Genetic mapping of quantitative trait loci for disease-related phenotypes.

Quantitative variation underlies normal as well as pathological traits, and large part of this variability is under the control of genetic loci. Thanks to a better understanding of the extent and nature of human genetic variability and the subsequent availability of an increasing number of genetic markers, genetic mapping of several such quantitative trait loci, or QTLs, has been accomplished in the past 20 years or so using linkage and association analysis in family-based and population-based studies. Rather than alternative, such methods are complementary as each has optimal power of detecting genetic variants underlying variability of quantitative traits under different scenarios defined by the QTL allele frequencies and magnitude of genetic effects. We describe how to apply such analyses to whole-genome or candidate-gene genetic marker data to correlate genetic variability to quantitative trait variability for the purpose of gene mapping and identification.

[1]  T. Mackay.  The genetic architecture of quantitative traits. , 2001, Annual review of genetics.

[2]  N. Schork,et al.  Testing the robustness of the likelihood-ratio test in a variance-component quantitative-trait loci-mapping procedure. , 1999, American journal of human genetics.

[3]  L. Almasy,et al.  Variance component methods for detecting complex trait loci. , 2001, Advances in genetics.

[4]  G. Abecasis,et al.  Estimating the power of variance component linkage analysis in large pedigrees , 2006, Genetic epidemiology.

[5]  Mario Falchi,et al.  PowQ: a user-friendly package for the design of variance component multipoint linkage analysis studies , 2006, Bioinform..

[6]  P. Gregersen,et al.  Accounting for ancestry: population substructure and genome-wide association studies. , 2008, Human molecular genetics.

[7]  Gonçalo R. Abecasis,et al.  PEDSTATS: descriptive statistics, graphics and quality assessment for gene mapping data , 2005, Bioinform..

[8]  J. Blangero,et al.  Genetic analysis of a common oligogenic trait with quantitative correlates: Summary of GAW9 results , 1995, Genetic epidemiology.

[9]  Simon C. Potter,et al.  Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls , 2007, Nature.

[10]  G. Abecasis,et al.  A general test of association for quantitative traits in nuclear families. , 2000, American journal of human genetics.

[11]  F. Kronenberg,et al.  Lost in the space of bioinformatic tools: a constantly updated survival guide for genetic epidemiology. The GenEpi Toolbox. , 2010, Atherosclerosis.

[12]  Sanjay Shete,et al.  Ignoring linkage disequilibrium among tightly linked markers induces false-positive evidence of linkage for affected sib pair analysis. , 2004, American journal of human genetics.

[13]  Jurg Ott,et al.  Handbook of Human Genetic Linkage , 1994 .

[14]  D. Allison,et al.  Effect of Box-Cox Transformation on Power of Haseman-Elston and Maximum-Likelihood Variance Components Tests to Detect Quantitative Trait Loci , 2003, Human Heredity.

[15]  Francis S Collins,et al.  A HapMap harvest of insights into the genetics of common disease. , 2008, The Journal of clinical investigation.

[16]  Pak Chung Sham,et al.  Genetic Power Calculator: design of linkage and association genetic mapping studies of complex traits , 2003, Bioinform..

[17]  D. Balding.  A tutorial on statistical methods for population association studies , 2006, Nature Reviews Genetics.

[18]  H. Cordell.  Detecting gene–gene interactions that underlie human diseases , 2009, Nature Reviews Genetics.

[19]  K. Mossman.  The Wellcome Trust Case Control Consortium, U.K. , 2008 .

[20]  D. Botstein,et al.  Discovering genotypes underlying human phenotypes: past successes for mendelian disease, future approaches for complex disease , 2003, Nature Genetics.

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

[22]  G. Abecasis,et al.  Merlin—rapid analysis of dense genetic maps using sparse gene flow trees , 2002, Nature Genetics.

[23]  Manuel A. R. Ferreira,et al.  PLINK: a tool set for whole-genome association and population-based linkage analyses. , 2007, American journal of human genetics.

[24]  Sharon R. Browning,et al.  Missing data imputation and haplotype phase inference for genome-wide association studies , 2008, Human Genetics.

[25]  K. Roeder,et al.  Genomic Control for Association Studies , 1999, Biometrics.

[26]  K. Lange,et al.  Prioritizing GWAS results: A review of statistical methods and recommendations for their application. , 2010, American journal of human genetics.

[27]  E. Stone,et al.  The genetics of quantitative traits: challenges and prospects , 2009, Nature Reviews Genetics.