Multiple Quantitative Trait Research Articles

Overexpression of a Transcription Factor Increases Lipid Content in a Woody Perennial Jatropha curcas.

Vegetable oil is an important renewable resource for dietary consumption for human and livestock, and more recently for biodiesel production. Lipid traits in crops are controlled by multiple quantitative trait loci (QTLs) and each of them has a small effect on lipid traits. So far, there is limited success to increase lipid yield and improve lipid quality in plants. Here, we reported the identification of a homolog of APETALA2 (AP2) transcription factor WRINKLED1 (JcWRI1) from an oleaginous plant Jatropha curcas and characterized its function in Jatropha and Arabidopsis thaliana. Using physical mapping data, we located JcWRI1 in a QTL region specifying high oleate and lipid content in Jatropha. Overexpression of JcWRI1 in Jatropha elevated seed lipid content and increased seed mass. Lipid profile in seeds of over-expression plants showed higher oleate content which will be beneficial to improve biodiesel quality. Overexpression of JcWRI1 activated lipid-related gene expression and JcWRI1 was shown to directly bind to the AW-box of promoters of some of these genes. In conclusion, we were able to increase seed lipid content and improve seed lipid quality in Jatropha by manipulating one key transcription factor JcWRI1.

The G-matrix Simulator Family: Software for Research and Teaching.

Genetic variation plays a fundamental role in all models of evolution. For phenotypes composed of multiple quantitative traits, genetic variation is best quantified as additive genetic variances and covariances, as these values determine the rate and trajectory of evolution. Additive genetic variances and covariances are often summarized conveniently in the G-matrix, which has additive genetic variances for each trait on the diagonal and additive genetic covariances as its off-diagonal elements. The evolution of the G-matrix is an interesting topic in its own right, because the processes that affect trait means also affect the distribution of standing genetic variation, which, in turn, feeds back to affect the rate of change of trait means. Theoretical studies of the G-matrix have profitably employed simulation-based models because the topic is often too complex to yield meaningful analytical results. Here, we present a series of G-matrix simulation software packages, which have emerged from about 15 years of research on this topic. These simulation models are useful for research and for building intuition regarding the evolution of the G-matrix under a wide variety of circumstances. A tutorial and source code also provide a foundation upon which future models can be built. These tools will be useful to students as well as researchers.

Linkage analysis and QTL mapping in a tetraploid russet mapping population of potato

BackgroundGenome-wide single nucleotide polymorphism (SNP) markers coupled with allele dosage information has emerged as a powerful tool for studying complex traits in cultivated autotetraploid potato (Solanum tuberosum L., 2n = 4× = 48). To date, this approach has been effectively applied to the identification of quantitative trait loci (QTLs) underlying highly heritable traits such as disease resistance, but largely unexplored for traits with complex patterns of inheritance.ResultsIn this study, an F1 tetraploid russet mapping population (162 individuals) was evaluated for multiple quantitative traits over two years and two locations to identify QTLs associated with tuber sugar concentration, processing quality, vine maturity, and other high-value agronomic traits. We report the linkage maps for the 12 potato chromosomes and the QTL location with corresponding genetic models and candidate SNPs explaining the highest phenotypic variation for tuber quality and maturity related traits. Significant QTLs for tuber glucose concentration and tuber fry color were detected on chromosomes 4, 5, 6, 10, and 11. Collectively, these QTLs explained between 24 and 46% of the total phenotypic variation for tuber glucose and fry color, respectively. The QTL on chromosome 10 was associated with apoplastic invertases, with ‘Premier Russet’ contributing the favorable allele for fry processing quality. On chromosome 5, minor-effect QTLs for tuber glucose concentration and fry color co-localized with various major-effect QTLs, including vine maturity, growth habit, tuber shape, early blight (Altenaria tenuis), and Verticillium wilt (Verticillium spp.).ConclusionsLinkage analysis and QTL mapping in a russet mapping population (A05141) using SNP dosage information successfully identified favorable alleles and candidate SNPs for resistance to the accumulation of tuber reducing sugars. These novel markers have a high potential for the improvement of tuber processing quality. Moreover, the discovery of different genetic models for traits with overlapping QTLs at the maturity locus clearly suggests an independent genetic control.

Novel Pathogenesis of Hypertension and Diastolic Dysfunction Caused by M3R (Muscarinic Cholinergic 3 Receptor) Signaling.

Multiple quantitative trait loci for blood pressure (BP) are localized in humans and rodent models. Model studies have not only produced human quantitative trait loci homologues but also provided unforeseen mechanistic insights into the function modality of quantitative trait loci actions. Presently, congenic knockins, gene-specific knockout, and in vitro and in vivo function studies were used in a rat model of polygenic hypertension, DSS (Dahl salt sensitive) rats. One gene previously unknown in regulating BP was detected with 1 structural mutation(s) for each of 2 quantitative trait loci classified into 2 separate epistatic modules 1 and 3. C17QTL1 in epistatic module 2 was identified to be the gene Chrm3 encoding the M3R (muscarinic cholinergic 3 receptor), since a single function-enhancing M3RT556M conversion correlated with elevated BP. To definitively prove that the enhanced M3R function is responsible for BP changes by the DSS alleles of C17QTL1, we generated a Chrm3 gene-specific rat knockout. We observed a reduction in BP without tachycardia in both sexes, regardless of the amount of dietary salt, and an improvement in diastolic and kidney dysfunctions. All occurred in spite of a significant reduction in M3R-dependent vasodilation. The previously seen sexual dimorphism for C17QTL1 on BP disappeared in the absence of M3R. A Chrm3-coding variation increased M3R signaling, correlating with higher BP. Removing the M3R signaling led to a decrease in BP and improvements in cardiac and renal malfunctions. A novel pathogenic pathway accounted for a portion of polygenic hypertension and has implications in applying new diagnostic and therapeutic uses against hypertension and diastolic dysfunction.

Multiplex QTL editing of grain-related genes improves yield in elite rice varieties.

Significant yield increase has been achieved by simultaneous introduction of three trait-related QTLs in three rice varieties with multiplex editing by CRISPR-Cas9. Using traditional breeding approaches to develop new elite rice varieties with high yield and superior quality is challenging. It usually requires introduction of multiple trait-related quantitative trait loci (QTLs) into an elite background through multiple rounds of crossing and selection. CRISPR-Cas9-based multiplex editing of QTLs represents a new breeding strategy that is straightforward and cost effective. To test this approach, we simultaneously targeted three yield-related QTLs for editing in three elite rice varieties, namely J809, L237 and CNXJ. The chosen yield-related QTL genes are OsGS3, OsGW2 and OsGn1a, which have been identified to negatively regulate the grain size, width and weight, and number, respectively. Our approach rapidly generated all seven combinations of single, double and triple mutants for the target genes in elite backgrounds. Detailed analysis of these mutants revealed differential contributions of QTL mutations to yield performance such as grain length, width, number and 1000-grain weight. Overall, the contributions are additive, resulting in 68 and 30% yield per panicle increase in triple mutants of J809 and L237, respectively. Our data hence demonstrates a promising genome editing approach for rapid breeding of QTLs in elite crop varieties.

A High-Resolution Genetic Map for the Laboratory Rat.

An accurate and high-resolution genetic map is critical for mapping complex traits, yet the resolution of the current rat genetic map is far lower than human and mouse, and has not been updated since the original Jensen-Seaman map in 2004. For the first time, we have refined the rat genetic map to sub-centimorgan (cM) resolution (<0.02 cM) by using 95,769 genetic markers and 870 informative meioses from a cohort of 528 heterogeneous stock (HS) rats. Global recombination rates in the revised sex-averaged map (0.66 cM/Mb) did not differ compared to the historical map (0.65 cM/Mb); however, substantial refinement was made to the localization of highly recombinant regions within the revised map. Also for the first time, sex-specific rat genetic maps were generated, which revealed both genomewide and fine-scale variation in recombination rates between male and female rats. Reanalysis of multiple quantitative trait loci (QTL) using the historical and refined rat genetic maps demonstrated marked changes to QTL localization, shape, and effect size. As a resource to the rat research community, we have provided revised centimorgan positions for all physical positions within the rat genome and commonly used genetic markers for trait mapping, including 44,828 SSLP markers and the RATDIV genotyping array. Collectively, this study provides a substantial improvement to the rat genetic map and an unprecedented resource for analysis of complex traits and recombination in the rat.

Speed breeding for multiple quantitative traits in durum wheat

BackgroundPlant breeding requires numerous generations to be cycled and evaluated before an improved cultivar is released. This lengthy process is required to introduce and test multiple traits of interest. However, a technology for rapid generation advance named ‘speed breeding’ was successfully deployed in bread wheat (Triticum aestivum L.) to achieve six generations per year while imposing phenotypic selection for foliar disease resistance and grain dormancy. Here, for the first time the deployment of this methodology is presented in durum wheat (Triticum durum Desf.) by integrating selection for key traits, including above and below ground traits on the same set of plants. This involved phenotyping for seminal root angle (RA), seminal root number (RN), tolerance to crown rot (CR), resistance to leaf rust (LR) and plant height (PH). In durum wheat, these traits are desirable in environments where yield is limited by in-season rainfall with the occurrence of CR and epidemics of LR. To evaluate this multi-trait screening approach, we applied selection to a large segregating F2 population (n = 1000) derived from a bi-parental cross (Outrob4/Caparoi). A weighted selection index (SI) was developed and applied. The gain for each trait was determined by evaluating F3 progeny derived from 100 ‘selected’ and 100 ‘unselected’ F2 individuals.ResultsTransgressive segregation was observed for all assayed traits in the Outrob4/Caparoi F2 population. Application of the SI successfully shifted the population mean for four traits, as determined by a significant mean difference between ‘selected’ and ‘unselected’ F3 families for CR tolerance, LR resistance, RA and RN. No significant shift for PH was observed.ConclusionsThe novel multi-trait phenotyping method presents a useful tool for rapid selection of early filial generations or for the characterization of fixed lines out-of-season. Further, it offers efficient use of resources by assaying multiple traits on the same set of plants. Results suggest that when performed in parallel with speed breeding in early generations, selection will enrich recombinant inbred lines with desirable alleles and will reduce the length and number of years required to combine these traits in elite breeding populations and therefore cultivars.

RETRACTED ARTICLE: Dissecting closely linked association signals in combination with the mammalian phenotype database can identify candidate genes in dairy cattle

BackgroundGenome-wide association studies (GWAS) have been successfully implemented in cattle research and breeding. However, moving from the associations to identifying the causal variants and revealing underlying mechanisms have proven complicated. In dairy cattle populations, we face a challenge due to long-range linkage disequilibrium (LD) arising from close familial relationships in the studied individuals. Long range LD makes it difficult to distinguish if one or multiple quantitative trait loci (QTL) are segregating in a genomic region showing association with a phenotype. We had two objectives in this study: 1) to distinguish between multiple QTL segregating in a genomic region, and 2) use of external information to prioritize candidate genes for a QTL along with the candidate variant.ResultsWe observed fixing the lead SNP as a covariate can help to distinguish additional close association signal(s). Thereafter, using the mammalian phenotype database, we successfully found candidate genes, in concordance with previous studies, demonstrating the power of this strategy. Secondly, we used variant annotation information to search for causative variants in our candidate genes. The variant information successfully identified known causal mutations and showed the potential to pinpoint the causative mutation(s) which are located in coding regions.ConclusionsOur approach can distinguish multiple QTL segregating on the same chromosome in a single analysis without manual input. Moreover, utilizing information from the mammalian phenotype database and variant effect predictor as post-GWAS analysis could benefit in candidate genes and causative mutations finding in cattle. Our study not only identified additional candidate genes for milk traits, but also can serve as a routine method for GWAS in dairy cattle.

Genetic variants regulate NR1H3 expression and contribute to multiple sclerosis risk

A recent study analyzed 2053 multiple sclerosis (MS) cases and 799 healthy controls to investigate whether five genetic variants (rs11039149, rs12221497, rs2279238, rs7120118 and rs7114704) in NR1H3 are associated with MS risk. However this study reported negative results. It is very important that the appropriate samples and approach should be used in replication studies, which may provide the correct interpretation of the results. Here, we evaluated the above findings using large-scale MS genome-wide association studies with a total of 27,148 samples including 9772 MS cases and 17,376 controls, and multiple expression quantitative trait loci datasets. The results suggest that rs7120118 and rs2279238 variants are significantly associated with MS risk, and could significantly regulate NR1H3 expression in kinds of human tissues and cells. In summary, these findings provide important supplementary information about the association between NR1H3 variants and MS risk.

Alternative splicing of OsLG3b controls grain length and yield in japonica rice.

SummaryGrain size, one of the important components determining grain yield in rice, is controlled by the multiple quantitative trait loci (QTLs). Intensive artificial selection for grain size during domestication is evidenced in modern cultivars compared to their wild relatives. Here, we report the molecular cloning and characterization of OsLG3b, a QTL for grain length in tropical japonica rice that encodes MADS‐box transcription factor 1 (OsMADS1). Six SNPs in the OsLG3b region led to alternative splicing, which were associated with grain length in an association analysis of candidate region. Quantitative PCR analysis indicated that OsLG3b expression was higher during the panicle and seed development stages. Analysis of haplotypes and introgression regions revealed that the long‐grain allele of OsLG3b might have arisen after domestication of tropical japonica and spread to subspecies indica or temperate japonica by natural crossing and artificial selection. OsLG3b is therefore a target of human selection for adaptation to tropical regions during domestication and/or improvement of rice. Phylogenetic analysis and pedigree records showed that OsLG3b had been employed by breeders, but the gene still has much breeding potential for increasing grain length in indica. These findings will not only aid efforts to elucidate the molecular basis of grain development and domestication, but also facilitate the genetic improvement of rice yield.

CRISPR mutagenesis confirms the role of oca2 in melanin pigmentation in Astyanax mexicanus

Understanding the genetic basis of trait evolution is critical to identifying the mechanisms that generated the immense amount of diversity observable in the living world. However, genetically manipulating organisms from natural populations with evolutionary adaptations remains a significant challenge. Astyanax mexicanus exists in two interfertile forms, a surface-dwelling form and multiple independently evolved cave-dwelling forms. Cavefish have evolved a number of morphological and behavioral traits and multiple quantitative trait loci (QTL) analyses have been performed to identify loci underlying these traits. These studies provide a unique opportunity to identify and test candidate genes for these cave-specific traits. We have leveraged the CRISPR/Cas9 genome editing techniques to characterize the effects of mutations in oculocutaneous albinism II (oca2), a candidate gene hypothesized to be responsible for the evolution of albinism in A. mexicanus cave populations. We generated oca2 mutant surface A. mexicanus. Surface fish with oca2 mutations are albino due to a disruption in the first step of the melanin synthesis pathway, the same step that is disrupted in albino cavefish. Hybrid offspring from crosses between oca2 mutant surface and cavefish are albino, definitively demonstrating the role of this gene in the evolution of albinism in this species. This research elucidates the role oca2 plays in pigmentation in fish, and establishes that this gene is solely responsible for the evolution of albinism in multiple cavefish populations. Finally, it demonstrates the utility of using genome editing to investigate the genetic basis of trait evolution.

Genetic Map Construction and Fiber Quality QTL Mapping Using the CottonSNP80K Array in Upland Cotton.

Cotton fiber quality traits are controlled by multiple quantitative trait loci (QTL), and the improvement of these traits requires extensive germplasm. Herein, an Upland cotton cultivar from America, Acala Maxxa, was crossed with a local high fiber quality cultivar, Yumian 1, and 180 recombinant inbred lines (RILs) were obtained. In order to dissect the genetic basis of fiber quality differences between these parents, a genetic map containing 12116 SNP markers was constructed using the CottonSNP80K assay, which covered 3741.81 cM with an average distance of 0.31 cM between markers. Based on the genetic map and growouts in three environments, we detected a total of 104 QTL controlling fiber quality traits. Among these QTL, 25 were detected in all three environments and 35 in two environments. Meanwhile, 19 QTL clusters were also identified, and nine contained at least one stable QTL (detected in three environments for a given trait). These stable QTL or QTL clusters are priorities for fine mapping, identifying candidate genes, elaborating molecular mechanisms of fiber development, and application in cotton breeding programs by marker-assisted selection (MAS).

Abstract P5-05-02: A laboratory/machine learning based comparative genetics model accurately predicts breast cancer in a human cohort

Abstract Inherited genetic variants are estimated to account for 30-35% of overall breast cancer risk. A few rare, highly penetrant, genetic determinants of risk in humans have been well defined. However, the actions of many common weakly penetrant breast cancer risk loci remain uncharacterized. Additionally, it is well established that endocrine factors in general, and estrogens in particular, influence breast cancer etiology. We are using the ACI rat model of 17β-estradiol (E2)-induced mammary cancer to parse the contributions of individual genetic risk variants to breast cancer susceptibility in a physiologically relevant context. ACI females develop mammary carcinomas at an incidence approaching 100% when exposed to physiological levels of E2, and these carcinomas share many features with luminal-type breast cancers in humans. In contrast, Brown Norway (BN) rats are highly resistant to E2-induced mammary cancer. Linkage analyses of progeny from intercrosses between susceptible ACI and resistant BN rats led to the identification of multiple quantitative trait loci for E2-induced mammary cancer. One such locus, Estrogen-induced mammary cancer 4 (Emca4), is the focus of the current investigation. We generated a series of novel congenic rat strains which carry BN alleles at distinct regions of interest across the Emca4 locus, introgressed onto the ACI genetic background. Characterization of mammary cancer phenotypes in the congenic strains facilitated fine resolution mapping of the Emca4 locus. These studies revealed that Emca4 is a complex locus harboring at least four interacting genetic determinants of risk, designated Emca4.1 – Emca4.4, and is orthologous to the 8q24.21 breast cancer risk locus in humans. To assess the relevance of the rat genetic data to human populations, novel machine learning methods were employed to generate risk prediction models using data from a human cohort. Genotype data for 76 SNPs located in the regions of the human genome orthologous to Emca4.1 – 4.4 were obtained from the Cancer Genetics Markers of Susceptibility case control population. Models generated from this data set were optimized with novel algorithms to identify a subset of 16 variants that significantly influenced the risk models. The best model distinguished breast cancer cases from controls with a remarkably high degree of accuracy for a model based on genotype (AUC = 0.6, P &amp;lt; 10-11 relative to random guessing). It is worth noting that the predictive power of this model arose from interactions between human SNPs. Our data show that Emca4 is a complex locus containing multiple interacting determinants of risk; variants in the orthologous 8q24.21 breast cancer risk locus in human interact to influence breast cancer risk as predicted by the rat model; and accounting for interactions between variants achieves a predictive power beyond what is observed with individual SNPs. We have demonstrated, for the first time, the ability to develop a multi-component genetic model in rats and test it in a human population. This illustrates the power of the rat model to elucidate the complex mechanisms through which common, weakly penetrant variants influence breast cancer risk in humans. Citation Format: Dennison KL, Chack A, Escanilla NS, Page D, Shull JD. A laboratory/machine learning based comparative genetics model accurately predicts breast cancer in a human cohort [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P5-05-02.

Joint Analysis of Strain and Parent-of-Origin Effects for Recombinant Inbred Intercrosses Generated from Multiparent Populations with the Collaborative Cross as an Example.

Multiparent populations (MPP) have become popular resources for complex trait mapping because of their wider allelic diversity and larger population size compared with traditional two-way recombinant inbred (RI) strains. In mice, the collaborative cross (CC) is one of the most popular MPP and is derived from eight genetically diverse inbred founder strains. The strategy of generating RI intercrosses (RIX) from MPP in general and from the CC in particular can produce a large number of completely reproducible heterozygote genomes that better represent the (outbred) human population. Since both maternal and paternal haplotypes of each RIX are readily available, RIX is a powerful resource for studying both standing genetic and epigenetic variations of complex traits, in particular, the parent-of-origin (PoO) effects, which are important contributors to many complex traits. Furthermore, most complex traits are affected by >1 genes, where multiple quantitative trait locus mapping could be more advantageous. In this paper, for MPP-RIX data but taking CC-RIX as a working example, we propose a general Bayesian variable selection procedure to simultaneously search for multiple genes with founder allelic effects and PoO effects. The proposed model respects the complex relationship among RIX samples, and the performance of the proposed method is examined by extensive simulations.

Fast and Accurate Genome-Wide Association Test of Multiple Quantitative Traits.

Multiple correlated traits are often collected in genetic studies. By jointly analyzing multiple traits, we can increase power by aggregating multiple weak effects and reveal additional insights into the genetic architecture of complex human diseases. In this article, we propose a multivariate linear regression-based method to test the joint association of multiple quantitative traits. It is flexible to accommodate any covariates, has very accurate control of type I errors, and offers very competitive performance. We also discuss fast and accurate significance p value computation especially for genome-wide association studies with small-to-medium sample sizes. We demonstrate through extensive numerical studies that the proposed method has competitive performance. Its usefulness is further illustrated with application to genome-wide association analysis of diabetes-related traits in the Atherosclerosis Risk in Communities (ARIC) study. We found some very interesting associations with diabetes traits which have not been reported before. We implemented the proposed methods in a publicly available R package.

Expression QTL analysis of glaucoma endophenotypes in the Norfolk Island isolate provides evidence that immune-related genes are associated with optic disc size.

Primary open-angle glaucoma (POAG) is influenced by both genetic and environmental factors. Despite significant progress in identifying genetic variants associated with POAG, there remains a substantial amount of unexplained heritability. Study design features that may enhance knowledge of the genetic architecture include focusing on multiple quantitative traits related to ocular disorders (i.e. endophenotypes), targeting genetic variants that directly influence gene expression (i.e. cis-eQTLs) and utilising genetically isolated populations to reduce genetic and environmental noise and thus enhance association signals. In this study we performed heritability and blood-based eQTL association analysis of five key POAG endophenotypes in 330 individuals from the Norfolk Island (NI) isolate. Results showed evidence of heritability for all five traits, with H2 estimates ranging from 0.35 for intraocular pressure (IOP) to 0.82 for central corneal thickness (CCT) (P < 0.05). The primary finding was for BTN3A2, whereby both cis-SNP and transcript were significantly associated with disc size within a conditional regression model. Specifically, this model included rs853676 (β = 0.23,P = 0.008) and transcript (β = 0.23, P = 0.03). We also observed a cis-SNP association between optic disc size and LPCAT2 independent of transcript (P = 0.0004). These genes have specific functions in immune system pathways and suggest a role for an inherited immune component of POAG risk. This study also demonstrates an alternate approach to understanding the functional genetic basis of POAG and ocular health more generally.

A first linkage map and downy mildew resistance QTL discovery for sweet basil (Ocimum basilicum) facilitated by double digestion restriction site associated DNA sequencing (ddRADseq).

Limited understanding of sweet basil (Ocimum basilicum L.) genetics and genome structure has reduced efficiency of breeding strategies. This is evidenced by the rapid, worldwide dissemination of basil downy mildew (Peronospora belbahrii) in the absence of resistant cultivars. In an effort to improve available genetic resources, expressed sequence tag simple sequence repeat (EST-SSR) and single nucleotide polymorphism (SNP) markers were developed and used to genotype the MRI x SB22 F2 mapping population, which segregates for response to downy mildew. SNP markers were generated from genomic sequences derived from double digestion restriction site associated DNA sequencing (ddRADseq). Disomic segregation was observed in both SNP and EST-SSR markers providing evidence of an O. basilicum allotetraploid genome structure and allowing for subsequent analysis of the mapping population as a diploid intercross. A dense linkage map was constructed using 42 EST-SSR and 1,847 SNP markers spanning 3,030.9 cM. Multiple quantitative trait loci (QTL) model (MQM) analysis identified three QTL that explained 37–55% of phenotypic variance associated with downy mildew response across three environments. A single major QTL, dm11.1 explained 21–28% of phenotypic variance and demonstrated dominant gene action. Two minor QTL dm9.1 and dm14.1 explained 5–16% and 4–18% of phenotypic variance, respectively. Evidence is provided for an additive effect between the two minor QTL and the major QTL dm11.1 increasing downy mildew susceptibility. Results indicate that ddRADseq-facilitated SNP and SSR marker genotyping is an effective approach for mapping the sweet basil genome.

Correlation analyses revealed global microRNA-mRNA expression associations in human peripheral blood mononuclear cells.

MicroRNAs (miRNAs) can regulate gene expression through binding to complementary sites in the 3'-untranslated regions of target mRNAs, which will lead to existence of correlation in expression between miRNA and mRNA. However, the miRNA-mRNA correlation patterns are complex and remain largely unclear yet. To establish the global correlation patterns in human peripheral blood mononuclear cells (PBMCs), multiple miRNA-mRNA correlation analyses and expression quantitative trait locus (eQTL) analysis were conducted in this study. We predicted and achieved 861 miRNA-mRNA pairs (65 miRNAs, 412 mRNAs) using multiple bioinformatics programs, and found global negative miRNA-mRNA correlations in PBMC from all 46 study subjects. Among the 861 pairs of correlations, 19.5% were significant (P<0.05) and ~70% were negative. The correlation network was complex and highlighted key miRNAs/genes in PBMC. Some miRNAs, such as hsa-miR-29a, hsa-miR-148a, regulate a cluster of target genes. Some genes, e.g., TNRC6A, are regulated by multiple miRNAs. The identified genes tend to be enriched in molecular functions of DNA and RNA binding, and biological processes such as protein transport, regulation of translation and chromatin modification. The results provided a global view of the miRNA-mRNA expression correlation profile in human PBMCs, which would facilitate in-depth investigation of biological functions of key miRNAs/mRNAs and better understanding of the pathogenesis underlying PBMC-related diseases.

SORL1 Variants Show Different Association with Early-Onset and Late-Onset Alzheimer's Disease Risk.

A recent study sequenced the full coding region of SORL1 in 1,255 early-onset Alzheimer's disease (EOAD) cases and 1,938 control individuals, and investigated the contribution of genetic variability in SORL1 to EOAD risk in a European cohort. This study identified six common variants and five low frequency variants in the SORL1 coding sequence. However, none of these 11 variants was significantly associated with EOAD risk after adjusting for multiple testing. We consider whether these 11 SORL1 variants identified in European EOAD contribute to late-onset Alzheimer's disease (LOAD) risk in individuals of European ancestry. Here, we investigated these 11 SORL1 variants identified in European EOAD and LOAD risk in individuals of European ancestry using a large-scale LOAD GWAS. Our results indicate that three genetic variants rs2070045, rs2276412, and rs17125548 as well as their tagged genetic variants contribute to LOAD risk in European population. We further investigate whether these variants could affect SORL1 expression using multiple expression quantitative trait loci (eQTLs) datasets. Our findings suggest that three genetic variants rs2070045, rs1699102, and rs3824968 could significantly regulate SORL1 expression in human brain tissues. We believe that our findings further provide important supplementary information about the involvement of the SORL1 variants in LOAD risk.

Genome-Wide Exome Analysis of Cmv5-Disparate Mouse Strains that Differ in Host Resistance to Murine Cytomegalovirus Infection.

Host resistance to murine cytomegalovirus (MCMV) varies in different strains of laboratory mice due to differences in expression of determinants that control and clear viral infection. The major histocompatibility complex class I Dk molecule is one such determinant that controls MCMV through the action of natural killer (NK) cells. However, the extent of NK cell–mediated Dk-dependent resistance to infection varies in different mouse strains. The molecular genetic basis of this variation remains unclear. Previous work to examine the Dk effect on MCMV resistance in MA/My × C57L offspring discovered multiple quantitative trait loci (QTL) that may serve to modify NK cells or their capacity to respond during MCMV infection. One QTL in particular, Cmv5, was found to regulate the frequency of NK cells and secondary lymphoid organ structure in spleen during MCMV infection. Cmv5 alleles, however, have not been identified. We therefore sequenced and analyzed genome-wide exome (GWE) variants, including those aligned to the critical genetic interval, in Cmv5-disparate mouse strains. Their GWE variant profiles were compared to assess strain-specific sequence data integrity and to analyze mouse strain relatedness across the genome. GWE content was further compared against data from the Mouse Genomes Project. This approach was developed as a platform for using GWE variants to define genomic regions of divergence and similarity in different mouse strains while also validating the overall quality of GWE sequence data. Moreover, the analysis provides a framework for the selection of novel QTL candidate sequences, including at the Cmv5 critical region.