Originally published as Genetics Published Articles Ahead of Print on September 19, 2005.

Genetics, Vol. 172, 647-661, January 2006, Copyright © 2006
doi:10.1534/genetics.105.045781

Improvement of Mapping Accuracy by Unifying Linkage and Association Analysis

Xiang-Yang Lou^*, Jennie Z. Ma, Mark C. K. Yang, Jun Zhu, Peng-Yuan Liu^**, Hong-Wen Deng^**, Robert C. Elston and Ming D. Li^*,1

^* Department of Psychiatric Medicine, University of Virginia, Charlottesville, Virginia 22911, Department of Psychiatry, University of Texas Health Science Center, San Antonio, Texas 78229, Department of Statistics, University of Florida, Gainesville, Florida 32611, Department of Agronomy, Zhejiang University, Hangzhou, Zhejiang 310029, People's Republic of China, ^** Osteoporosis Research Center, Creighton University Medical Center, Omaha, Nebraska 68131 and Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio 44109

¹ Corresponding author: 1670 Discovery Dr., Ste. 110, Charlottesville, VA 22911.
E-mail: ml2km{at}virginia.edu

It is well known that pedigree/family data record information on the coexistence in founder haplotypes of alleles at nearby loci and the cotransmission from parent to offspring that reveal different, but complementary, profiles of the genetic architecture. Either conventional linkage analysis that assumes linkage equilibrium or family-based association tests (FBATs) capture only partial information, leading to inefficiency. For example, FBATs will fail to detect even very tight linkage in the case where no allelic association exists, while a violation of the assumption of linkage equilibrium will result in biased estimation and reduced efficiency in linkage mapping. In this article, by using a data augmentation technique and the EM algorithm, we propose a likelihood-based approach that embeds both linkage and association analyses into a unified framework for general pedigree data. Relative to either linkage or association analysis, the proposed approach is expected to have greater estimation accuracy and power. Monte Carlo simulations support our theoretical expectations and demonstrate that our new methodology: (1) is more powerful than either FBATs or classic linkage analysis; (2) can unbiasedly estimate genetic parameters regardless of whether association exists, thus remedying the bias and less precision of traditional linkage analysis in the presence of association; and (3) is capable of identifying tight linkage alone. The new approach also holds the theoretical advantage that it can extract statistical information to the maximum extent and thereby improve mapping accuracy and power because it integrates multilocus population-based association study and pedigree-based linkage analysis into a coherent framework. Furthermore, our method is numerically stable and computationally efficient, as compared to existing parametric methods that use the simplex algorithm or Newton-type methods to maximize high-order multidimensional likelihood functions, and also offers the computation of Fisher's information matrix. Finally, we apply our methodology to a genetic study on bone mineral density (BMD) for the vitamin D receptor (VDR) gene and find that VDR is significantly linked to BMD at the one-third region of the wrist.

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