Genomic variation exists in cattle that affects their susceptibility to the complex of pathogens responsible for bovine respiratory disease (BRD). Heritability estimates and genome-wide association analyses (GWAA) support the role of host genomic variation in BRD susceptibility. Heritability estimates for BRD susceptibility range from 0.02 to 0.29 depending on the population, the definition of the disease, and the accuracy of diagnosis. GWAA have identified genomic regions (loci) associated with BRD in beef and dairy cattle based on a variety of BRD diagnostic criteria. National standards need to be developed for BRD diagnostics and reporting to facilitate selection. Commercial genotyping is available to predict BRD susceptibility in dairy cattle and for the selection of replacement animals. Disease pathogen profiles vary by region and can result in genetic heterogeneity where different loci are important for susceptibility to different BRD pathogens. Although the identification of the BRD pathogens may not be critical for treatment, it is of paramount importance in identifying loci that render cattle susceptible to the disease. Identification of loci associated with host susceptibility to BRD provides a foundation for genomic selection to reduce disease and opens the possibilities to a better understanding of how the host defends itself.

The objectives of the current study were to detect putative genomic loci and to identify candidate genes associated with milk production traits in Egyptian buffalo. A total number of 161 479 daily milk yield (DMY) records and 60 318 monthly measures for fat and protein percentages (FP and PP, respectively), along with fat and protein yields (FY and PY, respectively) from 1670 animals were used. Genotyping was performed using Axiom® Buffalo Genotyping 90 K array. Genome-wide association study (GWAS) for each trait was performed using PLINK. After Bonferroni correction, 47 SNPs were associated with one or more milk production traits. These SNPs were distributed over 36 quantitative trait loci (QTL) and located on 20 buffalo chromosomes (BBU). For the 47 SNPs, one was overlapped for three traits (DMY, FY, and PY), six were associated with two traits (one for PP and PY and five for FY and PY) while the rest were associated with only one trait. Out of 36 identified QTL, eleven were overlapped with previously reported loci in buffalo and/or cattle populations. Some of these SNPs are placed within or close to potential candidate genes, for example: TPD52, ZBTB10, RALYL and SNX16 on BBU15, ADGRD1 on BBU17, ESRRG on BBU5 and GRIP1 on BBU4. This is the first reported study between genome-wide markers and milk components in Egyptian buffalo. Our findings provide useful information to explore the genetic mechanisms and relevant genes contributing to the variation in milk production traits. Further confirmation studies with larger population size are necessary to validate the findings and detect the causal genetic variants.

The etiology of suicide is complex in nature with both environmental and genetic causes that are extremely diverse. This extensive heterogeneity weakens the relationship between genotype and phenotype and as a result, we face many challenges when studying the genetic etiology of suicide. We are now in the midst of a genetics revolution, where genotyping costs are decreasing and genotyping speed is increasing at a fast rate, allowing genetic association studies to genotype thousands to millions of SNPs that cover the entire human genome. As such, genome-wide association studies (GWAS) are now the norm. In this article we address several statistical challenges that occur when studying the genetic etiology of suicidality in the age of the genetics revolution. These challenges include: (1) the large number of statistical tests; (2) complex phenotypes that are difficult to quantify; and (3) modest genetic effect sizes. We address these statistical issues in the context of family-based study designs. Specifically, we discuss several statistical extensions of family-based association tests (FBATs) that work to alleviate these challenges. As our intention is to describe how statistical methodology may work to identify disease variants for suicidality, we avoid the mathematical details of the methodologies presented.

Feed efficiency traits (FETs) are important economic indicators in poultry production. Because feed intake (FI) is a time-dependent variable, longitudinal models can provide insights into the genetic basis of FET variation over time. It is expected that the application of longitudinal models as part of genome-wide association (GWA) and genomic selection (i.e. genome-wide selection (GS)) studies will lead to an increase in accuracy of selection. Thus, the objectives of this study were to evaluate the accuracy of estimated breeding values (EBVs) based on pedigree as well as high-density single nucleotide polymorphism (SNP) genotypes, and to conduct a GWA study on longitudinal FI and residual feed intake (RFI) in a total of 312 chickens with phenotype and genotype in the F2 population. The GWA and GS studies reported in this paper were conducted using β-spline random regression models for FI and RFI traits in a chicken F2 population, with FI and BW recorded for each bird weekly between 2 and 10 weeks of age. A single SNP regression approach was used on spline coefficients for weekly FI and RFI traits, with results showing that two significant SNPs for FI occur in the synuclein (SNCAIP) gene. Results also show that these regions are significantly associated with the spline coefficients (q2) for 5- and 6-week-old birds, while GWA study results showed no SNP association with RFI in F2 chickens. Estimated breeding value predictions obtained using a pedigree-based best linear unbiased prediction (ABLUP) model were then compared with predictions based on genomic best linear unbiased prediction (GBLUP). The accuracy was measured as correlation between genomic EBV and EBV with the phenotypic value corrected for fixed effects divided by the square root of heritability. The regression of observed on predicted values was used to estimate bias of methods. Results show that prediction accuracies using GBLUP and ABLUP for the FI measured from 2nd to 10th week were between 0.06 and 0.46 and 0.03 and 0.37, respectively. These results demonstrate that genomic methods are able to increase the accuracy of predicted breeding values at later ages on the basis of both traits, and indicate that use of a longitudinal model can improve selection accuracy for the trajectory of traits in F2 chickens when compared with conventional methods.

The powerful genome-wide association studies (GWAS) revealed common mutations that increase susceptibility for schizophrenia (SZ) and bipolar disorder (BD), but the vast majority were not known to be functional or associated with these illnesses. To help fill this gap, their impact on human brain structure and function has been examined. We systematically discuss this output to facilitate its timely integration in the psychosis research field; and encourage reflection for future research. Irrespective of imaging modality, studies addressing the effect of SZ/BD GWAS risk genes (ANK3, CACNA1C, MHC, TCF4, NRGN, DGKH, PBRM1, NCAN and ZNF804A) were included. Most GWAS risk variations were reported to affect neuroimaging phenotypes implicated in SZ/BD: white-matter integrity (ANK3 and ZNF804A), volume (CACNA1C and ZNF804A) and density (ZNF804A); grey-matter (CACNA1C, NRGN, TCF4 and ZNF804A) and ventricular (TCF4) volume; cortical folding (NCAN) and thickness (ZNF804A); regional activation during executive tasks (ANK3, CACNA1C, DGKH, NRGN and ZNF804A) and functional connectivity during executive tasks (CACNA1C and ZNF804A), facial affect recognition (CACNA1C and ZNF804A) and theory-of-mind (ZNF804A); but inconsistencies and non-replications also exist. Further efforts such as standardizing reporting and exploring complementary designs, are warranted to test the reproducibility of these early findings.

With the dramatic technological developments of genome-wide association single-nucleotide polymorphism (SNP) chips and next generation sequencing, human geneticists now have the ability to assay genetic variation at ever-rarer allele frequencies. To fully understand the impact of these rare variants on common, complex diseases, we must be able to accurately assess their statistical significance. However, it is well established that classical association tests are not appropriate for the analysis of low-frequency variation, giving spurious findings when observed counts are too few. To further our understanding of the asymptotic properties of traditional association tests, we conducted a range of simulations of a typical rare variant (~1%) under the null hypothesis and tested the allelic χ2, Cochran–Armitage trend, Wald, and Fisher's exact tests. We demonstrate that rare variation shows marked deviation from the expected distributional behavior for each test, with fewer minor alleles corresponding to a greater degree of test statistics deflation. The effect becomes more pronounced at progressively smaller α levels. We also show that the Wald test is particularly deflated at α levels consistent with genome-wide association significance, much more so than the other association tests considered. In general, these classical association tests are inappropriate for the analysis of variants for which the minor allele is observed fewer than 80 times, largely irrespective of sample size.

The biggest threat to the war on malaria is the continued evolution of drug resistance by the parasite. Resistance to almost all currently available antimalarials now exists in Plasmodium falciparum which causes the most suffering among all human malaria parasites. Monitoring of antimalarial efficacy and the development and subsequent spread of resistance has become an important part in the treatment and control of malaria. With recent reports of reduced efficacy of artemisinin, the current recommended treatment for uncomplicated malaria, there is urgent need for better methods to recognize and monitor drug resistance for effective treatment. Molecular markers have become a welcome addition to complement the more laborious and costly in vitro and in vivo methods that have traditionally been used to monitor drug resistance. However, there are currently no molecular markers for resistance to some antimalarials. This review highlights the role of the various genetic and genomic approaches that have been used in identifying the molecular markers that underlie drug resistance in P. falciparum. These approaches include; candidate genes, genetic linkage and genome-wide association studies. We discuss the requirements and limitations of each approach and use various examples to illustrate their contributions in identifying genomic regions of the parasite associated with antimalarial drug responses.

Genome-wide association studies for difficult-to-measure traits are generally limited by the sample size with accurate phenotypic data. The objective of this study was to utilise data on primiparous Holstein–Friesian cows from experimental farms in Ireland, the United Kingdom, the Netherlands and Sweden to identify genomic regions associated with the feed utilisation complex: fat and protein corrected milk yield (FPCM), dry matter intake (DMI), body condition score (BCS) and live-weight (LW). Phenotypic data and 37 590 single nucleotide polymorphisms (SNPs) were available on up to 1629 animals. Genetic parameters of the traits were estimated using a linear animal model with pedigree information, and univariate genome-wide association analyses were undertaken using Bayesian stochastic search variable selection performed using Gibbs sampling. The variation in the phenotypes explained by the SNPs on each chromosome was related to the size of the chromosome and was relatively consistent for each trait with the possible exceptions of BTA4 for BCS, BTA7, BTA13, BTA14, BTA18 for LW and BTA27 for DMI. For LW, BCS, DMI and FPCM, 266, 178, 206 and 254 SNPs had a Bayes factor >3, respectively. Olfactory genes and genes involved in the sensory smell process were overrepresented in a 500 kbp window around the significant SNPs. Potential candidate genes were involved with functions linked to insulin, epidermal growth factor and tryptophan.

Visual impairment is a leading cause of morbidity and poor quality of life in our community. Unravelling the mechanisms underpinning important blinding diseases could allow preventative or curative steps to be implemented. Twin siblings provide a unique opportunity in biology to discover genes associated with numerous eye diseases and ocular biometry. Twins are particularly useful for quantitative trait analysis through genome-wide association and linkage studies. Although many studies involving twins rely on twin registries, we present our approach to the Twins Eye Study in Tasmania to provide insight into possible recruitment strategies, expected participation rates and potential examination strategies that can be considered by other researchers for similar studies. Five separate avenues for cohort recruitment were adopted: (1) piggy-backing existing studies where twins had been recruited, (2) utilizing the national twin registry, (3) word-of-mouth and local media publicity, (4) directly approaching schools, and finally (5) collaborating with other research groups studying twins.

Aim: To describe the recruitment, ophthalmic examination methods and distribution of ocular biometry of participants in the Norfolk Island Eye Study, who were individuals descended from the English Bounty mutineers and their Polynesian wives. Methods: All 1,275 permanent residents of Norfolk Island aged over 15 years were invited to participate, including 602 individuals involved in a 2001 cardiovascular disease study. Participants completed a detailed questionnaire and underwent a comprehensive eye assessment including stereo disc and retinal photography, ocular coherence topography and conjunctival autofluorescence assessment. Additionally, blood or saliva was taken for DNA testing. Results: 781 participants aged over 15 years were seen (54% female), comprising 61% of the permanent Island population. 343 people (43.9%) could trace their family history to the Pitcairn Islanders (Norfolk Island Pitcairn Pedigree). Mean anterior chamber depth was 3.32mm, mean axial length (AL) was 23.5mm, and mean central corneal thickness was 546 microns. There were no statistically significant differences in these characteristics between persons with and without Pitcairn Island ancestry. Mean intra-ocular pressure was lower in people with Pitcairn Island ancestry: 15.89mmHg compared to those without Pitcairn Island ancestry 16.49mmHg (P = .007). The mean keratometry value was lower in people with Pitcairn Island ancestry (43.22 vs. 43.52, P = .007). The corneas were flatter in people of Pitcairn ancestry but there was no corresponding difference in AL or refraction. Conclusion: Our study population is highly representative of the permanent population of Norfolk Island. Ocular biometry was similar to that of other white populations. Heritability estimates, linkage analysis and genome-wide studies will further elucidate the genetic determinants of chronic ocular diseases in this genetic isolate.

Self-rated health questions have been proven to be a highly reliable and valid measure of overall health as measured by other indicators in many population groups. It also has been shown to be a very good predictor of mortality, chronic or severe diseases, and the need for services, and is positively correlated with clinical assessments. Genetic factors have been estimated to account for 25–64% of the variance in the liability of self-rated health. The aim of the present study was to identify Single Nucleotide Polymorphisms (SNPs) underlying the heritability of self-rated health by conducting a genome-wide association analysis in a large sample of 6,706 Australian individuals aged 18–92. No genome wide significant SNPs associated with self-rated health could be identified, indicating that self-rated health may be influenced by a large number of SNPs with very small effect size. A very large sample will be needed to identify these SNPs.

Genome-wide association studies for difficult-to-measure traits are generally limited by the sample population size with accurate phenotypic data. The objective of this study was to utilise data on primiparous Holstein–Friesian cows from experimental farms in Ireland, the United Kingdom, the Netherlands and Sweden to identify genomic regions associated with traditional measures of fertility, as well as a fertility phenotype derived from milk progesterone profiles. Traditional fertility measures investigated were days to first heat, days to first service, pregnancy rate to first service, number of services and calving interval (CI); post-partum interval to the commencement of luteal activity (CLA) was derived using routine milk progesterone assays. Phenotypic and genotypic data on 37 590 single nucleotide polymorphisms (SNPs) were available for up to 1570 primiparous cows. Genetic parameters were estimated using linear animal models, and univariate and bivariate genome-wide association analyses were undertaken using Bayesian stochastic search variable selection performed using Gibbs sampling. Heritability estimates of the traditional fertility traits varied from 0.03 to 0.16; the heritability for CLA was 0.13. The posterior quantitative trait locus (QTL) probabilities, across the genome, for the traditional fertility measures were all <0.021. Posterior QTL probabilities of 0.060 and 0.045 were observed for CLA on SNPs each on chromosome 2 and chromosome 21, respectively, in the univariate analyses; these probabilities increased when CLA was included in the bivariate analyses with the traditional fertility traits. For example, in the bivariate analysis with CI, the posterior QTL probability of the two aforementioned SNPs were 0.662 and 0.123. Candidate genes in the vicinity of these SNPs are discussed. The results from this study suggest that the power of genome-wide association studies in cattle may be increased by sharing of data and also possibly by using physiological measures of the trait under investigation.

For highly heritable brain disorders, such as schizophrenia and autism, investigating genetic effects on the level of neural systems seems an obvious approach. Nevertheless, the usefulness of the intermediate phenotypes (‘endo’ phenotypes) continues to be debated energetically. We argue that, while not all intermediate phenotypes are created equal, the hypothesis-driven investigation of the translational cascades linking genetic variation to disturbed behavior is a viable and important strategy that should not be supplanted by an exclusive focus on brainless, clinical/categorical phenotypes investigated in very large numbers of participants.