Dense Single Nucleotide Polymorphism Markers Research Articles

Reconstructing relationship matrices from dense SNP arrays for the prediction of genetic potential in unreplicated multilocation plantings of apple progeny

Prediction of the genetic potential of a candidate cultivar is fundamental to achieving a response to selection in plant breeding. In the early phases of advanced selection plantings, a common strategy to increase selection intensity is field testing of many candidates at multiple locations with incomplete or limited replication among trials. Genetic analyses that incorporate genetic relationships among candidates can be used to improve the prediction accuracy, and indirectly predict performance at locations where a candidate is not planted. Here, we report on the prediction of breeding value for sensory crispness for unreplicated apple candidate progeny and parents established at three locations across the USA (Minnesota, New York State, Washington State) using relationships constructed from (i) pedigree records and (ii) 8K single nucleotide polymorphism (SNP) array data. The correlation between relationship coefficients estimated from either historical pedigree or SNP arrays was 0.76. Estimates of phenotypic variation and heritability were similar among three analyses (i.e., full data using pedigree relationship matrix, reduced data for only individuals with SNP genotypes available using a pedigree-based relationship matrix, or reduced data using a genomic relationship matrix estimated from the SNP arrays). Significant G×E was detected with all analyses, although there were some differences in the estimates of additive genetic correlation among locations from different analyses. This study demonstrates that dense SNP marker arrays may be used to predict breeding values for incompletely or unreplicated candidate cultivars established across multiple locations. This approach will be particularly valuable where the cost or logistics of replication at multiple locations is prohibitive and when pedigree records are incomplete and are not able to fully characterize the diversity of a candidate population.

Estimating Additive and Non-Additive Genetic Variances and Predicting Genetic Merits Using Genome-Wide Dense Single Nucleotide Polymorphism Markers

Non-additive genetic variation is usually ignored when genome-wide markers are used to study the genetic architecture and genomic prediction of complex traits in human, wild life, model organisms or farm animals. However, non-additive genetic effects may have an important contribution to total genetic variation of complex traits. This study presented a genomic BLUP model including additive and non-additive genetic effects, in which additive and non-additive genetic relation matrices were constructed from information of genome-wide dense single nucleotide polymorphism (SNP) markers. In addition, this study for the first time proposed a method to construct dominance relationship matrix using SNP markers and demonstrated it in detail. The proposed model was implemented to investigate the amounts of additive genetic, dominance and epistatic variations, and assessed the accuracy and unbiasedness of genomic predictions for daily gain in pigs. In the analysis of daily gain, four linear models were used: 1) a simple additive genetic model (MA), 2) a model including both additive and additive by additive epistatic genetic effects (MAE), 3) a model including both additive and dominance genetic effects (MAD), and 4) a full model including all three genetic components (MAED). Estimates of narrow-sense heritability were 0.397, 0.373, 0.379 and 0.357 for models MA, MAE, MAD and MAED, respectively. Estimated dominance variance and additive by additive epistatic variance accounted for 5.6% and 9.5% of the total phenotypic variance, respectively. Based on model MAED, the estimate of broad-sense heritability was 0.506. Reliabilities of genomic predicted breeding values for the animals without performance records were 28.5%, 28.8%, 29.2% and 29.5% for models MA, MAE, MAD and MAED, respectively. In addition, models including non-additive genetic effects improved unbiasedness of genomic predictions.

QTL analysis of white blood cell, platelet and red blood cell-related traits in an F2 intercross between Landrace and Korean native pigs

Haematological traits play important roles in disease resistance and defence functions. The objective of this study was to locate quantitative trait loci (QTL) and the associated positional candidate genes influencing haematological traits in an F(2) intercross between Landrace and Korean native pigs. Eight blood-related traits (six erythrocyte traits, one leucocyte trait and one platelet trait) were measured in 816 F(2) progeny. All experimental animals were genotyped with 173 informative microsatellite markers located throughout the pig genome. We report that nine chromosomes harboured QTL for the baseline blood parameters: genomic regions on SSC 1, 4, 5, 6, 8, 9, 11, 13 and 17. Eight of twenty identified QTL reached genome-wide significance. In addition, we evaluated the KIT locus, an obvious candidate gene locus affecting variation in blood-related traits. Using dense single nucleotide polymorphism marker data on SSC 8 and the marker-assisted association test, the strong association of the KIT locus with blood phenotypes was confirmed. In conclusion, our study identified both previously reported and novel QTL affecting baseline haematological parameters in pigs. Additionally, the positional candidate genes identified here could play an important role in elucidating the genetic architecture of haematological phenotype variation in swine and in humans.

IgA deficiency and the MHC: assessment of relative risk and microheterogeneity within the HLA A1 B8, DR3 (8.1) haplotype.

Selective IgA deficiency (IgAD; serum IgA concentration of <0.07 g/l) is the most common primary immunodeficiency in Caucasians with an estimated prevalence of 1/600. The frequency of the extended major histocompatibility complex haplotype HLA A1, B8, DR3, DQ2 (the "8.1" haplotype) is increased among patients with IgAD. We carried out a direct measurement of the relative risk of homozygosity of the 8.1 haplotype for IgA deficiency in a population-based sample of 117 B8, DR3 homozygous individuals. IgA deficiency was found to be present in 2 of 117 (1.7%) of these subjects, a figure that is concordant with estimates of relative risk from large case-control studies in the Swedish population. These data are consistent with a multiplicative model for the 8.1 haplotype contribution to IgA deficiency and contrasts with prior studies, suggesting a much higher risk for 8.1 homozygosity. Using a dense single nucleotide polymorphism marker analysis of the MHC region in HLA B8, DR3, DQ2 homozygous individuals, we did not observe consistent differences between cases (n = 26) and controls (n = 24). Overall, our results do not support the hypothesis that IgA deficiency is associated with a distinct subgroup of 8.1 related haplotypes, but rather indicate that risk is conferred by the common 8.1 haplotype acting in multiplicative manner.

Comparison of single-nucleotide polymorphisms and microsatellite markers for linkage analysis in the COGA and simulated data sets for Genetic Analysis Workshop 14: Presentation Groups 1, 2, and 3

The papers in presentation groups 1-3 of Genetic Analysis Workshop 14 (GAW14) compared microsatellite (MS) markers and single-nucleotide polymorphism (SNP) markers for a variety of factors, using multiple methods in both data sets provided to GAW participants. Group 1 focused on data provided from the Collaborative Study on the Genetics of Alcoholism (COGA). Group 2 focused on data simulated for the workshop. Group 3 contained analyses of both data sets. Issues examined included: information content, signal strength, localization of the signal, use of haplotype blocks, population structure, power, type I error, control of type I error, the effect of linkage disequilibrium, and computational challenges. There were several broad resulting observations. 1) Information content was higher for dense SNP marker panels than for MS panels, and dense SNP markers sets appeared to provide slightly higher linkage scores and slightly higher power to detect linkage than MS markers. 2) Dense SNP panels also gave higher type I errors, suggesting that increased test thresholds may be needed to maintain the correct error rate. 3) Dense SNP panels provided better trait localization, but only in the COGA data, in which the MS markers were relatively loosely spaced. 4) The strength of linkage signals did not vary with the density of SNP panels, once the marker density was approximately 1 SNP/cM. 5) Analyses with SNPs were computationally challenging, and identified areas where improvements in analysis tools will be necessary to make analysis practical for widespread use.

Designing an optimum genetic association study using dense SNP markers and family-based sample.

Genetic association analysis using thousands of single nucleotide polymorphism (SNP) markers has become a promising alternative to genome-wide linkage scan. Analysis based on linkage-disequilibrium (LD) is more efficient because meiotic information of past generations is utilized. However, in addition to the physical distance between the disease locus and a marker locus, numerous other factors such as admixture, genetic drift, and multiple mutations can affect the observed value of LD. The effect of these factors in a genomic LD association study must be carefully analyzed to obtain an efficient study design. In the following review, we consider studies using family-based data and carefully study the effects of some of these important design factors, including the sample size, frequency of SNP markers, and marker density. For example, we conclude that (1) for reasonably frequent SNP markers, a moderately large sample of 500 families is appropriate for a moderately stringent significance level (alpha = 0.00009); (2) to maintain a power of 80%, maximal difference in allele frequencies between the disease gene and a SNP marker varies between 0.1 (under additive model) and 0.5 (multiplicative); (3) a map density of 10 cM is appropriate only under idea scenario (moderately large sample size, equal trait/marker allele frequencies, maximum LD strength etc.). Results shown here should have practical implications to designing efficient LD association studies using dense SNP markers.