Human protection drives the emergence of a new coping style in animals

Wild animals face novel environmental threats from human activities that may occur along a gradient of interactions with humans. Recent work has shown that merely living close to humans has major implications for a variety of antipredator traits and physiological responses. Here, we hypothesize that when human presence protects prey from their genuine predators (as sometimes seen in urban areas and at some tourist sites), this predator shield, followed by a process of habituation to humans, decouples commonly associated traits related to coping styles, which results in a new range of phenotypes. Such individuals are characterized by low aggressiveness and physiological stress responses, but have enhanced behavioral plasticity, boldness, and cognitive abilities. We refer to these individuals as “preactive,” because their physiological and behavioral coping style falls outside the classical proactive/reactive coping styles. While there is some support for this new coping style, formal multivariate studies are required to investigate behavioral and physiological responses to anthropogenic activities.

This Essay hypothesizes that when human presence protects wild animals from their natural predators, this predator shield decouples commonly associated traits, resulting in a new range of coping phenotypes, dubbed “preactive.”     

Comprehensive Characterization of Human Genome Variation by High Coverage Whole-Genome Sequencing of Forty Four Caucasians

Whole genome sequencing studies are essential to obtain a comprehensive understanding of the vast pattern of human genomic variations. Here we report the results of a high-coverage whole genome sequencing study for 44 unrelated healthy Caucasian adults, each sequenced to over 50-fold coverage (averaging 65.8×). We identified approximately 11 million single nucleotide polymorphisms (SNPs), 2.8 million short insertions and deletions, and over 500,000 block substitutions. We showed that, although previous studies, including the 1000 Genomes Project Phase 1 study, have catalogued the vast majority of common SNPs, many of the low-frequency and rare variants remain undiscovered. For instance, approximately 1.4 million SNPs and 1.3 million short indels that we found were novel to both the dbSNP and the 1000 Genomes Project Phase 1 data sets, and the majority of which (∼96%) have a minor allele frequency less than 5%. On average, each individual genome carried ∼3.3 million SNPs and ∼492,000 indels/block substitutions, including approximately 179 variants that were predicted to cause loss of function of the gene products. Moreover, each individual genome carried an average of 44 such loss-of-function variants in a homozygous state, which would completely “knock out” the corresponding genes. Across all the 44 genomes, a total of 182 genes were “knocked-out” in at least one individual genome, among which 46 genes were “knocked out” in over 30% of our samples, suggesting that a number of genes are commonly “knocked-out” in general populations. Gene ontology analysis suggested that these commonly “knocked-out” genes are enriched in biological process related to antigen processing and immune response. Our results contribute towards a comprehensive characterization of human genomic variation, especially for less-common and rare variants, and provide an invaluable resource for future genetic studies of human variation and diseases.     

Genome Wide Association Study Identifies New Loci Associated with Undesired Coat Color Phenotypes in Saanen Goats

This paper reports a quantitative genetics and genomic analysis of undesirable coat color patterns in goats. Two undesirable coat colors have routinely been recorded for the past 15 years in French Saanen goats. One fifth of Saanen females have been phenotyped “pink” (8.0%) or “pink neck” (11.5%) and consequently have not been included in the breeding program as elite animals. Heritability of the binary “pink” and “pink neck” phenotype, estimated from 103,443 females was 0.26 for “pink” and 0.21 for “pink neck”. Genome wide association studies (using haplotypes or single SNPs) were implemented using a daughter design of 810 Saanen goats sired by 9 Artificial Insemination bucks genotyped with the goatSNP50 chip. A highly significant signal (-log10pvalue = 10.2) was associated with the “pink neck” phenotype on chromosome 11, suggesting the presence of a major gene. Highly significant signals for the “pink” phenotype were found on chromosomes 5 and 13 (-log10p values of 7.2 and, 7.7 respectively). The most significant SNP on chromosome 13 was in the ASIP gene region, well known for its association with coat color phenotypes. Nine significant signals were also found for both traits. The highest signal for each trait was detected by both single SNP and haplotype approaches, whereas the smaller signals were not consistently detected by the two methods. Altogether these results demonstrated a strong genetic control of the “pink” and “pink neck” phenotypes in French Saanen goats suggesting that SNP information could be used to identify and remove undesired colored animals from the breeding program.     

Genetic and genomic analysis of a fat mass trait with complex inheritance reveals marked sex specificity

The integration of expression profiling with linkage analysis has increasingly been used to identify genes underlying complex phenotypes. The effects of gender on the regulation of many physiological traits are well documented; however, “genetical genomic” analyses have not yet addressed the degree to which their conclusions are affected by sex. We constructed and densely genotyped a large F2 intercross derived from the inbred mouse strains C57BL/6J and C3H/HeJ on an apolipoprotein E null (ApoE^−/−) background. This BXH.ApoE^−/− population recapitulates several “metabolic syndrome” phenotypes. The cross consists of 334 animals of both sexes, allowing us to specifically test for the dependence of linkage on sex. We detected several thousand liver gene expression quantitative trait loci, a significant proportion of which are sex-biased. We used these analyses to dissect the genetics of gonadal fat mass, a complex trait with sex-specific regulation. We present evidence for a remarkably high degree of sex-dependence on both the cis and trans regulation of gene expression. We demonstrate how these analyses can be applied to the study of the genetics underlying gonadal fat mass, a complex trait showing significantly female-biased heritability. These data have implications on the potential effects of sex on the genetic regulation of other complex traits.     

Mouse Models of Genomic Syndromes as Tools for Understanding the Basis of Complex Traits: An Example with the Smith-Magenis and the Potocki-Lupski Syndromes

Each human''s genome is distinguished by extra and missing DNA that can be “benign” or powerfully impact everything from development to disease. In the case of genomic disorders DNA rearrangements, such as deletions or duplications, correlate with a clinical specific phenotype. The clinical presentations of genomic disorders were thought to result from altered gene copy number of physically linked dosage sensitive genes. Genomic disorders are frequent diseases (~1 per 1,000 births). Smith-Magenis syndrome (SMS) and Potocki-Lupski syndrome (PTLS) are genomic disorders, associated with a deletion and a duplication, of 3.7 Mb respectively, within chromosome 17 band p11.2. This region includes 23 genes. Both syndromes have complex and distinctive phenotypes including multiple congenital and neurobehavioral abnormalities. Human chromosome 17p11.2 is syntenic to the 32-34 cM region of murine chromosome 11. The number and order of the genes are highly conserved. In this review, we will exemplify how genomic disorders can be modeled in mice and the advantages that such models can give in the study of genomic disorders in particular and gene copy number variation (CNV) in general. The contributions of the SMS and PTLS animal models in several aspects ranging from more specific ones, as the definition of the clinical aspects of the human clinical spectrum, the identification of dosage sensitive genes related to the human syndromes, to the more general contributions as the definition of genetic locus impacting obesity and behavior and the elucidation of general mechanisms related to the pathogenesis of gene CNV are discussed.Key Words: Gene copy number variation, complex traits, phenotypic consequences, mouse models.     

Hypermutation of an ancient human retrovirus by APOBEC3G

Human endogenous retroviruses (HERVs) comprise approximately 8% of the human genome, but all are remnants of ancient retroviral infections and harbor inactivating mutations that render them replication defective. Nevertheless, as viral “fossils,” HERVs may provide insights into ancient retrovirus-host interactions and their evolution. Indeed, one endogenous retrovirus [HERV-K(HML-2)], which has replicated in humans for the past few million years but is now thought to be extinct, was recently reconstituted in a functional form, and infection assays based on it have been established. Here, we show that several human APOBEC3 proteins are intrinsically capable of mutating and inhibiting infection by HERV-K(HML-2) in cell culture. We also present striking evidence that two HERV-K(HML-2) proviruses that are fixed in the modern human genome (HERV-K60 and HERV-KI) were subjected to hypermutation by a cytidine deaminase. Inspection of the spectrum of mutations that are found in HERV-K proviruses in the human genome and HERV-K DNA generated during in vitro replication in the presence of each of the human APOBEC3 proteins unequivocally identifies APOBEC3G as the cytidine deaminase responsible for hypermutation of HERV-K60 and HERV-KI. This is a rare example of the antiretroviral effects of APOBEC3G in the setting of natural human infection, whose consequences have been fossilized in human DNA, and a striking example of inactivation of ancient retroviruses in humans through enzymatic cytidine deamination.     

Systematic Screening of Drosophila Deficiency Mutations for Embryonic Phenotypes and Orphan Receptor Ligands

This paper defines a collection of Drosophila deletion mutations (deficiencies) that can be systematically screened for embryonic phenotypes, orphan receptor ligands, and genes affecting protein localization. It reports the results of deficiency screens we have conducted that have revealed new axon guidance phenotypes in the central nervous system and neuromuscular system and permitted a quantitative assessment of the number of potential genes involved in regulating guidance of specific motor axon branches. Deficiency “kits” that cover the genome with a minimum number of lines have been established to facilitate gene mapping. These kits cannot be systematically analyzed for phenotypes, however, since embryos homozygous for many deficiencies in these kits fail to develop due to the loss of key gene products encoded within the deficiency. To create new kits that can be screened for phenotype, we have examined the development of the nervous system in embryos homozygous for more than 700 distinct deficiency mutations. A kit of ∼400 deficiency lines for which homozygotes have a recognizable nervous system and intact body walls encompasses >80% of the genome. Here we show examples of screens of this kit for orphan receptor ligands and neuronal antigen expression. It can also be used to find genes involved in expression, patterning, and subcellular localization of any protein that can be visualized by antibody staining. A subset kit of 233 deficiency lines, for which homozygotes develop relatively normally to late stage 16, covers ∼50% of the genome. We have screened it for axon guidance phenotypes, and we present examples of new phenotypes we have identified. The subset kit can be used to screen for phenotypes affecting all embryonic organs. In the future, these deficiency kits will allow Drosophila researchers to rapidly and efficiently execute genome-wide anatomical screens that require examination of individual embryos at high magnification.     

Sequence and analysis of a whole genome from Kuwaiti population subgroup of Persian ancestry

Background

The 1000 Genome project paved the way for sequencing diverse human populations. New genome projects are being established to sequence underrepresented populations helping in understanding human genetic diversity. The Kuwait Genome Project an initiative to sequence individual genomes from the three subgroups of Kuwaiti population namely, Saudi Arabian tribe; “tent-dwelling” Bedouin; and Persian, attributing their ancestry to different regions in Arabian Peninsula and to modern-day Iran (West Asia). These subgroups were in line with settlement history and are confirmed by genetic studies. In this work, we report whole genome sequence of a Kuwaiti native from Persian subgroup at >37X coverage.

Results

We document 3,573,824 SNPs, 404,090 insertions/deletions, and 11,138 structural variations. Out of the reported SNPs and indels, 85,939 are novel. We identify 295 ‘loss-of-function’ and 2,314 ’deleterious’ coding variants, some of which carry homozygous genotypes in the sequenced genome; the associated phenotypes include pharmacogenomic traits such as greater triglyceride lowering ability with fenofibrate treatment, and requirement of high warfarin dosage to elicit anticoagulation response. 6,328 non-coding SNPs associate with 811 phenotype traits: in congruence with medical history of the participant for Type 2 diabetes and β-Thalassemia, and of participant’s family for migraine, 72 (of 159 known) Type 2 diabetes, 3 (of 4) β-Thalassemia, and 76 (of 169) migraine variants are seen in the genome. Intergenome comparisons based on shared disease-causing variants, positions the sequenced genome between Asian and European genomes in congruence with geographical location of the region. On comparison, bead arrays perform better than sequencing platforms in correctly calling genotypes in low-coverage sequenced genome regions however in the event of novel SNP or indel near genotype calling position can lead to false calls using bead arrays.

Conclusions

We report, for the first time, reference genome resource for the population of Persian ancestry. The resource provides a starting point for designing large-scale genetic studies in Peninsula including Kuwait, and Persian population. Such efforts on populations under-represented in global genome variation surveys help augment current knowledge on human genome diversity.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1233-x) contains supplementary material, which is available to authorized users.     

Use of artificial genomes in assessing methods for atypical gene detection

Parametric methods for identifying laterally transferred genes exploit the directional mutational biases unique to each genome. Yet the development of new, more robust methods—as well as the evaluation and proper implementation of existing methods—relies on an arbitrary assessment of performance using real genomes, where the evolutionary histories of genes are not known. We have used the framework of a generalized hidden Markov model to create artificial genomes modeled after genuine genomes. To model a genome, “core” genes—those displaying patterns of mutational biases shared among large numbers of genes—are identified by a novel gene clustering approach based on the Akaike information criterion. Gene models derived from multiple “core” gene clusters are used to generate an artificial genome that models the properties of a genuine genome. Chimeric artificial genomes—representing those having experienced lateral gene transfer—were created by combining genes from multiple artificial genomes, and the performance of the parametric methods for identifying “atypical” genes was assessed directly. We found that a hidden Markov model that included multiple gene models, each trained on sets of genes representing the range of genotypic variability within a genome, could produce artificial genomes that mimicked the properties of genuine genomes. Moreover, different methods for detecting foreign genes performed differently—i.e., they had different sets of strengths and weaknesses—when identifying atypical genes within chimeric artificial genomes.     

Survival of the Curviest: Noise-Driven Selection for Synergistic Epistasis

A major goal of human genetics is to elucidate the genetic architecture of human disease, with the goal of fueling improvements in diagnosis and the understanding of disease pathogenesis. The degree to which epistasis, or non-additive effects of risk alleles at different loci, accounts for common disease traits is hotly debated, in part because the conditions under which epistasis evolves are not well understood. Using both theory and evolutionary simulation, we show that the occurrence of common diseases (i.e. unfit phenotypes with frequencies on the order of 1%) can, under the right circumstances, be expected to be driven primarily by synergistic epistatic interactions. Conditions that are necessary, collectively, for this outcome include a strongly non-linear phenotypic landscape, strong (but not too strong) selection against the disease phenotype, and “noise” in the genotype-phenotype map that is both environmental (extrinsic, time-correlated) and developmental (intrinsic, uncorrelated) and, in both cases, neither too little nor too great. These results suggest ways in which geneticists might identify, a priori, those disease traits for which an “epistatic explanation” should be sought, and in the process better focus ongoing searches for risk alleles.     

Priors in Whole-Genome Regression: The Bayesian Alphabet Returns

Whole-genome enabled prediction of complex traits has received enormous attention in animal and plant breeding and is making inroads into human and even Drosophila genetics. The term “Bayesian alphabet” denotes a growing number of letters of the alphabet used to denote various Bayesian linear regressions that differ in the priors adopted, while sharing the same sampling model. We explore the role of the prior distribution in whole-genome regression models for dissecting complex traits in what is now a standard situation with genomic data where the number of unknown parameters (p) typically exceeds sample size (n). Members of the alphabet aim to confront this overparameterization in various manners, but it is shown here that the prior is always influential, unless n ≫ p. This happens because parameters are not likelihood identified, so Bayesian learning is imperfect. Since inferences are not devoid of the influence of the prior, claims about genetic architecture from these methods should be taken with caution. However, all such procedures may deliver reasonable predictions of complex traits, provided that some parameters (“tuning knobs”) are assessed via a properly conducted cross-validation. It is concluded that members of the alphabet have a room in whole-genome prediction of phenotypes, but have somewhat doubtful inferential value, at least when sample size is such that n ≪ p.     

A Naturally Occurring hPMS2 Mutation Can Confer a Dominant Negative Mutator Phenotype

Defects in mismatch repair (MMR) genes result in a mutator phenotype by inducing microsatellite instability (MI), a characteristic of hereditary nonpolyposis colorectal cancers (HNPCC) and a subset of sporadic colon tumors. Present models describing the mechanism by which germ line mutations in MMR genes predispose kindreds to HNPCC suggest a “two-hit” inactivation of both alleles of a particular MMR gene. Here we present experimental evidence that a nonsense mutation at codon 134 of the hPMS2 gene is sufficient to reduce MMR and induce MI in cells containing a wild-type hPMS2 allele. These results have significant implications for understanding the relationship between mutagenesis and carcinogenesis and the ability to generate mammalian cells with mutator phenotypes.     

Comparative Analyses of QTLs Influencing Obesity and Metabolic Phenotypes in Pigs and Humans

The pig is a well-known animal model used to investigate genetic and mechanistic aspects of human disease biology. They are particularly useful in the context of obesity and metabolic diseases because other widely used models (e.g. mice) do not completely recapitulate key pathophysiological features associated with these diseases in humans. Therefore, we established a F2 pig resource population (n = 564) designed to elucidate the genetics underlying obesity and metabolic phenotypes. Segregation of obesity traits was ensured by using breeds highly divergent with respect to obesity traits in the parental generation. Several obesity and metabolic phenotypes were recorded (n = 35) from birth to slaughter (242 ± 48 days), including body composition determined at about two months of age (63 ± 10 days) via dual-energy x-ray absorptiometry (DXA) scanning. All pigs were genotyped using Illumina Porcine 60k SNP Beadchip and a combined linkage disequilibrium-linkage analysis was used to identify genome-wide significant associations for collected phenotypes. We identified 229 QTLs which associated with adiposity- and metabolic phenotypes at genome-wide significant levels. Subsequently comparative analyses were performed to identify the extent of overlap between previously identified QTLs in both humans and pigs. The combined analysis of a large number of obesity phenotypes has provided insight in the genetic architecture of the molecular mechanisms underlying these traits indicating that QTLs underlying similar phenotypes are clustered in the genome. Our analyses have further confirmed that genetic heterogeneity is an inherent characteristic of obesity traits most likely caused by segregation or fixation of different variants of the individual components belonging to cellular pathways in different populations. Several important genes previously associated to obesity in human studies, along with novel genes were identified. Altogether, this study provides novel insight that may further the current understanding of the molecular mechanisms underlying human obesity.     

Modeling the Evolutionary Architectures of Transcribed Human Enhancer Sequences Reveals Distinct Origins,Functions, and Associations with Human Trait Variation

Despite the importance of gene regulatory enhancers in human biology and evolution, we lack a comprehensive model of enhancer evolution and function. This substantially limits our understanding of the genetic basis of species divergence and our ability to interpret the effects of noncoding variants on human traits.To explore enhancer sequence evolution and its relationship to regulatory function, we traced the evolutionary origins of transcribed human enhancer sequences with activity across diverse tissues and cellular contexts from the FANTOM5 consortium. The transcribed enhancers are enriched for sequences of a single evolutionary age (“simple” evolutionary architectures) compared with enhancers that are composites of sequences of multiple evolutionary ages (“complex” evolutionary architectures), likely indicating constraint against genomic rearrangements. Complex enhancers are older, more pleiotropic, and more active across species than simple enhancers. Genetic variants within complex enhancers are also less likely to associate with human traits and biochemical activity. Transposable-element-derived sequences (TEDS) have made diverse contributions to enhancers of both architectures; the majority of TEDS are found in enhancers with simple architectures, while a minority have remodeled older sequences to create complex architectures. Finally, we compare the evolutionary architectures of transcribed enhancers with histone-mark-defined enhancers.Our results reveal that most human transcribed enhancers are ancient sequences of a single age, and thus the evolution of most human enhancers was not driven by increases in evolutionary complexity over time. Our analyses further suggest that considering enhancer evolutionary histories provides context that can aid interpretation of the effects of variants on enhancer function. Based on these results, we propose a framework for analyzing enhancer evolutionary architecture.     

Gene-Gene and Gene-Environment Interactions in Meta-Analysis of Genetic Association Studies

Extensive genetic studies have identified a large number of causal genetic variations in many human phenotypes; however, these could not completely explain heritability in complex diseases. Some researchers have proposed that the “missing heritability” may be attributable to gene–gene and gene–environment interactions. Because there are billions of potential interaction combinations, the statistical power of a single study is often ineffective in detecting these interactions. Meta-analysis is a common method of increasing detection power; however, accessing individual data could be difficult. This study presents a simple method that employs aggregated summary values from a “case” group to detect these specific interactions that based on rare disease and independence assumptions. However, these assumptions, particularly the rare disease assumption, may be violated in real situations; therefore, this study further investigated the robustness of our proposed method when it violates the assumptions. In conclusion, we observed that the rare disease assumption is relatively nonessential, whereas the independence assumption is an essential component. Because single nucleotide polymorphisms (SNPs) are often unrelated to environmental factors and SNPs on other chromosomes, researchers should use this method to investigate gene–gene and gene–environment interactions when they are unable to obtain detailed individual patient data.     

Review of Non-coding RNAs and the epigenetic regulation of gene expression: A book edited by Kevin Morris

Advances in sequencing and detection technology over the past two decades, highlighted by the data explosion brought about by the human genome project, have transformed what was previously assumed to be a relatively simple genetic landscape into a new picture where the so-called “dark matter” of the genome has stolen the spotlight from the not so hip protein-coding genes. The simplified central dogma of molecular biology, in which a gene encodes for a protein via a messenger RNA (mRNA), is still at the core of genetics but is now caught in a much more complex web of regulation by the genomic region previously known as “junk” DNA. Books such as Non-coding RNAs and epigenetic regulation of gene expression, published by Caister Academic Press, become essential guidelines to help us understand the current status of the very fast paced field of RNA research, which has only just started to uncover the roles of non-coding RNAs (ncRNAs) in the regulation of gene expression.