Linkage Phase Research Articles

Genomic Predictability of Interconnected Biparental Maize Populations

Intense structuring of plant breeding populations challenges the design of the training set (TS) in genomic selection (GS). An important open question is how the TS should be constructed from multiple related or unrelated small biparental families to predict progeny from individual crosses. Here, we used a set of five interconnected maize (Zea mays L.) populations of doubled-haploid (DH) lines derived from four parents to systematically investigate how the composition of the TS affects the prediction accuracy for lines from individual crosses. A total of 635 DH lines genotyped with 16,741 polymorphic SNPs were evaluated for five traits including Gibberella ear rot severity and three kernel yield component traits. The populations showed a genomic similarity pattern, which reflects the crossing scheme with a clear separation of full sibs, half sibs, and unrelated groups. Prediction accuracies within full-sib families of DH lines followed closely theoretical expectations, accounting for the influence of sample size and heritability of the trait. Prediction accuracies declined by 42% if full-sib DH lines were replaced by half-sib DH lines, but statistically significantly better results could be achieved if half-sib DH lines were available from both instead of only one parent of the validation population. Once both parents of the validation population were represented in the TS, including more crosses with a constant TS size did not increase accuracies. Unrelated crosses showing opposite linkage phases with the validation population resulted in negative or reduced prediction accuracies, if used alone or in combination with related families, respectively. We suggest identifying and excluding such crosses from the TS. Moreover, the observed variability among populations and traits suggests that these uncertainties must be taken into account in models optimizing the allocation of resources in GS.

DECR1 and ME1 genotypes are associated with lipid composition traits in Duroc pigs.

Variation at the porcine DECR1 and ME1 genes has been associated with meat quality traits and backfat thickness in Landrace pigs, respectively. However, it has not been investigated yet whether DECR1 and ME1 genotypes influence lipid composition. With this aim, we have genotyped two missense DECR1 substitutions (c.160G>C and c.437G>C) and one silent ME1 (c.576C>T) polymorphism in 361 Duroc barrows distributed in five half-sib families and phenotyped for serum lipid concentrations and intramuscular fat content and composition traits. At the whole-population level, relevant associations, that is, with a posterior probability of the allele substitution effect to be over or below zero (PPN0)>0.90, were observed between DECR1 genotype and serum cholesterol (CHOL) (PPN0=0.932) and LDL concentrations (PPN0=0.945) at 190days, as well as between ME1 genotype and longissimus dorsi saturated fatty acid content (PPN0=0.924). At the within-family level, we found relevant associations between DECR1 and ME1 genotypes and diverse lipid composition traits, but most of them were family-specific. Discrepancies in allele substitution effects estimated in half-sib families might be produced by many factors such as number of individuals, marker allele frequencies and informativeness in each family, unaccounted random genetic and environmental effects, epistasis and family-specific differences in the linkage phase or amount of linkage disequilibrium between causal and marker mutations. This lack of consistency across families, combined with the fact that the ME1 mutation is synonymous and that the two DECR1 polymorphisms are conservative, suggests that the associations found are not causative.

Five novel glucose-6-phosphate dehydrogenase deficiency haplotypes correlating with disease severity

BackgroundGlucose-6-phosphate dehydrogenase (G6PD, EC 1.1.1.49) deficiency is caused by one or more mutations in the G6PD gene on chromosome X. An association between enzyme levels and gene haplotypes remains to be established.MethodsIn this study, we determined G6PD enzyme levels and sequenced the coding region, including the intron-exon boundaries, in a group of individuals (163 males and 86 females) who were referred to the clinic with suspected G6PD deficiency. The sequence data were analysed by physical linkage analysis and PHASE haplotype reconstruction.ResultsAll previously reported G6PD missense changes, including the AURES, MEDITERRANEAN, A-, SIBARI, VIANGCHAN and ANANT, were identified in our cohort. The AURES mutation (p.Ile48Thr) was the most common variant in the cohort (30% in males patients) followed by the Mediterranean variant (p.Ser188Phe) detectable in 17.79% in male patients. Variant forms of the A- mutation (p.Val68Met, p.Asn126Asp or a combination of both) were detectable in 15.33% of the male patients. However, unique to this study, several of such mutations co-existed in the same patient as shown by physical linkage in males or PHASE haplotype reconstruction in females. Based on 6 non-synonymous variants of G6PD, 13 different haplotypes (13 in males, 8 in females) were identified. Five of these were previously unreported (Jeddah A, B, C, D and E) and were defined by previously unreported combinations of extant mutations where patients harbouring these haplotypes exhibited severe G6PD deficiency.ConclusionsOur findings will help design a focused population screening approach and provide better management for G6PD deficiency patients.

Potential and Optimization of Genomic Selection for Fusarium Head Blight Resistance in Six‐Row Barley

ABSTRACTFusarium head blight (FHB),is a devastating disease of barley (Hordeum vulgare L.), causing reductions in yield and quality. Marker‐based selection for resistance to FHB and lowered deoxynivalenol (DON) grain concentration would save considerable costs and time associated with phenotyping. A marker‐based selection approach called genomic selection (GS) uses genomewide marker information to predict genetic value. We used a cross‐validation approach that separated training sets from validation sets by both entry and environment. We used this framework to test the potential of GS for genetic improvement of FHB and DON as well as test the effect of different factors on prediction accuracy. Prediction accuracy for FHB was found to be as high as 0.72 and that for DON was found to be as high as 0.68. Little difference was found between marker effect estimation methods in terms of prediction of entry genetic value. The extensive linkage disequilibrium (LD) present in this population allowed the marker set to be reduced to 384 markers and training population (TP) size to be reduced 200 with little effect on prediction accuracy. We found little to no advantage to combining subpopulations that correspond to neighboring breeding programs to increase TP size. Apparently, little genetic information is shared between subpopulations, either because of different marker–quantitative trait loci (QTL) linkage phases, different segregating QTL, or nonadditive gene action.

A novel linkage map of sugarcane with evidence for clustering of retrotransposon-based markers.

BackgroundThe development of sugarcane as a sustainable crop has unlimited applications. The crop is one of the most economically viable for renewable energy production, and CO2 balance. Linkage maps are valuable tools for understanding genetic and genomic organization, particularly in sugarcane due to its complex polyploid genome of multispecific origins. The overall objective of our study was to construct a novel sugarcane linkage map, compiling AFLP and EST-SSR markers, and to generate data on the distribution of markers anchored to sequences of scIvana_1, a complete sugarcane transposable element, and member of the Copia superfamily.ResultsThe mapping population parents (‘IAC66-6’ and ‘TUC71-7’) contributed equally to polymorphisms, independent of marker type, and generated markers that were distributed into nearly the same number of co-segregation groups (or CGs). Bi-parentally inherited alleles provided the integration of 19 CGs. The marker number per CG ranged from two to 39. The total map length was 4,843.19 cM, with a marker density of 8.87 cM. Markers were assembled into 92 CGs that ranged in length from 1.14 to 404.72 cM, with an estimated average length of 52.64 cM. The greatest distance between two adjacent markers was 48.25 cM. The scIvana_1-based markers (56) were positioned on 21 CGs, but were not regularly distributed. Interestingly, the distance between adjacent scIvana_1-based markers was less than 5 cM, and was observed on five CGs, suggesting a clustered organization.ConclusionsResults indicated the use of a NBS-profiling technique was efficient to develop retrotransposon-based markers in sugarcane. The simultaneous maximum-likelihood estimates of linkage and linkage phase based strategies confirmed the suitability of its approach to estimate linkage, and construct the linkage map. Interestingly, using our genetic data it was possible to calculate the number of retrotransposon scIvana_1 (~60) copies in the sugarcane genome, confirming previously reported molecular results. In addition, this research possibly will have indirect implications in crop economics e.g., productivity enhancement via QTL studies, as the mapping population parents differ in response to an important fungal disease.

ParentChecker: a computer program for automated inference of missing parental genotype calls and linkage phase correction

BackgroundAccurate genetic maps are the cornerstones of genetic discovery, but their construction can be hampered by missing parental genotype information. Inference of parental haplotypes and correction of phase errors can be done manually on a one by one basis with the aide of current software tools, but this is tedious and time consuming for the high marker density datasets currently being generated for many crop species. Tools that help automate the process of inferring parental genotypes can greatly speed the process of map building. We developed a software tool that infers and outputs missing parental genotype information based on observed patterns of segregation in mapping populations. When phases are correctly inferred, they can be fed back to the mapping software to quickly improve marker order and placement on genetic maps.ResultsParentChecker is a user-friendly tool that uses the segregation patterns of progeny to infer missing genotype information of parental lines that have been used to construct a mapping population. It can also be used to automate correction of linkage phase errors in genotypic data that are in ABH format.ConclusionParentChecker efficiently improves genetic mapping datasets for cases where parental information is incomplete by automating the process of inferring missing genotypes of inbred mapping populations and can also be used to correct linkage phase errors in ABH formatted datasets.

MtDNA lineage analysis of mouse L-cell lines reveals the accumulation of multiple mtDNA mutants and intermolecular recombination

The role of mitochondrial DNA (mtDNA) mutations and mtDNA recombination in cancer cell proliferation and developmental biology remains controversial. While analyzing the mtDNAs of several mouse L cell lines, we discovered that every cell line harbored multiple mtDNA mutants. These included four missense mutations, two frameshift mutations, and one tRNA homopolymer expansion. The LA9 cell lines lacked wild-type mtDNAs but harbored a heteroplasmic mixture of mtDNAs, each with a different combination of these variants. We isolated each of the mtDNAs in a separate cybrid cell line. This permitted determination of the linkage phase of each mtDNA and its physiological characteristics. All of the polypeptide mutations inhibited their oxidative phosphorylation (OXPHOS) complexes. However, they also increased mitochondrial reactive oxygen species (ROS) production, and the level of ROS production was proportional to the cellular proliferation rate. By comparing the mtDNA haplotypes of the different cell lines, we were able to reconstruct the mtDNA mutational history of the L-L929 cell line. This revealed that every heteroplasmic L-cell line harbored a mtDNA that had been generated by intracellular mtDNA homologous recombination. Therefore, deleterious mtDNA mutations that increase ROS production can provide a proliferative advantage to cancer or stem cells, and optimal combinations of mutant loci can be generated through recombination.

Model selection for quantitative trait loci mapping in a full-sib family

Statistical methods for mapping quantitative trait loci (QTLs) in full-sib forest trees, in which the number of alleles and linkage phase can vary from locus to locus, are still not well established. Previous studies assumed that the QTL segregation pattern was fixed throughout the genome in a full-sib family, despite the fact that this pattern can vary among regions of the genome. In this paper, we propose a method for selecting the appropriate model for QTL mapping based on the segregation of different types of markers and QTLs in a full-sib family. The QTL segregation patterns were classified into three types: test cross (1:1 segregation), F2 cross (1:2:1 segregation) and full cross (1:1:1:1 segregation). Akaike’s information criterion (AIC), the Bayesian information criterion (BIC) and the Laplace-empirical criterion (LEC) were used to select the most likely QTL segregation pattern. Simulations were used to evaluate the power of these criteria and the precision of parameter estimates. A Windows-based software was developed to run the selected QTL mapping method. A real example is presented to illustrate QTL mapping in forest trees based on an integrated linkage map with various segregation markers. The implications of this method for accurate QTL mapping in outbred species are discussed.

3FunMap: full-sib family functional mapping of dynamic traits

Functional mapping that embeds the developmental mechanisms of complex traits shows great power to study the dynamic pattern of genetic effects triggered by individual quantitative trait loci (QTLs). A full-sib family, produced by crossing two heterozygous parents, is characteristic of uncertainties about cross-type at a locus and linkage phase between different loci. Integrating functional mapping into a full-sib family requires a model selection procedure capable of addressing these uncertainties. 3FunMap, written in VC++ 6.0, provides a flexible and extensible platform to perform full-sib functional mapping of dynamic traits. Functions in the package encompass linkage phase determination, marker map construction and the pattern identification of QTL segregation, dynamic tests of QTL effects, permutation tests and numerical simulation. We demonstrate the features of 3FunMap through real data analysis and computer simulation. http://statgen.psu.edu/software.

Knowledge absorption of optical technology companies in Shanghai, Pudong: Successes, barriers and structural impediments

Knowledge absorption theories argue that technologically less advanced companies (latecomer firms) can substantially upgrade their knowledge base through active knowledge absorption. These theories view knowledge absorption as a stepwise process which includes a linkage phase, a leverage phase and a learning phase (LLL concept). Case studies in Taiwan and Korea have shown that latecomer firms can indeed follow this approach and catch-up technologically with advanced firms. This paper aims to discuss the relevance of the LLL concept in China using the example of Optical Technology (OT). It reveals that the concept is not universally applicable. In the case of China, the ownership of companies determines successes and failures of strategies for increasing knowledge absorption. Private firms face substantial difficulties in establishing the necessary linkages; the upgrading of foreign-invested enterprises (FIEs) is controlled by their parent companies. This result has implications for theoretical concepts of learning and innovation in China, as well as for policy.

O uso da variância como metodologia alternativa para integração de mapas genéticos

O objetivo deste trabalho foi desenvolver um processo de integração de mapas genéticos, com o uso do inverso da variância, e testar sua eficiência. Foram utilizadas populações simuladas F2 codominante e de retrocruzamento, com tamanhos populacionais de 100, 150, 200 e 400 indivíduos, tendo-se considerado uma espécie diploide fictícia com 2n = 2x = 2 cromossomos, com o comprimento total do genoma por grupo de ligação estipulado em 100 cM, 21 marcas por grupo de ligação e marcadores equidistantes em 5 cM. Os genomas foram comparados quanto ao tamanho do grupo de ligação, variância das distâncias entre marcas adjacentes, correlação de Spearman e quanto ao estresse relativo à adequação das distâncias estimadas. Cada genoma simulado foi fragmentado em quatro novos mapas: três com oito marcadores e um com nove marcadores, cada qual com quatro marcadores âncoras. Os mapas foram alinhados, ordenados, integrados e, em seguida, comparados ao mapa de origem. O processo de integração de mapas proposto mostrou-se eficiente. Os mapas gerados tiveram pequena tensão interna em comparação aos mapas dos quais se originaram. A integração de mapas depende do tipo de população utilizada, tamanho da população, tipo de marcador, da frequência de recombinação e da fase de ligação.

Application of single nucleotide polymorphism markers to chum salmon Oncorhynchus keta: discovery, genotyping and linkage phase resolution

This study describes (1) the application of new methods to the discovery of informative single nucleotide polymorphism (SNP) markers in chum salmon Oncorhynchus keta, (2) a method to resolve the linkage phase of closely linked SNPs and (3) a method to inexpensively genotype them. Finally, it demonstrates that these SNPs provide information that discriminates among O. keta populations from different geographical regions of the northern Pacific Ocean. These informative markers can be used in conjunction with mixed-stock analysis to learn about the spatial and temporal marine distributions of O. keta and the factors that influence the distributions.

Functional Mapping Model of Quantitative Trait Loci in a Full-sib Family

Functional mapping of quantitative trait loci (QTL) has embedded the biological mechanisms and processes of dynamic traits in the statistical method of QTL mapping. Although the statistical model for functional mapping has been well developed in inbred lines, it has not been proposed in a full-sib family, in which the number of alleles may vary over loci and the linkage phases are usually unknown between loci. Since forest trees have several unique biological characteristics and most of them are outbred species, we should not directly apply the functional mapping model for inbred species to them. The objective of this study is to develop a functional mapping statistical model for a full-sib family in forest trees. We suppose that the QTL genotype of one parent is heterozygous but the other is homozygous, and consider all pairwise combinations of marker segregation types and all possible linkage phases between mark loci in a diploid full-sib family into our model. A maximum­ likelihood approach based on a logistic-mixture model, implemented with EM algorithm, was developed to provide the estimation of QTL positions and the parameters responsible for growth trajectories. The results of a large number of simulations showed that our model was robust for identifying the QTL position and estimating the parameters of QTL effects and residual (co)variance.

Linkage analysis between dominant and co-dominant makers in full-sib families of out-breeding species

As high-throughput genomic tools, such as the DNA microarray platform, have lead to the development of novel genotyping procedures, such as Diversity Arrays Technology (DArT) and Single Nucleotide Polymorphisms (SNPs), it is likely that, in the future, high density linkage maps will be constructed from both dominant and co-dominant markers. Recently, a strictly genetic approach was described for estimating recombination frequency (r) between co-dominant markers in full-sib families. The complete set of maximum likelihood estimators for r in full-sib families was almost obtained, but unfortunately, one particular configuration involving dominant markers, segregating in a 3:1 ratio and co-dominant markers, was not considered. Here we add nine further estimators to the previously published set, thereby making it possible to cover all combinations of molecular markers with two to four alleles (without epistasis) in a full-sib family. This includes segregation in one or both parents, dominance and all linkage phase configurations.

A genetic algorithm based method for stringent haplotyping of family data

BackgroundThe linkage phase, or haplotype, is an extra level of information that in addition to genotype and pedigree can be useful for reconstructing the inheritance pattern of the alleles in a pedigree, and computing for example Identity By Descent probabilities. If a haplotype is provided, the precision of estimated IBD probabilities increases, as long as the haplotype is estimated without errors. It is therefore important to only use haplotypes that are strongly supported by the available data for IBD estimation, to avoid introducing new errors due to erroneous linkage phases.ResultsWe propose a genetic algorithm based method for haplotype estimation in family data that includes a stringency parameter. This allows the user to decide the error tolerance level when inferring parental origin of the alleles. This is a novel feature compared to existing methods for haplotype estimation. We show that using a high stringency produces haplotype data with few errors, whereas a low stringency provides haplotype estimates in most situations, but with an increased number of errors.ConclusionBy including a stringency criterion in our haplotyping method, the user is able to maintain the error rate at a suitable level for the particular study; one can select anything from haplotyped data with very small proportion of errors and a higher proportion of non-inferred haplotypes, to data with phase estimates for every marker, when haplotype errors are tolerable. Giving this choice makes the method more flexible and useful in a wide range of applications as it is able to fulfil different requirements regarding the tolerance for haplotype errors, or uncertain marker-phases.

Bayesian shrinkage mapping for multiple QTL in half-sib families

Recently, an effective Bayesian shrinkage estimation method has been proposed for mapping QTL in inbred line crosses. However, with regard to outbred populations, such as half-sib populations with maternal information unavailable, it is not straightforward to utilize such a shrinkage estimation for QTL mapping. The reasons are: (1) the linkage phase of markers in the outbred population is usually unknown; and (2) only paternal genotypes can be used for inferring QTL genotypes of offspring. In this article, a novel Bayesian shrinkage method was proposed for mapping QTL under the half-sib design using a mixed model. A simulation study clearly demonstrated that the proposed method was powerful for detecting multiple QTL. In addition, we applied the proposed method to map QTL for economic traits in the Chinese dairy cattle population. Two or more novel QTL harbored in the chromosomal region were detected for each trait of interest, whereas only one QTL was found using traditional maximum likelihood analyses in our earlier studies. This further validated that our shrinkage estimation method could perform well in empirical data analyses and had practical significance in the field of linkage studies for outbred populations.

Marker Imputation in Barley Association Studies

Association mapping requires high marker density, potentially leading to many missing marker data and to high genotyping costs. In human genetics, methods exist to impute missing marker data and whole markers typed in a reference panel but not in the experimental dataset. We sought to determine if an imputation method developed for human data would function effectively in a barley (Hordeum vulgare L.) panel. The panel contained 98 lines, 2517 single nucleotide polymorphism (SNP) markers, and 716 Diversity Arrays Technology (DArT) markers. Averaged over markers, masked scores were correctly imputed 97.1% of the time. We chose 610 and 273 tag markers in two‐ and six‐row barley subpopulations, respectively. Despite this low number of tags, imputation accuracy was such that for about 80% of non‐tag markers, the prediction r2 between imputed and true scores was 0.8 or higher. When DArT markers were used as tags, SNP markers were imputed with similar accuracy, suggesting that the method can convert association information from one marker system (e.g., DArT) to another marker system (e.g., SNP). We believe marker imputation methods will have an important future in association studies as a component of tagging methods and in reducing problems due to missing data.

HLA haplotype and supertype associations with cellular immune responses and cytokine production in healthy children after rubella vaccine

Secreted rubella virus-specific cytokines reflect the immunologic mechanisms underlying adoptive immune responses and are significant markers of immunity to rubella. We studied the association between measures of cellular (cytokine and frequency of cytokine-secreted cells) immune responses and HLA haplotypes (with frequencies of ≥1%) and supertypes among 738 healthy children following two doses of rubella vaccine. Haplotype effects were estimated while accounting for linkage phase ambiguity via an expectation maximization algorithm. Importantly, the majority of HLA class I and class II haplotype associations with different cytokines were consistent between Th1, Th2 and/or innate/proinflammatory cytokine groups. We found few class I supertypes (A1, A2, A3, and B7) with potential associations with IL-10 ELISPOT counts and rubella-specific IL-2, IL-10, TNF-α, and IL-6 cytokine secretion levels. Our data indicate that the presence or absence of certain HLA haplotypes and/or supertypes may influence the cytokine immune response to rubella vaccine, and represents a more advanced analysis compared to individual candidate gene association studies.

Confirmation and fine-mapping of a major QTL for resistance to infectious pancreatic necrosis in Atlantic salmon (Salmo salar): population-level associations between markers and trait.

BackgroundInfectious pancreatic necrosis (IPN) is one of the most prevalent and economically devastating diseases in Atlantic salmon (Salmo salar) farming worldwide. The disease causes large mortalities at both the fry- and post-smolt stages. Family selection for increased IPN resistance is performed through the use of controlled challenge tests, where survival rates of sib-groups are recorded. However, since challenge-tested animals cannot be used as breeding candidates, within-family selection is not performed and only half of the genetic variation for IPN resistance is being exploited. DNA markers linked to quantitative trait loci (QTL) affecting IPN resistance would therefore be a powerful selection tool. The aim of this study was to identify and fine-map QTL for IPN-resistance in Atlantic salmon, for use in marker-assisted selection to increase the rate of genetic improvement for this trait.ResultsA genome scan was carried out using 10 large full-sib families of challenge-tested Atlantic salmon post-smolts and microsatellite markers distributed across the genome. One major QTL for IPN-resistance was detected, explaining 29% and 83% of the phenotypic and genetic variances, respectively. This QTL mapped to the same location as a QTL recently detected in a Scottish Atlantic salmon population. The QTL was found to be segregating in 10 out of 20 mapping parents, and subsequent fine-mapping with additional markers narrowed the QTL peak to a 4 cM region on linkage group 21. Challenge-tested fry were used to show that the QTL had the same effect on fry as on post-smolt, with the confidence interval for QTL position in fry overlapping the confidence interval found in post-smolts. A total of 178 parents were tested for segregation of the QTL, identifying 72 QTL-heterozygous parents. Genotypes at QTL-heterozygous parents were used to determine linkage phases between alleles at the underlying DNA polymorphism and alleles at single markers or multi-marker haplotypes. One four-marker haplotype was found to be the best predictor of QTL alleles, and was successfully used to deduce genotypes of the underlying polymorphism in 72% of the parents of the next generation within a breeding nucleus. A highly significant population-level correlation was found between deduced alleles at the underlying polymorphism and survival of offspring groups in the fry challenge test, parents with the three deduced genotypes (QQ, Qq, qq) having mean offspring mortality rates of 0.13, 0.32, and 0.49, respectively. The frequency of the high-resistance allele (Q) in the population was estimated to be 0.30. Apart from this major QTL, one other experiment-wise significant QTL for IPN-resistance was detected, located on linkage group 4.ConclusionThe QTL confirmed in this study represents a case of a major gene explaining the bulk of genetic variation for a presumed complex trait. QTL genotypes were deduced within most parents of the 2005 generation of a major breeding company, providing a solid framework for linkage-based MAS within the whole population in subsequent generations. Since haplotype-trait associations valid at the population level were found, there is also a potential for MAS based on linkage disequilibrium (LD). However, in order to use MAS across many generations without reassessment of linkage phases between markers and the underlying polymorphism, the QTL needs to be positioned with even greater accuracy. This will require higher marker densities than are currently available.

Composite measure of linkage disequilibrium for testing interaction between unlinked loci

Widely used statistical interaction models essentially treated the interaction effect as a residual term and hence are likely to limit the power to detect interaction. Alternatively, interactions between two loci can be understood as irreducible dependencies between loci causing disease or viewed as the linkage disequilibrium (LD) between them. This motivated the development of LD-based statistics for the detection of interaction between two loci. Although LD-based statistics have demonstrated high power to detect interaction between two loci, in general, linkage phase information of marker loci for unrelated individuals is unknown. To overcome this limitation, we classify the interaction between two loci into intragametic interaction that characterizes interaction of two alleles from different loci on the same haplotype and intergametic interaction that characterizes the interaction of two alleles from different loci on different haplotypes. Then we show that intragametic and intergametic interaction will lead to the corresponding intragametic and intergametic LD. This stimulates the use of composite measure of LD for developing statistics to detect interaction between two unlinked loci. To study the validity of the composite LD-based statistic for testing interaction, we estimate its type 1 error rates by simulation. To evaluate the performance of the composite LD-based statistic for detection of interaction between two loci, we compare its power with logistic regression and apply it to two real examples. The preliminary results demonstrate that the composite LD-based statistic is a strong alternative to the logistic regressions and the intragametic LD-based statistic for the detection of interaction between two unlinked loci.