The effect of measurement error of phenotypes on genome wide association studies

Clarifying Chronic Obstructive Pulmonary Disease Genetic Associations Observed in Biobanks via Mediation Analysis of Smoking

Varying case definitions of COPD have heterogenous genetic risk profiles, potentially reflective of disease subtypes or classification bias (e.g., smokers more likely to be diagnosed with COPD). To better understand differences in genetic loci associated with ICD-defined versus spirometry-defined COPD we contrasted their GWAS results with those for heavy smoking among 337,138 UK Biobank participants. Overlapping risk loci were found in/near the genes ZEB2, FAM136B, CHRNA3, and CHRNA4, with the CHRNA3 locus shared across all three traits. Mediation analysis to estimate the effects of lead genotyped variants mediated by smoking found significant indirect effects for the FAM136B, CHRNA3, and CHRNA4 loci for both COPD definitions. Adjustment for mediator-outcome confounders modestly attenuated indirect effects, though in the CHRNA4 locus for spirometry-defined COPD the proportion mediated increased an additional 8.47%. Our results suggest that differences between ICD-defined and spirometry-defined COPD associated genetic loci are not a result of smoking biasing classification.

Comparison of two multi-trait association testing methods and sequence-based fine mapping of six additive QTL in Swiss Large White pigs

BACKGROUND

Genetic correlations between complex traits suggest that pleiotropic variants contribute to trait variation. Genome-wide association studies (GWAS) aim to uncover the genetic underpinnings of traits. Multivariate association testing and the meta-analysis of summary statistics from single-trait GWAS enable detecting variants associated with multiple phenotypes. In this study, we used array-derived genotypes and phenotypes for 24 reproduction, production, and conformation traits to explore differences between the two methods and used imputed sequence variant genotypes to fine-map six quantitative trait loci (QTL).

RESULTS

We considered genotypes at 44,733 SNPs for 5,753 pigs from the Swiss Large White breed that had deregressed breeding values for 24 traits. Single-trait association analyses revealed eleven QTL that affected 15 traits. Multi-trait association testing and the meta-analysis of the single-trait GWAS revealed between 3 and 6 QTL, respectively, in three groups of traits. The multi-trait methods revealed three loci that were not detected in the single-trait GWAS. Four QTL that were identified in the single-trait GWAS, remained undetected in the multi-trait analyses. To pinpoint candidate causal variants for the QTL, we imputed the array-derived genotypes to the sequence level using a sequenced reference panel consisting of 421 pigs. This approach provided genotypes at 16 million imputed sequence variants with a mean accuracy of imputation of 0.94. The fine-mapping of six QTL with imputed sequence variant genotypes revealed four previously proposed causal mutations among the top variants.

CONCLUSIONS

Our findings in a medium-size cohort of pigs suggest that multivariate association testing and the meta-analysis of summary statistics from single-trait GWAS provide very similar results. Although multi-trait association methods provide a useful overview of pleiotropic loci segregating in mapping populations, the investigation of single-trait association studies is still advised, as multi-trait methods may miss QTL that are uncovered in single-trait GWAS.

De-novo and genome-wide meta-analyses identify a risk haplotype for congenital sensorineural deafness in Dalmatian dogs

Congenital sensorineural deafness (CSD) has been reported to affect up to 30% of Dalmatian dogs world-wide and while unilaterally deaf dogs can live a close to normal life, dogs suffering bilateral deafness are frequently euthanized. Extreme-white coat patterning as encoded by the gene Melanocyte Inducing Transcription Factor (MITF) has long been postulated as the major risk factor for CSD in the Dalmatian breed. While attempts to identify causative risk variants associated with CSD have been numerous, no genome-wide association study has positively identified MITF as a risk locus for either bilateral or unilateral deafness in the Dalmatian breed to date. In this study, we identified an association with CSD on CFA20 in the vicinity of MITF within Australian Dalmatian dogs. Although not genome-wide significant, the association signal was validated by reanalysing publicly available data and merging the wider data resource with the local data to improve statistical power. The merged data, representing three major global populations of Dalmatian dogs, enabled us to identify a single, well-defined genome-wide significant risk haplotype for CSD. The haplotype was formed by three genome-wide significant associated markers (BICF2G630233852T>C, BICF2G630233861T>C, BICF2G630233888G>A) on CFA20 with 62% of bilaterally deaf dogs homozygous for the risk haplotype (CCA), while 30% of bilaterally deaf and 45% of hearing dogs carried one copy of the risk haplotype. Animals homozygous or heterozygous for the low-risk haplotype were less likely to be unilaterally deaf. While the association between the risk haplotype and deafness is incomplete, animals homozygous for the risk haplotype were 10-times more likely to be bilaterally deaf. Although the underlying causative variants are yet to be discovered, results from this study can now assist with reducing deafness in Dalmatian dogs.

Autosomal recessive loci contribute significantly to quantitative variation of male fertility in a dairy cattle population

BACKGROUND

Cattle are ideally suited to investigate the genetics of male fertility. Semen from individual bulls is used for thousands of artificial inseminations for which the fertilization success is monitored. Results from the breeding soundness examination and repeated observations of semen quality complement the fertility evaluation for each bull.

RESULTS

In a cohort of 3881 Brown Swiss bulls that had genotypes at 683,609 SNPs, we reveal four novel recessive QTL for male fertility on BTA1, 18, 25, and 26 using haplotype-based association testing. A QTL for bull fertility on BTA1 is also associated with sperm head shape anomalies. All other QTL are not associated with any of the semen quality traits investigated. We perform complementary fine-mapping approaches using publicly available transcriptomes as well as whole-genome sequencing data of 125 Brown Swiss bulls to reveal candidate causal variants. We show that missense or nonsense variants in SPATA16, VWA3A, ENSBTAG00000006717 and ENSBTAG00000019919 are in linkage disequilibrium with the QTL. Using whole-genome sequence data, we detect strong association (P = 4.83 × 10^- 12) of a missense variant (p.Ile193Met) in SPATA16 with male fertility. However, non-coding variants exhibit stronger association at all QTL suggesting that variants in regulatory regions contribute to variation in bull fertility.

CONCLUSION

Our findings in a dairy cattle population provide evidence that recessive variants may contribute substantially to quantitative variation in male fertility in mammals. Detecting causal variants that underpin variation in male fertility remains difficult because the most strongly associated variants reside in poorly annotated non-coding regions.

Integrating Genome-Wide CNVs Into QTLs and High Confidence GWAScore Regions Identified Positional Candidates for Sheep Economic Traits

Copy number variations (CNVs) are important source of genetic variation, which can affect diverse economic traits through a variety of mechanisms. In addition, genome scan can identify many quantitative trait loci (QTLs) for the economic traits, while genome-wide association studies (GWAS) can localize genetic variants associated with the phenotypic variations. Here, we developed a method called GWAScore which collected GWAS summary data to identify potential candidates, and integrated CNVs into QTLs and high confidence GWAScore regions to detect crucial CNV markers for sheep growth traits. We got 197 candidate genes which were overlapping with the candidate CNVs. Some crucial genes (MYLK3, TTC29, HERC6, ABCG2, RUNX1, etc.) showed significantly elevated GWAScore peaks than other candidate genes. In this study, we developed the GWAScore method to excavate the potential value of candidate genes as markers for the sheep molecular breeding.

Identifying novel associations in GWAS by hierarchical Bayesian latent variable detection of differentially misclassified phenotypes

Background

Heterogeneity in the definition and measurement of complex diseases in Genome-Wide Association Studies (GWAS) may lead to misdiagnoses and misclassification errors that can significantly impact discovery of disease loci. While well appreciated, almost all analyses of GWAS data consider reported disease phenotype values as is without accounting for potential misclassification.

Results

Here, we introduce Phenotype Latent variable Extraction of disease misdiagnosis (PheLEx), a GWAS analysis framework that learns and corrects misclassified phenotypes using structured genotype associations within a dataset. PheLEx consists of a hierarchical Bayesian latent variable model, where inference of differential misclassification is accomplished using filtered genotypes while implementing a full mixed model to account for population structure and genetic relatedness in study populations. Through simulations, we show that the PheLEx framework dramatically improves recovery of the correct disease state when considering realistic allele effect sizes compared to existing methodologies designed for Bayesian recovery of disease phenotypes. We also demonstrate the potential of PheLEx for extracting new potential loci from existing GWAS data by analyzing bipolar disorder and epilepsy phenotypes available from the UK Biobank. From the PheLEx analysis of these data, we identified new candidate disease loci not previously reported for these datasets that have value for supplemental hypothesis generation.

Conclusion

PheLEx shows promise in reanalyzing GWAS datasets to provide supplemental candidate loci that are ignored by traditional GWAS analysis methodologies.

Sources of bias in genomics research of oral and dental traits

Evidence regarding the genomic basis of oral/dental traits and diseases is a fundamental pillar of the emerging notion of precision health. During the last decade, technological advances have improved the feasibility and affordability of conducting genome-wide association studies (GWAS) and studying the associations of emanating data with both common and rare oral conditions. Most evidence thus far emanates from GWAS of dental caries and periodontal disease that have tested the associations of several million single nucleotide polymorphisms (SNPs) with typically binary, health vs. disease phenotypes. GWAS offer advantages over the previous candidate-gene studies, mainly owing to their agnostic (i.e., unbiased, or hypothesis-free) nature. Nevertheless, GWAS are prone to virtually all sources of random and systematic error. Here, we review common sources of bias in genomics research with focus on GWAS including: type I and II errors, population stratification and heterogeneity, selection bias, adjustment for heritable covariates, appropriate reference panels for imputation, and gene annotation. We argue that valid and precise phenotype measurement is a key requirement, as GWAS sample sizes and thus statistical power increase. Finally, we stress that the lack of diversity of populations with phenotypes and genotypes is a major limitation for the generalizability and ultimate translation of the emerging genomics evidence-base into oral health promotion for all.

Reflection on modern methods: five myths about measurement error in epidemiological research

Epidemiologists are often confronted with datasets to analyse which contain measurement error due to, for instance, mistaken data entries, inaccurate recordings and measurement instrument or procedural errors. If the effect of measurement error is misjudged, the data analyses are hampered and the validity of the study's inferences may be affected. In this paper, we describe five myths that contribute to misjudgments about measurement error, regarding expected structure, impact and solutions to mitigate the problems resulting from mismeasurements. The aim is to clarify these measurement error misconceptions. We show that the influence of measurement error in an epidemiological data analysis can play out in ways that go beyond simple heuristics, such as heuristics about whether or not to expect attenuation of the effect estimates. Whereas we encourage epidemiologists to deliberate about the structure and potential impact of measurement error in their analyses, we also recommend exercising restraint when making claims about the magnitude or even direction of effect of measurement error if not accompanied by statistical measurement error corrections or quantitative bias analysis. Suggestions for alleviating the problems or investigating the structure and magnitude of measurement error are given.

Longitudinal RNA-Seq Analysis of the Repeatability of Gene Expression and Splicing in Human Platelets Identifies a Platelet SELP Splice QTL

RATIONALE

Longitudinal studies are required to distinguish within versus between-individual variation and repeatability of gene expression. They are uniquely positioned to decipher genetic signal from environmental noise, with potential application to gene variant and expression studies. However, longitudinal analyses of gene expression in healthy individuals-especially with regards to alternative splicing-are lacking for most primary cell types, including platelets.

OBJECTIVE

To assess repeatability of gene expression and splicing in platelets and use repeatability to identify novel platelet expression quantitative trait loci (QTLs) and splice QTLs.

METHODS AND RESULTS

We sequenced the transcriptome of platelets isolated repeatedly up to 4 years from healthy individuals. We examined within and between individual variation and repeatability of platelet RNA expression and exon skipping, a readily measured alternative splicing event. We find that platelet gene expression is generally stable between and within-individuals over time-with the exception of a subset of genes enriched for the inflammation gene ontology. We show an enrichment among repeatable genes for associations with heritable traits, including known and novel platelet expression QTLs. Several exon skipping events were also highly repeatable, suggesting heritable patterns of splicing in platelets. One of the most repeatable was exon 14 skipping of SELP. Accordingly, we identify rs6128 as a platelet splice QTL and define an rs6128-dependent association between SELP exon 14 skipping and race. In vitro experiments demonstrate that this single nucleotide variant directly affects exon 14 skipping and changes the ratio of transmembrane versus soluble P-selectin protein production.

CONCLUSIONS

We conclude that the platelet transcriptome is generally stable over 4 years. We demonstrate the use of repeatability of gene expression and splicing to identify novel platelet expression QTLs and splice QTLs. rs6128 is a platelet splice QTL that alters SELP exon 14 skipping and soluble versus transmembrane P-selectin protein production.

Growth Performance and Meat Quality Evaluations in Three-Way Cross Cattle Developed for the Tibetan Plateau and their Molecular Understanding by Integrative Omics Analysis

Despite of favorable characteristics of high protein, low fat, and free-pollution, yak meat has intrinsically poor performance in tenderness and color, which is ever challenging yak sector. To this end, a three-way cross system was first developed for high quality beef of the Tibetan Plateau using Angus cattle ( Bos taurus) as terminal sire to mate with 1/2 yak (F1) generated from♂Qaidam cattle ( Bos taurus) × ♀yak ( Bos grunniens). The withers height, chest girth, and body weight of 1/4 yak (F2) were all great higher than that of yak and 1/2 yak ( P < 0.01), especially at later period, suggesting the faster growth rate of 1/4 yak. Also the dressing percentage was much better in 1/4 yak ( P < 0.01). Tenderness and meat color were both significantly improved in 1/4 yak with some unpleasant sacrifice of PUFAs, such as EPA and DHA, and meat protein, given the significantly lower shear force and higher L* ( P < 0.01). A total of 769 genes, including SREBF1, GHR, and FASN, the widely recognized causal genes of meat quality, were identified from 11947 differently expressed genes by the data integration of transcriptome, GWAS and QTL. These genes were significantly enriched for important pathway and GO terms, such as insulin signaling pathway, fatty acid biosynthesis, calcium signaling pathway, metabolic pathway, and cellular response to stress ( P < 0.01). And 12 promising candidates were exemplified with annotation of H3K4me3 data from divergent meat quality, such as OSTF1, NRAS1, and KCNJ11. Interestingly, 75 high-altitude adaptive candidate genes were also detected in the list. This study is a first step toward high quality beef of the Tibetan Plateau and provides useful information for their molecular understanding.

Evaluation of the accuracy of imputed sequence variant genotypes and their utility for causal variant detection in cattle

Background

The availability of dense genotypes and whole-genome sequence variants from various sources offers the opportunity to compile large datasets consisting of tens of thousands of individuals with genotypes at millions of polymorphic sites that may enhance the power of genomic analyses. The imputation of missing genotypes ensures that all individuals have genotypes for a shared set of variants.

Results

We evaluated the accuracy of imputation from dense genotypes to whole-genome sequence variants in 249 Fleckvieh and 450 Holstein cattle using Minimac and FImpute. The sequence variants of a subset of the animals were reduced to the variants that were included on the Illumina BovineHD genotyping array and subsequently inferred in silico using either within- or multi-breed reference populations. The accuracy of imputation varied considerably across chromosomes and dropped at regions where the bovine genome contains segmental duplications. Depending on the imputation strategy, the correlation between imputed and true genotypes ranged from 0.898 to 0.952. The accuracy of imputation was higher with Minimac than FImpute particularly for variants with a low minor allele frequency. Using a multi-breed reference population increased the accuracy of imputation, particularly when FImpute was used to infer genotypes. When the sequence variants were imputed using Minimac, the true genotypes were more correlated to predicted allele dosages than best-guess genotypes. The computing costs to impute 23,256,743 sequence variants in 6958 animals were ten-fold higher with Minimac than FImpute. Association studies with imputed sequence variants revealed seven quantitative trait loci (QTL) for milk fat percentage. Two causal mutations in the DGAT1 and GHR genes were the most significantly associated variants at two QTL on chromosomes 14 and 20 when Minimac was used to infer genotypes.

Conclusions

The population-based imputation of millions of sequence variants in large cohorts is computationally feasible and provides accurate genotypes. However, the accuracy of imputation is low in regions where the genome contains large segmental duplications or the coverage with array-derived single nucleotide polymorphisms is poor. Using a reference population that includes individuals from many breeds increases the accuracy of imputation particularly at low-frequency variants. Considering allele dosages rather than best-guess genotypes as explanatory variables is advantageous to detect causal mutations in association studies with imputed sequence variants.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-017-0301-x) contains supplementary material, which is available to authorized users.

Genetic Research and Women's Heart Disease: a Primer

PURPOSE OF REVIEW

This review provides a brief synopsis of sexual dimorphism in atherosclerosis with an emphasis on genetic studies aimed to better understand the atherosclerotic process and clinical outcomes in women. Such studies are warranted because development of atherosclerosis, impact of several traditional risk factors, and burden of coronary heart disease (CHD) differ between women and men.

RECENT FINDINGS

While most candidate gene studies pool women and men and adjust for sex, some sex-specific studies provide evidence of association between candidate genes and prevalent and incident CHD in women. So far, most genome-wide association studies (GWAS) also failed to consider sex-specific associations. The few GWAS focused on women tended to have small sample sizes and insufficient power to reject the null hypothesis of no association even if associations exist. Few studies consider that sex can modify the effect of gene variants on CHD. Sufficiently large-scale genetic studies in women of different race/ethnic groups, taking into account possible gene-gene and gene-environment interactions as well as hormone-mediated epigenetic mechanisms, are needed. Using the same disease definition for women and men might not be appropriate. Accurate phenotyping and inclusion of relevant outcomes in women, together with targeting the entire spectrum of atherosclerosis, could help address the contribution of genes to sexual dimorphism in atherosclerosis. Discovered genetic loci should be taken forward for replication and functional studies to elucidate the plausible underlying biological mechanisms. A better understanding of the etiology of atherosclerosis in women would facilitate future prevention efforts and interventions.

Development and application of a livestock phenomics platform to enhance productivity and efficiency at pasture

Our capacity to measure performance- and efficiency-related phenotypes in grazing livestock in a timely manner, ideally in real-time without human interference, has been severely limited. Future demands and constraints on grazing livestock production will require a step change beyond our current approaches to obtaining phenotypic data. Animal phenomics is a relatively new term that describes the next generation of animal trait measurement, including methodologies and equipment used to acquire data on traits, and computational approaches required to turn data into phenotypic information. Phenomics offers a range of emerging opportunities to define new traits specific to grazing livestock, including intake and efficiency at pasture, and to measure many traits simultaneously or at a level of detail previously unachievable in the grazing environment. Application of this approach to phenotyping can improve the precision with which nutritional and other management strategies are applied, enable development of predictive biological traits, and accelerate the rate at which genetic gain is achieved for existing and new traits. In the present paper, we briefly outline the potential for livestock phenomics and describe (1) on-animal sensory-based approaches to develop traits diagnostic of productivity and efficiency, as well as resilience, health and welfare and (2) on-farm methods for data collection that drive management solutions to reduce input costs and accelerate genetic gain. The technological and analytical challenges associated with these objectives are also briefly considered, along with a brief overview of a promising field of work in which phenomics will affect animal agriculture, namely efficiency at pasture.

Limitations of GCTA as a solution to the missing heritability problem

Genome-wide association studies (GWASs) seek to understand the relationship between complex phenotype(s) (e.g., height) and up to millions of single-nucleotide polymorphisms (SNPs). Early analyses of GWASs are commonly believed to have "missed" much of the additive genetic variance estimated from correlations between relatives. A more recent method, genome-wide complex trait analysis (GCTA), obtains much higher estimates of heritability using a model of random SNP effects correlated between genotypically similar individuals. GCTA has now been applied to many phenotypes from schizophrenia to scholastic achievement. However, recent studies question GCTA's estimates of heritability. Here, we show that GCTA applied to current SNP data cannot produce reliable or stable estimates of heritability. We show first that GCTA depends sensitively on all singular values of a high-dimensional genetic relatedness matrix (GRM). When the assumptions in GCTA are satisfied exactly, we show that the heritability estimates produced by GCTA will be biased and the standard errors will likely be inaccurate. When the population is stratified, we find that GRMs typically have highly skewed singular values, and we prove that the many small singular values cannot be estimated reliably. Hence, GWAS data are necessarily overfit by GCTA which, as a result, produces high estimates of heritability. We also show that GCTA's heritability estimates are sensitive to the chosen sample and to measurement errors in the phenotype. We illustrate our results using the Framingham dataset. Our analysis suggests that results obtained using GCTA, and the results' qualitative interpretations, should be interpreted with great caution.

Quantitative measurement of odor detection thresholds using an air dilution olfactometer, and association with genetic variants in a sample of diverse ancestry

Genetic association studies require a quantitative and reliable method for odor threshold assessment in order to examine the contribution of genetic variants to complex olfactory phenotypes. Our main goal was to assess the feasibility of a portable Scentroid air dilution olfactometer for use in such studies. Using the Scentroid SM110C and the SK5 n-butanol Sensitivity Kit (IDES Canada Inc.), n-butanol odor thresholds were determined for 182 individuals of diverse ancestry (mean age: 20.4 ± 2.5 years; n = 128 female; n = 54 male). Threshold scores from repeat participants were used to calculate a test–retest reliability coefficient, which was statistically significant (r = 0.754, p < 0.001, n = 29), indicating that the Scentroid provides reliable estimates of odor thresholds. In addition, we performed a preliminary genetic analysis evaluating the potential association of n-butanol odor thresholds to six single-nucleotide polymorphisms (SNPs) putatively involved in general olfactory sensitivity (GOS). The results of multiple linear regression analysis revealed no significant association between the SNPs tested and threshold scores. However, our sample size was relatively small, and our study was only powered to identify genetic markers with strong effects on olfactory sensitivity. Overall, we find that the Scentroid provides reliable quantitative measures of odor detection threshold and is well suited for genetic studies of olfactory sensitivity.

Dark matter RNA illuminates the puzzle of genome-wide association studies

In the past decade, numerous studies have made connections between sequence variants in human genomes and predisposition to complex diseases. However, most of these variants lie outside of the charted regions of the human genome whose function we understand; that is, the sequences that encode proteins. Consequently, the general concept of a mechanism that translates these variants into predisposition to diseases has been lacking, potentially calling into question the validity of these studies. Here we make a connection between the growing class of apparently functional RNAs that do not encode proteins and whose function we do not yet understand (the so-called 'dark matter' RNAs) and the disease-associated variants. We review advances made in a different genomic mapping effort - unbiased profiling of all RNA transcribed from the human genome - and provide arguments that the disease-associated variants exert their effects via perturbation of regulatory properties of non-coding RNAs existing in mammalian cells.

Impact of measurement error on testing genetic association with quantitative traits

Measurement error of a phenotypic trait reduces the power to detect genetic associations. We examined the impact of sample size, allele frequency and effect size in presence of measurement error for quantitative traits. The statistical power to detect genetic association with phenotype mean and variability was investigated analytically. The non-centrality parameter for a non-central F distribution was derived and verified using computer simulations. We obtained equivalent formulas for the cost of phenotype measurement error. Effects of differences in measurements were examined in a genome-wide association study (GWAS) of two grading scales for cataract and a replication study of genetic variants influencing blood pressure. The mean absolute difference between the analytic power and simulation power for comparison of phenotypic means and variances was less than 0.005, and the absolute difference did not exceed 0.02. To maintain the same power, a one standard deviation (SD) in measurement error of a standard normal distributed trait required a one-fold increase in sample size for comparison of means, and a three-fold increase in sample size for comparison of variances. GWAS results revealed almost no overlap in the significant SNPs (p<10⁻⁵) for the two cataract grading scales while replication results in genetic variants of blood pressure displayed no significant differences between averaged blood pressure measurements and single blood pressure measurements. We have developed a framework for researchers to quantify power in the presence of measurement error, which will be applicable to studies of phenotypes in which the measurement is highly variable.

Genome wide association studies in presence of misclassified binary responses

BACKGROUND

Misclassification has been shown to have a high prevalence in binary responses in both livestock and human populations. Leaving these errors uncorrected before analyses will have a negative impact on the overall goal of genome-wide association studies (GWAS) including reducing predictive power. A liability threshold model that contemplates misclassification was developed to assess the effects of mis-diagnostic errors on GWAS. Four simulated scenarios of case-control datasets were generated. Each dataset consisted of 2000 individuals and was analyzed with varying odds ratios of the influential SNPs and misclassification rates of 5% and 10%.

RESULTS

Analyses of binary responses subject to misclassification resulted in underestimation of influential SNPs and failed to estimate the true magnitude and direction of the effects. Once the misclassification algorithm was applied there was a 12% to 29% increase in accuracy, and a substantial reduction in bias. The proposed method was able to capture the majority of the most significant SNPs that were not identified in the analysis of the misclassified data. In fact, in one of the simulation scenarios, 33% of the influential SNPs were not identified using the misclassified data, compared with the analysis using the data without misclassification. However, using the proposed method, only 13% were not identified. Furthermore, the proposed method was able to identify with high probability a large portion of the truly misclassified observations.

CONCLUSIONS

The proposed model provides a statistical tool to correct or at least attenuate the negative effects of misclassified binary responses in GWAS. Across different levels of misclassification probability as well as odds ratios of significant SNPs, the model proved to be robust. In fact, SNP effects, and misclassification probability were accurately estimated and the truly misclassified observations were identified with high probabilities compared to non-misclassified responses. This study was limited to situations where the misclassification probability was assumed to be the same in cases and controls which is not always the case based on real human disease data. Thus, it is of interest to evaluate the performance of the proposed model in that situation which is the current focus of our research.

Evidence for pleiotropism and recent selection in the PLAG1 region in Australian Beef cattle

A putative functional mutation (rs109231213) near PLAG1 (BTA14) associated with stature was studied in beef cattle. Data from 8199 Bos taurus, Bos indicus and Tropical Composite cattle were used to test the associations between rs109231213 and various phenotypes. Further, 23 496 SNPs located on BTA14 were tested for association with these phenotypes, both independently and fitted together with rs109231213. The C allele of rs109231213 significantly increased hip height, weight, net food intake, age at puberty in males and females and decreased IGF-I concentration in blood and fat depth. When rs109231213 was fitted as a fixed effect in the model, there was an overall reduction in associations between other SNPs and these traits but some SNPs remained associated (P < 10(-4) ). Frequency of the mutant C allele of rs109231213 differed among B. indicus (0.52), B. taurus (0.96) and Tropical Composite (0.68). Most chromosomes carrying the C allele had the same surrounding 10 SNP haplotype, probably because the C allele was introgressed into Brahman from B. taurus cattle. A region of reduced heterozygosity surrounds the C allele; this is small in B. taurus but 20 Mb long in Brahmans, indicating recent and strong selection for the mutant allele. Thus, the C allele appears to mark a mutation that has been selected almost to fixation in the B. taurus breeds studied here and introduced into Brahman cattle during grading up and selected to a frequency of 0.52 despite its negative effects on fertility.

Density estimation of a biomedical variable subject to measurement error using an auxiliary set of replicate observations

Correcting for measurement error when estimating the density of a routinely collected biomedical variable is an important issue when describing reference values for both healthy and pathological states. The present work addresses the problem of estimating the density of a biomedical variable observed with measurement error without any a priori knowledge on the error density. Assuming the availability of a sample of replicate observations, either internal or external, which is generally easily obtained in clinical settings, we propose an estimator based on the non-parametric deconvolution theory with an adaptive procedure for cutoff selection, the replicates being used for an estimation of the error density. We illustrate this approach in two applicative examples: (i) the systolic blood pressure distribution density, using the Framingham Study data set, and (ii) the distribution of the timing of onset of pregnancy within the female cycle, using ultrasound measurements in the first trimester of pregnancy.

Haplotype analysis improved evidence for candidate genes for intramuscular fat percentage from a genome wide association study of cattle

In genome wide association studies (GWAS), haplotype analyses of SNP data are neglected in favour of single point analysis of associations. In a recent GWAS, we found that none of the known candidate genes for intramuscular fat (IMF) had been identified. In this study, data from the GWAS for these candidate genes were re-analysed as haplotypes. First, we confirmed that the methodology would find evidence for association between haplotypes in candidate genes of the calpain-calpastatin complex and musculus longissimus lumborum peak force (LLPF), because these genes had been confirmed through single point analysis in the GWAS. Then, for intramuscular fat percent (IMF), we found significant partial haplotype substitution effects for the genes ADIPOQ and CXCR4, as well as suggestive associations to the genes CEBPA, FASN, and CAPN1. Haplotypes for these genes explained 80% more of the phenotypic variance compared to the best single SNP. For some genes the analyses suggested that there was more than one causative mutation in some genes, or confirmed that some causative mutations are limited to particular subgroups of a species. Fitting the SNPs and their interactions simultaneously explained a similar amount of the phenotypic variance compared to haplotype analyses. Haplotype analysis is a neglected part of the suite of tools used to analyse GWAS data, would be a useful method to extract more information from these data sets, and may contribute to reducing the missing heritability problem.