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Background Over the last few years, genome-wide association (GWA) studies became a tool of choice for the identification of loci associated with complex traits. Currently, imputed single nucleotide polymorphisms (SNP) data are frequently used in GWA analyzes. Correct analysis of imputed data calls for the implementation of specific methods which take genotype imputation uncertainty into account. Results We developed the ProbABEL software package for the analysis of genome-wide imputed SNP data and quantitative, binary, and time-till-event outcomes under linear, logistic, and Cox proportional hazards models, respectively. For quantitative traits, the package also implements a fast two-step mixed model-based score test for association in samples with differential relationships, facilitating analysis in family-based studies, studies performed in human genetically isolated populations and outbred animal populations. Conclusions ProbABEL package provides fast efficient way to analyze imputed data in genome-wide context and will facilitate future identification of complex trait loci.

Background Lactose provides an easily-digested energy source for neonates, and is the primary carbohydrate in milk in most species. Bovine lactose is also a key component of many human food products. However, compared to analyses of other milk components, the genetic control of lactose has been little studied. Here we present the first GWAS focussed on analysis of milk lactose traits. Results Using a discovery population of 12,000 taurine dairy cattle, we detail 27 QTL for lactose concentration and yield, and subsequently validate the effects of 26 of these loci in a distinct population of 18,000 cows. We next present data implicating causative genes and variants for these QTL. Fine mapping of these regions using imputed, whole genome sequence-resolution genotypes reveals protein-coding candidate causative variants affecting the ABCG2 , DGAT1 , STAT5B , KCNH4 , NPFFR2 and RNF214 genes. Eleven of the remaining QTL appear to be driven by regulatory effects, suggested by the presence of co-locating, co-segregating eQTL discovered using mammary RNA sequence data from a population of 357 lactating cows. Pathway analysis of genes representing all lactose-associated loci shows significant enrichment of genes located in the endoplasmic reticulum, with functions related to ion channel activity mediated through the LRRC8C , P2RX4 , KCNJ2 and ANKH genes. A number of the validated QTL are also found to be associated with additional milk volume, fat and protein phenotypes. Conclusions Overall, these findings highlight novel candidate genes and variants involved in milk lactose regulation, whose impacts on membrane transport mechanisms reinforce the key osmo-regulatory roles of lactose in milk.

In the Victorian era, Sir Francis Galton showed that ‘when dealing with the transmission of stature from parents to children, the average height of the two parents, … is all we need care to know about them’ (1886). One hundred and twenty-two years after Galton's work was published, 54 loci showing strong statistical evidence for association to human height were described, providing us with potential genomic means of human height prediction. In a population-based study of 5748 people, we find that a 54-loci genomic profile explained 4–6% of the sex- and age-adjusted height variance, and had limited ability to discriminate tall/short people, as characterized by the area under the receiver-operating characteristic curve (AUC). In a family-based study of 550 people, with both parents having height measurements, we find that the Galtonian mid-parental prediction method explained 40% of the sex- and age-adjusted height variance, and showed high discriminative accuracy. We have also explored how much variance a genomic profile should explain to reach certain AUC values. For highly heritable traits such as height, we conclude that in applications in which parental phenotypic information is available (eg, medicine), the Victorian Galton's method will long stay unsurpassed, in terms of both discriminative accuracy and costs. For less heritable traits, and in situations in which parental information is not available (eg, forensics), genomic methods may provide an alternative, given that the variants determining an essential proportion of the trait's variation can be identified.

Multiple loss-of-function (LOF) alleles at the same gene may influence a phenotype not only in the homozygote state when alleles are considered individually, but also in the compound heterozygote (CH) state. Such LOF alleles typically have low frequencies and moderate to large effects. Detecting such variants is of interest to the genetics community, and relevant statistical methods for detecting and quantifying their effects are sorely needed. We present a collapsed double heterozygosity (CDH) test to detect the presence of multiple LOF alleles at a gene. When causal SNPs are available, which may be the case in next generation genome sequencing studies, this CDH test has overwhelmingly higher power than single SNP analysis. When causal SNPs are not directly available such as in current GWA settings, we show the CDH test has higher power than standard single SNP analysis if tagging SNPs are in linkage disequilibrium with the underlying causal SNPs to at least a moderate degree (r²>0.1). The test is implemented for genome-wide analysis in the publically available software package GenABEL which is based on a sliding window approach. We provide the proof of principle by conducting a genome-wide CDH analysis of red hair color, a trait known to be influenced by multiple loss-of-function alleles, in a total of 7,732 Dutch individuals with hair color ascertained. The association signals at the MC1R gene locus from CDH were uniformly more significant than traditional GWA analyses (the most significant P for CDH = 3.11×10⁻¹⁴² vs. P for rs258322 = 1.33×10⁻⁶⁶). The CDH test will contribute towards finding rare LOF variants in GWAS and sequencing studies.

Caroline Fox and colleagues report results of a large genome-wide association meta-analysis and replication study for indices of renal function. Their work identifies 13 new loci associated with renal function and 7 loci associated with creatinine production and secretion. Chronic kidney disease (CKD) is a significant public health problem, and recent genetic studies have identified common CKD susceptibility variants. The CKDGen consortium performed a meta-analysis of genome-wide association data in 67,093 individuals of European ancestry from 20 predominantly population-based studies in order to identify new susceptibility loci for reduced renal function as estimated by serum creatinine (eGFRcrea), serum cystatin c (eGFRcys) and CKD (eGFRcrea < 60 ml/min/1.73 m 2 ; n = 5,807 individuals with CKD (cases)). Follow-up of the 23 new genome-wide–significant loci ( P < 5 × 10 −8 ) in 22,982 replication samples identified 13 new loci affecting renal function and CKD (in or near LASS2, GCKR, ALMS1, TFDP2, DAB2, SLC34A1, VEGFA, PRKAG2, PIP5K1B, ATXN2, DACH1, UBE2Q2 and SLC7A9 ) and 7 loci suspected to affect creatinine production and secretion ( CPS1, SLC22A2, TMEM60, WDR37, SLC6A13, WDR72 and BCAS3 ). These results further our understanding of the biologic mechanisms of kidney function by identifying loci that potentially influence nephrogenesis, podocyte function, angiogenesis, solute transport and metabolic functions of the kidney.

Abstract Background: Presence of interaction between a genotype and certain factor in determination of a trait's value, it is expected that the trait's variance is increased in the group of subjects having this genotype. Thus, test of heterogeneity of variances can be used as a test to screen for potentially interacting single-nucleotide polymorphisms (SNPs). In this work, we evaluated statistical properties of variance heterogeneity analysis in respect to the detection of potentially interacting SNPs in a case when an interaction variable is unknown. Results: Through simulations, we investigated type I error for Bartlett's test, Bartlett's test with prior rank transformation of a trait to normality, and Levene's test for different genetic models. Additionally, we derived an analytical expression for power estimation. We showed that Bartlett's test has acceptable type I error in the case of trait following a normal distribution, whereas Levene's test kept nominal Type I error under all scenarios investigated. For the power of variance homogeneity test, we showed (as opposed to the power of direct test which uses information about known interacting factor) that, given the same interaction effect, the power can vary widely depending on the non-estimable direct effect of the unobserved interacting variable. Thus, for a given interaction effect, only very wide limits of power of the variance homogeneity test can be estimated. Also we applied Levene's approach to test genome-wide homogeneity of variances of the C-reactive protein in the Rotterdam Study population (n = 5959). In this analysis, we replicate previous results of Pare and colleagues (2010) for the SNP rs12753193 (n = 21, 799). Conclusions: Screening for differences in variances among genotypes of a SNP is a promising approach as a number of biologically interesting models may lead to the heterogeneity of variances. However, it should be kept in mind that the absence of variance heterogeneity for a SNP can not be interpreted as the absence of involvement of the SNP in the interaction network.

Phospho- and sphingolipids are crucial cellular and intracellular compounds. These lipids are required for active transport, a number of enzymatic processes, membrane formation, and cell signalling. Disruption of their metabolism leads to several diseases, with diverse neurological, psychiatric, and metabolic consequences. A large number of phospholipid and sphingolipid species can be detected and measured in human plasma. We conducted a meta-analysis of five European family-based genome-wide association studies (N = 4034) on plasma levels of 24 sphingomyelins (SPM), 9 ceramides (CER), 57 phosphatidylcholines (PC), 20 lysophosphatidylcholines (LPC), 27 phosphatidylethanolamines (PE), and 16 PE-based plasmalogens (PLPE), as well as their proportions in each major class. This effort yielded 25 genome-wide significant loci for phospholipids (smallest P-value = 9.88×10(-204)) and 10 loci for sphingolipids (smallest P-value = 3.10×10(-57)). After a correction for multiple comparisons (P-value<2.2×10(-9)), we observed four novel loci significantly associated with phospholipids (PAQR9, AGPAT1, PKD2L1, PDXDC1) and two with sphingolipids (PLD2 and APOE) explaining up to 3.1% of the variance. Further analysis of the top findings with respect to within class molar proportions uncovered three additional loci for phospholipids (PNLIPRP2, PCDH20, and ABDH3) suggesting their involvement in either fatty acid elongation/saturation processes or fatty acid specific turnover mechanisms. Among those, 14 loci (KCNH7, AGPAT1, PNLIPRP2, SYT9, FADS1-2-3, DLG2, APOA1, ELOVL2, CDK17, LIPC, PDXDC1, PLD2, LASS4, and APOE) mapped into the glycerophospholipid and 12 loci (ILKAP, ITGA9, AGPAT1, FADS1-2-3, APOA1, PCDH20, LIPC, PDXDC1, SGPP1, APOE, LASS4, and PLD2) to the sphingolipid pathways. In large meta-analyses, associations between FADS1-2-3 and carotid intima media thickness, AGPAT1 and type 2 diabetes, and APOA1 and coronary artery disease were observed. In conclusion, our study identified nine novel phospho- and sphingolipid loci, substantially increasing our knowledge of the genetic basis for these traits.

A genomewide association study indicates that a locus on chromosome 12 confers susceptibility (with a hazard ratio of approximately 1.3) to ischemic stroke. This locus is close to NINJ2, which encodes a cell adhesion molecule that shows increased expression on glia in response to nerve injury. A second gene near this locus influences blood pressure and hypertension. A genomewide association study indicates that a locus on chromosome 12 confers susceptibility (with a hazard ratio of approximately 1.3) to ischemic stroke. This locus is close to NINJ2, which encodes a cell adhesion molecule that shows increased expression on glia in response to nerve injury. Stroke is the leading neurologic cause of death and disability. 1 Twin and familial aggregation studies suggest that the risk of stroke has a substantial genetic component, 2 – 4 but the genes underlying this risk in the general population remain undetermined. Studies of candidate genes or studies that use classical linkage approaches have yielded inconsistent findings. 5 Genomewide association studies have uncovered previously unsuspected common variants underlying the risk of complex diseases such as diabetes 6 and coronary disease. 7 , 8 Two previous genomewide association studies of stroke were limited by a case–control design that is more susceptible to survival and selection biases than the . . .

Caroline Klaver and colleagues report a genome-wide association study for myopia and refractive error in the general population, identifying a susceptibility locus at 15q14. Refractive errors are the most common ocular disorders worldwide and may lead to blindness. Although this trait is highly heritable, identification of susceptibility genes has been challenging. We conducted a genome-wide association study for refractive error in 5,328 individuals from a Dutch population-based study with replication in four independent cohorts (combined 10,280 individuals in the replication stage). We identified a significant association at chromosome 15q14 (rs634990, P = 2.21 × 10 −14 ). The odds ratio of myopia compared to hyperopia for the minor allele (minor allele frequency = 0.47) was 1.41 (95% CI 1.16–1.70) for individuals heterozygous for the allele and 1.83 (95% CI 1.42–2.36) for individuals homozygous for the allele. The associated locus is near two genes that are expressed in the retina, GJD2 and ACTC1 , and appears to harbor regulatory elements which may influence transcription of these genes. Our data suggest that common variants at 15q14 influence susceptibility for refractive errors in the general population.

Abstract Background: Hundreds of new loci have been discovered by genome-wide association studies of human traits. These studies mostly focused on associations between single locus and a trait. Interactions between genes and between genes and environmental factors are of interest as they can improve our understanding of the genetic background underlying complex traits. Genome-wide testing of complex genetic models is a computationally demanding task. Moreover, testing of such models leads to multiple comparison problems that reduce the probability of new findings. Assuming that the genetic model underlying a complex trait can include hundreds of genes and environmental factors, testing of these models in genome-wide association studies represent substantial difficulties. We and Pare with colleagues (2010) developed a method allowing to overcome such difficulties. The method is based on the fact that loci which are involved in interactions can show genotypic variance heterogeneity of a trait. Genome-wide testing of such heterogeneity can be a fast scanning approach which can point to the interacting genetic variants. Results: In this work we present a new method, SVLM, allowing for variance heterogeneity analysis of imputed genetic variation. Type I error and power of this test are investigated and contracted with these of the Levene's test. We also present an R package, VariABEL, implementing existing and newly developed tests. Conclusions: Variance heterogeneity analysis is a promising method for detection of potentially interacting loci. New method and software package developed in this work will facilitate such analysis in genome-wide context.