Finding genes that underlie complex traits

Jaw size variation is associated with a novel craniofacial function for galanin receptor 2 in an adaptive radiation of pupfishes

Understanding the genetic basis of novel adaptations in new species is a fundamental question in biology. Here we demonstrate a new role for galr2 in vertebrate craniofacial development using an adaptive radiation of trophic specialist pupfishes endemic to San Salvador Island, Bahamas. We confirmed the loss of a putative Sry transcription factor binding site upstream of galr2 in scale-eating pupfish and found significant spatial differences in galr2 expression among pupfish species in Meckel's cartilage using in situ hybridization chain reaction (HCR). We then experimentally demonstrated a novel role for Galr2 in craniofacial development by exposing embryos to Garl2-inhibiting drugs. Galr2-inhibition reduced Meckel's cartilage length and increased chondrocyte density in both trophic specialists but not in the generalist genetic background. We propose a mechanism for jaw elongation in scale-eaters based on the reduced expression of galr2 due to the loss of a putative Sry binding site. Fewer Galr2 receptors in the scale-eater Meckel's cartilage may result in their enlarged jaw lengths as adults by limiting opportunities for a circulating Galr2 agonist to bind to these receptors during development. Our findings illustrate the growing utility of linking candidate adaptive SNPs in non-model systems with highly divergent phenotypes to novel vertebrate gene functions.

Predictive modeling of antibiotic eradication therapy success for new-onset Pseudomonas aeruginosa pulmonary infections in children with cystic fibrosis

Chronic Pseudomonas aeruginosa (Pa) lung infections are the leading cause of mortality among cystic fibrosis (CF) patients; therefore, the eradication of new-onset Pa lung infections is an important therapeutic goal that can have long-term health benefits. The use of early antibiotic eradication therapy (AET) has been shown to clear the majority of new-onset Pa infections, and it is hoped that identifying the underlying basis for AET failure will further improve treatment outcomes. Here we generated machine learning models to predict AET outcomes based on pathogen genomic data. We used a nested cross validation design, population structure control, and recursive feature selection to improve model performance and showed that incorporating population structure control was crucial for improving model interpretation and generalizability. Our best model, controlling for population structure and using only 30 recursively selected features, had an area under the curve of 0.87 for a holdout test dataset. The top-ranked features were generally associated with motility, adhesion, and biofilm formation.

Jaw size variation is associated with a novel craniofacial function for galanin receptor 2 in an adaptive radiation of pupfishes

Understanding the genetic basis of novel adaptations in new species is a fundamental question in biology that also provides an opportunity to uncover new genes and regulatory networks with potential clinical relevance. Here we demonstrate a new role for galr2 in vertebrate craniofacial development using an adaptive radiation of trophic specialist pupfishes endemic to San Salvador Island in the Bahamas. We confirmed the loss of a putative Sry transcription factor binding site in the upstream region of galr2 in scale-eating pupfish and found significant spatial differences in galr2 expression among pupfish species in Meckel's cartilage and premaxilla using in situ hybridization chain reaction (HCR). We then experimentally demonstrated a novel function for Galr2 in craniofacial development and jaw elongation by exposing embryos to drugs that inhibit Galr2 activity. Galr2-inhibition reduced Meckel's cartilage length and increased chondrocyte density in both trophic specialists but not in the generalist genetic background. We propose a mechanism for jaw elongation in scale-eaters based on the reduced expression of galr2 due to the loss of a putative Sry binding site. Fewer Galr2 receptors in the scale-eater Meckel's cartilage may result in their enlarged jaw lengths as adults by limiting opportunities for a postulated Galr2 agonist to bind to these receptors during development. Our findings illustrate the growing utility of linking candidate adaptive SNPs in non-model systems with highly divergent phenotypes to novel vertebrate gene functions.

Functional characterization of human genomic variation linked to polygenic diseases

The burden of human disease lies predominantly in polygenic diseases. Since the early 2000s, genome-wide association studies (GWAS) have identified genetic variants and loci associated with complex traits. These have ranged from variants in coding sequences to mutations in regulatory regions, such as promoters and enhancers, as well as mutations affecting mediators of mRNA stability and other downstream regulators, such as 5' and 3'-untranslated regions (UTRs), long noncoding RNA (lncRNA), and miRNA. Recent research advances in genetics have utilized a combination of computational techniques, high-throughput in vitro and in vivo screening modalities, and precise genome editing to impute the function of diverse classes of genetic variants identified through GWAS. In this review, we highlight the vastness of genomic variants associated with polygenic disease risk and address recent advances in how genetic tools can be used to functionally characterize them.

Genome-wide association study of traits in sacred lotus uncovers MITE-associated variants underlying stamen petaloid and petal number variations

Understanding the genetic variants responsible for floral trait diversity is important for the molecular breeding of ornamental flowers. Widely used in water gardening for thousands of years, the sacred lotus exhibits a wide range of diversity in floral organs. Nevertheless, the genetic variations underlying various morphological characteristics in lotus remain largely unclear. Here, we performed a genome-wide association study of sacred lotus for 12 well-recorded ornamental traits. Given a moderate linkage disequilibrium level of 32.9 kb, we successfully identified 149 candidate genes responsible for seven flower traits and plant size variations, including many pleiotropic genes affecting multiple floral-organ-related traits, such as NnKUP2. Notably, we found a 2.75-kb presence-and-absence genomic fragment significantly associated with stamen petaloid and petal number variations, which was further confirmed by re-examining another independent population dataset with petal number records. Intriguingly, this fragment carries MITE transposons bound by siRNAs and is related to the expression differentiation of a nearby candidate gene between few-petalled and double-petalled lotuses. Overall, these genetic variations and candidate genes responsible for diverse lotus traits revealed by our GWAS highlight the role of transposon variations, particularly MITEs, in shaping floral trait diversity.

Epi-MEIF: detecting higher order epistatic interactions for complex traits using mixed effect conditional inference forests

Understanding the relationship between genetic variations and variations in complex and quantitative phenotypes remains an ongoing challenge. While Genome-wide association studies (GWAS) have become a vital tool for identifying single-locus associations, we lack methods for identifying epistatic interactions. In this article, we propose a novel method for higher-order epistasis detection using mixed effect conditional inference forest (epiMEIF). The proposed method is fitted on a group of single nucleotide polymorphisms (SNPs) potentially associated with the phenotype and the tree structure in the forest facilitates the identification of n-way interactions between the SNPs. Additional testing strategies further improve the robustness of the method. We demonstrate its ability to detect true n-way interactions via extensive simulations in both cross-sectional and longitudinal synthetic datasets. This is further illustrated in an application to reveal epistatic interactions from natural variations of cardiac traits in flies (Drosophila). Overall, the method provides a generalized way to identify higher-order interactions from any GWAS data, thereby greatly improving the detection of the genetic architecture underlying complex phenotypes.

Socioeconomic and genomic roots of verbal ability from current evidence

This research examines how the human genome and SES jointly and interactively shape verbal ability among youth in the U.S. The youth are aged 12-18 when the study starts. The research draws on findings from the latest GWAS as well as a rich set of longitudinal SES measures at individual, family and neighborhood levels from Add Health (N = 7194). Both SES and genome measures predict verbal ability well separately and jointly. More interestingly, the inclusion of both sets of predictors in the same model corrects for about 20% upward bias in the effect of the education PGS, and implies that about 20-30% of the effects of parental SES are not environmental, but parentally genomic. The three incremental R²s that measure the relative contributions of the two PGSs, the genomic component in parental SES, and the environmental component in parental SES are estimated to be about 1.5%, 1.5%, and 7.8%, respectively. The total environmental R² and the total genomic R² are, thus, 7.8% and 3%, respectively. These findings confirm the importance of SES environment and also pose challenges to traditional social-science research. Not only does an individual's genome have an important direct influence on verbal ability, parental genomes also influence verbal ability through parental SES. The decades-long blueprint of including SES in a model and interpreting their effects as those of SES needs to be amended accordingly. A straightforward solution is to routinely collect DNA data for large social-science studies granted that the primary purpose is to understand social and environmental influences.

Genome-wide association study for carcass traits in Hanwoo cattle using additional relatives' information of non-genotyped animals

The aim of this study was to find significant genomic regions associated with carcass traits in Hanwoo cattle and to compare the benefit of using additional information from non-genotyped animals. Imputed whole-genome sequence data were used along with phenotypic data on 13 715 genotyped animals as well as phenotypes of 440 284 non-genotyped animals that were offspring of 454 genotyped sires. For carcass weight, 15 083 SNPs in 33 QTL regions and 313 candidate genes were identified. We found 410 SNPs in 17 QTL regions containing 122 candidate genes for back fat thickness. In total, 656 SNPs in 19 QTLs with 137 candidate genes for eye muscle area and 79 SNPs in 12 QTL regions with 77 candidate genes were identified for marbling score. The most important candidate genes included ZFAT, TG, PLAG1, CHCHD7, and TOX for carcass weight and eye muscle area, NOG for back fat thickness, and EVOVL5 for marbling score. This study showed that the use of phenotypic records on non-genotyped progeny along with imputed whole-genome sequence data increased the power of detecting new significant genomic regions.

Morphometric and Genetic Description of Trophic Adaptations in Cichlid Fishes

Since Darwin, biologists have sought to understand the evolution and origins of phenotypic adaptations. The skull is particularly diverse due to intense natural selection on feeding biomechanics. We investigated the genetic and molecular origins of trophic adaptation using Lake Malawi cichlids, which have undergone an exemplary evolutionary radiation. We analyzed morphological differences in the lateral and ventral head shape among an insectivore that eats by suction feeding, an obligate biting herbivore, and their F2 hybrids. We identified variation in a series of morphological traits—including mandible width, mandible length, and buccal length—that directly affect feeding kinematics and function. Using quantitative trait loci (QTL) mapping, we found that many genes of small effects influence these craniofacial adaptations. Intervals for some traits were enriched in genes related to potassium transport and sensory systems, the latter suggesting co-evolution of feeding structures and sensory adaptations for foraging. Despite these indications of co-evolution of structures, morphological traits did not show covariation. Furthermore, phenotypes largely mapped to distinct genetic intervals, suggesting that a common genetic basis does not generate coordinated changes in shape. Together, these suggest that craniofacial traits are mostly inherited as separate modules, which confers a high potential for the evolution of morphological diversity. Though these traits are not restricted by genetic pleiotropy, functional demands of feeding and sensory structures likely introduce constraints on variation. In all, we provide insights into the quantitative genetic basis of trophic adaptation, identify mechanisms that influence the direction of morphological evolution, and provide molecular inroads to craniofacial variation.

Whole-Genome Sequencing of 117 Chromosome Segment Substitution Lines for Genetic Analyses of Complex Traits in Rice

Rice is one of the most important food crops in Asia. Genetic analyses of complex traits and molecular breeding studies in rice greatly rely on the construction of various genetic populations. Chromosome segment substitution lines (CSSLs) serve as a powerful genetic population for quantitative trait locus (QTL) mapping in rice. Moreover, CSSLs containing target genomic regions can be used as improved varieties in rice breeding. In this study, we developed a set of CSSLs consisting of 117 lines derived from the recipient 'Huanghuazhan' (HHZ) and the donor 'Basmati Surkb 89-15' (BAS). The 117 lines were extensively genotyped by whole-genome resequencing, and a high-density genotype map was constructed for the CSSL population. The 117 CSSLs covered 99.78% of the BAS genome. Each line contained a single segment, and the average segment length was 6.02 Mb. Using the CSSL population, we investigated three agronomic traits in Shanghai and Hangzhou, China, and a total of 25 QTLs were detected in both environments. Among those QTLs, we found that RFT1 was the causal gene for heading date variance between HHZ and BAS. RFT1 from BAS was found to contain a loss-of-function allele based on yeast two-hybrid assay, and its causal variation was a P to S change in the 94th amino acid of the RFT1 protein. The combination of high-throughput genotyping and marker-assisted selection (MAS) is a highly efficient way to construct CSSLs in rice, and extensively genotyped CSSLs will be a powerful tool for the genetic mapping of agronomic traits and molecular breeding for target QTLs/genes.

Identifying Breast Cancer-Related Genes Based on a Novel Computational Framework Involving KEGG Pathways and PPI Network Modularity

Complex diseases, such as breast cancer, are often caused by mutations of multiple functional genes. Identifying disease-related genes is a critical and challenging task for unveiling the biological mechanisms behind these diseases. In this study, we develop a novel computational framework to analyze the network properties of the known breast cancer–associated genes, based on which we develop a random-walk-with-restart (RCRWR) algorithm to predict novel disease genes. Specifically, we first curated a set of breast cancer–associated genes from the Genome-Wide Association Studies catalog and Online Mendelian Inheritance in Man database and then studied the distribution of these genes on an integrated protein–protein interaction (PPI) network. We found that the breast cancer–associated genes are significantly closer to each other than random, which confirms the modularity property of disease genes in a PPI network as revealed by previous studies. We then retrieved PPI subnetworks spanning top breast cancer–associated KEGG pathways and found that the distribution of these genes on the subnetworks are non-random, suggesting that these KEGG pathways are activated non-uniformly. Taking advantage of the non-random distribution of breast cancer–associated genes, we developed an improved RCRWR algorithm to predict novel cancer genes, which integrates network reconstruction based on local random walk dynamics and subnetworks spanning KEGG pathways. Compared with the disease gene prediction without using the information from the KEGG pathways, this method has a better prediction performance on inferring breast cancer–associated genes, and the top predicted genes are better enriched on known breast cancer–associated gene ontologies. Finally, we performed a literature search on top predicted novel genes and found that most of them are supported by at least wet-lab experiments on cell lines. In summary, we propose a robust computational framework to prioritize novel breast cancer–associated genes, which could be used for further in vitro and in vivo experimental validation.

Average semivariance yields accurate estimates of the fraction of marker-associated genetic variance and heritability in complex trait analyses

The development of genome-informed methods for identifying quantitative trait loci (QTL) and studying the genetic basis of quantitative variation in natural and experimental populations has been driven by advances in high-throughput genotyping. For many complex traits, the underlying genetic variation is caused by the segregation of one or more ‘large-effect’ loci, in addition to an unknown number of loci with effects below the threshold of statistical detection. The large-effect loci segregating in populations are often necessary but not sufficient for predicting quantitative phenotypes. They are, nevertheless, important enough to warrant deeper study and direct modelling in genomic prediction problems. We explored the accuracy of statistical methods for estimating the fraction of marker-associated genetic variance (p) and heritability (HM2) for large-effect loci underlying complex phenotypes. We found that commonly used statistical methods overestimate p and HM2. The source of the upward bias was traced to inequalities between the expected values of variance components in the numerators and denominators of these parameters. Algebraic solutions for bias-correcting estimates of p and HM2 were found that only depend on the degrees of freedom and are constant for a given study design. We discovered that average semivariance methods, which have heretofore not been used in complex trait analyses, yielded unbiased estimates of p and HM2, in addition to best linear unbiased predictors of the additive and dominance effects of the underlying loci. The cryptic bias problem described here is unrelated to selection bias, although both cause the overestimation of p and HM2. The solutions we described are predicted to more accurately describe the contributions of large-effect loci to the genetic variation underlying complex traits of medical, biological, and agricultural importance.

The contributions of individual genes to the phenotypic variation observed for genetically complex traits has been an ongoing and important challenge in biology, medicine, and agriculture. While many genes have statistically undetectable effects, those with large effects often warrant in-depth study and can be important predictors of complex phenotypes such as disease risk in humans or disease resistance in domesticated plants and animals. The genes identified through associations with genetic markers in complex trait analyses typically account for a fraction of the heritable variation, a genetic parameter we called ‘marker heritability’. We discovered that textbook statistical methods systematically overestimate marker heritability and thus overestimate the contributions of specific genes to the phenotypic variation observed for complex traits in natural and experimental populations. We describe the source of the upward bias, validate our findings through computer simulation, describe methods for bias-correcting estimates of marker heritability, and illustrate their application through empirical examples. The statistical methods we describe supply investigators with more accurate estimates of the contributions of specific genes or networks of interacting genes to the heritable variation observed in complex trait studies.

Identifying Breast Cancer-Related Genes Based on a Novel Computational Framework Involving KEGG Pathways and PPI Network Modularity

Complex diseases, such as breast cancer, are often caused by mutations of multiple functional genes. Identifying disease-related genes is a critical and challenging task for unveiling the biological mechanisms behind these diseases. In this study, we develop a novel computational framework to analyze the network properties of the known breast cancer-associated genes, based on which we develop a random-walk-with-restart (RCRWR) algorithm to predict novel disease genes. Specifically, we first curated a set of breast cancer-associated genes from the Genome-Wide Association Studies catalog and Online Mendelian Inheritance in Man database and then studied the distribution of these genes on an integrated protein-protein interaction (PPI) network. We found that the breast cancer-associated genes are significantly closer to each other than random, which confirms the modularity property of disease genes in a PPI network as revealed by previous studies. We then retrieved PPI subnetworks spanning top breast cancer-associated KEGG pathways and found that the distribution of these genes on the subnetworks are non-random, suggesting that these KEGG pathways are activated non-uniformly. Taking advantage of the non-random distribution of breast cancer-associated genes, we developed an improved RCRWR algorithm to predict novel cancer genes, which integrates network reconstruction based on local random walk dynamics and subnetworks spanning KEGG pathways. Compared with the disease gene prediction without using the information from the KEGG pathways, this method has a better prediction performance on inferring breast cancer-associated genes, and the top predicted genes are better enriched on known breast cancer-associated gene ontologies. Finally, we performed a literature search on top predicted novel genes and found that most of them are supported by at least wet-lab experiments on cell lines. In summary, we propose a robust computational framework to prioritize novel breast cancer-associated genes, which could be used for further in vitro and in vivo experimental validation.

Understanding the fundamentals of microbial remediation with emphasize on metabolomics

The post-genomic tool metabolomics is a great advancement in science and technology which acquires novel strategies and pathways to analyze various biological compounds. Metabolomics aids in retrieving the qualitative and quantitative data from the various biological system. The current review is focused on the application of metabolomics in bioremediation and helps to focus on the xenobiotic compounds which are discharged into the environment and have long term impact. The microbial based biodegradation can be effectively used along with the combination of metabolomic approach for a better understanding of the breakdown of certain recalcitrant. Additionally, this review also discusses the candidate gene approach which helps to comprehend the functional analysis of microbial genes in response to different contaminants. Therefore, this review intends to discuss the metabolomics in bioremediation by studying the complete set of metabolites involved during the process of degradation and their interaction with the environment.

Genetic loci shared between major depression and intelligence with mixed directions of effect

Genome-wide association studies (GWAS) have identified several common genetic variants influencing major depression and general cognitive abilities, but little is known about whether the two share any of their genetic aetiology. Here we investigate shared genomic architectures between major depression (MD) and general intelligence (INT) with the MiXeR statistical tool and their overlapping susceptibility loci with conjunctional false discovery rate (conjFDR), which evaluate the level of overlap in genetic variants and improve the power for gene discovery between two phenotypes. We analysed GWAS data on MD (n = 480,359) and INT (n = 269,867) to characterize polygenic architecture and identify genetic loci shared between these phenotypes. Despite non-significant genetic correlation (r_g = -0.0148, P = 0.50), we observed large polygenic overlap and identified 92 loci shared between MD and INT at conjFDR < 0.05. Among the shared loci, 69 and 64 are new for MD and INT, respectively. Our study demonstrates polygenic overlap between these phenotypes with a balanced mixture of effect.

Transhumanist Genetic Enhancement: Creation of a 'New Man' Through Technological Innovation

The transhumanist project of reshaping human beings by promoting their improvement through technological innovations has a broad agenda. This study focuses on the enhancement of the human organism through genetic modification techniques. Transhumanism values and a discussion of their philosophical background provide a framework to understand its ideals. Genetics and ethics are employed to assess the claims of the transhumanist program of human enhancement. A succinct description of central concepts in genetics and an explanation of current techniques to edit the human genome serve to assess the capabilities and limitations of editing techniques. Potential benefits and liabilities of human enhancement through genome editing are discussed to appraise its feasibility. Ethical considerations of genome editing inform a reflection on the implications of introducing heritable changes in the genome of individuals. It is concluded that the transhumanist program is underpinned by a large number of hypotheses rather than by sufficient evidence.

NIDM: network impulsive dynamics on multiplex biological network for disease-gene prediction

The prediction of genes related to diseases is important to the study of the diseases due to high cost and time consumption of biological experiments. Network propagation is a popular strategy for disease-gene prediction. However, existing methods focus on the stable solution of dynamics while ignoring the useful information hidden in the dynamical process, and it is still a challenge to make use of multiple types of physical/functional relationships between proteins/genes to effectively predict disease-related genes. Therefore, we proposed a framework of network impulsive dynamics on multiplex biological network (NIDM) to predict disease-related genes, along with four variants of NIDM models and four kinds of impulsive dynamical signatures (IDSs). NIDM is to identify disease-related genes by mining the dynamical responses of nodes to impulsive signals being exerted at specific nodes. By a series of experimental evaluations in various types of biological networks, we confirmed the advantage of multiplex network and the important roles of functional associations in disease-gene prediction, demonstrated superior performance of NIDM compared with four types of network-based algorithms and then gave the effective recommendations of NIDM models and IDS signatures. To facilitate the prioritization and analysis of (candidate) genes associated to specific diseases, we developed a user-friendly web server, which provides three kinds of filtering patterns for genes, network visualization, enrichment analysis and a wealth of external links (http://bioinformatics.csu.edu.cn/DGP/NID.jsp). NIDM is a protocol for disease-gene prediction integrating different types of biological networks, which may become a very useful computational tool for the study of disease-related genes.

Massively Parallel Sequencing for Rare Genetic Disorders: Potential and Pitfalls

There have been two major eras in the history of gene discovery. The first was the era of linkage analysis, with approximately 1,300 disease-related genes identified by positional cloning by the turn of the millennium. The second era has been powered by two major breakthroughs: the publication of the human genome and the development of massively parallel sequencing (MPS). MPS has greatly accelerated disease gene identification, such that disease genes that would have taken years to map previously can now be determined in a matter of weeks. Additionally, the number of affected families needed to map a causative gene and the size of such families have fallen: de novo mutations, previously intractable by linkage analysis, can be identified through sequencing of the parent-child trio, and genes for recessive disease can be identified through MPS even of a single affected individual. MPS technologies include whole exome sequencing (WES), whole genome sequencing (WGS), and panel sequencing, each with their strengths. While WES has been responsible for most gene discoveries through MPS, WGS is superior in detecting copy number variants, chromosomal rearrangements, and repeat-rich regions. Panels are commonly used for diagnostic purposes as they are extremely cost-effective and generate manageable quantities of data, with no risk of unexpected findings. However, in instances of diagnostic uncertainty, it can be challenging to choose the right panel, and in these circumstances WES has a higher diagnostic yield. MPS has ethical, social, and legal implications, many of which are common to genetic testing generally but amplified due to the magnitude of data (e.g., relationship misattribution, identification of variants of uncertain significance, and genetic discrimination); others are unique to WES and WGS technologies (e.g., incidental or secondary findings). Nonetheless, MPS is rapidly translating into clinical practice as an extremely useful part of the clinical armamentarium.

Genetic Diversity, QTL Mapping, and Marker-Assisted Selection Technology in Cotton (Gossypium spp.)

Cotton genetic resources contain diverse economically important traits that can be used widely in breeding approaches to create of high-yielding elite cultivars with superior fiber quality and adapted to biotic and abiotic stresses. Nevertheless, the creation of new cultivars using conventional breeding methods is limited by the cost and proved to be time consuming process, also requires a space to make field observations and measurements. Decoding genomes of cotton species greatly facilitated generating large-scale high-throughput DNA markers and identification of QTLs that allows confirmation of candidate genes, and use them in marker-assisted selection (MAS)-based breeding programs. With the advances of quantitative trait loci (QTL) mapping and genome-wide-association study approaches, DNA markers associated with valuable traits significantly accelerate breeding processes by replacing the selection with a phenotype to the selection at the DNA or gene level. In this review, we discuss the evolution and genetic diversity of cotton Gossypium genus, molecular markers and their types, genetic mapping and QTL analysis, application, and perspectives of MAS-based approaches in cotton breeding.

Rats deficient in the GAD65 isoform exhibit epilepsy and premature lethality

GABA is synthesized by glutamate decarboxylase (GAD), which has two isoforms, namely, GAD65 and GAD67, encoded by the Gad2 and Gad1 genes, respectively. GAD65-deficient (Gad2^-/- ) mice exhibit a reduction in brain GABA content after 1 month of age and show spontaneous seizures in adulthood. Approximately 25% of Gad2^-/- mice died by 6 months of age. Our Western blot analysis demonstrated that the protein expression ratio of GAD65 to GAD67 in the brain was greater in rats than in mice during postnatal development, suggesting that the contribution of each GAD isoform to GABA functions differs between these two species. To evaluate whether GAD65 deficiency causes different phenotypes between rats and mice, we generated Gad2^-/- rats using TALEN genome editing technology. Western blot and immunohistochemical analyses with new antibodies demonstrated that the GAD65 protein was undetectable in the Gad2^-/- rat brain. Gad2^-/- pups exhibited spontaneous seizures and paroxysmal discharge in EEG at postnatal weeks 3-4. More than 80% of the Gad2^-/- rats died at postnatal days (PNDs) 17-23. GABA content in Gad2^-/- brains was significantly lower than those in Gad2^+/- and Gad2^+/+ brains at PND17-19. These results suggest that the low levels of brain GABA content in Gad2^-/- rats may lead to epilepsy followed by premature death, and that Gad2^-/- rats are more severely affected than Gad2^-/- mice. Considering that the GAD65/GAD67 ratio in human brains is more similar to that in rat brains than in mouse brains, Gad2^-/- rats would be useful for further investigating the roles of GAD65 in vivo.

Rat models of human diseases and related phenotypes: a systematic inventory of the causative genes

The laboratory rat has been used for a long time as the model of choice in several biomedical disciplines. Numerous inbred strains have been isolated, displaying a wide range of phenotypes and providing many models of human traits and diseases. Rat genome mapping and genomics was considerably developed in the last decades. The availability of these resources has stimulated numerous studies aimed at discovering causal disease genes by positional identification. Numerous rat genes have now been identified that underlie monogenic or complex diseases and remarkably, these results have been translated to the human in a significant proportion of cases, leading to the identification of novel human disease susceptibility genes, helping in studying the mechanisms underlying the pathological abnormalities and also suggesting new therapeutic approaches. In addition, reverse genetic tools have been developed. Several genome-editing methods were introduced to generate targeted mutations in genes the function of which could be clarified in this manner [generally these are knockout mutations]. Furthermore, even when the human gene causing a disease had been identified without resorting to a rat model, mutated rat strains (in particular KO strains) were created to analyze the gene function and the disease pathogenesis. Today, over 350 rat genes have been identified as underlying diseases or playing a key role in critical biological processes that are altered in diseases, thereby providing a rich resource of disease models. This article is an update of the progress made in this research and provides the reader with an inventory of these disease genes, a significant number of which have similar effects in rat and humans.

Genetics in bicuspid aortic valve disease: Where are we?

Bicuspid aortic valve (BAV) is the most common congenital heart defect, found in up to 2% of the population and associated with a 30% lifetime risk of complications. BAV is inherited as an autosomal dominant trait with incomplete penetrance and variable expressivity due to a complex genetic architecture that involves many interacting genes. In this review, we highlight the current state of knowledge about BAV genetics, principles and methods for BAV gene discovery, clinical applications of BAV genetics, and important future directions.

Why are Women More Religious than Men? Do Risk Preferences and Genetic Risk Predispositions Explain the Gender Gap?

Risk preference theory argues that the gender gap in religiosity is caused by greater female risk aversion. Although widely debated, risk preference theory has been inadequately tested. Our study tests the theory directly with phenotypic and genetic risk preferences in three dimensions-general, impulsive, and sensation-seeking risk. Moreover, we examine whether the effect of different dimension of risk preference on the gender gap varies across different dimensions of religiosity. We find that general and impulsive risk preferences do not explain gender differences in religiosity, whereas sensation-seeking risk preference makes the gender gap in self-assessed religiousness and church attendance insignificant, but not belief in God, prayer, or importance of religion. Genetic risk preferences do not remove any of the gender gaps in religiosity, suggesting that the causal order is not from risk preference to religiosity. Evidence suggests that risk preferences are not a strong predictor for gender differences in religiosity.

A review of traditional and machine learning methods applied to animal breeding

The current livestock management landscape is transitioning to a high-throughput digital era where large amounts of information captured by systems of electro-optical, acoustical, mechanical, and biosensors is stored and analyzed on a daily and hourly basis, and actionable decisions are made based on quantitative and qualitative analytic results. While traditional animal breeding prediction methods have been used with great success until recently, the deluge of information starts to create a computational and storage bottleneck that could lead to negative long-term impacts on herd management strategies if not handled properly. A plethora of machine learning approaches, successfully used in various industrial and scientific applications, made their way in the mainstream approaches for livestock breeding techniques, and current results show that such methods have the potential to match or surpass the traditional approaches, while most of the time they are more scalable from a computational and storage perspective. This article provides a succinct view on what traditional and novel prediction methods are currently used in the livestock breeding field, how successful they are, and how the future of the field looks in the new digital agriculture era.

The mutational spectrum of Myocilin gene among familial versus sporadic cases of Juvenile onset open angle glaucoma

PURPOSE

Juvenile onset primary open angle glaucoma (JOAG) is a rare disorder associated with high IOP and progressive optic neuropathy in patients diagnosed before the age of 40 years. While in some populations it has primarily an autosomal dominant pattern of inheritance, in others it occurs in a primarily sporadic form. The main aim of the study was to assess the relative prevalence of Myocilin (MYOC) mutations in familial versus sporadic cases of JOAG.

METHODS

We screened 92 unrelated (sporadic) JOAG patients, and 22 affected families (70 affected members and 36 unaffected) for variations in the MYOC gene. We also analyzed the clinical features associated with these variations.

RESULTS

Three coding sequence variants were identified as mutations causing JOAG. Four families segregated distinct mutations at Gly367Arg, and two families at Gln337Arg, while only two sporadic JOAG cases harbored MYOC mutations (Gly367Arg and Gln48His). The frequency of MYOC mutations in familial cases (27%) was significantly higher than in sporadic JOAG cases (2%); p = 0.001. A 90% penetrance for the Gly367Arg variant was seen by the age of 40 years in our patients. Characteristic allele signatures, indicative of specific founder effects, were not observed for the Gly367Arg mutation that was looked for in 12 patients among 2 geographically close families, which harbored this mutation.

CONCLUSION

Our data demonstrated that genetic screening for MYOC mutations should be focused toward cases with familial rather than sporadically occurring JOAG.

Chromosomal Substitution Strategies to Localize Genomic Regions Related to Complex Traits

Chromosomal substitution strategies provide a powerful tool to anonymously reveal the relationship between DNA sequence variants and a normal or disease phenotype of interest. Even in this age of CRISPR-Cas9 genome engineering, the knockdown or overexpression of a gene provides relevant information to our understanding of complex disease only when a close association of an allelic variant with the phenotype has first been established. Limitations of genetic linkage approaches led to the development of more efficient breeding strategies to substitute chromosomal segments from one animal strain into the genetic background of a different strain, enabling a direct comparison of the phenotypes of the strains with variant(s) that differ only at a defined locus. This substitution can be a whole chromosome (consomic), a part of a chromosome (congenic), or as small as only a single or several alleles (subcongenics). In contrast to complete knockout of a specific candidate gene of interest, which simply studies the effects of complete elimination of the gene, the substitution of naturally occurring variants can provide special insights into the functional actions of wild-type alleles. Strategies for production of these inbred strains are reviewed, and a number of examples are used to illustrate the utility of these model systems. Consomic/congenic strains provide a number of experimental advantages in the study of functions of genes and their variants, which are emphasized in this article, such as replication of experimental studies; determination of temporal relationships throughout a life; rigorously controlled experiments in which relations between genotype and phenotype can be tested with the confounding effects of heterogeneous genetic backgrounds, both targeted and multilayered; and "omic" studies performed at many levels of functionality, from molecules to organelles, cells to organs, and organs to organismal behavior across the life span. The application of chromosomal substitution strategies and development of consomic/congenic rat and mouse strains have greatly expanded our knowledge of genomic variants and their phenotypic relationship to physiological functions and to complex diseases such as hypertension and cancer. © 2020 American Physiological Society. Compr Physiol 10:365-388, 2020.

PANDA: Prioritization of autism-genes using network-based deep-learning approach

Understanding the genetic background of complex diseases and disorders plays an essential role in the promising precision medicine. The evaluation of candidate genes, however, requires time-consuming and expensive experiments given a large number of possibilities. Thus, computational methods have seen increasing applications in predicting gene-disease associations. We proposed a bioinformatics framework, Prioritization of Autism-genes using Network-based Deep-learning Approach (PANDA). Our approach aims to identify autism-genes across the human genome based on patterns of gene-gene interactions and topological similarity of genes in the interaction network. PANDA trains a graph deep learning classifier using the input of the human molecular interaction network and predicts and ranks the probability of autism association of every node (gene) in the network. PANDA was able to achieve a high classification accuracy of 89%, outperforming three other commonly used machine learning algorithms. Moreover, the gene prioritization ranking list produced by PANDA was evaluated and validated using an independent large-scale exome-sequencing study. The top 10% of PANDA-ranked genes were found significantly enriched for autism association.

A network embedding model for pathogenic genes prediction by multi-path random walking on heterogeneous network

BACKGROUND

Prediction of pathogenic genes is crucial for disease prevention, diagnosis, and treatment. But traditional genetic localization methods are often technique-difficulty and time-consuming. With the development of computer science, computational biology has gradually become one of the main methods for finding candidate pathogenic genes.

METHODS

We propose a pathogenic genes prediction method based on network embedding which is called Multipath2vec. Firstly, we construct an heterogeneous network which is called GP-network. It is constructed based on three kinds of relationships between genes and phenotypes, including correlations between phenotypes, interactions between genes and known gene-phenotype pairs. Then in order to embedding the network better, we design the multi-path to guide random walk in GP-network. The multi-path includes multiple paths between genes and phenotypes which can capture complex structural information of heterogeneous network. Finally, we use the learned vector representation of each phenotype and protein to calculate the similarities and rank according to the similarities between candidate genes and the target phenotype.

RESULTS

We implemented Multipath2vec and four baseline approaches (i.e., CATAPULT, PRINCE, Deepwalk and Metapath2vec) on many-genes gene-phenotype data, single-gene gene-phenotype data and whole gene-phenotype data. Experimental results show that Multipath2vec outperformed the state-of-the-art baselines in pathogenic genes prediction task.

CONCLUSIONS

We propose Multipath2vec that can be utilized to predict pathogenic genes and experimental results show the higher accuracy of pathogenic genes prediction.

Identification of Novel Breast Cancer Genes based on Gene Expression Profiles and PPI Data

Background:

Breast cancer is one of the most common malignancies, and a threat to female health all over the world. However, the molecular mechanism of breast cancer has not been fully discovered yet.

Objective:

It is crucial to identify breast cancer-related genes, which could provide new biomarker for breast cancer diagnosis as well as potential treatment targets.

Methods:

Here we used the minimum redundancy-maximum relevance (mRMR) method to select significant genes, then mapped the transcripts of the genes on the Protein-Protein Interaction (PPI) network and traced the shortest path between each pair of two proteins.

Results:

As a result, we identified 24 breast cancer-related genes whose betweenness were over 700. The GO enrichment analysis indicated that the transcription and oxygen level are very important in breast cancer. And the pathway analysis indicated that most of these 24 genes are enriched in prostate cancer, endocrine resistance, and pathways in cancer.

Conclusion:

We hope these 24 genes might be useful for diagnosis, prognosis and treatment for breast cancer.

Analysis of the genomic architecture of a complex trait locus in hypertensive rat models links Tmem63c to kidney damage

Unraveling the genetic susceptibility of complex diseases such as chronic kidney disease remains challenging. Here, we used inbred rat models of kidney damage associated with elevated blood pressure for the comprehensive analysis of a major albuminuria susceptibility locus detected in these models. We characterized its genomic architecture by congenic substitution mapping, targeted next-generation sequencing, and compartment-specific RNA sequencing analysis in isolated glomeruli. This led to prioritization of transmembrane protein Tmem63c as a novel potential target. Tmem63c is differentially expressed in glomeruli of allele-specific rat models during onset of albuminuria. Patients with focal segmental glomerulosclerosis exhibited specific TMEM63C loss in podocytes. Functional analysis in zebrafish revealed a role for tmem63c in mediating the glomerular filtration barrier function. Our data demonstrate that integrative analysis of the genomic architecture of a complex trait locus is a powerful tool for identification of new targets such as Tmem63c for further translational investigation.

The human kidneys filter the entire volume of the blood about 300 times each day. This ability depends on specialized cells, known as podocytes, which wrap around some of the blood vessels in the kidney. These cells control which molecules leave the blood based on their size. Normally large molecules like proteins are blocked, while smaller molecules including waste products, toxins, excess water and salts pass through into the urine.

If this filtration system is damaged, by high blood pressure, for example, it can lead to chronic kidney disease. A hallmark of this disease, often called CKD for short, is high levels of the protein albumin in the urine. Previous studies involving rats with high blood pressure have found several regions of the genome that contribute to high levels of albumin in the urine, including one on chromosome 6. However, this region contains several genes and it was unclear which genes affected the condition.

Schulz et al. set out to narrow down the list and find specific genes that might contribute to elevated albumin in the urine of rats with high blood pressure. This search identified the gene for a protein called TMEM63c as a likely candidate. This protein spans the outer membrane of podocyte cells. Analysis of kidney biopsies showed that patients with chronic kidney disease also had low levels of this protein in their podocytes. Further experiments, this time in zebrafish, showed that reducing the activity of the gene for tmem63c led to damaged podocytes and a leakier filter in the kidneys.

The results suggest that this gene plays an important role in the integrity of the kidneys filtration barrier. It is possible that faulty versions of this gene are behind some cases of chronic kidney disease. If this proves to be the case, a better understanding of the role of this gene may lead to new treatments for the condition.

Identifying Multi-Omics Causers and Causal Pathways for Complex Traits

The central dogma of molecular biology delineates a unidirectional causal flow, i.e., DNA → RNA → protein → trait. Genome-wide association studies, next-generation sequencing association studies, and their meta-analyses have successfully identified ~12,000 susceptibility genetic variants that are associated with a broad array of human physiological traits. However, such conventional association studies ignore the mediate causers (i.e., RNA, protein) and the unidirectional causal pathway. Such studies may not be ideally powerful; and the genetic variants identified may not necessarily be genuine causal variants. In this article, we model the central dogma by a mediate causal model and analytically prove that the more remote an omics level is from a physiological trait, the smaller the magnitude of their correlation is. Under both random and extreme sampling schemes, we numerically demonstrate that the proteome-trait correlation test is more powerful than the transcriptome-trait correlation test, which in turn is more powerful than the genotype-trait association test. In conclusion, integrating RNA and protein expressions with DNA data and causal inference are necessary to gain a full understanding of how genetic causal variants contribute to phenotype variations.

BLINK: a package for the next level of genome-wide association studies with both individuals and markers in the millions

Big datasets, accumulated from biomedical and agronomic studies, provide the potential to identify genes that control complex human diseases and agriculturally important traits through genome-wide association studies (GWAS). However, big datasets also lead to extreme computational challenges, especially when sophisticated statistical models are employed to simultaneously reduce false positives and false negatives. The newly developed fixed and random model circulating probability unification (FarmCPU) method uses a bin method under the assumption that quantitative trait nucleotides (QTNs) are evenly distributed throughout the genome. The estimated QTNs are used to separate a mixed linear model into a computationally efficient fixed effect model (FEM) and a computationally expensive random effect model (REM), which are then used iteratively. To completely eliminate the computationally expensive REM, we replaced REM with FEM by using Bayesian information criteria. To eliminate the requirement that QTNs be evenly distributed throughout the genome, we replaced the bin method with linkage disequilibrium information. The new method is called Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK). Both real and simulated data analyses demonstrated that BLINK improves statistical power compared to FarmCPU, in addition to remarkably reducing computing time. Now, a dataset with one million individuals and one-half million markers can be analyzed within three hours, instead of one week using FarmCPU.

Meta-analysis of Alzheimer's disease on 9,751 samples from Norway and IGAP study identifies four risk loci

A large fraction of genetic risk factors for Alzheimer's Disease (AD) is still not identified, limiting the understanding of AD pathology and study of therapeutic targets. We conducted a genome-wide association study (GWAS) of AD cases and controls of European descent from the multi-center DemGene network across Norway and two independent European cohorts. In a two-stage process, we first performed a meta-analysis using GWAS results from 2,893 AD cases and 6,858 cognitively normal controls from Norway and 25,580 cases and 48,466 controls from the International Genomics of Alzheimer's Project (IGAP), denoted the discovery sample. Second, we selected the top hits (p < 1 × 10^-6) from the discovery analysis for replication in an Icelandic cohort consisting of 5,341 cases and 110,008 controls. We identified a novel genomic region with genome-wide significant association with AD on chromosome 4 (combined analysis OR = 1.07, p = 2.48 x 10^-8). This finding implicated HS3ST1, a gene expressed throughout the brain particularly in the cerebellar cortex. In addition, we identified IGHV1-68 in the discovery sample, previously not associated with AD. We also associated USP6NL/ECHDC3 and BZRAP1-AS1 to AD, confirming findings from a follow-up transethnic study. These new gene loci provide further evidence for AD as a polygenic disorder, and suggest new mechanistic pathways that warrant further investigation.

DGAT1 polymorphism in Riverine buffalo, Swamp buffalo and crossbred buffalo

This Research Communication describes the polymorphisms in the coding region of DGAT1 gene in Riverine buffalo, Swamp buffalo and crossbred buffalo, and associations between polymorphisms and milk production performance in Riverine buffalo. Two polymorphisms of DGAT1were identified, located in exon 13 and exon 17, respectively. The distribution of the genotypes of the two SNP loci in different buffalo population varied, especially the polymorphism located in exon 13 which was not found in the Swamp buffalo. Moreover, SNP located in exon 17 was a nonsynonymous switch resulting in the animo acid sequence changed from an arginine (Arg) to a histidine (His) at position 484. Both SNPs were in Hardy-Weinberg equilibrium, and the polymorphism of g.8330T>C in the exon 13 was significantly associated with peak milk yield, total milk yield and protein percentage. The C variant was associated with an increase in milk yield and peak yield but less in protein percentage compared with the T variant. The polymorphisms of g.9046T>C in exon 17 were significantly associated with fat percentage, in that the buffaloes with TT genotype had a significantly higher fat percentage than those with CC genotype. These findings reveal the difference in the genetic evolution of the DGAT1 between Riverine buffalo and Swamp buffalo, and provide evidence that the DGAT1 gene has potential effects for Riverine buffalo milk production traits, which can be used as a candidate gene for marker-assisted selection in buffalo breeding.

Determination of quantitative trait nucleotides by concordance analysis between quantitative trait loci and marker genotypes of US Holsteins

Experimental designs that exploit family information can provide substantial predictive power in quantitative trait nucleotide discovery projects. Concordance between quantitative trait locus genotype as determined by the a posteriori granddaughter design and marker genotype was determined for 30 trait-by-chromosomal segment effects segregating in the US Holstein population with probabilities of <10^-20 to accept the null hypotheses of no segregating gene affecting the trait within the chromosomal segment. Genotypes for 83 grandsires and 17,217 sons were determined by either complete sequence or imputation for 3,148,506 polymorphisms across the entire genome; 471 Holstein bulls had a complete genome sequence, including 64 of the grandsires. Complete concordance was obtained only for stature on chromosome 14 and daughter pregnancy rate on chromosome 18. For each quantitative trait locus, effects of the 30 polymorphisms with highest concordance scores for the analyzed trait were computed by stepwise regression for predicted transmitting abilities of 26,750 bulls with progeny test and imputed genotypes. Effects for stature on chromosome 11, daughter pregnancy rate on chromosome 18, and protein percentage on chromosome 20 met 3 criteria: complete or almost complete concordance, nominal significance of the polymorphism effect after correction for all other polymorphisms, and marker coefficient of determination >40% of total multiple-regression coefficient of determination for the 30 polymorphisms with highest concordance. An intronic variant marker on chromosome 5 at 93,945,738 bp explained 7% of variance for fat percentage and 74% of total multiple-marker regression variance but was concordant for only 24 of 30 families. The missense polymorphism Phe279Tyr in GHR at 31,909,478 bp on chromosome 20 was confirmed as the causative mutation for fat and protein concentration. For effect on fat percentage on chromosome 14, 12 additional missense polymorphisms were found that had almost complete concordance with the suggested causative polymorphism (missense mutation Ala232Glu in DGAT1). The only polymorphism found likely to improve predictive power for genomic evaluation of dairy cattle was on chromosome 15; that polymorphism had a frequency of 0.45 for the allele with economically positive effects on all production traits.

Utility of pooled sequencing for association mapping in nonmodel organisms

High-density genome-wide sequencing increases the likelihood of discovering genes of major effect and genomic structural variation in organisms. While there is an increasing availability of reference genomes across broad taxa, the greatest limitation to whole-genome sequencing of multiple individuals continues to be the costs associated with sequencing. To alleviate excessive costs, pooling multiple individuals with similar phenotypes and sequencing the homogenized DNA (Pool-Seq) can achieve high genome coverage, but at the loss of individual genotypes. Although Pool-Seq has been an effective method for association mapping in model organisms, it has not been frequently utilized in natural populations. To extend bioinformatic tools for rapid implementation of Pool-Seq data in nonmodel organisms, we developed a pipeline called PoolParty and illustrate its effectiveness in genetic association mapping. Alignment expectations based on five pooled Chinook salmon (Oncorhynchus tshawytscha) libraries showed that approximately 48% genome coverage per library could be achieved with reasonable sequencing effort. We additionally examined male and female O. tshawytscha libraries to illustrate how Pool-Seq techniques can successfully map known genes associated with functional differences among sexes such as growth hormone 2. Finally, we compared pools of individuals of different spawning ages for each sex to discover novel genes involved with age at maturity in O. tshawytscha such as opsin4 and transmembrane protein19. While not appropriate for every system, Pool-Seq data processed by the PoolParty pipeline is a practical method for identifying genes of major effect in nonmodel organisms when high genome coverage is necessary and cost is a limiting factor.

Intraspecific variation of residual heterozygosity and its utility for quantitative genetic studies in maize

BACKGROUND

Residual heterozygosity (RH) in advanced inbred lines of plants benefits quantitative trait locus (QTL) mapping studies. However, knowledge of factors affecting the genome-wide distribution of RH remains limited.

RESULTS

A set of 2196 heterogeneous inbred family (HIF) maize lines derived from 12 recombinant inbred line (RIL) populations was genotyped using the Maize50K SNP chip. A total of 18,615 unique RH intervals were identified, ranging from 505 to 2095 intervals per population, with average maize genome coverage of 94.8%. Across all populations, there were 8.6 RH intervals per HIF line on average, ranging from 1.8 to 14 intervals; the average size of an RH interval was approximately 58.7 Mb, ranging from 7.2 to 74.1 Mb. A given RH region was present in an average of 5 different individuals within a population. Seven RH hotspots, where RH segments were enriched in the genome, were found to be subject to selection during population development. The RH patterns varied significantly across populations, presumably reflecting differences in the genetic background of each population, and 8 QTLs were found to affect heterozygosity levels in the RH hotspots. The potential use of this HIF library for the fine mapping of QTLs was assessed based on publicly available QTL information, achieving a ≤ 1 Mb resolution on average.

CONCLUSION

The examined library of HIF lines offers insight into the RH landscape and its intraspecific variation and provides a useful resource for the QTL cloning of important agronomic traits in maize.

Genome-wide association across Saccharomyces cerevisiae strains reveals substantial variation in underlying gene requirements for toxin tolerance

Cellulosic plant biomass is a promising sustainable resource for generating alternative biofuels and biochemicals with microbial factories. But a remaining bottleneck is engineering microbes that are tolerant of toxins generated during biomass processing, because mechanisms of toxin defense are only beginning to emerge. Here, we exploited natural diversity in 165 Saccharomyces cerevisiae strains isolated from diverse geographical and ecological niches, to identify mechanisms of hydrolysate-toxin tolerance. We performed genome-wide association (GWA) analysis to identify genetic variants underlying toxin tolerance, and gene knockouts and allele-swap experiments to validate the involvement of implicated genes. In the process of this work, we uncovered a surprising difference in genetic architecture depending on strain background: in all but one case, knockout of implicated genes had a significant effect on toxin tolerance in one strain, but no significant effect in another strain. In fact, whether or not the gene was involved in tolerance in each strain background had a bigger contribution to strain-specific variation than allelic differences. Our results suggest a major difference in the underlying network of causal genes in different strains, suggesting that mechanisms of hydrolysate tolerance are very dependent on the genetic background. These results could have significant implications for interpreting GWA results and raise important considerations for engineering strategies for industrial strain improvement.

Understanding the genetic architecture of complex traits is important for elucidating the genotype-phenotype relationship. Many studies have sought genetic variants that underlie phenotypic variation across individuals, both to implicate causal variants and to inform on architecture. Here we used genome-wide association analysis to identify genes and processes involved in tolerance of toxins found in plant-biomass hydrolysate, an important substrate for sustainable biofuel production. We found substantial variation in whether or not individual genes were important for tolerance across genetic backgrounds. Whether or not a gene was important in a given strain background explained more variation than the alleleic differences in the gene. These results suggest substantial variation in gene contributions, and perhaps underlying mechanisms, of toxin tolerance.

Population-Wide Genetic Risk Prediction of Complex Diseases: A Pilot Feasibility Study in Macau Population for Precision Public Healthcare Planning

The genetic bases of many common diseases have been identified through genome-wide association studies in the past decade. However, the application of this approach on public healthcare planning has not been well established. Using Macau with population of around 650,000 as a basis, we conducted a pilot study to evaluate the feasibility of population genomic research and its potential on public health decisions. By performing genome-wide SNP genotyping of over a thousand Macau individuals, we evaluated the population genetic risk profiles of 47 non-communicable diseases and traits, as well as two traits associated with influenza infection. We found that for most of the diseases, the genetic risks of Macau population were different from those of Caucasian, but with similar profile with mainland Chinese. We also identified a panel of diseases that Macau population may have a high or elevated genetic risks. This pilot study showed that (1) population genomic study is feasible in Asian regions like Macau; (2) Macau may have different profile of population-based genetic risks than Caucasians, (3) the different prevalence of genetic risk profile indicates the importance of Asian-specific studies for Asian populations; and (4) the results generated may have an impact for going forward healthcare planning.

Examination of Clock and Adcyap1 gene variation in a neotropical migratory passerine

Complex behavioral traits, such as those making up a migratory phenotype, are regulated by multiple environmental factors and multiple genes. We investigated possible relationships between microsatellite variation at two candidate genes implicated in the control of migratory behavior, Clock and Adcyap1, and several aspects of migratory life-history and evolutionary divergence in the Painted Bunting (Passerina ciris), a species that shows wide variation in migratory and molting strategies across a disjunct distribution. We focused on Clock and Adcyap1 microsatellite variation across three Painted Bunting populations in Oklahoma, Louisiana, and North Carolina, and for the Oklahoma breeding population we used published migration tracking data on adult males to explore phenotypic variation in individual migratory behavior. We found no correlation between microsatellite allele size within either Clock and Adcyap1 relative to the initiation or duration of fall migration in adult males breeding in Oklahoma. We also show the lack of significant correlations with aspects of the migratory phenotype for the Louisiana population. Our research highlights the limitations of studying microsatellite allelic mutations that are of undetermined functional influence relative to complex behavioral phenotypes.

Genetic dissection of QTLs and differentiation analysis of alleles for heading date genes in rice

Heading date is an important agronomic trait in rice (Oryza sativa L.); it determines the geographical and seasonal adaptability of the crop. Single segment substitution lines (SSSLs) have become the preferred experimental materials in mapping functional genetic variations as the particular chromosome segments from donor genotypes can be evaluated for their impact on the phenotype in a recurrent recipient background. The phenotypic differences can be attributed to the control of quantitative trait loci (QTLs). Here, we evaluated a library consisting of 1,123 SSSLs in the same genetic background of an elite rice variety, Huajingxian74 (HJX74), and revealed four SSSLs, W05-1-11-2-7-6 (W05), W08-16-3-2 (W08), W12-28-58-03-19-1 (W12), and W22-9-5-2-4-9-3 (W22), which had a significantly different heading date compared to HJX74. To further genetically dissect the QTLs controlling heading date on chromosomes 3, 6, and 10, four SSSLs were used to develop 15 secondary SSSLs with the smaller substituted segments. The qHD-3 heading date QTL detected in W05 and W08 was delimited to an interval of 4.15 cM, whereas qHD-6-1 and qHD-6-2 heading date QTLs dissected from the substituted segments in W12 were mapped to the intervals of 2.25-cM and 2.55-cM, respectively. The qHD-10 QTL detected on the substituted segment in W22 was mapped to an interval of 6.85-cM. The nucleotide and amino acid sequence changes for those genes in the secondary SSSLs were also revealed. The allele variations of those genes might contribute to the heading date QTLs on chromosome 3 (DTH3, OsDof12, and EHD4), chromosome 6 (Hd3a, Hd17, and RFT1), and chromosome 10 (Ehd1 and Ehd2). These sequence variations in heading date genes would be useful resources for further studying the function of genes, and would be important for rice breeding. Overall, our results indicate that secondary SSSLs were powerful tools for genetic dissection of QTLs and identification of differentiation in the genes.

Unraveling the molecules hidden in the gray shadows of quantitative disease resistance to pathogens

Una de las preguntas más desafiantes del fitomejoramiento y de la fitopatología molecular es ¿cuáles son las bases genéticas y moleculares de la resistencia cuantitativa a enfermedades?. El escaso conocimiento de cómo este tipo de resistencia funciona ha obstaculizado que los fitomejoradores la aprovecharlo plenamente. Para superar estos obstáculos se han desarrollado nuevas metodologías para el estudio de rasgos cuantitativos. Los enfoques como el mapeo genético, la identificación de loci de rasgos cuantitativos (QTL) y el mapeo por asociaciones, incluyendo el enfoque de genes candidatos y los estudios de asociación amplia del genoma, se han llevado a cabo históricamente para describir rasgos cuantitativos y por lo tanto para estudiar QDR. Además, se han proporcionado grandes avances en la obtención de datos fenotípicos cuantitativos para mejorar estos análisis. Recientemente, algunos genes asociados a QDR han sido clonados, lo que conduce a nuevas hipótesis sobre las bases moleculares de este tipo de resistencia. En esta revisión presentamos los avances más recientes sobre QDR y la correspondiente aplicación, que han permitido postular nuevas ideas que pueden ayudar a construir nuevos modelos. Algunas de las hipótesis presentadas aquí como posibles explicaciones para QDR están relacionadas con el nivel de expresión y el splicing alternativo de algunos genes relacionados con la defensa, la acción de "alelos débiles" de genes R, la presencia de variantes alélicas en los genes implicados en la respuesta de defensa y un papel central de quinasas o pseudoqinasas. Con la información recapitulada en esta revisión es posible concluir que la distinción conceptual entre resistencia cualitativa y cuantitativa puede ser cuestionada ya que ambos comparten importantes componentes.

Exploiting the potential of next-generation sequencing in genomic medicine

INTRODUCTION

The review highlights the impact of next-generation sequencing (NGS) on genomic medicine and the consequences of the progression from a single-gene panel technology to a whole exome sequencing approach.

AREAS COVERED

We brought together literature-based evidences, personal unpublished data and clinical experience to provide a critical overview of the impact of NGS on our daily clinical practice. Expert commentary: NGS has changed the role of clinical geneticist and has broadened the view accomplishing a transition from a monogenic Mendelian perspective to an oligogenic approach to disorders. Thus, it is a compelling new expertise which combines clinical evaluation with big omics data interpretation and moves forward to phenotype re-evaluation in light of data analysis. We introduced the term, 'exotyping', to highlight this holistic approach. Further, the review discusses the impact that the combination of genetic reprogramming and transcriptome analysis will have on the discovery of evidence-based therapies.

Primary Pediatric Hypertension: Current Understanding and Emerging Concepts

The rising prevalence of primary pediatric hypertension and its tracking into adult hypertension point to the importance of determining its pathogenesis to gain insights into its current and emerging management. Considering that the intricate control of BP is governed by a myriad of anatomical, molecular biological, biochemical, and physiological systems, multiple genes are likely to influence an individual's BP and susceptibility to develop hypertension. The long-term regulation of BP rests on renal and non-renal mechanisms. One renal mechanism relates to sodium transport. The impaired renal sodium handling in primary hypertension and salt sensitivity may be caused by aberrant counter-regulatory natriuretic and anti-natriuretic pathways. The sympathetic nervous and renin-angiotensin-aldosterone systems are examples of antinatriuretic pathways. An important counter-regulatory natriuretic pathway is afforded by the renal autocrine/paracrine dopamine system, aberrations of which are involved in the pathogenesis of hypertension, including that associated with obesity. We present updates on the complex interactions of these two systems with dietary salt intake in relation to obesity, insulin resistance, inflammation, and oxidative stress. We review how insults during pregnancy such as maternal and paternal malnutrition, glucocorticoid exposure, infection, placental insufficiency, and treatments during the neonatal period have long-lasting effects in the regulation of renal function and BP. Moreover, these effects have sex differences. There is a need for early diagnosis, frequent monitoring, and timely management due to increasing evidence of premature target organ damage. Large controlled studies are needed to evaluate the long-term consequences of the treatment of elevated BP during childhood, especially to establish the validity of the current definition and treatment of pediatric hypertension.