Genomewide Twomarker linkage disequilibrium mapping of quantitative trait loci
 Jie Yang^{1},
 Wei Zhu^{2},
 Jiansong Chen^{2},
 Qiao Zhang^{2} and
 Song Wu^{2}Email author
DOI: 10.1186/147121561520
© Yang et al.; licensee BioMed Central Ltd. 2014
Received: 28 August 2013
Accepted: 31 January 2014
Published: 8 February 2014
Abstract
Background
In a natural population, the alleles of multiple tightly linked loci on the same chromosome cosegregate and are passed nonrandomly from generation to generation. Capitalizing on this phenomenon, a group of mapping methods, commonly referred to as the linkage disequilibriumbased mapping (LD mapping), have been developed recently for detecting genetic associations. However, most current LD mapping methods mainly employed singlemarker analysis, overlooking the rich information contained within adjacent linked loci.
Results
We extend the singlemarker LD mapping to include two linked loci and explicitly incorporate their LD information into genetic mapping models (tmLD). We establish the theoretical foundations for the tmLD mapping method and also provide a thorough examination of its statistical properties. Our simulation studies demonstrate that the tmLD mapping method significantly improves the detection power of association compared to the singlemarker based and also haplotype based mapping methods. The practical usage and properties of the tmLD mapping method were further elucidated through the analysis of a largescale dental caries GWAS data set. It shows that the tmLD mapping method can identify significant SNPs that are missed by the traditional singlemarker association analysis and haplotype based mapping method. An R package for our proposed method has been developed and is freely available.
Conclusions
The proposed tmLD mapping method is more powerful than single marker mapping generally used in GWAS data analysis. We recommend the usage of this improved method over the traditional single marker association analysis.
Keywords
Genetic mapping Linkage disequilibrium mapping Linked loci Genome wide association studyBackground
Most economically, biologically and clinically important traits, such as those linked to poplar growth, cancer development and dental caries risk, are inherently complex in terms of their polygenic control and sensitivity to the environment [1]. The number of genes involved in these traits is typically large, each exerting a small effect and acting singly or interactively with others in a complicated network. For this reason, the genetic analysis of complex traits has been very difficult. However, a profound understanding of the genetic control mechanisms of complex traits is crucial to economy and life. Therefore, the development of more powerful and complex genetic mapping methods has become increasingly urgent.
In recent years, with the advancement of new DNAbased biotechnologies, such as singlenucleotide polymorphism (SNP) arrays, genomewide association studies (GWAS) have become feasible to dissect the phenotypic variation of a complex trait into individual genetic components. Particularly, SNP arrays have gained popularity due to their costeffectiveness: in year 2011 alone, 1068 GWAS were performed, each with at least 100,000 SNPs genotyped (http://www.genome.gov/gwastudies). Based on the most recent summary data of dbSNP database (http://www.ncbi.nlm.nih.gov/projects/SNP), there are ~ $38 million (about 1 percent of the total genome) of validated SNPs in human genome. However, even the densest SNP array on the market can only accommodate ~1 million SNPs, and hence a great percentage of SNPs is not able to be sampled in a real genetic study. Fortunately, SNPs in the genome are not independent from each other, i.e. they are locally connected and form the socalled linkage disequilibrium (LD) blocks. Because of this unique correlation structure, the sampled genetic markers carry partial information about the unsampled SNPs and may be used for genomewide association analyses.
where D^{(t+1)} is the LD value at generation t + 1 and r is the recombination rate between the two loci. Therefore, the LD value approaches to zero gradually at a geometric rate of 1r. The larger the r, the faster the rate of convergence. According to Equation ([1]), if a significant D^{(t+1)} value can be detected in the current generation, it implies r must be very small, almost close to 0, under the assumption that the initial LD was generated long time ago (i.e. t is large). This assumption is plausible because it does take a long time for mutations/LD to be spread in a population. Therefore, the principle of linkage disequilibrium decaying with generation builds up an alternative mapping strategy [10, 11], which provides an important tool for the fine mapping of genes affecting a quantitative trait.
The LD mapping based on a single marker has been greatly studied [12–14]. However, little effort has been put on the LD mapping with multiple markers. Motivated by the seminal work of interval mapping proposed by Lander and Botstein in 1989 [15], in which genetic mapping was performed based on two neighboring genetic markers in controlled experiments, we propose to develop a new LD mapping framework that utilizes two SNP markers in a natural population. The new model explicitly incorporates the LD information between two markers into the mapping analysis, and thus we expect the analysis based on two markers is more powerful than that based on a single marker in a natural population just as Lander and Botstein have discovered in the controlled experiment. In the following sections, we first laid out the modeling framework for the twomarker LD mapping (tmLD), with details on parameter estimation and hypothesis testing. We then further elucidated our method through extensive simulation studies. Finally, we applied our method to a GWAS dental caries data set, followed by some discussions.
Methods
Twomarker LD (tmLD) mapping
and ${\mathit{D}}_{\mathit{ijk}}=\frac{1}{2}\left[{\left(1\right)}^{\left\mathit{i}\mathit{j}\right}{\mathit{D}}_{12}+{\left(1\right)}^{\left\mathit{j}\mathit{k}\right}{\mathit{D}}_{23}+{\left(1\right)}^{\left\mathit{i}\mathit{k}\right}{\mathit{D}}_{13}{\left(1\right)}^{\left\mathit{i}+\mathit{j}+\mathit{k}1\right}{\mathit{D}}_{123}\right]$ where i, j, k = 0, 1, D_{12}, D_{23}, D_{13} have exactly the same meaning as those in digenic disequilibria models for loci at positions 1/2, 2/3 and 1/3; and D_{123} is an additional trigenic disequilibria parameter for three loci together. Model (1) implies that D_{12}, D_{23}, D_{13} all geometrically decay with generations. It can be shown that with some reasonable assumptions, the D_{123} decreases with generations at a rate of (1r_{13}) and therefore also changes very slowly with time (Additional file 2). Hence, significant D_{12}, D_{23}, and D_{123} at current generation imply r_{12}and r_{23} are very small, which form the basis for LD mapping using two genetic markers.
Likelihood function
Joint zygote probabilities of the QTL genotypes at QTL Q and twomarker genotypes at markers M1 and M2, as expressed in terms of zygote configurations in a natural population
Marker  Joint markerQTL genotype frequency  

Genotype  Frequency  qq(0)  Qq(1)  QQ(2)  
m _{1} m _{1} m _{2} m _{2}  (00)  ${\mathit{p}}_{00}^{2}$  ${\mathit{p}}_{000}^{2}$  2p_{010}p_{000}  ${\mathit{p}}_{010}^{2}$ 
(n_{000})  (n_{010})  (n_{020})  
m _{1} m _{1} M _{2} m _{2}  (01)  2p_{01}p_{00}  2p_{001}p_{000}  2p_{011}p_{000} + 2p_{010}p_{001}  2p_{010}p_{011} 
(n_{001})  (n_{011})  (n_{021})  
m _{1} m _{1} M _{2} M _{2}  (02)  ${\mathit{p}}_{01}^{2}$  ${\mathit{p}}_{001}^{2}$  2p_{011}p_{001}  ${\mathit{p}}_{011}^{2}$ 
(n_{002})  (n_{012})  (n_{022})  
M _{1} m _{1} m _{2} m _{2}  (10)  2p_{00}p_{10}  2p_{100}p_{000}  2p_{110}p_{000} + 2p_{100}p_{010}  2p_{110}p_{010} 
(n_{100})  (n_{110})  (n_{120})  
M _{1} m _{1} M _{2} m _{2}  (11)  2p_{11}p_{00}  2p_{101}p_{000} + 2p_{100}p_{001}  2p_{111}p_{000} + 2p_{110}p_{001}  2p_{111}p_{010} + 2p_{110}p_{011} 
+ 2p_{10}p_{01}  + 2p_{101}p_{010} + 2p_{100}p_{011}  
(n_{101})  (n_{111})  (n_{121})  
M _{1} m _{1} M _{2} M _{2}  (12)  2p_{11}p_{01}  2p_{101}p_{001}  2p_{111}p_{001} + 2p_{101}p_{011}  2p_{111}p_{011} 
(n_{102})  (n_{112})  (n_{122})  
M _{1} M _{1} m _{2} m _{2}  (20)  ${\mathit{p}}_{10}^{2}$  ${\mathit{p}}_{100}^{2}$  2p_{110}p_{100}  ${\mathit{p}}_{110}^{2}$ 
(n_{200})  (n_{210})  (n_{220})  
M _{1} M _{1} M _{2} m _{2}  (21)  2p_{11}p_{10}  2p_{101}p_{100}  2p_{111}p_{100} + 2p_{110}p_{101}  2p_{110}p_{111} 
(n_{201})  (n_{211})  (n_{221})  
M _{1} M _{1} M _{2} M _{2}  (22)  ${\mathit{p}}_{11}^{2}$  ${\mathit{p}}_{101}^{2}$  2p_{111}p_{101}  ${\mathit{p}}_{111}^{2}$ 
(n_{202})  (n_{212})  (n_{222}) 
Computational algorithms
Within the maximum likelihood estimation framework, an efficient EM algorithm can be implemented to obtain the MLEs of (Ω_{ p }, Ω_{ q }), and is summarized into the following steps:
Step 1. Give initial values for the unknown parameters (Ω_{ p }, Ω_{ q });
Step 2. E step – Calculate the posterior probabilities for each subject i to carry a particular QTL genotype j using the equation ${\mathrm{\Pi}}_{\mathit{j}\mathit{i}}=\frac{{\mathit{\pi}}_{\mathit{j}\mathit{i}}{\mathit{f}}_{\mathit{j}}\left({\mathit{y}}_{\mathit{i}}{\mathrm{\Omega}}_{\mathit{q}}\right)}{{\displaystyle {\sum}_{\mathit{j}=0}^{2}}{\mathit{\pi}}_{\mathit{j}\mathit{i}}{\mathit{f}}_{\mathit{j}}\left({\mathit{y}}_{\mathit{i}}{\mathrm{\Omega}}_{\mathit{q}}\right)}.$
Step 3. M step – Solve the loglikelihood equations for each parameter based on observed data and Π_{ji} to obtain its estimate. To estimate the quantitative genetic parameters (Ω_{ q }), their expressions in closed forms can be derived based on the estimation equations. For the estimates of the population genetic parameters (Ω_{ p }), another inner layer of EM algorithm can be employed.
Step 4. Repeat the E and M steps until the estimates converge to stable values. The estimates at convergence are the MLEs of parameters.
The detailed derivation for the EM algorithm is given in Additional file 3.
Hypothesis testing
The estimates of the parameters under the null hypotheses can be obtained with the same EM algorithm derived for the alternative hypotheses, but with a constraint that all subjects have the same posterior probability. A likelihood ratio test (LRT) statistics can be constructed and computed to draw the inference about whether a QTL may be associated with given markers. Under the H_{0}, the LRT statistics asymptotically follows a χ^{2}distribution with three degrees of freedom.
Results
Simulation settings
Let us randomly choose a sample of n subjects from a human population at HardyWeinberg equilibrium. In this population, one QTL is segregating and is inferred by a pair of markers. The allele frequencies of the markers (ℳ_{1} and ℳ_{2}) and QTL ($\mathcal{Q}$) and their linkage disequilibria values are given as follows: p_{1} = 0.5 for allele M_{1} of ℳ_{1}; p_{2} = 0.5 for allele Q of $\mathcal{Q}$; p_{3} = 0.5 for allele M_{2} of ℳ_{2}. The LD parameters among the markers and QTL loci are given as: D_{12} = 0.05, D_{13} = 0.15, D_{23} = 0.05 and D_{123} = 0.04. For subjects who carry QTL genotype j, their phenotypic values were simulated based on Model (3), with μ_{2} = 10, μ_{1} = 5, μ_{0} = 0. The variances in phenotypic values were calculated based on different heritability values (H^{2}). H^{2} quantifies the genetic contribution from the QTL to the overall trait and H^{2} = 0 implies that the means for three QTL genotype groups are the same, which are set to be 0. With the above given parameters and design, we simulated the phenotypic and marker information by assuming different sample sizes (N = 100, 250, 500, 1000, 1500, 2000, 2500, 3000), and different heritability values (H^{2} = 0, 0.05, 0.1, 0.2, 0.3, 0.4). Each simulation setting is carried out 1000 times for the evaluation of power and type I error.
Type I error evaluation and power comparison
Simulated data were used to compare our proposed tmLD method with singlemarker based association analyses, including the singlemarker LD mapping method (smLD) and singlemarker based association test (smAT), and twomarker based haplotype analysis (haplo). The smLD was performed as described in Additional file 4. The smAT is a simple linear regression model with phenotypic trait as response variable and marker genotypes as categorical independent variable. The haplotype analysis was conducted as described in [16]; briefly, the haplotype that yields the best model fitting among those formed by two markers is used in comparison with tmLD.
Real data example
Dental caries or cavities, more commonly known as tooth decay, is one of the most common chronic disorders in humans, affecting approximately 40% children and adolescents and 90% adults in the US. The etiology and pathogenesis of dental caries have been determined to be multifactorial, such as environmental factors related to social behaviors [17]. However, it is also apparent that some individuals are very susceptible to caries while some others are more resistant, almost irrelevant to the environmental risk factors they are exposed to, suggesting that genetic factors may play prominent roles in the caries development. Supported by evidence in both human and animal studies [18–21], the caries heritability has been estimated to be between 3060%. The most compelling evidence come from the twin studies that the significant resemblance of dental caries lies within monozygotic but not dizygotic twin pairs [22, 23]. So it is without question that in addition to environmental factors, genetic components also profoundly influence the dental caries trait. To understand the genetic mechanisms of the dental caries, a GWAS study has been conducted and the dataset has been deposited in dbGaP (Study Accession: phs000095.v2.p1). Here we will apply our proposed model to analyze this caries GWAS dataset, in which 1843 adults were genotyped with a large panel of SNPs (610,000). We carried out the analysis using the caries outcomes that have been well defined in other GWAS studies, i.e. the D1MFT index which quantifies the total permanent tooth caries with white spots.
List of significant SNPs with pvalue < 1.1e7 in the Caries dataset
SNP ID  Gene  Chr  Coordinate  Allele  MAF  P_{smAT}  P_{smLD}  P_{haplo}  P_{tmLD} 

rs7607421  –  2  220500564  C/T  0.390  3.2E08 ^{ * }  2.1E04  2.0E06  6.9E05 
rs10790497  CNTN5  11  98539071  A/G  0.346  8.8E01  8.2E01  1.7E03  2.6E08 ^{ ‡ } 
rs7319311  COL4A2  13  109828579  A/G  0.326  5.8E02  2.7E02  2.8E02  1.0E07 ^{ ‡ } 
Discussion
It is well recognized that naturally occurring variations in most complex disease traits have a genetic basis and consequently many GWAS studies have been conducted in the past few years. In analyzing these data, a phenomenon, called “missing heritability”, has been observed that the detected genetic variants can explain only a small portion of the heritability of phenotypic traits while a majority part remains mysterious [25]. Part of the reason may be attributed to the lack of power in current methods. Thus, developing novel and powerful methods to better detect significant genes has been of great interest. Currently the routine GWAS analyses seek singlemarker association between SNPs and phenotype, and when a significant association is detected, it implies that there might be some SNP(s) in linkage that are causal. Note that it cannot imply the test SNP itself is causal because there is no guarantee that the truly causal SNPs would have been genotyped. Since the interpretation of a significant association relies on the linkage concept, it is sensible to directly incorporate the LD information into association models. Additionally, due to the structure of LD blocks, a causal SNP is usually in linkage with multiple neighboring SNPs, all of which carry partial information about it. So in this sense, a new model that can incorporates more genetic information of linked SNPs should draw better inferences about the causal SNP.
In this article, we proposed a novel statistical method by considering two SNPs simultaneously. Our model is built upon the general LD mapping framework, and extends the previous methods based on singlemarker LD. The simulation studies demonstrated that our new methods dramatically improved the detection power of the underlying QTLs. This is intuitively reasonable since our model can capture the linkage information between SNP markers, and hence has more power to detect the particular QTL that are in LD with both markers. Furthermore, the simulation studies indicated that even when the underlying QTL is indeed genotyped and is one of the markers, the performance of the tmLD analysis is nearly identical to that of the optimal test resulting from the causal SNP, suggesting the robustness of our model.
We applied our model to a GWAS date set that aimed to understand the genetic mechanisms of the dental caries. The data set contains a large cohort of 1,843 subjects as well as a very large number of SNPs (443,175). This shows that both our proposed method and the corresponding software package in R can be well applied to a typical GWAS data set. In addition, we also observed that the association analyses based on the singlemarker and the twomarker models yielded different profiles of significant SNPs. This is somewhat expected since their assumptions are different. For the tmLD method, we assume that both markers must obey HWE and have to be in LD with the casual SNP. It might be possible that some SNPs would violate these assumptions and become unsuitable to the tmLD. In this sense, the single and twomarker analyses may be complementary to each other, and therefore it might be beneficial to use both methods in analyzing a real data set.
Sometimes population structure may be a concern in a GWAS analysis if subpopulations indeed exist in the sample, as it may lead to spurious associations. Several wellknown methods developed to account for population structure [26] can be incorporated into our LD mapping framework to address this issue. For instance, the principal component analysis (PCA) can be applied to correct for stratifications [27]. That is, we may first apply PCA on the genotype data and then choose the first few large principal components to be included in the Model (3) as additional covariates. With slight modifications, the computation algorithms and hypothesis testing described in the Method section can be readily applied.
In this work, we generalized the single marker LD analysis to a more general LD mapping framework using two adjacent markers. There are several ongoing works worthy of further investigation. First, the model can be easily extended to other types of phenotypic data, such as case–control binary and count data. Second, currently the two adjacent markers were used for the analysis; however, it is possible that another two markers in the same LD block might have better power, so it would be very interesting to determine how to choose the best SNP pair. Third, typically, one LD block may contain several SNPs, and if there exists one causal SNP within the LD block, it would be very interesting to see if we can summarize all SNPs in one LD block to make even better inference about the unobserved QTL.
Conclusions
The proposed tmLD model is a novel mapping method that can simultaneously consider two linked SNPs in a natural population. Through the extensive simulation studies, the tmLD method demonstrates better power than singlemarker mapping strategies traditionally used in GWAS association analysis. The practical usage of the tmLD method was also shown in the analysis of a largescale dental GWAS dataset. Hence, we recommend the usage of this improved method over the traditional singlemarker association analysis.
Software availability
Abbreviations
 LD:

Linkage disequilibrium
 SNP:

Singlenucleotide polymorphism
 QTL:

Quantitative trait loci
 GWAS:

Genomewise association study
 smAT:

Singlemarker association test
 smLD:

Singlemarker linkage disequilibrium method
 tmLD:

Twomarker linkage disequilibrium method
 haplo:

Twomarker based haplotype analysis
 MAF:

Minor allele frequency
 HWE:

HardyWeinberg equilibrium.
Declarations
Acknowledgements
The authors would like to thank the anonymous reviewers for their valuable comments and suggestions that have helped improve the quality of the paper significantly. This work is partly supported by the FUSION award from the Stony Brook University to SW.
The dataset used in the real data example was obtained from dbGaP through dbGaP accession number [phs000095]. Funding support for collecting this dataset was provided by the National Institute of Dental and Craniofacial Research (NIDCR, grant number U01DE018903). Data and samples were provided by: (1) the Center for Oral Health Research in Appalachia (NIDCR R01DE 014899); (2) the University of Pittsburgh School of Dental Medicine (SDM) DNA Bank and Research Registry (NIH/NCRR/CTSA Grant UL1RR024153); (3) the Iowa Fluoride Study and the Iowa Bone Development Study (NIDCR R01DE09551and R01DE12101); and (4) the Iowa Comprehensive Program to Investigate Craniofacial and Dental Anomalies (NIDCR, P60DE013076).
Authors’ Affiliations
References
 Lynch M, Waslsh B: Genetics and analysis of quantitative traits. 1998, Sunderland, MA: Sinauer Associates, Inc.Google Scholar
 Wu RL, Ma CX, Casella G: Statistical Genetics of Quantitative Traits: Linkage, Map and QTL. 2007, New York: SpringerVerlagGoogle Scholar
 Lewontin RC: The interaction of selection and linkage. I. General considerations; heterotic models. Genetics. 1964, 49 (1): 4967.PubMedPubMed CentralGoogle Scholar
 Hedrick PW: Gametic disequilibrium measures: proceed with caution. Genetics. 1987, 117 (2): 331341.PubMedPubMed CentralGoogle Scholar
 Weir BS: Genetic data analysis II. 1996, Sunderland, MA: Sinauer AssociatesGoogle Scholar
 Kruglyak L: Genetic isolates: separate but equal?. Proc Natl Acad Sci U S A. 1999, 96 (4): 11701172. 10.1073/pnas.96.4.1170.PubMedPubMed CentralView ArticleGoogle Scholar
 Farnir F, Grisart B, Coppieters W, Riquet J, Berzi P, Cambisano N, Karim L, Mni M, Moisio S, Simon P, Wagenaar D, Vilkki J, Georges M: Simultaneous mining of linkage and linkage disequilibrium to fine map quantitative trait loci in outbred halfsib pedigrees: revisiting the location of a quantitative trait locus with major effect on milk production on bovine chromosome 14. Genetics. 2002, 161 (1): 275287.PubMedPubMed CentralGoogle Scholar
 McRae AF, McEwan JC, Dodds KG, Wilson T, Crawford AM, Slate J: Linkage disequilibrium in domestic sheep. Genetics. 2002, 160 (3): 11131122.PubMedPubMed CentralGoogle Scholar
 Liu T, Todhunter RJ, Lu Q, Schoettinger L, Li HY, Littell RC, BurtonWurster N, Acland GM, Lust G, Wu RL: Modeling extent and distribution of zygotic disequilibrium: implications for a multigenerational canine pedigree. Genetics. 2006, 174 (1): 439453. 10.1534/genetics.106.060137.PubMedPubMed CentralView ArticleGoogle Scholar
 Lou XY, Casella G, Todhunter RJ, Yang MCK, Wu RL: A general statistical framework for unifying interval and linkage disequilibrium mapping: toward highresolution mapping of quantitative traits. J Am Stat Assoc. 2005, 100 (469): 158171. 10.1198/016214504000001295.View ArticleGoogle Scholar
 Weiss KM, Clark AG: Linkage disequilibrium and the mapping of complex human traits. Trends Genet. 2002, 18 (1): 1924. 10.1016/S01689525(01)025501.PubMedView ArticleGoogle Scholar
 Wu R, Ma CX, Casella G: Joint linkage and linkage disequilibrium mapping of quantitative trait loci in natural populations. Genetics. 2002, 160 (2): 779792.PubMedPubMed CentralGoogle Scholar
 Wu R, Zeng ZB: Joint linkage and linkage disequilibrium mapping in natural populations. Genetics. 2001, 157 (2): 899909.PubMedPubMed CentralGoogle Scholar
 Wang Z, Wu R: A statistical model for highresolution mapping of quantitative trait loci determining HIV dynamics. Stat Med. 2004, 23 (19): 30333051. 10.1002/sim.1870.PubMedView ArticleGoogle Scholar
 Lander ES, Botstein D: Mapping mendelian factors underlying quantitative traits using rflp linkage maps. Genetics. 1989, 121 (1): 185199.PubMedPubMed CentralGoogle Scholar
 Wu S, Yang J, Wang C, Wu R: A general quantitative genetic model for haplotyping a complex trait in humans. Curr Genomics. 2007, 8 (5): 343350. 10.2174/138920207782446179.PubMedPubMed CentralView ArticleGoogle Scholar
 Ditmyer MM, Dounis G, Howard KM, Mobley C, Cappelli D: Validation of a multifactorial risk factor model used for predicting future caries risk with Nevada adolescents. BMC Oral Health. 2011, 11: 1810.1186/147268311118.PubMedPubMed CentralView ArticleGoogle Scholar
 Boraas JC, Messer LB, Till MJ: A genetic contribution to dentalcaries, occlusion, and morphology as demonstrated by twins reared apart. J Dent Res. 1988, 67 (9): 11501155. 10.1177/00220345880670090201.PubMedView ArticleGoogle Scholar
 Bretz WA, Corby PM, Schork NJ, Robinson MT, Coelho M, Costa S, Melo MR, Weyant RJ, Hart TC: Longitudinal analysis of heritability for dental caries traits. J Dent Res. 2005, 84 (11): 10471051. 10.1177/154405910508401115.PubMedPubMed CentralView ArticleGoogle Scholar
 Bretz WA, Corby PMA, Melo MR, Coelho MQ, Costa SM, Robinson M, Schork NJ, Drewnowski A, Hart TC: Heritability estimates for dental caries and sucrose sweetness preference. Arch Oral Biol. 2006, 51 (12): 11561160. 10.1016/j.archoralbio.2006.06.003.PubMedView ArticleGoogle Scholar
 Goodman HO, Luke JE, Rosen S, Hackel E: Heritability in dental caries, certain oral microflora and salivary components. Am J Hum Genet. 1959, 11 (3): 263273.PubMedPubMed CentralGoogle Scholar
 Bretz WA, Corby PMA, Hart TC, Costa S, Coelho MQ, Weyant RJ, Robinson M, Schork NJ: Dental caries and microbial acid production in twins. Caries Res. 2005, 39 (3): 168172. 10.1159/000084793.PubMedView ArticleGoogle Scholar
 Liu H, Deng H, Cao CF, Ono H: Genetic analysis of dental traits in 82 pairs of femalefemale twins. Chin J Dent Res. 1998, 1 (3): 1216.PubMedGoogle Scholar
 Bueno DF, Sunaga DY, Kobayashi GS, Aguena M, RaposoAmaral CE, Masotti C, Cruz LA, Pearson PL, PassosBueno MR: Human stem cell cultures from cleft lip/palate patients show enrichment of transcripts involved in extracellular matrix modeling by comparison to controls. Stem Cell Rev. 2011, 7 (2): 446457. 10.1007/s1201501091973.PubMedPubMed CentralView ArticleGoogle Scholar
 Zuk O, Hechter E, Sunyaev SR, Lander ES: The mystery of missing heritability: genetic interactions create phantom heritability. Proc Natl Acad Sci U S A. 2012, 109 (4): 11931198. 10.1073/pnas.1119675109.PubMedPubMed CentralView ArticleGoogle Scholar
 Wu C, DeWan A, Hoh J, Wang Z: A comparison of association methods correcting for population stratification in case–control studies. Ann Hum Genet. 2011, 75 (3): 418427. 10.1111/j.14691809.2010.00639.x.PubMedPubMed CentralView ArticleGoogle Scholar
 Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D: Principal components analysis corrects for stratification in genomewide association studies. Nat Genet. 2006, 38 (8): 904909. 10.1038/ng1847.PubMedView ArticleGoogle Scholar
Copyright
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.