Multi-locus Association Analysis Research Articles

Multi-locus genome-wide association analysis for root and shoot traits at seedling stage in Ethiopian sorghum (Sorghum bicolor (L.) Moench) accessions

Multi-locus genome-wide association analysis for root and shoot traits at seedling stage in Ethiopian sorghum (Sorghum bicolor (L.) Moench) accessions

Genetic potential of grain-related traits in rice landraces: phenomics and multi-locus association analyses.

Grain length, width, weight, and the number of grains per panicle are crucial determinants contributing to yield in cereal crops. Understanding the genetic basis of grain-related traits has been the main research object in crop science. Kerala has a collection of different rice landraces. Characterization of these valuable genetic resources for 39 distinct agro-morphological traits was carried out in two seasons from 2017 to 2019 directly in farmers field. Most characteristics were polymorphic except ligule shape, leaf angle, and panicle axis. The results of principal component analysis implied that leaf length, plant height, culm length, flag leaf length, and grain-related traits were the principal discriminatory characteristics of rice landraces. For identifying the genetic basis of key grain traits of rice, three multi locus GWAS models were performed based on 1,47,994 SNPs in 73 rice accessions. As a result, 48 quantitative trait nucleotides (QTNs) were identified to be associated with these traits. After characterization of their function and expression, 15 significant candidate genes involved in regulating grain width, number of grains per panicle, and yield were identified. The detected QTNs and candidate genes in this study could be further used for marker-assisted high-quality breeding of rice.

Genome-wide association and genomic prediction for a reproductive index summarizing fertility outcomes in U.S. Holsteins.

Subfertility represents one major challenge to enhancing dairy production and efficiency. Herein, we use a reproductive index (RI) expressing the predicted probability of pregnancy following artificial insemination (AI) with Illumina 778K genotypes to perform single and multi-locus genome-wide association analyses (GWAA) on 2,448 geographically diverse U.S. Holstein cows and produce genomic heritability estimates. Moreover, we use genomic best linear unbiased prediction (GBLUP) to investigate the potential utility of the RI by performing genomic predictions with cross validation. Notably, genomic heritability estimates for the U.S. Holstein RI were moderate (h2 = 0.1654 ± 0.0317-0.2550 ± 0.0348), while single and multi-locus GWAA revealed overlapping quantitative trait loci (QTL) on BTA6 and BTA29, including the known QTL for the daughter pregnancy rate (DPR) and cow conception rate (CCR). Multi-locus GWAA revealed seven additional QTL, including one on BTA7 (60 Mb) which is adjacent to a known heifer conception rate (HCR) QTL (59 Mb). Positional candidate genes for the detected QTL included male and female fertility loci (i.e. spermatogenesis and oogenesis), meiotic and mitotic regulators, and genes associated with immune response, milk yield, enhanced pregnancy rates, and the reproductive longevity pathway. Based on the proportion of the phenotypic variance explained (PVE), all detected QTL (n = 13; P ≤ 5e - 05) were estimated to have moderate (1.0% < PVE ≤ 2.0%) or small effects (PVE ≤ 1.0%) on the predicted probability of pregnancy. Genomic prediction using GBLUP with cross validation (k = 3) produced mean predictive abilities (0.1692-0.2301) and mean genomic prediction accuracies (0.4119-0.4557) that were similar to bovine health and production traits previously investigated.

Genetic dissection of and genomic selection for seed weight, pod length, and pod width in soybean

A biparental soybean population of 364 recombinant inbred lines (RILs) derived from Zhongdou 41 × ZYD 02.878 was used to identify quantitative trait loci (QTL) associated with hundred-seed weight (100-SW), pod length (PL), and pod width (PW). 100-SW, PL, and PW showed moderate correlations among one another, and 100-SW was correlated most strongly with PW (0.64–0.74). Respectively 74, 70, 75 and 19 QTL accounting for 38.7%–78.8% of total phenotypic variance were identified by inclusive composite interval mapping, restricted two-stage multi-locus genome-wide association analysis, 3 variance-component multi-locus random-SNP-effect mixed linear model analysis, and conditional genome-wide association analysis. Of these QTL, 189 were novel, and 24 were detected by multiple methods. Six loci were associated with 100-SW, PL, and PW and may be pleiotropic loci. A total of 284 candidate genes were identified in colocalizing QTL regions, including the verified gene Seed thickness 1 (ST1). Eleven genes with functions involved in pectin biosynthesis, phytohormone, ubiquitin-protein, and photosynthesis pathways were prioritized by examining single nucleotide polymorphism (SNP) variation, calculating genetic differentiation index, and inquiring gene expression. The prediction accuracies of genomic selection (GS) for 100-SW, PL, and PW based on single trait-associated markers reached 0.82, 0.76, and 0.86 respectively, but selection index (SI)-assisted GS strategy did not increase GS efficiency and inclusion of trait-associated markers as fixed effects reduced prediction accuracy. These results shed light on the genetic basis of 100-SW, PL, and PW and provide GS models for these traits with potential application in breeding programs.

A comprehensive comparison of multilocus association methods with summary statistics in genome-wide association studies

BackgroundMultilocus analysis on a set of single nucleotide polymorphisms (SNPs) pre-assigned within a gene constitutes a valuable complement to single-marker analysis by aggregating data on complex traits in a biologically meaningful way. However, despite the existence of a wide variety of SNP-set methods, few comprehensive comparison studies have been previously performed to evaluate the effectiveness of these methods.ResultsWe herein sought to fill this knowledge gap by conducting a comprehensive empirical comparison for 22 commonly-used summary-statistics based SNP-set methods. We showed that only seven methods could effectively control the type I error, and that these well-calibrated approaches had varying power performance under the simulation scenarios. Overall, we confirmed that the burden test was generally underpowered and score-based variance component tests (e.g., sequence kernel association test) were much powerful under the polygenic genetic architecture in both common and rare variant association analyses. We further revealed that two linkage-disequilibrium-free P value combination methods (e.g., harmonic mean P value method and aggregated Cauchy association test) behaved very well under the sparse genetic architecture in simulations and real-data applications to common and rare variant association analyses as well as in expression quantitative trait loci weighted integrative analysis. We also assessed the scalability of these approaches by recording computational time and found that all these methods can be scalable to biobank-scale data although some might be relatively slow.ConclusionIn conclusion, we hope that our findings can offer an important guidance on how to choose appropriate multilocus association analysis methods in post-GWAS era. All the SNP-set methods are implemented in the R package called MCA, which is freely available at https://github.com/biostatpzeng/.

Detecting QTL and Candidate Genes for Plant Height in Soybean via Linkage Analysis and GWAS.

Soybean is an important global crop for edible protein and oil, and plant height is a main breeding goal which is closely related to its plant shape and yield. In this research, a high-density genetic linkage map was constructed by 1996 SNP-bin markers on the basis of a recombinant inbred line population derived from Dongnong L13 × Henong 60. A total of 33 QTL related to plant height were identified, of which five were repeatedly detected in multiple environments. In addition, a 455-germplasm population with 63,306 SNP markers was used for multi-locus association analysis. A total of 62 plant height QTN were detected, of which 26 were detected repeatedly under multiple methods. Two candidate genes, Glyma.02G133000 and Glyma.05G240600, involving in plant height were predicted by pathway analysis in the regions identified by multiple environments and backgrounds, and validated by qRT-PCR. These results enriched the soybean plant height regulatory network and contributed to molecular selection-assisted breeding.

Multi-Locus Genome-Wide Association Studies Reveal Fruit Quality Hotspots in Peach Genome.

Peach is one of the most important fruit crops in the world, with the global annual production about 24.6 million tons. The United States is the fourth-largest producer after China, Spain, and Italy. Peach consumption has decreased over the last decade, most likely due to inconsistent quality of the fruit on the market. Thus, marker-assisted selection for fruit quality traits is highly desired in fresh market peach breeding programs and one of the major goals of the RosBREED project. The ability to use DNA information to select for desirable traits would enable peach breeders to efficiently plan crosses and select seedlings with desired quality traits early in the selection process before fruiting. Therefore, we assembled a multi-locus genome wide association study (GWAS) of 620 individuals from three public fresh market peach breeding programs (Arkansas, Texas, and South Carolina). The material was genotyped using 9K SNP array and the traits were phenotyped for three phenological (bloom date, ripening date, and days after bloom) and 11 fruit quality-related traits (blush, fruit diameter, fruit weight, adherence, fruit firmness, redness around pit, fruit texture, pit weight, soluble solid concentration, titratable acidity, and pH) over three seasons (2010, 2011, and 2012). Multi-locus association analyses, carried out using mrMLM 4.0 and FarmCPU R packages, revealed a total of 967 and 180 quantitative trait nucleotides (QTNs), respectively. Among the 88 consistently reliable QTNs detected using multiple multi-locus GWAS methods and/or at least two seasons, 44 were detected for the first time. Fruit quality hotspots were identified on chromosomes 1, 3, 4, 5, 6, and 8. Out of 566 candidate genes detected in the genomic regions harboring the QTN clusters, 435 were functionally annotated. Gene enrichment analyses revealed 68 different gene ontology (GO) terms associated with fruit quality traits. Data reported here advance our understanding of genetic mechanisms underlying important fruit quality traits and further support the development of DNA tools for breeding.

Molecular characterization of QTL-allele system for drought tolerance at seedling stage and optimal genotype design using multi-locus multi-allele genome-wide association analysis in a half-sib population of soybean (Glycine max (L.) Merr.)

AbstractCharacterizing the whole genetic architecture of drought tolerance (DT) is a persistent challenge for the breeders. Here we developed a half-sib population comprising of 404 lines of two recombinant inbred line (RIL) populations with M8206 as the joint parent (M8206 × TongShan and ZhengYang × M8206) and tested for its DT under sand culture. The population was sequenced using restriction-site-associated DNA sequencing filtered with minor allele frequency ≥0.01; 55,936 single nucleotide polymorphisms (SNPs) were obtained and assembled into 6137 SNPLDBs (SNP linkage disequilibrium blocks). The restricted two-stage multi-locus genome-wide association analysis characterized with error and false-positive control identified 40 QTLs with 93 alleles on an average of 34.75% of the phenotypic variance (PV) collectively for relative root length (RRL) and relative shoot length (RSL) that served as potential DT indicators. Among these, eight loci corresponded to previously reported QTLs, whereas 32 loci were therefore novel. The identified QTLs with their corresponding alleles for RRL and RSL were organized into QTL-allele matrices, depicting the comprehensive DT genetic architecture of the three parents/half-sib population. From the matrices, we predicted the possible best/optimal genotype with weighted average value (WAV) 1.553 over two indicators, while for the top 10 single crosses among RILs with 95th percentile WAV was 1.218–1.257, transgressive over the parents (0.693–0.794) yet much less than 1.553. From the detected QTL-allele system, 65 potential candidate genes collectively for both indicators explaining on an average of 24.41% PV were annotated and χ2-tested as a DT candidate gene system involving nine biological processes.

The combined effect of nuclear and mitochondrial genomes on the risk of developing multiple sclerosis

Multiple sclerosis (MS) is a severe chronic CNS disease characterized by autoimmune inflammation, demyelination, and neurodegeneration. The interaction of mitochondrial and nuclear genomes is shown to be important in the formation of a predisposition to many diseases.Objective: to analyze the association of MS with the carriage of biallelic combinations, including as components the polymorphisms of three genes of mitochondrial DNA (mtDNA) and those of 16 nuclear genes, the products of which are involved in the functioning of the immune system and may participate in the development of autoimmune inflammation in MS; and, if these combinations are identified, to determine the nature of an interaction between their components. Patients and methods. The investigation enrolled 540 MS patients and 406 control group individuals; all were Russians. The mitochondrial genome was genotyped by polymerase chain reaction-restriction fragment length polymorphism analysis. APSampler software was used for multilocus association analysis. Results and discussion. The investigators identified five biallelic combinations that were associated with MS (p=0.0036–0.022) and possessed protective properties (odds ratio (OR) 0.67–0.75). The mitochondrial component of the identified combinations was the polymorphisms m.4580 (rs28357975), m.13368 (rs3899498), and m.13708 (rs28359178) mtDNA; the nuclear component was CXCR5 (rs523604), TNFRSF1A (rs1800693), and CD86 (rs2255214) gene polymorphisms. The interaction between the components of the identified combinations was additive. Conclusion. The data obtained in the Russian population suggest that the combined contribution of the mitochondrial and nuclear genomes may affect the risk of developing MS.

Adaptive weighted sum tests via LASSO method in multi-locus family-based association analysis

Family based multi-locus tests integrate information from individual loci by weighted averaging of the marginal statistics, and have been proven to be more efficient and robust than the single-locus tests in genetic association studies. The power depends on how much information the weights can extract from data. The currently published weighted sum methods are only applicable to either common or rare variants and may suffer from substantial power loss especially for rare variants. In this paper, we propose a novel data-driven weight to improve the power under both common and rare variant circumstances. We use the l1 regularization in Least Absolute Shrinkage and Selection Operator (LASSO) regression to construct the weight serving as a simultaneously adaptive marker selection process. Simulations for a dichotomous phenotype demonstrated that our LASSO-based approach outperformed the existing multi-locus methods in the sense of providing the highest statistical power while well controlled type I error rate under different scenarios. We also applied our methods to a real dataset for rheumatoid arthritis (GAW15 Problem 2). Two groups of alleles, in which individual SNPs had only modest and non-significant effects, were detected (P < 0.00001) using our proposed methods, whereas traditional multi-locus methods failed to identify them. In conclusion, the novel LASSO-based approach represents a superior weight-choosing strategy for multi-locus tests.

Linkage Analysis and Multi-Locus Genome-Wide Association Studies Identify QTNs Controlling Soybean Plant Height.

Plant height is an important target for soybean breeding. It is a typical quantitative trait controlled by multiple genes and is susceptible to environmental influences. Here, we carried out phenotypic analysis of 156 recombinant inbred lines derived from “Dongnong L13” and “Henong 60” in nine environments at four locations over 6 years using interval mapping and inclusive composite interval mapping methods. We performed quantitative trait locus (QTL) analysis by applying pre-built simple-sequence repeat maps. We detected 48 QTLs, including nine significant QTLs detected by multiple methods and in multiple environments. Meanwhile, genotyping of all lines using the SoySNP660k BeadChip produced 54,836 non-redundant single-nucleotide polymorphism (SNP) genotypes. We used five multi-locus genome-wide association analysis methods to locate 10 quantitative trait nucleotides (QTNs), four of which overlap with previously located QTLs. Five candidate genes related to plant height are predicted to lie within 200 kb of these four QTNs. We identified 19 homologous genes in Arabidopsis, two of which may be associated with plant height. These findings further our understanding of the multi-gene regulatory network and genetic determinants of soybean plant height, which will be important for breeding high-yielding soybean.

Single-Locus and Multi-Locus Genome-Wide Association Studies for Intramuscular Fat in Duroc Pigs.

Intramuscular fat (IMF) is an important quantitative trait of meat, which affects the associated sensory properties and nutritional value of pork. To gain a better understanding of the genetic determinants of IMF, we used a composite strategy, including single-locus and multi-locus association analyses to perform genome-wide association studies (GWAS) for IMF in 1,490 Duroc boars. We estimated the genomic heritability of IMF to be 0.23 ± 0.04. A total of 30 single nucleotide polymorphisms (SNPs) were found to be significantly associated with IMF. The single-locus mixed linear model (MLM) and multiple-locus methods multi-locus random-SNP-effect mixed linear model (mrMLM), fast multi-locus random-SNP-effect efficient mixed model association (FASTmrEMMA), and integrative sure independence screening expectation maximization Bayesian least absolute shrinkage and selection operator model (ISIS EM-BLASSO) analyses identified 5, 9, 8, and 21 significant SNPs, respectively. Interestingly, a novel quantitative trait locus (QTL) on SSC 7 was found to affect IMF. In addition, 10 candidate genes (BDKRB2, GTF2IRD1, UTRN, TMEM138, DPYD, CASQ2, ZNF518B, S1PR1, GPC6, and GLI1) were found to be associated with IMF based on their potential functional roles in IMF. GO analysis showed that most of the genes were involved in muscle and organ development. A significantly enriched KEGG pathway, the sphingolipid signaling pathway, was reported to be associated with fat deposition and obesity. Identification of novel variants and functional genes will advance our understanding of the genetic mechanisms of IMF and provide specific opportunities for marker-assisted or genomic selection in pigs. In general, such a composite single-locus and multi-locus strategy for GWAS may be useful for understanding the genetic architecture of economic traits in livestock.

Dissecting the Genetic Architecture of Seed Protein and Oil Content in Soybean from the Yangtze and Huaihe River Valleys Using Multi-Locus Genome-Wide Association Studies.

Soybean is a globally important legume crop that provides a primary source of high-quality vegetable protein and oil. Seed protein and oil content are two valuable quality traits controlled by multiple genes in soybean. In this study, the restricted two-stage multi-locus genome-wide association analysis (RTM-GWAS) procedure was performed to dissect the genetic architecture of seed protein and oil content in a diverse panel of 279 soybean accessions from the Yangtze and Huaihe River Valleys in China. We identified 26 quantitative trait loci (QTLs) for seed protein content and 23 for seed oil content, including five associated with both traits. Among these, 39 QTLs corresponded to previously reported QTLs, whereas 10 loci were novel. As reported previously, the QTL on chromosome 20 was associated with both seed protein and oil content. This QTL exhibited opposing effects on these traits and contributed the most to phenotype variation. From the detected QTLs, 55 and 51 candidate genes were identified for seed protein and oil content, respectively. Among these genes, eight may be promising candidate genes for improving soybean nutritional quality. These results will facilitate marker-assisted selective breeding for soybean protein and oil content traits.

Non-additive effects of ACVR2A in preeclampsia in a Philippine population

BackgroundMultiple interrelated pathways contribute to the pathogenesis of preeclampsia, and variants in susceptibility genes may play a role among Filipinos, an ethnically distinct group with high prevalence of the disease. The objective of this study was to examine the association between variants in maternal candidate genes and the development of preeclampsia in a Philippine population.MethodsA case-control study involving 29 single nucleotide polymorphisms (SNPs) in 21 candidate genes was conducted in 150 patients with preeclampsia (cases) and 175 women with uncomplicated normal pregnancies (controls). Genotyping for the GRK4 and DRD1 gene variants was carried out using the TaqMan Assay, and all other variants were assayed using the Sequenom MassARRAY Iplex Platform. PLINK was used for SNP association testing. Multilocus association analysis was performed using multifactor dimensionality reduction (MDR) analysis.ResultsAmong the clinical factors, older age (P < 1 × 10–4), higher BMI (P < 1 × 10–4), having a new partner (P = 0.006), and increased time interval from previous pregnancy (P = 0.018) associated with preeclampsia. The MDR algorithm identified the genetic variant ACVR2A rs1014064 as interacting with age and BMI in association with preeclampsia among Filipino women.ConclusionsThe MDR algorithm identified an interaction between age, BMI and ACVR2A rs1014064, indicating that context among genetic variants and demographic/clinical factors may be crucial to understanding the pathogenesis of preeclampsia among Filipino women.

Detecting the QTL-Allele System of Seed Oil Traits Using Multi-Locus Genome-Wide Association Analysis for Population Characterization and Optimal Cross Prediction in Soybean.

Soybean is one of the world's major vegetative oil sources, while oleic acid and linolenic acid content are the major quality traits of soybean oil. The restricted two-stage multi-locus genome-wide association analysis (RTM-GWAS), characterized with error and false-positive control, has provided a potential approach for a relatively thorough detection of whole-genome QTL-alleles. The Chinese soybean landrace population (CSLRP) composed of 366 accessions was tested under four environments to identify the QTL-allele constitution of seed oil, oleic acid and linolenic acid content (SOC, OAC, and LAC). Using RTM-GWAS with 29,119 SNPLDBs (SNP linkage disequilibrium blocks) as genomic markers, 50, 98, and 50 QTLs with 136, 283, and 154 alleles (2–9 per locus) were detected, with their contribution 82.52, 90.31, and 83.86% to phenotypic variance, corresponding to their heritability 91.29, 90.97, and 90.24% for SOC, OAC, and LAC, respectively. The RTM-GWAS was shown to be more powerful and efficient than previous single-locus model GWAS procedures. For each trait, the detected QTL-alleles were organized into a QTL-allele matrix as the population genetic constitution. From which the genetic differentiation among 6 eco-populations was characterized as significant allele frequency differentiation on 28, 56, and 30 loci for the three traits, respectively. The QTL-allele matrices were also used for genomic selection for optimal crosses, which predicted transgressive potential up to 24.76, 40.30, and 2.37% for the respective traits, respectively. From the detected major QTLs, 38, 27, and 25 candidate genes were annotated for the respective traits, and two common QTL covering eight genes were identified for further study.

Association between maternal/newborn genetic variants, placental pathology and spontaneous preterm birth risk: a Romanian population-based study

Objective: The objective of this study was to determine the association between maternal/newborn single-nucleotide polymorphisms (SNPs) in three candidate genes, placental pathology and the risk of spontaneous preterm birth (SPTB) in a Romanian population.Methods: We performed a prospective case-control study in a tertiary maternity in Romania, including 79 mother-newborn pairs with SPTB and 81 mother-newborn pairs with term delivery. Using real-time Polymerase Chain Reaction (PCR), three SNPs rs8192282 A > G, rs2277698 C > T and rs34003 A > C located on interleukin 6 receptor (IL6R), tissue inhibitor of matrix metalloproteinase-2 (TIMP2) and fibroblast growth factor 1 (FGF1) genes were assessed. The minor allele and genotype frequencies were compared between groups. Multilocus genetic association analyses were performed. From pathology reports, the morphological and histopathological examination of the placentas were extracted.Results: The rs34003 C/C genotype frequency in newborns FGF1 gene was significantly higher in the spontaneous preterm birth (SPTB) group compared to the control group (p = .045). In single-locus analyses, C/C genotype was associated with an increased risk of spontaneous preterm birth (OR = 2.59, 95%CI: 1.02–6.58). Additionally, this homozygote genotype was correlated with the presence of placental pathology, especially with the inflammatory and vascular lesions (p < .01). The prediction model based on rs34003 C/C genotype – placental pathology joint influence had a statistically significant regression coefficient (p < .01, OR = 7.76, 95%CI: 4.03–14.93). Single nucleotide polymorphisms related to IL6R gene in maternal samples and FGF1 gene in newborns were associated with spontaneous preterm delivery in multilocus genetic association analyses (p = .028, OR of 2.375).Conclusions: Our results indicate that rs34003 C/C genotype in newborns FGF1gene is correlated with the occurrence of placental pathological lesions and with an increased SPTB risk. The association of two SNPs in maternal and fetal genes doubled the risk of spontaneous preterm birth in our population.

A multilocus association analysis method integrating phenotype and expression data reveals multiple novel associations to flowering time variation in wild-collected Arabidopsis thaliana.

The adaptation to a new habitat often results in a confounding between genomewide genotype and beneficial alleles. When the confounding is strong, or the allelic effects is weak, it is a major statistical challenge to detect the adaptive polymorphisms. We describe a novel approach to dissect polygenic traits in natural populations. First, candidate adaptive loci are identified by screening for loci directly associated with the adaptive trait or the expression of genes known to affect it. Then, a multilocus genetic architecture is inferred using a backward elimination association analysis across all candidate loci with an adaptive false discovery rate-based threshold. Effects of population stratification are controlled by accounting for genomic kinship in both steps of the analysis and also by simultaneously testing all candidate loci in the multilocus model. We illustrate the method by exploring the polygenic basis of an important adaptive trait, flowering time in Arabidopsis thaliana, using public data from the 1,001 genomes project. We revealed associations between 33 (29) loci and flowering time at 10 (16)°C in this collection of natural accessions, where standard genomewide association analysis methods detected five (3) loci. The 33 (29) loci explained approximately 55.1 (48.7)% of the total phenotypic variance of the respective traits. Our work illustrates how the genetic basis of highly polygenic adaptive traits in natural populations can be explored in much greater detail using new multilocus mapping approaches taking advantage of prior biological information, genome and transcriptome data.

Characterization and Analytical Programs of the Restricted Two-stage Multi-locus Genome-wide Association Analysis

Characterization and Analytical Programs of the Restricted Two-stage Multi-locus Genome-wide Association Analysis

Single-Locus versus Multilocus Patterns of Local Adaptation to Climate in Eastern White Pine (Pinus strobus, Pinaceae).

Natural plant populations are often adapted to their local climate and environmental conditions, and populations of forest trees offer some of the best examples of this pattern. However, little empirical work has focused on the relative contribution of single-locus versus multilocus effects to the genetic architecture of local adaptation in plants/forest trees. Here, we employ eastern white pine (Pinus strobus) to test the hypothesis that it is the inter-genic effects that primarily drive climate-induced local adaptation. The genetic structure of 29 range-wide natural populations of eastern white pine was determined in relation to local climatic factors using both a reference set of SSR markers, and SNPs located in candidate genes putatively involved in adaptive response to climate. Comparisons were made between marker sets using standard single-locus outlier analysis, single-locus and multilocus environment association analyses and a novel implementation of Population Graphs. Magnitudes of population structure were similar between the two marker sets. Outlier loci consistent with diversifying selection were rare for both SNPs and SSRs. However, genetic distances based on the multilocus among population covariances (cGD) were significantly more correlated to climate, even after correcting for spatial effects, for SNPs as compared to SSRs. Coalescent simulations confirmed that the differences in mutation rates between SSRs and SNPs did not affect the topologies of the Population Graphs, and hence values of cGD and their correlations with associated climate variables. We conclude that the multilocus covariances among populations primarily reflect adaptation to local climate and environment in eastern white pine. This result highlights the complexity of the genetic architecture of adaptive traits, as well as the need to consider multilocus effects in studies of local adaptation.

Permutation-based variance component test in generalized linear mixed model with application to multilocus genetic association study.

BackgroundIn many medical studies the likelihood ratio test (LRT) has been widely applied to examine whether the random effects variance component is zero within the mixed effects models framework; whereas little work about likelihood-ratio based variance component test has been done in the generalized linear mixed models (GLMM), where the response is discrete and the log-likelihood cannot be computed exactly. Before applying the LRT for variance component in GLMM, several difficulties need to be overcome, including the computation of the log-likelihood, the parameter estimation and the derivation of the null distribution for the LRT statistic.MethodsTo overcome these problems, in this paper we make use of the penalized quasi-likelihood algorithm and calculate the LRT statistic based on the resulting working response and the quasi-likelihood. The permutation procedure is used to obtain the null distribution of the LRT statistic. We evaluate the permutation-based LRT via simulations and compare it with the score-based variance component test and the tests based on the mixture of chi-square distributions. Finally we apply the permutation-based LRT to multilocus association analysis in the case–control study, where the problem can be investigated under the framework of logistic mixed effects model.ResultsThe simulations show that the permutation-based LRT can effectively control the type I error rate, while the score test is sometimes slightly conservative and the tests based on mixtures cannot maintain the type I error rate. Our studies also show that the permutation-based LRT has higher power than these existing tests and still maintains a reasonably high power even when the random effects do not follow a normal distribution. The application to GAW17 data also demonstrates that the proposed LRT has a higher probability to identify the association signals than the score test and the tests based on mixtures.ConclusionsIn the present paper the permutation-based LRT was developed for variance component in GLMM. The LRT outperforms existing tests and has a reasonably higher power under various scenarios; additionally, it is conceptually simple and easy to implement.