The effects of genotype-by-environment interactions on body condition score across three winter supplemental feed environments in a composite beef cattle breed in Montana

Cattle operations in the Northern Great Plains region of the United States face extreme cold weather conditions and require nutritional supplementation over the winter season in order for animals to maintain body condition. In cow-calf operations, body condition scores (BCS) measured at calving and breeding have been shown to be associated with several economically important health and fertility traits, so maintenance of BCS is both an animal welfare and economic concern. A low-to-medium heritability has been found for BCS when measured across various production stages, indicating a large environmental influence but sufficient genetic basis for selection. The present study evaluated BCS measured prior to calving (late winter) and breeding (early summer) under three winter supplementation environments in a multitrait linear mixed model. Traits were discretized by winter supplementation and genetic correlations between environments were considered a reflection of evidence for genotype-by-environment interactions between BCS and diet. Winter supplementation treatments were fed October through April and varied by range access and protein content: 1) feedlot environment with approximately 15% crude protein (CP) corn/silage diet, 2) native rangeland access with 1.8 kg of an 18% CP pellet supplement, and 3) native rangeland access with a self-fed 50% CP and mineral supplement. A total of 2,988 and 2,353 records were collected across multiple parities on 1,010 and 800 individuals for prebreeding and precalving BCS, respectively. Heifers and cows came from a composite beef cattle breed developed and maintained by the USDA Fort Keogh Livestock and Range Research Laboratory near Miles City, Montana. Genetic correlations between treatments 1 and 2, 1 and 3, and 2 and 3 were 0.98, 0.78, and 0.65 and 1.00, 0.98, and 0.99 for precalving and prebreeding BCS, respectively. This provides moderate evidence of genotype-by-environment interactions for precalving BCS under treatment 3 relative to treatments 1 and 2, but no evidence for genotype-by-environment interactions for prebreeding BCS. Treatment 3 differed substantially in CP content relative to treatments 1 and 2, indicating that some animals differ in their ability to maintain BCS up to spring calving across a protein gradient. These results indicate the potential for selection of animals with increased resilience under cold weather conditions and high protein, restricted energy diets to maintain BCS.

Fuzzy Logic as a Strategy for Combining Marker Statistics to Optimize Preselection of High-Density and Sequence Genotype Data

The high dimensionality of genotype data available for genomic evaluations has presented a motivation for developing strategies to identify subsets of markers capable of increasing the accuracy of predictions compared to the current commercial single nucleotide polymorphism (SNP) chips. In this simulation study, an algorithm for combining statistics used in the preselection and prioritization of SNP markers from a high-density panel (1.3 million SNPs) into a composite "fuzzy" ranking score based on a Sugeno-type fuzzy inference system (FIS) was developed and evaluated for performance in preselection for genomic predictions. FST scores, and p-values were evaluated as inputs for the FIS. The accuracy of genomic predictions for fuzzy-score-preselected panel sizes of 1-50 k SNPs ranged from -0.4-11.7 and -0.3-3.8% higher than FST and p-value preselection, respectively. Though gains in prediction accuracies using only two inputs to the FIS were modest, preselection based on fuzzy scores yielded more accurate predictions than both FST scores and p-values for the majority of evaluated panel sizes under all genetic architectures. FIS have the potential to aggregate information from multiple criteria that reflect SNP-trait associations and biological relevance in a flexible and efficient way to yield higher quality genomic predictions.

39 Combining Different Marker Prioritization Methods in the Analysis of High-density and Sequence Data

High-density and sequence genotypes were expected to increase accuracy of genomic predictions through inclusion of markers in high linkage disequilibrium with causal loci, yet the realized increase has been minimal. Marker preselection has been proposed as a strategy to prioritize the most relevant markers to reduce the dimensionality of the association model and potentially increase accuracy. Strength of association statistics (estimated effect, p-value) and population differentiation measurements (FST score) have both been explored as criteria for preselection, but sensitivity to identify relevant markers decreases as random noise exceeds true signal variation. Combining both criteria into an index would leverage the unique contributions of each criterion and potentially increase prediction accuracies. A simulation consisting of 200 QTL, 777k SNP, and 7 generations under selection was generated (10 replicates). Marker preselection was compared across three criteria: only estimated effect (EFF), only FST score (FST), or an index combining the two previous statistics (COMB). In the COMB scenario, markers from genomic regions with high correlation (>0.7) between estimated effect and FST score were selected along with markers whose estimated effect or FST score exceeded a certain threshold. Across replicates, COMB identified additional markers tagging between 1 and 7 QTL not tagged by EFF or FST that explain 0.2–5.4% of the genetic variance. The highest accuracy for EFF and FST was 0.76 and 0.73 when preselecting 2k and 10k markers, respectively. Under the best-case scenario (3,297 preselected markers), COMB improved accuracy by less than 1% and 4% compared to EFF and FST scenarios, respectively. Though an index combining multiple statistics may increase the number of QTL tagged by preselected markers and genetic variance explained relative to single-statistic preselection, this does not necessarily translate to a meaningful increase in accuracy. However, the results are dependent on the indexing method.

PSXI-7 Genomic analysis of heterosis in a composite beef cattle breed

Crossbreeding is widely used in the beef cattle industry to exploit benefits of heterosis. This study evaluated the effects of heterozygosity on growth traits in an Angus x Hereford cross population. Moreover, a genome wide association study was conducted to detect regions in the genome with significant dominance effects on growth traits contributing to heterosis. A total of 1,530 animals, comprised of pure Line 1 Hereford, Angus and Angus x Line 1 Hereford crosses, were evaluated. Phenotypes included birth weight, weaning weight and yearling weight. All animals were genotyped with GeneSeek GGP LD 50k. Effects of genomic heterozygosity on growth traits were estimated. These effects were -0.76 kg (P < 0.001), 4.67 kg (P < 0.0001), 42.39 kg (P < 0.02) on birth weight, weaning weight and yearling weight respectively. A genome wide association study revealed several SNP markers with significant heterotic effects associated with birth weight, weaning weight and yearling weight. These SNP markers were located on chromosomes 1, 2, 14, 19, 13 and 12. Genes in these regions were reported to be involved in growth and other important physiological mechanisms. Our study revealed several regions associated with dominance effects and contributing to heterosis. These results could be beneficial in optimizing crossbreeding.

Dissection of the impact of prioritized QTL-linked and -unlinked SNP markers on the accuracy of genomic selection1

Background

Use of genomic information has resulted in an undeniable improvement in prediction accuracies and an increase in genetic gain in animal and plant genetic selection programs in spite of oversimplified assumptions about the true biological processes. Even for complex traits, a large portion of markers do not segregate with or effectively track genomic regions contributing to trait variation; yet it is not clear how genomic prediction accuracies are impacted by such potentially nonrelevant markers. In this study, a simulation was carried out to evaluate genomic predictions in the presence of markers unlinked with trait-relevant QTL. Further, we compared the ability of the population statistic F_ST and absolute estimated marker effect as preselection statistics to discriminate between linked and unlinked markers and the corresponding impact on accuracy.

Results

We found that the accuracy of genomic predictions decreased as the proportion of unlinked markers used to calculate the genomic relationships increased. Using all, only linked, and only unlinked marker sets yielded prediction accuracies of 0.62, 0.89, and 0.22, respectively. Furthermore, it was found that prediction accuracies are severely impacted by unlinked markers with large spurious associations. F_ST-preselected marker sets of 10 k and larger yielded accuracies 8.97 to 17.91% higher than those achieved using preselection by absolute estimated marker effects, despite selecting 5.1 to 37.7% more unlinked markers and explaining 2.4 to 5.0% less of the genetic variance. This was attributed to false positives selected by absolute estimated marker effects having a larger spurious association with the trait of interest and more negative impact on predictions. The Pearson correlation between F_ST scores and absolute estimated marker effects was 0.77 and 0.27 among only linked and only unlinked markers, respectively. The sensitivity of F_ST scores to detect truly linked markers is comparable to absolute estimated marker effects but the consistency between the two statistics regarding false positives is weak.

Conclusion

Identification and exclusion of markers that have little to no relevance to the trait of interest may significantly increase genomic prediction accuracies. The population statistic F_ST presents an efficient and effective tool for preselection of trait-relevant markers.

Grid search approach to discriminate between old and recent inbreeding using phenotypic, pedigree and genomic information

Background

Although inbreeding caused by the mating of animals related through a recent common ancestor is expected to have more harmful effects on phenotypes than ancient inbreeding (old inbreeding), estimating these effects requires a clear definition of recent (new) and ancient (old) inbreeding. Several methods have been proposed to classify inbreeding using pedigree and genomic data. Unfortunately, these methods are largely based on heuristic criteria such as the number of generations from a common ancestor or length of runs of homozygosity (ROH) segments. To mitigate these deficiencies, this study aimed to develop a method to classify pedigree and genomic inbreeding into recent and ancient classes based on a grid search algorithm driven by the assumption that new inbreeding tends to have a more pronounced detrimental effect on traits. The proposed method was tested using a cattle population characterized by a deep pedigree.

Results

Effects of recent and ancient inbreeding were assessed on four growth traits (birth, weaning and yearling weights and average daily gain). Thresholds to classify inbreeding into recent and ancient classes were trait-specific and varied across traits and sources of information. Using pedigree information, inbreeding generated in the last 10 to 11 generations was considered as recent. When genomic information (ROH) was used, thresholds ranged between four to seven generations, indicating, in part, the ability of ROH segments to characterize the harmful effects of inbreeding in shorter periods of time. Nevertheless, using the proposed classification method, the discrimination between new and old inbreeding was less robust when ROH segments were used compared to pedigree. Using several model comparison criteria, the proposed approach was generally better than existing methods. Recent inbreeding appeared to be more harmful across the growth traits analyzed. However, both new and old inbreeding were found to be associated with decreased yearling weight and average daily gain.

Conclusions

The proposed method provided a more objective quantitative approach for the classification of inbreeding. The proposed method detected a clear divergence in the effects of old and recent inbreeding using pedigree data and it was superior to existing methods for all analyzed traits. Using ROH data, the discrimination between old and recent inbreeding was less clear and the proposed method was superior to existing approaches for two out of the four analyzed traits. Deleterious effects of recent inbreeding were detected sooner (fewer generations) using genomic information than pedigree. Difference in the results using genomic and pedigree information could be due to the dissimilarity in the number of generations to a common ancestor. Additionally, the uncertainty associated with the identification of ROH segments and associated inbreeding could have an effect on the results. Potential biases in the estimation of inbreeding effects may occur when new and old inbreeding are discriminated based on arbitrary thresholds. To minimize the impact of inbreeding, mating designs should take the different inbreeding origins into consideration.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07872-z.

Effects of management decisions on genetic evaluation of simulated calving records using random regression

The objective of this study was to evaluate the effects of various data structures on the genetic evaluation for the binary phenotype of reproductive success. The data were simulated based on an existing pedigree and an underlying fertility phenotype with a heritability of 0.10. A data set of complete observations was generated for all cows. This data set was then modified mimicking the culling of cows when they first failed to reproduce, cows having a missing observation at either their second or fifth opportunity to reproduce as if they had been selected as donors for embryo transfer, and censoring records following the sixth opportunity to reproduce as in a cull-for-age strategy. The data were analyzed using a third-order polynomial random regression model. The EBV of interest for each animal was the sum of the age-specific EBV over the first 10 observations (reproductive success at ages 2-11). Thus, the EBV might be interpreted as the genetic expectation of number of calves produced when a female is given 10 opportunities to calve. Culling open cows resulted in the EBV for 3-yr-old cows being reduced from 8.27 ± 0.03 when open cows were retained to 7.60 ± 0.02 when they were culled. The magnitude of this effect decreased as cows grew older when they first failed to reproduce and were subsequently culled. Cows that did not fail over the 11 yr of simulated data had an EBV of 9.43 ± 0.01 and 9.35 ± 0.01 based on analyses of the complete data and the data in which cows that failed to reproduce were culled, respectively. Cows that had a missing observation for their second record had a significantly reduced EBV, but the corresponding effect at the fifth record was negligible. The current study illustrates that culling and management decisions, and particularly those that affect the beginning of the trajectory of sustained reproductive success, can influence both the magnitude and accuracy of resulting EBV.

Runs of homozygosity and analysis of inbreeding depression

Pedigree information was traditionally used to assess inbreeding. The availability of high-density marker panels provides an alternative to assess inbreeding, particularly in the presence of incomplete and error-prone pedigrees. Assessment of autozygosity across chromosomal segments using runs of homozygosity (ROH) has emerged as a valuable tool to estimate inbreeding due to its general flexibility and ability to quantify the chromosomal contribution to genome-wide inbreeding. Unfortunately, the identification of ROH segments is sensitive to the parameters used during the search process. These parameters are heuristically set, leading to significant variation in the results. The minimum length required to identify an ROH segment has major effects on the estimation of inbreeding and inbreeding depression, yet it is arbitrarily set. To overcome this limitation, a search algorithm to approximate mutation enrichment was developed to determine the minimum length of ROH segments. It consists of finding genome segments with significant effect differences in trait means between animals with high and low burdens of autozygous intervals with a specific length. The minimum length could be determined heuristically as the smallest interval at which a significant signal is detected. The proposed method was tested in an inbred Hereford cattle population genotyped for 30,220 SNPs. Phenotypes recorded for six traits were used for the approximation of mutation loads. The estimated minimum length was around 1 Mb for yearling weight (YW) and average daily gain (ADG) and 4 Mb for birth weight and weaning weight. These trait-specific thresholds estimated using the proposed method could be attributed to a trait-dependent effect of homozygosity. The detection of significant inbreeding effects was well aligned with the estimated thresholds, especially for YW and ADG. Although highly deleterious alleles are expected to be more frequent in recent inbreeding (long ROH), short ROH segments (<5 Mb) could contain a large number of less deleterious mutations with substantial joint effects on some traits (YW and ADG). Our results highlight the importance of accurate estimation of the ROH-based inbreeding and the necessity to consider a trait-specific minimum length threshold for the identification of ROH segments in inbreeding depression analyses. These thresholds could be determined using the proposed method provided the availability of phenotypic information.

23 Current progress in the Agricultural Research Service Beef Grand Challenge: A large-scale genetics by environment by management evaluation project

The Agricultural Research Service (ARS) Beef Grand Challenge is a cooperative, multidisciplinary effort evaluating differences in performance of genetic lines across production environments representative of different geographical regions. Weaned spring-born calves (n = 120 per location), representing natural service matings to Angus, Hereford, Simmental, Charolais, or indicus-composite (Beefmaster or Brangus) bulls from the U.S. Meat Animal Research Center Germplasm Evaluation program in south central Nebraska, are sent to wheat pasture (central Oklahoma) and winter range (eastern Montana), and weaned fall-born calves (n=40 per location) are sent to summer grazing on shortgrass prairie (northeastern Colorado) and southern mixed-grass rangeland (western Oklahoma). All cattle are fed a finishing ration representative of the region that approximately matches energy content across locations. Each calving season has a matching counterpart of calves that remain in Nebraska on a calf-fed drylot program (receiving ration followed by longer finishing ration). Breeds and sires are represented equally, to the extent possible, at each location. To detect differences in breed effects at each location and average over yearly variation, the study is being replicated for 4 years. Weights, stress measures, carcass composition (marbling, yield grade, quality grade, etc.), steak tenderness and steak fatty acid composition are collected from each location. Additionally, rumen metagenomic composition, metagenomic samples, preharvest food safety samples, and feed intake measures are collected at some locations. Grazing impacts and supplemental range feeding are also being evaluated. One year of sampling has been completed, with numeric differences observed for marbling and tenderness as well as growth performance among locations. Statistical differences will be evaluated when replicate years are collected. The USDA is an equal opportunity provider and employer.

A Weighted Genomic Relationship Matrix Based on Fixation Index (FST) Prioritized SNPs for Genomic Selection

A dramatic increase in the density of marker panels has been expected to increase the accuracy of genomic selection (GS), unfortunately, little to no improvement has been observed. By including all variants in the association model, the dimensionality of the problem should be dramatically increased, and it could undoubtedly reduce the statistical power. Using all Single nucleotide polymorphisms (SNPs) to compute the genomic relationship matrix (G) does not necessarily increase accuracy as the additive relationships can be accurately estimated using a much smaller number of markers. Due to these limitations, variant prioritization has become a necessity to improve accuracy. The fixation index (F_ST) as a measure of population differentiation has been used to identify genome segments and variants under selection pressure. Using prioritized variants has increased the accuracy of GS. Additionally, F_ST can be used to weight the relative contribution of prioritized SNPs in computing G. In this study, relative weights based on F_ST scores were developed and incorporated into the calculation of G and their impact on the estimation of variance components and accuracy was assessed. The results showed that prioritizing SNPs based on their F_ST scores resulted in an increase in the genetic similarity between training and validation animals and improved the accuracy of GS by more than 5%.

Multigenerational Effects

Environmental influences resulting in epigenetic mediation of gene expression can affect multiple generations via direct effect (first generation); direct or maternally mediated effects on the fetus (second generation), or gonadal cell lines of the fetus (third generation) when pregnant animals are exposed to the stimuli; and through generational inheritance. The cumulative effects are rapid changes in phenotypic characteristics of the population when compared with rate of phenotypic change from genetic selection. With extensive data collection, significant potential exists to propagate desired characteristics in the livestock industry through epigenetic pathways.

Inbreeding depression in line 1 Hereford cattle population using pedigree and genomic information

This study aimed at assessing inbreeding and its effect on growth and fertility traits using the longtime closed line 1 Hereford cattle population. Inbreeding was estimated based on pedigree (FPED) and genomic information. For the latter, three estimates were derived based on the diagonal elements of the genomic relationship matrix using estimated (FGRM) or fixed (FGRM0.5) minor allele frequencies or runs of homozygosity (ROH) (FROH). A pedigree containing 10,186 animals was used to calculate FPED. Genomic inbreeding was evaluated using 785 animals genotyped for 30,810 SNP. Traits analyzed were birth weight (BWT), weaning weight (WWT), yearling weight (YWT), ADG, and age at first calving (AFC). The number of ROH per animal ranged between 6 and 119 segments with an average of 83. The shortest and longest segments were 1.36 and 64.86 Mb long, respectively, reflecting both ancient and recent inbreeding occurring in the last 30 to 40 generations. The average inbreeding was 29.2%, 16.1%, 30.2%, and 22.9% for FPED, FGRM, FGRM0.5, and FROH, respectively. FROH provided the highest correlations with FPED (r = 0.66). Across paternal half-sib families, with minimal variation in FPED, there were substantial variations in their genomic inbreeding. Inbreeding depression analyses showed that a 1% increase in an animal's FPED resulted in a decrease of 1.20 kg, 2.03 kg, and 0.004 kg/d in WWT, YWT, and ADG, respectively. Maternal inbreeding showed significantly negative effects on progeny growth performance. AFC increased by 1.4 and 0.8 d for each 1% increase in FPED of the cow and her dam, respectively. Using genomic inbreeding, similar impact on growth traits was observed although the magnitude of the effect varied between methods. Across all genomic measures, WWT, YWT, and ADG decreased by 0.21 to 0.53 kg, 0.46 to 1.13 kg, and 0.002 to 0.006 kg/d for each 1% increase in genomic inbreeding, respectively. Four chromosomes (9, 12, 17, and 27) were identified to have a significant association between their homozygosity (FROH-CHR) and growth traits. Variability in genomic inbreeding could be useful when deciding between full and half-sib selection candidates. Despite the high level of inbreeding in this study, its negative impact on growth performance was not as severe as expected, which may be attributed to the purging of the deleterious alleles due to natural or artificial selection over time.

A Bayesian approach for analysis of ordered categorical responses subject to misclassification

Ordinal categorical responses are frequently collected in survey studies, human medicine, and animal and plant improvement programs, just to mention a few. Errors in this type of data are neither rare nor easy to detect. These errors tend to bias the inference, reduce the statistical power and ultimately the efficiency of the decision-making process. Contrarily to the binary situation where misclassification occurs between two response classes, noise in ordinal categorical data is more complex due to the increased number of categories, diversity and asymmetry of errors. Although several approaches have been presented for dealing with misclassification in binary data, only limited practical methods have been proposed to analyze noisy categorical responses. A latent variable model implemented within a Bayesian framework was proposed to analyze ordinal categorical data subject to misclassification using simulated and real datasets. The simulated scenario consisted of a discrete response with three categories and a symmetric error rate of 5% between any two classes. The real data consisted of calving ease records of beef cows. Using real and simulated data, ignoring misclassification resulted in substantial bias in the estimation of genetic parameters and reduction of the accuracy of predicted breeding values. Using our proposed approach, a significant reduction in bias and increase in accuracy ranging from 11% to 17% was observed. Furthermore, most of the misclassified observations (in the simulated data) were identified with a substantially higher probability. Similar results were observed for a scenario with asymmetric misclassification. While the extension to traits with more categories between adjacent classes is straightforward, it could be computationally costly. For traits with high heritability, the performance of the methodology would be expected to improve.

Genotype × prenatal and post-weaning nutritional environment interaction in a composite beef cattle breed using reaction norms and a multi-trait model

Environmental effects have been shown to influence several economically important traits in beef cattle. In this study, genotype × nutritional environment interaction has been evaluated in a composite beef cattle breed (50% Red Angus, 25% Charolais, 25% Tarentaise). Four nutritional environments (marginal-restricted [MARG-RES], marginal-control [MARG-CTRL], adequate-restricted [ADEQ-RES], and adequate-control [ADEQ-CTRL]) were created based on two levels of winter supplement provided to dams grazing winter range during gestation (MARG and ADEQ) and two levels of input to offspring during post-weaning development (RES and CTRL). Genetic parameters of average daily gain (ADG) during the 140-d post-wean trial, yearling weight (YW), and ultrasound measurement of fat depth (FAT) at the 12th rib and intramuscular fat percentage (IMF) of 3,020 individuals in the four environments were estimated. The heritabilities estimated using a single trait mixed linear model were: ADG: 0.21, 0.23, 0.19 and 0.21; YW: 0.27, 0.33, 0.20 and 0.26; FAT: 0.30, 0.29, 0.29, 0.55; IMF: 0.45, 0.51, 0.33, 0.53 for MARG-RES, ADEQ-RES, MARG-CTRL and ADEQ-CTRL, respectively. The extent of genotype × environment interaction was modeled using two separate methods: reaction norms and multi-trait models. The genetic correlations were estimated using a multi-trait model for ADG, YW, FAT and IMF. Growth traits (ADG, YW) and FAT showed correlations less than 0.80 across the four different environments indicating genotype by environment interaction. For example, genetic correlation for ADG between MARG-CTRL and MARG-RES was 0.65 and 0.73 between ADEQ-RES and MARG-RES. In this example, the former genetic correlation corresponds to differences in post-weaning nutritional environment, and the later represents a nutritional difference imposed on dams (i.e., prenatal environment), potentially mediated via fetal programming. The reaction norm model results were in concordance with the multi-trait model, genotype by environment interaction had a higher effect on traits with a lower heritability.

Analysis of binary responses with outcome-specific misclassification probability in genome-wide association studies

Errors in the binary status of some response traits are frequent in human, animal, and plant applications. These error rates tend to differ between cases and controls because diagnostic and screening tests have different sensitivity and specificity. This increases the inaccuracies of classifying individuals into correct groups, giving rise to both false-positive and false-negative cases. The analysis of these noisy binary responses due to misclassification will undoubtedly reduce the statistical power of genome-wide association studies (GWAS). A threshold model that accommodates varying diagnostic errors between cases and controls was investigated. A simulation study was carried out where several binary data sets (case-control) were generated with varying effects for the most influential single nucleotide polymorphisms (SNPs) and different diagnostic error rate for cases and controls. Each simulated data set consisted of 2000 individuals. Ignoring misclassification resulted in biased estimates of true influential SNP effects and inflated estimates for true noninfluential markers. A substantial reduction in bias and increase in accuracy ranging from 12% to 32% was observed when the misclassification procedure was invoked. In fact, the majority of influential SNPs that were not identified using the noisy data were captured using the proposed method. Additionally, truly misclassified binary records were identified with high probability using the proposed method. The superiority of the proposed method was maintained across different simulation parameters (misclassification rates and odds ratios) attesting to its robustness.

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.

Misclassification in binary responses and effect on genome-wide association studies

Misclassification of dependent variables is a major issue in many areas of science that can arise when indirect markers are used to classify subjects or continuous traits are treated as categorical. In human medicine, this can have significant impacts on diagnostic accuracy. In animal science applications, misclassification can negatively affect both the accuracy of selection and the ability to ascertain the biological mechanisms for traits of interest. When dealing with traits influenced by genetic factors, genomic markers, such as SNP, can provide direct measurements of the underlying mechanisms controlling phenotypic responses. Unfortunately, in the presence of misclassification in the discrete dependent variables, the robustness of the analysis and the validity of the results could be severely compromised. To quantify the impact of misclassification on genome-wide association studies for binary responses, a real databased simulation was carried out. The simulated data consisted of 2,400 animals genotyped for 50K SNP. A binary trait with heritability equal to 0.10 and prevalence of 20% was generated. A rate of 1, 5, and 10% misclassification was artificially introduced to the true binary responses. Using a latent-threshold model, 3 analyses were carried out for each misclassification rate using 1) the true data (M1), 2) the contaminated data and ignoring misclassification (M2), and 3) the contaminated data and accounting for misclassification (M3). The results indicate that ignoring misclassification, when it exists in the data such as in M2, will lead to major deterioration in the performance of the model. When misclassification was contemplated in the model (M2), the results indicated a strong capacity of the procedure in dealing with potential misclassification in the training set. In fact, a large portion of miscoded samples in the training set was identified and corrected. The results of this study suggest that the proposed method is adequate and effective for practical genome-wide association studies for binary response classification.