over development, the duration of genetic effects, as well as what causes developmental trajectories to change or stop changing. In statistics, fGWAS displays increased power for gene detection by capitalizing on cumulative phenotypic variation in a longitudinal trait over time and increased robustness for manipulating sparse longitudinal data.

[1]  T. Dawber,et al.  Epidemiological approaches to heart disease: the Framingham Study. , 1951, American journal of public health and the nation's health.

[2]  L. Bertalanffy.  Quantitative Laws in Metabolism and Growth , 1957 .

[3]  F. J. Richards.  A Flexible Growth Function for Empirical Use , 1959 .

[4]  H. Kacser,et al.  The molecular basis of dominance. , 1981, Genetics.

[5]  Husˇkova´ Marie,et al.  On sequentially adaptive asymptotically efficient rank statistics , 1985 .

[6]  H. Kacser,et al.  Dominance, pleiotropy and metabolic structure. , 1987, Genetics.

[7]  M. Kirkpatrick,et al.  Analysis of the inheritance, selection and evolution of growth trajectories. , 1990, Genetics.

[8]  W. G. Hill,et al.  Estimating the covariance structure of traits during growth and ageing, illustrated with lactation in dairy cattle. , 1994, Genetical research.

[9]  Michael D. McKay,et al.  Nonparametric variance-based methods of assessing uncertainty importance , 1997 .

[10]  W. Atchley,et al.  Developmental quantitative genetics, conditional epigenetic variability and growth in mice. , 1997, Genetics.

[11]  C. Geyer,et al.  The genetic analysis of age-dependent traits: modeling the character process. , 1999, Genetics.

[12]  Karin Meyer,et al.  Random regressions to model phenotypic variation in monthly weights of Australian beef cows , 2000 .

[13]  James H. Brown,et al.  A general model for ontogenetic growth , 2001, Nature.

[14]  M. Kenward,et al.  Parametric modelling of growth curve data: An overview , 2001 .

[15]  G. Casella,et al.  Functional mapping of quantitative trait loci underlying the character process: a theoretical framework. , 2002, Genetics.

[16]  Pier Paolo Delsanto,et al.  Does tumor growth follow a "universal law"? , 2003, Journal of theoretical biology.

[17]  T. Mackay,et al.  Quantitative genetic analyses of complex behaviours in Drosophila , 2004, Nature Reviews Genetics.

[18]  Rongling Wu,et al.  A joint model for nonparametric functional mapping of longitudinal trajectory and time-to-event , 2006, BMC Bioinformatics.

[19]  A. Hartemink,et al.  Genome-wide prediction of imprinted murine genes. , 2005, Genome research.

[20]  Wei Zhao,et al.  A non-stationary model for functional mapping of complex traits , 2005, Bioinform..

[21]  Pier Paolo Delsanto,et al.  The dynamic evolution of the power exponent in a universal growth model of tumors. , 2005, Journal of theoretical biology.

[22]  R. Wu,et al.  Functional mapping — how to map and study the genetic architecture of dynamic complex traits , 2006, Nature Reviews Genetics.

[23]  R. Wu,et al.  Functional mapping for genetic control of programmed cell death. , 2006, Physiological genomics.

[24]  Runze Li,et al.  Analysis of Longitudinal Data With Semiparametric Estimation of Covariance Function , 2007, Journal of the American Statistical Association.

[25]  James Strait,et al.  Genome-Wide Association Scan Shows Genetic Variants in the FTO Gene Are Associated with Obesity-Related Traits , 2007, PLoS genetics.

[26]  M. Jarvelin,et al.  A Common Variant in the FTO Gene Is Associated with Body Mass Index and Predisposes to Childhood and Adult Obesity , 2007, Science.

[27]  T. Frayling.  Genome–wide association studies provide new insights into type 2 diabetes aetiology , 2007, Nature Reviews Genetics.

[28]  R. Vasan,et al.  Bmc Medical Genetics Genome-wide Association to Body Mass Index and Waist Circumference: the Framingham Heart Study 100k Project , 2022 .

[29]  C. Jaquish,et al.  The Framingham Heart Study, on its way to becoming the gold standard for Cardiovascular Genetic Epidemiology? , 2007, BMC Medical Genetics.

[30]  Joshua M. Korn,et al.  Integrated detection and population-genetic analysis of SNPs and copy number variation , 2008, Nature Genetics.

[31]  G. Abecasis,et al.  Metabolic and cardiovascular traits: an abundance of recently identified common genetic variants. , 2008, Human molecular genetics.

[32]  Subhajyoti De,et al.  Common variants near MC4R are associated with fat mass, weight and risk of obesity , 2008, Nature Genetics.

[33]  J. Rioux,et al.  Autoimmune diseases: insights from genome-wide association studies. , 2008, Human molecular genetics.

[34]  Jianqing Fan,et al.  Semiparametric Estimation of Covariance Matrixes for Longitudinal Data , 2008, Journal of the American Statistical Association.

[35]  M. Daly,et al.  Genetic Mapping in Human Disease , 2008, Science.

[36]  Judy H. Cho,et al.  Finding the missing heritability of complex diseases , 2009, Nature.

[37]  Melissa Bondy,et al.  Genome-wide association study identifies five susceptibility loci for glioma , 2009, Nature Genetics.

[38]  S. Cichon,et al.  Genomewide association studies: history, rationale, and prospects for psychiatric disorders. , 2009, The American journal of psychiatry.

[39]  M. Fornage,et al.  Genomewide association studies of stroke. , 2009, The New England journal of medicine.

[40]  Rongling Wu,et al.  Nonparametric Functional Mapping of Quantitative Trait Loci , 2009, Biometrics.

[41]  J. Hirschhorn.  Genomewide association studies--illuminating biologic pathways. , 2009, The New England journal of medicine.

[42]  C. Bogardus.  Missing Heritability and GWAS Utility , 2009, Obesity.

[43]  Bjarni V. Halldórsson,et al.  New sequence variants associated with bone mineral density , 2009, Nature Genetics.

[44]  J. Hirschhorn,et al.  Progress in Genome-Wide Association Studies of Human Height , 2009, Hormone Research in Paediatrics.

[45]  Rongling Wu,et al.  Mapping genes for plant structure, development and evolution: functional mapping meets ontology. , 2010, Trends in genetics : TIG.

[46]  Deborah Hughes,et al.  Genome-wide association study identifies five new breast cancer susceptibility loci , 2010, Nature Genetics.

[47]  R. Wu,et al.  Functional Clustering of Periodic Transcriptional Profiles through ARMA(p,q) , 2010, PloS one.