Family-based designs in the age of large-scale gene-association studies

BMP4 was associated with NSCL/P in an Asian population

BACKGROUND

The Bone Morphogenetic Protein 4 gene (BMP4) is located in chromosome 14q22-q23 which has shown evidence of linkage for isolated nonsyndromic cleft lip with or without cleft palate (NSCL/P) in a genome wide linkage analysis of human multiplex families. BMP4 has been shown to play crucial roles in lip and palatal development in animal models. Several candidate gene association analyses also supported its potential risk for NSCL/P, however, results across these association studies have been inconsistent. The aim of the current study was to test for possible association between markers in and around the BMP4 gene and NSCL/P in Asian and Maryland trios.

METHODOLOGY/PRINCIPAL FINDINGS

Family Based Association Test was used to test for deviation from Mendelian assortment for 12 SNPs in and around BMP4. Nominal significant evidence of linkage and association was seen for three SNPs (rs10130587, rs2738265 and rs2761887) in 221 Asian trios and for one SNP (rs762642) in 76 Maryland trios. Statistical significance still held for rs10130587 after Bonferroni correction (corrected p = 0.019) among the Asian group. Estimated odds ratio for carrying the apparent high risk allele at this SNP was 1.61 (95%CI = 1.20, 2.18).

CONCLUSIONS

Our results provided further evidence of association between BMP4 and NSCL/P.

Evaluation of the LIM homeobox genes LHX6 and LHX8 as candidates for Tourette syndrome

The etiology and pathophysiology of Tourette Syndrome (TS) remain poorly understood. Multiple lines of evidence suggest that a complex genetic background and the cortico-striato-thalamo-cortical circuit are involved. The role of Lhx6 and Lhx8 in the development of the striatal interneurons, prompted us to investigate them as novel candidate genes for TS. We performed a comparative study of the expression of Lhx6 and Lhx8 and investigated genetic association with TS using two samples of trios (TSGeneSEE and German sample - 222 families). We show that Lhx6 and Lhx8 expression in the forebrain is evolutionarily conserved, underlining their possible importance in TS-related pathophysiological pathways. Our tagging-single nucleotide polymorphism (tSNP)-based association analysis was negative for association with LHX8. However, we found positive association with LHX6 in the TSGeneSEE sample (corrected P-value = 0.006 for three-site haplotype around SNP rs3808901) but no association in the sample of German families. Interestingly, the SNP allele that was identified to be significantly associated in the TSGeneSEE dataset, showed an opposite trend of transmission in the German dataset. Our analysis of the correlation of the LHX6 region with individual ancestry within Europe, revealed the fact that this particular SNP demonstrates a high degree of population differentiation and is correlated with the North to South axis of European genetic variation. Our results indicate that further study of the LHX6 gene in relation to the TS phenotype is warranted and suggest the intriguing hypothesis that different genetic factors may contribute to the etiology of TS in different populations, even within Europe.

Variants of NOD1 and NOD2 genes display opposite associations with familial risk of Crohn's disease and anti-saccharomyces cerevisiae antibody levels

BACKGROUND

NOD2 is involved in Crohn's disease (CD), but the role of NOD1 remains unclear. Anti-Saccharomyces cerevisiae antibodies (ASCA) are higher in CD patients and some of their relatives. Using family-based analyses we investigated the relationships between NOD2 mutations, NOD1 +32656 variant, and both the risk of CD and ASCA levels. We compared allelic frequencies between families with multiple CD cases (multiplex), those with one case of CD (simplex), and control families, searching for a gradient of at risk alleles according to the prevalence of the disease among families.

METHODS

In all, 93 CD patients, 160 healthy relatives from 22 multiplex families, 22 CD patients and 81 healthy relatives from 22 simplex families, and 169 subjects from 27 control families were included in the study. ASCA levels were determined by enzyme-linked immunosorbent assay. NOD1 +32656, NOD2 R702W, G908R, and 1007fs were genotyped by polymerase chain reaction / restriction fragment length polymorphism.

RESULTS

In family-based analyses NOD2 mutations and the NOD1 wildtype allele were associated with CD in multiplex families, with a synergetic effect when risk alleles of both genes were transmitted. Lower ASCA levels were strongly associated with the NOD1 variant allele. Simplex families had a lower frequency of the "at risk" +32656 allele than multiplex families.

CONCLUSIONS

The +32656 variant was associated with low ASCA level and low risk of CD in multiplex families. NOD2 and NOD1 variants displayed antagonist effects on the risk of CD and ASCA level. A gradient of NOD1, NOD2 at-risk alleles was associated with the variable prevalence of CD in families.

Genetic association analysis using sibship data: a multilevel model approach

Family based association study (FBAS) has the advantages of controlling for population stratification and testing for linkage and association simultaneously. We propose a retrospective multilevel model (rMLM) approach to analyze sibship data by using genotypic information as the dependent variable. Simulated data sets were generated using the simulation of linkage and association (SIMLA) program. We compared rMLM to sib transmission/disequilibrium test (S-TDT), sibling disequilibrium test (SDT), conditional logistic regression (CLR) and generalized estimation equations (GEE) on the measures of power, type I error, estimation bias and standard error. The results indicated that rMLM was a valid test of association in the presence of linkage using sibship data. The advantages of rMLM became more evident when the data contained concordant sibships. Compared to GEE, rMLM had less underestimated odds ratio (OR). Our results support the application of rMLM to detect gene-disease associations using sibship data. However, the risk of increasing type I error rate should be cautioned when there is association without linkage between the disease locus and the genotyped marker.

Genetic analysis: moving between linkage and association

The approaches to identifying genes and genomic regions associated with human disease can be grouped into two categories: linkage analysis and genetic association analysis. Linkage analysis is useful for diseases of high penetrance that run strongly within families, but is limited in its ability to detect situations where there are multiple genes with smaller effects. An alternative is genetic association studies, which were initially performed on small numbers of candidate genes. This approach identified relatively few genes that were consistently associated with disease, but it is now possible to do a genetic association for the whole genome, making this approach more powerful. In practice, the two types of analysis are often interlinked. This article provides information on the tools needed to perform both genetic linkage and genetic association analysis.

On the meta-analysis of genome-wide association studies: a robust and efficient approach to combine population and family-based studies

For the meta-analysis of genome-wide association studies, we propose a new method to adjust for the population stratification and a linear mixed approach that combines family-based and unrelated samples. The proposed approach achieves similar power levels as a standard meta-analysis which combines the different test statistics or p values across studies. However, by virtue of its design, the proposed approach is robust against population admixture and stratification, and no adjustments for population admixture and stratification, even in unrelated samples, are required. Using simulation studies, we examine the power of the proposed method and compare it to standard approaches in the meta-analysis of genome-wide association studies. The practical features of the approach are illustrated with a meta-analysis of three genome-wide association studies for Alzheimer's disease. We identify three single nucleotide polymorphisms showing significant genome-wide association with affection status. Two single nucleotide polymorphisms are novel and will be verified in other populations in our follow-up study.

Family-based association tests using genotype data with uncertainty

Family-based association studies have been widely used to identify association between diseases and genetic markers. It is known that genotyping uncertainty is inherent in both directly genotyped or sequenced DNA variations and imputed data in silico. The uncertainty can lead to genotyping errors and missingness and can negatively impact the power and Type I error rates of family-based association studies even if the uncertainty is independent of disease status. Compared with studies using unrelated subjects, there are very few methods that address the issue of genotyping uncertainty for family-based designs. The limited attempts have mostly been made to correct the bias caused by genotyping errors. Without properly addressing the issue, the conventional testing strategy, i.e. family-based association tests using called genotypes, can yield invalid statistical inferences. Here, we propose a new test to address the challenges in analyzing case-parents data by using calls with high accuracy and modeling genotype-specific call rates. Our simulations show that compared with the conventional strategy and an alternative test, our new test has an improved performance in the presence of substantial uncertainty and has a similar performance when the uncertainty level is low. We also demonstrate the advantages of our new method by applying it to imputed markers from a genome-wide case-parents association study.

Rapid testing of SNPs and gene-environment interactions in case-parent trio data based on exact analytic parameter estimation

Case-parent trio studies concerned with children affected by a disease and their parents aim to detect single nucleotide polymorphisms (SNPs) showing a preferential transmission of alleles from the parents to their affected offspring. A popular statistical test for detecting such SNPs associated with disease in this study design is the genotypic transmission/disequilibrium test (gTDT) based on a conditional logistic regression model, which usually needs to be fitted by an iterative procedure. In this article, we derive exact closed-form solutions for the parameter estimates of the conditional logistic regression models when testing for an additive, a dominant, or a recessive effect of a SNP, and show that such analytic parameter estimates also exist when considering gene-environment interactions with binary environmental variables. Because the genetic model underlying the association between a SNP and a disease is typically unknown, it might further be beneficial to use the maximum over the gTDT statistics for the possible effects of a SNP as test statistic. We therefore propose a procedure enabling a fast computation of the test statistic and the permutation-based p-value of this MAX gTDT. All these methods are applied to whole-genome scans of the case-parent trios from the International Cleft Consortium. These applications show our procedures dramatically reduce the required computing time compared to the conventional iterative methods allowing, for example, the analysis of hundreds of thousands of SNPs in a few minutes instead of several hours.

Genome-wide association study in German patients with attention deficit/hyperactivity disorder

The heritability of attention deficit hyperactivity disorder (ADHD) is approximately 0.8. Despite several larger scale attempts, genome-wide association studies (GWAS) have not led to the identification of significant results. We performed a GWAS based on 495 German young patients with ADHD (according to DSM-IV criteria; Human660W-Quadv1; Illumina, San Diego, CA) and on 1,300 population-based adult controls (HumanHap550v3; Illumina). Some genes neighboring the single nucleotide polymorphisms (SNPs) with the lowest P-values (best P-value: 8.38 × 10(-7)) have potential relevance for ADHD (e.g., glutamate receptor, metabotropic 5 gene, GRM5). After quality control, the 30 independent SNPs with the lowest P-values (P-values ≤ 7.57 × 10(-5) ) were chosen for confirmation. Genotyping of these SNPs in up to 320 independent German families comprising at least one child with ADHD revealed directionally consistent effect-size point estimates for 19 (10 not consistent) of the SNPs. In silico analyses of the 30 SNPs in the largest meta-analysis so far (2,064 trios, 896 cases, and 2,455 controls) revealed directionally consistent effect-size point estimates for 16 SNPs (11 not consistent). None of the combined analyses revealed a genome-wide significant result. SNPs in previously described autosomal candidate genes did not show significantly lower P-values compared to SNPs within random sets of genes of the same size. We did not find genome-wide significant results in a GWAS of German children with ADHD compared to controls. The second best SNP is located in an intron of GRM5, a gene located within a recently described region with an infrequent copy number variation in patients with ADHD.

The FGF and FGFR Gene Family and Risk of Cleft Lip With or Without Cleft Palate

Background : Isolated, nonsyndromic cleft lip with or without cleft palate is a common human congenital malformation with a complex and heterogeneous etiology. Genes coding for fibroblast growth factors and their receptors (FGF/FGFR genes) are excellent candidate genes. Methods : We tested single-nucleotide polymorphic markers in 10 FGF/FGFR genes (including FGFBP1, FGF2, FGF10, FGF18, FGFR1, FGFR2, FGF19, FGF4, FGF3, and FGF9) for genotypic effects, interactions with one another, and with common maternal environmental exposures in 221 Asian and 76 Maryland case-parent trios ascertained through a child with isolated, nonsyndromic cleft lip with or without cleft palate. Results : Both FGFR1 and FGF19 yielded evidence of linkage and association in the transmission disequilibrium test, confirming previous evidence. Haplotypes of three single-nucleotide polymorphisms in FGFR1 were nominally significant among Asian trios. Estimated odds ratios for individual single-nucleotide polymorphic markers and haplotypes of multiple markers in FGF19 ranged from 1.31 to 1.87. We also found suggestive evidence of maternal genotypic effects for markers in FGF2 and FGF10 among Asian trios. Tests for gene-environment (G × E) interaction between markers in FGFR2 and maternal smoking or multivitamin supplementation yielded significant evidence of G × E interaction separately. Tests of gene-gene (G × G) interaction using Cordell's method yielded significant evidence between single-nucleotide polymorphisms in FGF9 and FGF18, which was confirmed in an independent sample of trios from an international consortium. Conclusion : Our results suggest several genes in the FGF/FGFR family may influence risk for isolated, nonsyndromic cleft lip with or without cleft palate through distinct biological mechanisms.

Genome-wide association study reveals class I MHC-restricted T cell-associated molecule gene (CRTAM) variants interact with vitamin D levels to affect asthma exacerbations

BACKGROUND

It has recently been shown that vitamin D deficiency can increase asthma development and severity and that variations in vitamin D receptor genes are associated with asthma susceptibility.

OBJECTIVE

We sought to find genetic factors that might interact with vitamin D levels to affect the risk of asthma exacerbation.

METHODS

We conducted a genome-wide study of gene-vitamin D interaction on asthma exacerbations using population-based and family-based approaches on 403 subjects and trios from the Childhood Asthma Management Program. Twenty-three polymorphisms with significant interactions were studied in a replication analysis in 584 children from a Costa Rican cohort.

RESULTS

We identified 3 common variants in the class I MHC-restricted T cell-associated molecule gene (CRTAM) that were associated with an increased rate of asthma exacerbations based on the presence of a low circulating vitamin D level. These results were replicated in a second independent population (unadjusted combined interaction, P = .00028-.00097; combined odds ratio, 3.28-5.38). One variant, rs2272094, is a nonsynonymous coding polymorphism of CRTAM. Functional studies on cell lines confirmed the interaction of vitamin D and rs2272094 on CRTAM expression. CRTAM is highly expressed in activated human CD8(+) and natural killer T cells, both of which have been implicated in asthmatic patients.

CONCLUSION

The findings highlight an important gene-environment interaction that elucidates the role of vitamin D and CD8(+) and natural killer T cells in asthma exacerbation in a genome-wide gene-environment interaction study that has been replicated in an independent population. The results suggest the potential importance of maintaining adequate vitamin D levels in subsets of high-risk asthmatic patients.

A functional microsatellite of the macrophage migration inhibitory factor gene associated with meningococcal disease

Macrophage migration inhibitory factor (MIF) is an abundantly expressed proinflammatory cytokine playing a critical role in innate immunity and sepsis and other inflammatory diseases. We examined whether functional MIF gene polymorphisms (-794 CATT(5-8) microsatellite and -173 G/C SNP) were associated with the occurrence and outcome of meningococcal disease in children. The CATT(5) allele was associated with the probability of death predicted by the Pediatric Index of Mortality 2 (P=0.001), which increased in correlation with the CATT(5) copy number (P=0.04). The CATT(5) allele, but not the -173 G/C alleles, was also associated with the actual mortality from meningoccal sepsis [OR 2.72 (1.2-6.4), P=0.02]. A family-based association test (i.e., transmission disequilibrium test) performed in 240 trios with 1 afflicted offspring indicated that CATT(5) was a protective allele (P=0.02) for the occurrence of meningococcal disease. At baseline and after stimulation with Neisseria meningitidis in THP-1 monocytic cells or in a whole-blood assay, CATT(5) was found to be a low-expression MIF allele (P=0.005 and P=0.04 for transcriptional activity; P=0.09 and P=0.09 for MIF production). Taken together, these data suggest that polymorphisms of the MIF gene affecting MIF expression are associated with the occurrence, severity, and outcome of meningococcal disease in children.

AVPR1A variant associated with preschoolers' lower altruistic behavior

The genetic origins of altruism, defined here as a costly act aimed to benefit non-kin individuals, have not been examined in young children. However, previous findings concerning adults pointed at the arginine vasopressin receptor 1A (AVPR1A) gene as a possible candidate. AVPR1A has been associated with a range of behaviors including aggressive, affiliative and altruistic phenotypes, and recently a specific allele (327 bp) of one of its promoter region polymorphisms (RS3) has been singled out in particular. We modeled altruistic behavior in preschoolers using a laboratory-based economic paradigm, a modified dictator game (DG), and tested for association between DG allocations and the RS3 “target allele.” Using both population and family-based analyses we show a significant link between lower allocations and the RS3 “target allele,” associating it, for the first time, with a lower proclivity toward altruistic behavior in children. This finding helps further the understanding of the intricate mechanisms underlying early altruistic behavior.

Identification of new schizophrenia susceptibility loci in an ethnically homogeneous, family-based, Arab-Israeli sample

While the use of population-based samples is a common strategy in genome-wide association studies (GWASs), family-based samples have considerable advantages, such as robustness against population stratification and false-positive associations, better quality control, and the possibility to check for both linkage and association. In a genome-wide linkage study of schizophrenia in Arab-Israeli families with multiple affected individuals, we previously reported significant evidence for a susceptibility locus at chromosome 6q23.2-q24.1 and suggestive evidence at chromosomes 10q22.3-26.3, 2q36.1-37.3 and 7p21.1-22.3. To identify schizophrenia susceptibility genes, we applied a family-based GWAS strategy in an enlarged, ethnically homogeneous, Arab-Israeli family sample. We performed genome-wide single nucleotide polymorphism (SNP) genotyping and single SNP transmission disequilibrium test association analysis and found genome-wide significant association (best value of P=1.22×10(-11)) for 8 SNPs within or near highly reasonable functional candidate genes for schizophrenia. Of particular interest are a group of SNPs within and flanking the transcriptional factor LRRFIP1 gene. To determine replicability of the significant associations beyond the Arab-Israeli population, we studied the association of the significant SNPs in a German case-control validation sample and found replication of associations near the UGT1 subfamily and EFHD1 genes. Applying an exploratory homozygosity mapping approach as a complementary strategy to identify schizophrenia susceptibility genes in our Arab Israeli sample, we identified 8 putative disease loci. Overall, this GWAS, which emphasizes the important contribution of family based studies, identifies promising candidate genes for schizophrenia.

Association mapping

Association mapping seeks to identify marker alleles present at significantly different frequencies in cases carrying a particular disease or trait compared with controls. Genome-wide association studies are increasingly replacing candidate gene-based association studies for complex diseases, where a number of loci are likely to contribute to disease risk and the effect size of each particular risk allele is typically modest or low. Good study design is essential to the success of an association study, and factors such as the heritability of the disease under investigation, the choice of controls, statistical power, multiple testing and whether the association can be replicated need to be considered before beginning. Likewise, thorough quality control of the genotype data needs to be undertaken prior to running any association analyses. Finally, it should be kept in mind that a significant genetic association is not proof positive that a particular genetic locus causes a disease, but rather an important first step in discovering the genetic variants underlying a complex disease.

Association of HLA and post-schistosomal hepatic disorder: a systematic review and meta-analysis

Several human genetic variants, HLA antigens and alleles are reportedly linked to post-schistosomal hepatic disorder (PSHD), but the results from these reports are highly inconclusive. In order to estimate overall associations between human genetic variants, HLA antigens, HLA alleles and PSHD, we systematically reviewed and performed a meta-analysis of relevant studies in both post-schistosomal hepatic disorder and post-schistosomal non-hepatic disorder patients. PubMed, Scopus, Google Scholar, The HuGE Published Literature database, Cochrane Library, and manual search of reference lists of articles published before July 2009 were used to retrieve relevant studies. Two reviewers independently selected articles and extracted data on study characteristics and data regarding the association between genetic variants, HLA antigens, HLA alleles and PSHD in the form of 2×2 tables. A meta-analysis using fixed-effects or random-effects models to pooled odds ratios (OR) with corresponding 95% confidence intervals were calculated only if more than one study had investigated particular variation. We found 17 articles that met our eligibility criteria. Schistosoma mansoni and Schistosoma japonicum were reported as the species causing PSHD. Since human genetic variants were only investigated in one study, these markers were not assessed by meta-analysis. Thus, only HLA-genes (a total of 66 HLA markers) were conducted in the meta-analysis. Our meta-analysis showed that human leucocyte antigens HLA-DQB1*0201 (OR=2.64, P=0.018), DQB1*0303 (OR=1.93, P=0.008), and DRB1*0901 (OR=2.14, P=0.002) alleles and HLA-A1 (OR=5.10, P=0.001), A2 (OR=2.17, P=0.005), B5 (OR=4.63, P=0.001), B8 (OR=2.99, P=0.02), and B12 (OR=5.49, P=0.005) serotypes enhanced susceptibility to PSHD, whereas HLA-DQA1*0501 (OR=0.29, P≤0.001) and DQB1*0301 (OR=0.58, P=0.007) were protective factors against the disease. We further suggested that the DRB1*0901-DQB1*0201, DRB1*0901-DQB1*0303 and A1-B8 haplotypes enhanced susceptibility to PSHD, whereas DQA1*0501-DQB1*0301 linkage decreased the risk of PSHD. The result improved our understanding of the association between the HLA loci and PSHD with regard to pathogenic or protective T-cells and provided novel evidence that HLA alleles may influence disease severity.

Family-based designs for genome-wide association studies

Association mapping has successfully identified common SNPs associated with many diseases. However, the inability of this class of variation to account for most of the supposed heritability has led to a renewed interest in methods - primarily linkage analysis - to detect rare variants. Family designs allow for control of population stratification, investigations of questions such as parent-of-origin effects and other applications that are imperfectly or not readily addressed in case-control association studies. This article guides readers through the interface between linkage and association analysis, reviews the new methodologies and provides useful guidelines for applications. Just as effective SNP-genotyping tools helped to realize the potential of association studies, next-generation sequencing tools will benefit genetic studies by improving the power of family-based approaches.

Evaluation of methods accounting for population structure with pedigree data and continuous outcomes

Methods to account for population structure (PS) in genome-wide association studies have been well developed in samples of unrelated individuals, but when a sample is composed of families, the task of finding and accounting for PS is not as straight forward. Family-based tests that condition on parental genotypes or their sufficient statistics are immune to biases due to PS, but are known to have low power, particularly for unselected samples. Population-based approaches that use all available data are an attractive alternative, but the methods have not been evaluated for continuous outcomes when a sample has both family and PS. Therefore, we compare through simulation the performance of population-based regression models that account for family and PS with continuous outcomes using a range of family sizes and structures, including two and three generational families with admixed and discrete PS. We find that when computation time is a concern, the Dupuis et al. efficient score test performs very well. When computational time is not an issue, a linear mixed effects model adjusting for genetic principal components tends to have slightly better power than the score test and may be preferred.

Evidence for gene-environment interaction in a genome wide study of nonsyndromic cleft palate

Nonsyndromic cleft palate (CP) is a common birth defect with a complex and heterogeneous etiology involving both genetic and environmental risk factors. We conducted a genome-wide association study (GWAS) using 550 case-parent trios, ascertained through a CP case collected in an international consortium. Family-based association tests of single nucleotide polymorphisms (SNP) and three common maternal exposures (maternal smoking, alcohol consumption, and multivitamin supplementation) were used in a combined 2 df test for gene (G) and gene-environment (G × E) interaction simultaneously, plus a separate 1 df test for G × E interaction alone. Conditional logistic regression models were used to estimate effects on risk to exposed and unexposed children. While no SNP achieved genome-wide significance when considered alone, markers in several genes attained or approached genome-wide significance when G × E interaction was included. Among these, MLLT3 and SMC2 on chromosome 9 showed multiple SNPs resulting in an increased risk if the mother consumed alcohol during the peri-conceptual period (3 months prior to conception through the first trimester). TBK1 on chr. 12 and ZNF236 on chr. 18 showed multiple SNPs associated with higher risk of CP in the presence of maternal smoking. Additional evidence of reduced risk due to G × E interaction in the presence of multivitamin supplementation was observed for SNPs in BAALC on chr. 8. These results emphasize the need to consider G × E interaction when searching for genes influencing risk to complex and heterogeneous disorders, such as nonsyndromic CP.

New approaches to population stratification in genome-wide association studies

Genome-wide association (GWA) studies are an effective approach for identifying genetic variants associated with disease risk. GWA studies can be confounded by population stratification--systematic ancestry differences between cases and controls--which has previously been addressed by methods that infer genetic ancestry. Those methods perform well in data sets in which population structure is the only kind of structure present but are inadequate in data sets that also contain family structure or cryptic relatedness. Here, we review recent progress on methods that correct for stratification while accounting for these additional complexities.

Familial cutaneous lupus erythematosus (CLE) in the German shorthaired pointer maps to CFA18, a canine orthologue to human CLE

A familial form of lupus, termed exfoliative cutaneous lupus erythematosus (ECLE) has been recognized for decades in German shorthaired pointer dogs (GSP). Previous studies were suggestive of autosomal recessive inheritance. The disease presents as a severe dermatitis with age of onset between 16 and 40 weeks, and mirrors cutaneous lupus erythematosus (CLE) in humans. Lameness and, in advanced cases, renal disease may be present. Most affected dogs are euthanized before reaching the age of 4 years. The diagnosis is made by clinical observations and microscopic examination of skin biopsies. In humans, many different forms of CLE exist and various genes and chromosomal locations have been implicated. The large number of potential candidate loci combined with often weak association prevented in depth screening of the dog population thus far. During the course of our studies, we developed a colony of dogs with ECLE as a model for human CLE and the genetic analysis of these dogs confirmed the autosomal recessive mode of inheritance of CLE in GSPs. Using canine patient material, we performed a genome-wide association study (GWAS) to identify the genomic region harboring the gene involved in the development of the disease in GSPs. We identified a SNP allele on canine chromosome 18 that segregated with the disease in the 267 dogs tested. The data generated should allow identification of the mutant gene responsible for this form of cutaneous lupus erythematosus in dogs and assist in the understanding of the development of similar disease in humans.

Combining disease models to test for gene-environment interaction in nuclear families

It is useful to have robust gene-environment interaction tests that can utilize a variety of family structures in an efficient way. This article focuses on tests for gene-environment interaction in the presence of main genetic and environmental effects. The objective is to develop powerful tests that can combine trio data with parental genotypes and discordant sibships when parents' genotypes are missing. We first make a modest improvement on a method for discordant sibs (discordant on phenotype), but the approach does not allow one to use families when all offspring are affected, e.g., trios. We then make a modest improvement on a Mendelian transmission-based approach that is inefficient when discordant sibs are available, but can be applied to any nuclear family. Finally, we propose a hybrid approach that utilizes the most efficient method for a specific family type, then combines over families. We utilize this hybrid approach to analyze a chronic obstructive pulmonary disorder dataset to test for gene-environment interaction in the Serpine2 gene with smoking. The methods are freely available in the R package fbati.

CAPL: a novel association test using case-control and family data and accounting for population stratification

The recent successes of GWAS based on large sample sizes motivate combining independent datasets to obtain larger sample sizes and thereby increase statistical power. Analysis methods that can accommodate different study designs, such as family-based and case-control designs, are of general interest. However, population stratification can cause spurious association for population-based association analyses. For family-based association analysis that infers missing parental genotypes based on the allele frequencies estimated in the entire sample, the parental mating-type probabilities may not be correctly estimated in the presence of population stratification. Therefore, any approach to combining family and case-control data should also properly account for population stratification. Although several methods have been proposed to accommodate family-based and case-control data, all have restrictions. Most of them require sampling a homogeneous population, which may not be a reasonable assumption for data from a large consortium. One of the methods, FamCC, can account for population stratification and uses nuclear families with arbitrary number of siblings but requires parental genotype data, which are often unavailable for late-onset diseases. We extended the family-based test, Association in the Presence of Linkage (APL), to combine family and case-control data (CAPL). CAPL can accommodate case-control data and families with multiple affected siblings and missing parents in the presence of population stratification. We used simulations to demonstrate that CAPL is a valid test either in a homogeneous population or in the presence of population stratification. We also showed that CAPL can have more power than other methods that combine family and case-control data.

Thymic stromal lymphopoietin (TSLP) is associated with allergic rhinitis in children with asthma

Background

Allergic rhinitis (AR) affects up to 80% of children with asthma and increases asthma severity. Thymic stromal lymphopoietin (TSLP) is a key mediator of allergic inflammation. The role of the TSLP gene (TSLP) in the pathogenesis of AR has not been studied.

Objective

To test for associations between variants in TSLP, TSLP-related genes, and AR in children with asthma.

Methods

We genotyped 15 single nucleotide polymorphisms (SNPs) in TSLP, OX40L, IL7R, and RXRα in three independent cohorts: 592 asthmatic Costa Rican children and their parents, 422 nuclear families of North American children with asthma, and 239 Swedish children with asthma. We tested for associations between these SNPs and AR. As we previously reported sex-specific effects for TSLP, we performed overall and sex-stratified analyses. We additionally performed secondary analyses for gene-by-gene interactions.

Results

Across the three cohorts, the T allele of TSLP SNP rs1837253 was undertransmitted in boys with AR and asthma as compared to boys with asthma alone. The SNP was associated with reduced odds for AR (odds ratios ranging from 0.56 to 0.63, with corresponding Fisher's combined P value of 1.2 × 10^-4). Our findings were significant after accounting for multiple comparisons. SNPs in OX40L, IL7R, and RXRα were not consistently associated with AR in children with asthma. There were nominally significant interactions between gene pairs.

Conclusions

TSLP SNP rs1837253 is associated with reduced odds for AR in boys with asthma. Our findings support a role for TSLP in the pathogenesis of AR in children with asthma.

NTM and NR3C2 polymorphisms influencing intelligence: family-based association studies

Family, twin, and adoption studies have indicated that human intelligence quotient (IQ) has significant genetic components. We performed a low-density genome-wide association analysis with a family-based association test to identify genetic variants influencing IQ, as measured by Wechsler Adult Intelligence Scale full-score IQ (FSIQ). We examined 11,120 single-nucleotide polymorphisms (SNPs) from the Affymetrix GeneChips 10K mapping array genotyped in 292 nuclear families from Genetic Analysis Workshop 14, a subset from the Collaborative Study on the Genetics of Alcoholism (COGA). A replication analysis was performed using part of International Multi-Center ADHD Genetics Project (IMAGE) dataset. Twenty-two SNPs were identified as having suggestive associations with IQ (p<10(-3)) in the COGA sample and eleven of the SNPs were located within known genes. In particular, NTM at 11q25 (rs411280, p = 0.000764) and NR3C2 at 4q31.1 (rs3846329, p = 0.000675) were two novel genes which have not been associated with IQ in other studies. It has been reported that NTM might play a role in late-onset Alzheimer disease while NR3C2 may be associated with cognitive function and major depression. The associations of these two genes were well-replicated by single-marker and haplotype analyses in the IMAGE sample. In conclusion, our findings provide evidence that chromosome regions of 11q25 and 4q31.1 contain genes affecting IQ. This study will serve as a resource for replication in other populations.

A family based association study of DRD4, DAT1, and 5HTT and continuous traits of attention-deficit hyperactivity disorder

Despite its high heritability, genetic association studies of attention deficit-hyperactivity disorder (ADHD) have often resulted in somewhat small, inconsistent effects. Refining the ADHD phenotype beyond a dichotomous diagnosis and testing associations with continuous information from the underlying symptom dimensions may result in more consistent genetic findings. This study further examined the association between ADHD and the DRD4, DAT1, and 5HTT genes by testing their association with multivariate phenotypes derived from continuous measures of ADHD symptom severity. DNA was collected in 202 families consisting of at least one ADHD proband and at least one parent or sibling. VNTR polymorphisms of the DRD4 and DAT1 genes were significantly associated with the continuous ADHD phenotype. The association with DRD4 was driven by both inattentive and hyperactive symptoms, while the association with DAT1 was driven primarily by inattentive symptoms. These results use novel methods to build upon important connections between dopamine genes and their final behavioral manifestation as symptoms of ADHD.

Capitalizing on admixture in genome-wide association studies: a two-stage testing procedure and application to height in African-Americans

As genome-wide association studies expand beyond populations of European ancestry, the role of admixture will become increasingly important in the continued discovery and fine-mapping of variation influencing complex traits. Although admixture is commonly viewed as a confounding influence in association studies, approaches such as admixture mapping have demonstrated its ability to highlight disease susceptibility regions of the genome. In this study, we illustrate a powerful two-stage testing strategy designed to uncover trait-associated single nucleotide polymorphisms in the presence of ancestral allele frequency differentiation. In the first stage, we conduct an association scan by using predicted genotypic values based on regional admixture estimates. We then select a subset of promising markers for inclusion in a second-stage analysis, where association is tested between the observed genotype and the phenotype conditional on the predicted genotype. We prove that, under the null hypothesis, the test statistics used in each stage are orthogonal and asymptotically independent. Using simulated data designed to mimic African-American populations in the case of a quantitative trait, we show that our two-stage procedure maintains appropriate control of the family wise error rate and has higher power under realistic effect sizes than the one-stage testing procedure in which all markers are tested for association simultaneously with control of admixture. We apply the proposed procedure to a study of height in 201 African-Americans genotyped at 108 ancestry informative markers. The two-stage procedure identified two statistically significant markers rs1985080 (PTHB1/BBS9) and rs952718 (ABCA12). PTHB1/BBS9 is downregulated by parathyroid hormone in osteoblastic cells and is thought to be involved in parathyroid hormone action in bones and may play a role in height. ABCA12 is a member of the superfamily of ATP binding cassette transporters and its potential involvement in height is unclear.

Distribution of the number of false discoveries in large-scale family-based association testing with application to the association between PTPN1 and hypertension and obesity

We present a model-free approach to the study of the number of false discoveries for large-scale simultaneous family-based association tests (FBATs) in which the set of discoveries is decided by applying a threshold to the test statistics. When the association between a set of markers in a candidate gene and a group of phenotypes is studied by a class of FBATs, we indicate that a joint null hypothesis distribution for these statistics can be obtained by the fundamental statistical method of conditioning on sufficient statistics for the null hypothesis. Based on the joint null distribution of these statistics, we can obtain the distribution of the number of false discoveries for the set of discoveries defined by a threshold; the size of this set is referred to as its tail count. Simulation studies are presented to demonstrate that the conditional, not the unconditional, distribution of the tail count is appropriate for the study of false discoveries. The usefulness of this approach is illustrated by re-examining the association between PTPN1 and a group of blood-pressure-related phenotypes reported by Olivier et al. (Hum Mol Genet 13:1885-1892, 2004); our results refine and reinforce this association.

Family-based association studies

Family-based association methods are useful because they offer improved matching of controls to cases, with the result that they are not susceptible to confounding by population stratification. They also allow analysis of parent-of-origin effects and maternal-fetal interactions. The transmission/disequilibrium test (TDT) is a test of linkage and association that is equivalent to a matched case/control analysis, from which various extensions are possible. A logistic regression formulation leads to modifications for multiallelic markers, haplotypes, and quantitative traits. Some pitfalls are described, for the situations in which one parent is missing, genotyping errors have occurred, and haplotype phase is uncertain. The problem of testing association in general pedigrees is discussed, with particular reference to sib pairs without parents.

Detection of SNP-SNP interactions in trios of parents with schizophrenic children

Schizophrenia (SZ) is a heritable and complex psychiatric disorder with an estimated worldwide prevalence of about 1%. Research on the risk factors for SZ has thus far yielded few clues to causes, but has pointed to a heterogeneous etiology that likely involves multiple genes and gene-environment interactions. In this manuscript, we apply a novel method (trio logic regression, Li et al., 2009) to case-parent trio data from a SZ candidate gene study conducted on families of Ashkenazi Jewish descent, and demonstrate the method's ability to detect multi-gene models for SZ risk in the family-based design. In particular, we demonstrate how this method revealed a genotype-phenotype association that includes an allele without marginal effect.

Tests for compositional epistasis under single interaction-parameter models

Compositional epistasis is said to be present when the effect of a genetic factor at one locus is masked by a variant at another locus. Although such compositional epistasis is not equivalent to the presence of an interaction in a statistical model, non-standard tests can sometimes be used to detect compositional epistasis. In this paper we consider empirical tests for compositional epistasis under models for the joint effect of two genetic factors which place no restrictions on the main effects of each factor but constrain the interactive effects of the two factors so as to be captured by a single parameter in the model. We describe the implications of these tests for cohort, case-control, case-only and family-based study designs and we illustrate the methods using an example of gene-gene interaction already reported in the literature.

Gene-environment interaction tests for family studies with quantitative phenotypes: A review and extension to longitudinal measures

Longitudinal studies are an important tool for analysing traits that change over time, depending on individual characteristics and environmental exposures. Complex quantitative traits, such as lung function, may change over time and appear to depend on genetic and environmental factors, as well as on potential gene-environment interactions. There is a growing interest in modelling both marginal genetic effects and gene-environment interactions. In an admixed population, the use of traditional statistical models may fail to adjust for confounding by ethnicity, leading to bias in the genetic effect estimates. A variety of methods have been developed to account for the genetic substructure of human populations. Family-based designs provide an important resource for avoiding confounding due to admixture. To date, however, most genetic analyses have been applied to cross-sectional designs. In this paper, we propose a methodology which aims to improve the assessment of main genetic effect and gene-environment interaction effects by combining the advantages of both longitudinal studies for continuous phenotypes, and the family-based designs. This approach is based on an extension of ordinary linear mixed models for quantitative phenotypes, which incorporates information from a case-parent design. Our results indicate that use of this method allows both main genetic and gene-environment interaction effects to be estimated without bias, even in the presence of population substructure.

Estimating gene penetrance from family data

Family data are useful for estimating disease risk in carriers of specific genotypes of a given gene (penetrance). Penetrance is frequently estimated assuming that relatives' phenotypes are independent, given their genotypes for the gene of interest. This assumption is unrealistic when multiple shared risk factors contribute to disease risk. In this setting, the phenotypes of relatives are correlated even after adjustment for the genotypes of any one gene (residual correlation). Many methods have been proposed to address this problem, but their performance has not been evaluated systematically. In simulations we generated genotypes for a rare (frequency 0.35%) allele of moderate penetrance, and a common (frequency 15%) allele of low penetrance, and then generated correlated disease survival times using the Clayton-Oakes copula model. We ascertained families using both population and clinic designs. We then compared the estimates of several methods to the optimal ones obtained from the model used to generate the data. We found that penetrance estimates for common low-risk genotypes were more robust to model misspecification than those for rare, moderate-risk genotypes. For the latter, penetrance estimates obtained ignoring residual disease correlation had large biases. Also biased were estimates based only on families that segregate the risk allele. In contrast, a method for accommodating phenotype correlation by assuming the presence of genetic heterogeneity performed nearly optimally, even when the survival data were coded as binary outcomes. We conclude that penetrance estimates that accommodate residual phenotype correlation (even only approximately) outperform those that ignore it, and that coding censored survival outcomes as binary does not substantially increase the mean-square error of the estimates, provided the censoring is not extensive.

Gene--environment-wide association studies: emerging approaches

Despite the yield of recent genome-wide association (GWA) studies, the identified variants explain only a small proportion of the heritability of most complex diseases. This unexplained heritability could be partly due to gene--environment (G×E) interactions or more complex pathways involving multiple genes and exposures. This Review provides a tutorial on the available epidemiological designs and statistical analysis approaches for studying specific G×E interactions and choosing the most appropriate methods. I discuss the approaches that are being developed for studying entire pathways and available techniques for mining interactions in GWA data. I also explore methods for marrying hypothesis-driven pathway-based approaches with 'agnostic' GWA studies.

Efficiency robust statistics for genetic linkage and association studies under genetic model uncertainty

When testing genetic linkage and association, test statistics that follow a normal or Chi-square distributions are often used. These statistics are usually derived under a specific mode of inheritance (genetic model). Common genetic models include, but not limited to, the recessive, additive, multiplicative, and dominant models. For many diseases, their underlying genetic models are often unknown. Instead, a family of scientifically plausible genetic models may be available, which includes the four commonly used models. Hence, the optimal test is not available. Employing a single test statistic which is optimal for one model may suffer from substantial loss of power when the model is misspecified. In this situation efficient robust tests are useful. In this tutorial, we first review several commonly used robust statistics, including maximum efficiency robust tests, maximal tests, and constrained likelihood ratio tests for three common designs in genetic studies: (i) linkage analysis using affected sib-pairs, (ii) association studies using parents-offspring trios, and (iii) case-control association studies (unmatched and matched). Codes in the R statistical language for applying these robust statistics to test for linkage and association are presented with examples. We also provide some comparisons of the performance of the various robust tests via simulation studies. Guidelines for applications are also given for each study design. Finally, applications of robust tests to genome-wide association studies and meta-analysis are discussed.

Gene-environment interaction tests for dichotomous traits in trios and sibships

When testing for genetic effects, failure to account for a gene-environment interaction can mask the true association effects of a genetic marker with disease. Family-based association tests are popular because they are completely robust to population substructure and model misspecification. However, when testing for an interaction, failure to model the main genetic effect correctly can lead to spurious results. Here we propose a family-based test for interaction that is robust to model misspecification, but still sensitive to an interaction effect, and can handle continuous covariates and missing parents. We extend the FBAT-I gene-environment interaction test for dichotomous traits to using both trios and sibships. We then compare this extension to joint tests of gene and gene-environment interaction, and compare the joint test additionally to the main effects test of the gene. Lastly, we apply these three tests to a group of nuclear families ascertained according to affection with Bipolar Disorder.

Classical twin design in modern pharmacogenomics studies

Response to medication is highly variable, unpredictable and, at times, may be fatal. All drugs are more effective in certain groups of the population while showing no or minimal benefit in other groups. Although the current data on the subject are piecemeal, anecdotal evidence suggests that, in line with other common multifactorial traits, a myriad of genomic as well as environmental factors underpin population variability in drug response. Pharmacogenomics is the study of how variations in the human genome affect the variability in response to medication. Efforts to personalize treatment based on results from pharmacogenomics studies have the potential to increase efficacy, lower the overall cost of treatment, and decrease the incidence of adverse drug reactions, and are one of the major challenges of the modern era. The classical twin design has traditionally been used to assess the relative contribution of genetic and environmental factors to population variation in common, complex phenotypes, including drug response. Twins are not commonly regarded as providing the optimal design in genomic studies. However, we argue that, through their precise ‘matching’ for confounding variables (age, sex, cohort and common environmental effects), their amenability to numerous nonclassical study designs (genome-wide association studies or the role of epigenetic factors), and the availability of large, established registries worldwide, the twin model represents a flexible study design for systems-biology studies of drug response in humans. In this review, we describe the ‘classical twin model’ and its application in traditional pharmacogenetics studies, discuss the value of the twin design in the modern systems biology era, and highlight the potential of existing twin registries in formulating future strategies in pharmacogenomics research. We argue that the usefulness of this design goes beyond its traditional applications. Moreover, the flexibility of the model in concert with the amenability of large, established registries of twins worldwide to the collecting of new phenotypes will mean that the study of identical and nonidentical twins will play a considerable role in shaping our understanding of the important factors that underpin population variability in common, complex phenotypes, including response to medication.

Magnitude of stratification in human populations and impacts on genome wide association studies

Genome-wide association studies (GWAS) may be biased by population stratification (PS). We conducted empirical quantification of the magnitude of PS among human populations and its impact on GWAS. Liver tissues were collected from 979, 59 and 49 Caucasian Americans (CA), African Americans (AA) and Hispanic Americans (HA), respectively, and genotyped using Illumina650Y (Ilmn650Y) arrays. RNA was also isolated and hybridized to Agilent whole-genome gene expression arrays. We propose a new method (i.e., hgdp-eigen) for detecting PS by projecting genotype vectors for each sample to the eigenvector space defined by the Human Genetic Diversity Panel (HGDP). Further, we conducted GWAS to map expression quantitative trait loci (eQTL) for the approximately 40,000 liver gene expression traits monitored by the Agilent arrays. HGDP-eigen performed similarly to the conventional self-eigen methods in capturing PS. However, leveraging the HGDP offered a significant advantage in revealing the origins, directions and magnitude of PS. Adjusting for eigenvectors had minor impacts on eQTL detection rates in CA. In contrast, for AA and HA, adjustment dramatically reduced association findings. At an FDR = 10%, we identified 65 eQTLs in AA with the unadjusted analysis, but only 18 eQTLs after the eigenvector adjustment. Strikingly, 55 out of the 65 unadjusted AA eQTLs were validated in CA, indicating that the adjustment procedure significantly reduced GWAS power. A number of the 55 AA eQTLs validated in CA overlapped with published disease associated SNPs. For example, rs646776 and rs10903129 have previously been associated with lipid levels and coronary heart disease risk, however, the rs10903129 eQTL was missed in the eigenvector adjusted analysis.

Epistatic interactions

The term "epistasis" is sometimes used to describe some form of statistical interaction between genetic factors and is alternatively sometimes used to describe instances in which the effect of a particular genetic variant is masked by a variant at another locus. In general statistical tests for interaction are of limited use in detecting "epistasis" in the sense of masking. It is, however, shown that there are relations between empirical data patterns and epistasis that have not been previously noted. These relations can sometimes be exploited to empirically test for "epistatic interactions" in the sense of the masking of the effect of a particular genetic variant by a variant at another locus.

Two-stage testing strategies for genome-wide association studies in family-based designs

The analysis of genome-wide association studies (GWAS) poses statistical hurdles that have to be handled efficiently in order for the study to be successful. The two largest impediments in the analysis phase of the study are the multiple comparisons problem and maintaining robustness against confounding due to population admixture and stratification. For quantitative traits in family-based designs, Van Steen (1) proposed a two-stage testing strategy that can be considered a hybrid approach between family-based and population-based analysis. By including the population-based component into the family-based analysis, the Van Steen algorithm maximizes the statistical power, while at the same time, maintains the original robustness of family-based association tests (FBATs) (2-4). The Van Steen approach consists of two statistically independent steps, a screening step and a testing step. For all genotyped single nucleotide polymorphisms (SNPs), the screening step examines the evidence for association at a population-based level. Based on support for a potential genetic association from the screening step, the SNPs are prioritized for testing in the next step, where they are analyzed with a FBAT (3). By exploiting population-based information in the screening step that is not utilized in family-based association testing step, the two steps are statistically independent. Therefore, the use of the population-based data for the purposes of screening does not bias the FBAT statistic calculated in the testing step. Depending on the trait type and the ascertainment conditions, Van Steen-type testing strategies can achieve statistical power levels that are comparable to those of population-based studies with the same number of probands. In this chapter, we review the original Van Steen algorithm, its numerous extensions, and discuss its advantages and disadvantages.

Designs for linkage analysis and association studies of complex diseases

Genetic linkage analysis has been a traditional means for identifying regions of the genome with large genetic effects that contribute to a disease. Following linkage analysis, association studies are widely pursued to fine-tune regions with significant linkage signals. For complex diseases which often involve function of multi-genetic variants each with small or moderate effect, linkage analysis has little power compared to association studies. In this chapter, we give a brief review of design issues related to linkage analysis and association studies with human genetic data. We introduce methods commonly used for linkage and association studies and compared the relative merits of the family-based and population-based association studies. Compared to candidate gene studies, a genomewide blind searching of disease variant is proving to be a more powerful approach. We briefly review the commonly used two-stage designs in genome-wide association studies. As more and more biological evidences indicate the role of genomic imprinting in disease, identifying imprinted genes becomes critically important. Design and analysis in genetic mapping imprinted genes are introduced in this chapter. Recent efforts in integrating gene expression analysis and genetic mapping, termed expression quantitative trait loci (eQTLs) mapping or genetical genomics analysis, offer new prospect in elucidating the genetic architecture of gene expression. Designs in genetical genomics analysis are also covered in this chapter.

Parsing the effects of individual SNPs in candidate genes with family data

We introduce a stepwise approach for family-based designs for selecting a set of markers in a gene that are independently associated with the disease. The approach is based on testing the effect of a set of markers conditional on another set of markers. Several likelihood-based approaches have been proposed for special cases, but no model-free based tests have been proposed. We propose two types of tests in a family-based framework that are applicable to arbitrary family structures and completely robust to population stratification. We propose methods for ascertained dichotomous traits and unascertained quantitative traits. We first propose a completely model-free extension of the FBAT main genetic effect test. Then, for power issues, we introduce two model-based tests, one for dichotomous traits and one for continuous traits. Lastly, we utilize these tests to analyze a continuous lung function phenotype as a proxy for asthma in the Childhood Asthma Management Program. The methods are implemented in the free R package fbati.

Maximizing the Power of Genome-Wide Association Studies: A Novel Class of Powerful Family-Based Association Tests

For genome-wide association studies in family-based designs, a new, universally applicable approach is proposed. Using a modified Liptak's method, we combine the p-value of the family-based association test (FBAT) statistic with the p-value for the Van Steen-statistic. The Van Steen-statistic is independent of the FBAT-statistic and utilizes information that is ignored by traditional FBAT-approaches. The new test statistic takes advantages of all available information about the genetic association, while, by virtue of its design, it achieves complete robustness against confounding due to population stratification. The approach is suitable for the analysis of almost any trait type for which FBATs are available, e.g. binary, continuous, time to-onset, multivariate, etc. The efficiency and the validity of the new approach depend on the specification of a nuisance/tuning parameter and the weight parameters in the modified Liptak's method. For different trait types and ascertainment conditions, we discuss general guidelines for the optimal specification of the tuning parameter and the weight parameters. Our simulation experiments and an application to an Alzheimer study show the validity and the efficiency of the new method, which achieves power levels that are comparable to those of population-based approaches.

Robust QTL fine mapping by applying a quantitative transmission disequilibrium test to the Mendelian sampling term

In many farm animal populations, high-density single nucleotide polymorphism (SNP) genotypes are becoming available on a large scale, and routine estimation of breeding values is implemented for a multiplicity of traits. We propose to apply the basic principle of the quantitative transmission disequilibrium test (QTDT) to estimated Mendelian sampling terms. A two-step procedure is suggested, where in the first step additive breeding values are estimated with a mixed linear model and the Mendelian sampling terms are calculated from the estimated breeding values. In the second step, the QTDT is applied to these estimated Mendelian sampling terms. The resulting test is expected to yield significant results if the SNP is in sufficient linkage disequilibrium and linkage with quantitative trait loci (QTL). This principle is illustrated with a simulated data set comprising 4665 individuals genotyped for 6000 SNP and 15 true QTL. Thirteen of the fifteen QTL were significant on a genome-wide 0.1% error level. Results for the empirical power are derived from repeated samples of 1000 and 3000 genotyped individuals, respectively. General properties and potential extensions of the methodology are indicated. Owing to its computational simplicity and speed, the suggested procedure is well suited to scan whole genomes with high-density SNP coverage in samples of substantial size and for a multiplicity of different traits.