BACKGROUND
it is usually assumed that a trait is measured with a degree of imprecision or error, but that the details of the measurement of a trait will not affect the results of a genome wide association study.
specifically, that 1) the particular details of how the trait is measured, 2) who measured it, and 3) whether the trait is subtly differently measured in a second sample will not i) materially affect the location of the most significant associations between a dna marker and the trait of interest or ii) affect the confirmation of the results in a second sample, because it is assumed that the errors in measurement are not systematic.
genome wide association studies  compare a large number of single nucleotide polymorphisms  to a trait measurement and snps with strong associations are usually reported  <cit> .
the effect of genotypic error is well known and there is a substantial amount of error checking of genotypes that usually occurs before the gwas is performed.
thereafter, a second sample is used for confirmation usually measured in the same way but often by a different set of phenotypers, and there may be subtle differences in the way the phenotypes were measured.
the lack of confirmation of some of the associations is usually ascribed to differences in some aspect of the genetics of the trait or aspects of the sample, and lack of confirmation is most generally ascribed to false discovery due to the large number of tests performed or insufficient power in the original gwas  <cit> .
for some traits, particularly ones that rely upon qualitative diagnosis, care is taken to ensure that the same diagnostic criteria are used  <cit> , but there is always the possibility that different individuals will interpret the criteria in their own way, leading to heterogeneity in classifying individuals from one sample to the next.
with quantitative traits, such as a height or weight measurement, this is usually not thought to be an important source of variability, but many quantitative traits are not automatically sampled by machine nor have precise and unambiguous guidelines for measurement, and alternative measurements of a trait may have correlations r <  <dig>   <cit> .
two measurements of subcutaneous fat thickness were collected on the same animals by two groups working independently  <cit>  and this provided the opportunity to examine the effects of measurement error of phenotypes on the results of a gwas.
of course, subcutaneous fat thickness is also of intrinsic interest in biological studies in many mammalian species because it is linked to the overall fatness of an animal or how well it is doing in a particular environment, to onset of puberty, and is easily measured  <cit> .
the thickness of the fat layer in these studies was measured manually using a ruler with  <dig> mm gradations adjacent to the crest of the 3rd sacral vertebra, but as fat layers are not uniformly thick there is clearly scope for variation in measurements performed by different individuals.
these animals had been genotyped for a gwas of body composition  <cit>  and one of the fat thickness measurements had been analysed along with estimates of growth and food efficiency.
to determine how successful confirmation would be in future studies of the same trait, and to evaluate how successful others would be in confirming our findings, the likely importance of such imprecision was investigated by comparing the results of the gwas for this trait to a gwas performed using the unpublished second measurement of subcutaneous fat thickness.
this analysis took previously collected genotypic data, additional unpublished phenotypes and then reanalysed the entire data set.
the expectation was that there would not be systematic differences in the two measurements, that the underlying genetic basis for fat thickness would be evident in both gwas, and that the differences in trait values might merely affect the degree of significance of the most significant snp associations, which would largely be in common.
the data were trimmed in two ways to remove outliers to determine whether this would be successful in controlling the differences between the gwas.
the fat thickness data were also coded as a threshold trait to determine how important such measurement error might be for discrete traits, where individuals may be coded as affected or unaffected.
RESULTS
the two subcutaneous fat thickness measurements for the animals that were genotyped differed by an average of  <dig>  mm , the correlation was r =  <dig>  between them, and the regression coefficient of chillp <dig> on p8fat was b =  <dig> , s.e.
=  <dig>  .
in all untrimmed data sets the p8fat measurement had a larger range and greater variance than the chillp <dig> measurement.
the histograms for the chillp <dig> and p8fat distribution  both have a longer right tail than left tail and are similar in shape but the chillp <dig> histogram appears smoother than the p8fat histogram.
most measurements were similar to each other but a substantial number of individuals showed highly divergent results .
univariate trimming, by removing the top and bottom 5% of values, reduced the range substantially for chillp <dig> and p8fat, but did not improve the correlation between traits, which reduced to r =  <dig> .
bivariate trimming substantially improved the correlation between measurements but left the range and variance relatively intact.
using the mean of the two p <dig> measurements resulted in correlations that were similar to that obtained with bivariate trimming at diff <dig> <  <dig> between the two measurements, but resulted in a larger sample for analysis.
r
b
a subcutaneous fat thickness measured in millimetres aligned with the crest of the 3rd sacral vertebra by two independent groups, with p8mean the average between the two values
b correlation between chillp <dig> and p8fat for the same method of data trimming
c correlation between p8mean to chillp <dig> and p8fat respectively
d for samples in common for the two univariate trimmed data sets
p8fat gwas and difference to the chillp <dig> gwas
the gwas of the p8fat measurement found  <dig> snps significantly  associated to the trait, which represents a false positive rate  = 76% .
this is a slightly lower fpr than that found for the gwas of the chillp <dig> measurement, which reported  <dig> snps significantly  associated to that trait measurement, and fpr = 86%.
these associations showed a distinct excess of large t-test values compared to the theoretical expectation, consistent with the discovery of real associations .
there were  <dig> significant  snp in common .
the largest aggregation of significant snp associations in the p8fat gwas was  <dig> snps out of  <dig> with p <  <dig>  at bta14: <dig> mb, in a  <dig>  kb region that includes the genes nsmaf and tox.
the largest aggregation of significant snp association in the chillp <dig> gwas was  <dig> snps out of  <dig> with p <  <dig>  on bta14: <dig> mb, in a  <dig>  kb region that included the genes xkr <dig>  tmem <dig>  tgs <dig>  lyn, rps <dig>  loc <dig>  mos, plag <dig>  chchd <dig>  sdr16c <dig>  and penk.
six of these  <dig> snp on bta <dig> associated to either measurement were significant  including btb- <dig>  which had p =  <dig>  × 10- <dig> .
these two aggregations of snps are adjacent to each other but do not overlap and are separated by  <dig>  mb .
this qtl region on bta <dig> appears to be substantially stronger than all others for this trait although its exact location appears to shift depending on the trait measurement used.
to summarise the differences in the gwas, the correlation of allele effects was r =  <dig>  and of -logp values was r =  <dig>  .
these correlations are substantially less than the correlation between measurements, and the correlation between the allele effects was approximately equal to the square of the correlation between the trait measurements .
snp
a
b
sig
snps
c
r
d
chr
e
%
f
region
g
h
penk
including
a number snp significant at p <  <dig>
b false positive rate
c number of significant  snp in common between a chillp <dig> and p8fat gwas of the same type
d correlation between allele effects of gwas of chillp <dig> and p8fat for the same method of data trimming
e top  <dig> chromosomes with largest number of snp significant at p <  <dig>  in descending order of number of significant snp
f percent of snp with p <  <dig>  on the top  <dig> chromosomes compared to all chromosomes
g genomic region with the largest number of snp significant at p <  <dig>
h gene located to this genomic region
i p8mean and chillp <dig> or p8fat respectively
a
b
p
ars-bfgl-
btb-
btb-
hapmap55575-
ars-bfgl-
btb-
btb-
btb-
btb-
hapmap53460-
hapmap42233-
hapmap50089-
ιhapmap55575-
ars-bfgl-
btb-
hapmap32434-
ua-ifasa-
a allele frequency across the entire sample
the effect of data trimming
univariate trimming of the top and bottom 5% of data for chillp <dig> or p8fat decreased the similarity between the two gwas.
the correlation of allele effects decreased to r =  <dig> , a value similar to the square of the correlation between the chillp <dig> and p8fat measurements after trimming the top and bottom 5% of measurements .
none of the significant  snps were significant for both chillp <dig> and p8fat .
the clusters of snp on bta <dig> were in different locations in the two gwas, similar to that found in the full data set , with a slightly greater distance between the clusters.
the most significant snp in the chillp <dig> gwas was now on bta8: <dig> mb  and the most significant snp in the p8fat gwas was on bta14: <dig> mb .
bivariate trimming improved the correlation between allele effects for a gwas of p8fat and chillp <dig>  trimming at diff <dig> <  <dig> decreased the sample size by  <dig> and  <dig> respectively for the p8fat and chillp <dig> datasets but increased the correlation of allele effects to r =  <dig> , a value similar to the square of the correlation between the chillp <dig> and p8fat measurements after trimming at diff <dig> <  <dig> .
the number of significant  associations decreased for chillp <dig> and p8fat, but there was an increase in the number of snps that were significant in common.
the chillp <dig> and p8fat gwas showed two clusters of snp, one at bta14: <dig> and one at bta14: <dig> mb, although there were more snp in the bta14: <dig> mb cluster than in the bta14: <dig> mb cluster for both gwas .
bivariate trimming at diff <dig> <  <dig> decreased the sample by  <dig> for p8fat and  <dig> for chillp <dig> and not only increased the correlation between allele effects it reduced the fpr for the experiment and generated smaller p-values for the snp.
the correlation between allele effects between the gwas increased to r =  <dig>  which is approximately equal to the square of the correlation of the trait values trimmed at diff <dig> <  <dig> .
the number of significant snp nearly doubled , the number of significant snp in common increased to  <dig> and there were  <dig> snp in common at p <  <dig> .
on bta  <dig>  each of the gwas showed two clusters of snp separated by approximately  <dig> mb, with both gwas showing snp significant  in both clusters , and the snp hapmap25761-btc- <dig>  was associated with p8fat with p =  <dig>  × 10- <dig>
an alternative to trimming is to average the two trait measurements , which may reduce the number of highly divergent measurements and preserve as high a sample size as possible.
overall, the correlation between chillp <dig> and the p8mean allele effects was r =  <dig>  and between p8fat and p8mean allele effects was r =  <dig> .
these are approximately equal to the square of the correlations between the trait measurements of chillp <dig> and p8mean and p8fat and p8mean .
the number of significant snps were relatively low  but a large proportion were shared and  <dig> significant snps were shared between all three measurements.
importantly, there was evidence for both clusters of significant snps associated to p8mean on bta <dig>  however, there were fewer significant snp in each cluster, and the p-values were not as small .
averaging the trait measurements was not as efficient as stringent bivariate trimming.
the effect on discrete phenotypes
when the phenotypes were coded as affected or unaffected, depending upon whether they exceeded the + <dig> s.d.
threshold above the mean, the effect of misclassification was greater on these results, but bivariate trimming was able to mitigate these effects substantially .
the correlation between allele effects for the full data set was r =  <dig>  and increased to r =  <dig>  for bivariate trimming at diff <dig> <  <dig>  the percentage discordantly scored individuals declined from  <dig>  to  <dig> %.
with discrete phenotypes the associations were more significant, and at a stringency of diff <dig> <  <dig> there were  <dig> significant  snps in the chillp <dig> gwas,  <dig> significant  snps in the p8fat gwas.
there were  <dig> significant  snps common to both of the gwas, four of these common snps were significant at the  <dig> × 10- <dig> threshold, and one of these snps was significant at the  <dig> × 10- <dig> threshold.
obviously, with discrete phenotypes it is possible to exclude all discrepantly coded individuals, and all +a and a+ individuals  could be removed to give exactly the same output from a gwas.
after bivariate trimming at diff <dig> <  <dig>  and then removal of the  <dig> discordant phenotypes, there were  <dig> snps that were significant at p <  <dig> × 10- <dig> and  <dig> snps that were significant at p <  <dig> , obtained with a sample of n =  <dig> animals.
a
a numbers of individuals coded as unaffected  or affected  using a threshold of > + <dig> s.d.
in the chillp <dig> and p8fat data, respectively
b percentage of discrepant codes for both traits
c correlation of allele effects for the discretised chillp <dig> and p8fat data
discussion
the error in measuring phenotypes had a strong effect on the profiles of significant snp identified in the gwas.
there was a small overlap in the significant snps, the chromosomes with the largest number of significant snps were similar but not the same, and the region with the largest number of significant snps, occurring on bta <dig> in both of the trait measurements, was shifted by at least  <dig> mb towards the telomere.
follow up studies of this important qtl on bta <dig> could have ended up wasting time studying a non-overlapping set of genes.
overall, the correlation in allele effects was moderate and was approximately equal to the square of the correlation between the trait measurements for all data sets.
as the correlation between trait measurements declines below approximately r =  <dig>  between duplicate trait measurements, the similarity between gwas will decline rapidly.
for qualitative traits using a threshold to determine whether an individual is affected, measurement error appeared to generate greater incompatibility between the gwas, possibly because it is a more powerful statistical design.
these results suggest that one would expect to find minimal overlap in the significant snp from one study to the next if a different but correlated trait was used in a confirmation study, such as rib fat thickness or intramuscular fat percentage.
the attempts to control measurement error were not all equally successful, and even small amounts of measurement error still caused differences in the gwas.
univariate trimming, which is generally used to remove obviously erroneous outliers, such as a  <dig>  kg human, or a  <dig> kg peregrine falcon, made the comparison between gwas worse.
nevertheless, despite the increased divergence between measurements, the qtl on bta <dig> was still found in both gwas, except that the disparity in the location of the qtl in each gwas increased.
bivariate trimming, even at an equivalent of the removal of around 10% of the most divergent measurements, showed an improvement in similarity between the gwas.
at the most stringent level, the overall experimental statistics such as the fpr and the size of the p-values improved, so that smaller p-values were generated for the significant snp despite the decrease in sample size.
more importantly, for the qtl on bta <dig>  its location was clarified into two adjacent qtl, separated by  <dig> mb.
using the mean of the two measurements helped to reduce phenotypic error, also showed two qtl separated by  <dig> mb on bta <dig>  but the fpr was close to 100% and the p-values were substantially larger.
this suggests that taking the average does not remove error, and it does not reduce error sufficiently that the errors will not affect a gwas study.
nevertheless, even with the most stringent amounts of bivariate trimming, so that the trait measurements were very highly correlated, the list of significant snp of the two gwas did not coincide.
this suggests that the deterministic methods used to analyse gwas data are sensitive to small differences when mapping quantitative traits and every effort needs to be made to improve the precision of trait measurement, or to develop methods of analysis that are not so sensitive to error.
it is therefore recommended that trait values in gwas experiments be examined for repeatability before the experiment is performed.
at present, duplicate, independent measures of the phenotype are not made for gwa studies, so routine bivariate trimming would not be possible for most data sets.
for traits that do not have high repeatability , two or more independent measurements of the same trait should be obtained for all samples, and individuals genotyped that have highly correlated trait measurements.
the major differences in the trait measurements in this study were that 1) a different group of individuals performed the measurement , and 2) the p8fat measurement was performed about an hour after slaughter on a warm carcass whereas the chillp <dig> measurement was performed after the carcass had been refrigerated, and would have been measured within  <dig> hours of the p8fat measurement.
although one cannot explicitly disentangle the effects of warm vs chiller and inspectors vs meat scientists, because the comparison was warm+inspectors vs cold+scientists, the differences between the mean chillp <dig> and p8fat were ~  <dig> mm and the regression coefficient of one measure on the other was nearly  <dig> in the entire sample  with a small standard error of estimation , which suggests that overall, there was little shrinkage in the fat thickness.
furthermore, the plot of divergence suggests that most of the difference is due to strongly divergent measurements for a minority of samples, implicating operator error rather than systematic differences due to the chiller.
nevertheless, given these factors of when, where and by whom the trait was measured, these measurement differences may be at the extreme end of divergence of measurement of the same trait, and studies of other traits may not be as strongly affected.
however, until other analyses of independently performed repeated measurements are obtained one cannot be sure that the results obtained here are atypical or that they will represent a general phenomenon.
more importantly, these results suggest that a wide variety of phenotypes may be subject to unreproducible results due to technical issues associated with phenotype measurement.
in the measurement of fat thickness, fat layers are not uniformly thick and so slight differences in where an individual or group of scorers placed the ruler could potentially affect the measure obtained.
some phenotypes are more likely to be affected by such measurement problems.
for example, metabolite or hormone concentrations may show diurnal or weekly cycles or may show different values in different assays  <cit> .
a bone thickness or length will depend upon a landmark being identified and used consistently  <cit> .
a waist measurement, defined as the region of smallest circumference, might be made at a different location in each individual, or if measured at a particular part of the abdomen might not be the smallest circumference  <cit> .
these results also show that the effects of measurement accuracy are stronger on the coding of affected or unaffected status, such as when an individual passes a threshold.
traits that may be affected by such thresholds are schizophrenia or obesity.
these effects on gwas studies yield sobering implications for genomic selection or phenotype prediction.
genomic selection or phenotype prediction uses ld between markers and traits in one study to predict the performance of a separate set of animals using their genotypes for the same panel of markers, usually a set of tens to hundreds of thousands of snps in a snp array  <cit> .
there is the implicit assumption in those studies that if large enough numbers of snps and large enough sample sizes of animals are used then high predictive accuracy will be obtained.
genomic selection depends for its success not only on the significance of the most significant snps but includes loci with minimal evidence for significance and even snps with non-significant effects into these models  <cit> .
the results in this study show that evidence for larger qtl can appear to shift by  <dig> mb along the chromosome, and the evidence for smaller qtl can disappear altogether, which will affect the list of snp used in these predictions.
in addition, genomic selection analyses may be affected by trait measurement error because even after the majority of discordant data have been removed the allele effects in the two gwas still showed clear differences.
these results suggest that the prediction accuracy of such genomic selection or prediction studies will only improve once better phenotypic measures are collected, either by double scoring the phenotypes or by more accurate machine based collection of precisely defined phenotypes.
CONCLUSIONS
it is recommended that trait values in gwas experiments be examined for repeatability before the experiment is performed.
wherever possible, independent scorers should collect the repeated data.
for traits that do not have high repeatability , two or more independent measurements of the same trait should be obtained for all samples.
this threshold is suggested because the square of a decimal fraction begins to depart substantially from the original fraction below a value of  <dig> , and the correlation between allele effects between two gwas is approximately equal to the square of the correlation between trait values.
for prospective individuals in a gwas, only those with accurately measured trait values should be genotyped, such as those that have highly correlated trait measurements.

 it is usually assumed that a trait is measured with a degree of imprecision or error, but that the details of the measurement of a trait will not affect the results of a genome wide association study. specifically, that 1) the particular details of how the trait is measured, 2) who measured it, and 3) whether the trait is subtly differently measured in a second sample will not i) materially affect the location of the most significant associations between a dna marker and the trait of interest or ii) affect the confirmation of the results in a second sample, because it is assumed that the errors in measurement are not systematic. genome wide association studies compare a large number of single nucleotide polymorphisms to a trait measurement and snps with strong associations are usually reported. the effect of genotypic error is well known and there is a substantial amount of error checking of genotypes that usually occurs before the gwas is performed. thereafter, a second sample is used for confirmation usually measured in the same way but often by a different set of phenotypers, and there may be subtle differences in the way the phenotypes were measured. the lack of confirmation of some of the associations is usually ascribed to differences in some aspect of the genetics of the trait or aspects of the sample, and lack of confirmation is most generally ascribed to false discovery due to the large number of tests performed or insufficient power in the original gwas. for some traits, particularly ones that rely upon qualitative diagnosis, care is taken to ensure that the same diagnostic criteria are used, but there is always the possibility that different individuals will interpret the criteria in their own way, leading to heterogeneity in classifying individuals from one sample to the next. with quantitative traits, such as a height or weight measurement, this is usually not thought to be an important source of variability, but many quantitative traits are not automatically sampled by machine nor have precise and unambiguous guidelines for measurement, and alternative measurements of a trait may have correlations r <. two measurements of subcutaneous fat thickness were collected on the same animals by two groups working independently and this provided the opportunity to examine the effects of measurement error of phenotypes on the results of a gwas. of course, subcutaneous fat thickness is also of intrinsic interest in biological studies in many mammalian species because it is linked to the overall fatness of an animal or how well it is doing in a particular environment, to onset of puberty, and is easily measured. the thickness of the fat layer in these studies was measured manually using a ruler with mm gradations adjacent to the crest of the 3rd sacral vertebra, but as fat layers are not uniformly thick there is clearly scope for variation in measurements performed by different individuals. these animals had been genotyped for a gwas of body composition and one of the fat thickness measurements had been analysed along with estimates of growth and food efficiency. to determine how successful confirmation would be in future studies of the same trait, and to evaluate how successful others would be in confirming our findings, the likely importance of such imprecision was investigated by comparing the results of the gwas for this trait to a gwas performed using the unpublished second measurement of subcutaneous fat thickness. this analysis took previously collected genotypic data, additional unpublished phenotypes and then reanalysed the entire data set. the expectation was that there would not be systematic differences in the two measurements, that the underlying genetic basis for fat thickness would be evident in both gwas, and that the differences in trait values might merely affect the degree of significance of the most significant snp associations, which would largely be in common. the data were trimmed in two ways to remove outliers to determine whether this would be successful in controlling the differences between the gwas. the fat thickness data were also coded as a threshold trait to determine how important such measurement error might be for discrete traits, where individuals may be coded as affected or unaffected.  the two subcutaneous fat thickness measurements for the animals that were genotyped differed by an average of mm, the correlation was r = between them, and the regression coefficient of chillp on p8fat was b =, s.e. =. in all untrimmed data sets the p8fat measurement had a larger range and greater variance than the chillp measurement. the histograms for the chillp and p8fat distribution both have a longer right tail than left tail and are similar in shape but the chillp histogram appears smoother than the p8fat histogram. most measurements were similar to each other but a substantial number of individuals showed highly divergent results. univariate trimming, by removing the top and bottom 5% of values, reduced the range substantially for chillp and p8fat, but did not improve the correlation between traits, which reduced to r =. bivariate trimming substantially improved the correlation between measurements but left the range and variance relatively intact. using the mean of the two p measurements resulted in correlations that were similar to that obtained with bivariate trimming at diff < between the two measurements, but resulted in a larger sample for analysis. r b a subcutaneous fat thickness measured in millimetres aligned with the crest of the 3rd sacral vertebra by two independent groups, with p8mean the average between the two values b correlation between chillp and p8fat for the same method of data trimming c correlation between p8mean to chillp and p8fat respectively d for samples in common for the two univariate trimmed data sets p8fat gwas and difference to the chillp gwas the gwas of the p8fat measurement found snps significantly associated to the trait, which represents a false positive rate = 76%. this is a slightly lower fpr than that found for the gwas of the chillp measurement, which reported snps significantly associated to that trait measurement, and fpr = 86%. these associations showed a distinct excess of large t-test values compared to the theoretical expectation, consistent with the discovery of real associations. there were significant snp in common. the largest aggregation of significant snp associations in the p8fat gwas was snps out of with p < at bta14: mb, in a kb region that includes the genes nsmaf and tox. the largest aggregation of significant snp association in the chillp gwas was snps out of with p < on bta14: mb, in a kb region that included the genes xkr tmem tgs lyn, rps loc mos, plag chchd sdr16c and penk. six of these snp on bta associated to either measurement were significant including btb- which had p = × 10-. these two aggregations of snps are adjacent to each other but do not overlap and are separated by mb. this qtl region on bta appears to be substantially stronger than all others for this trait although its exact location appears to shift depending on the trait measurement used. to summarise the differences in the gwas, the correlation of allele effects was r = and of -logp values was r =. these correlations are substantially less than the correlation between measurements, and the correlation between the allele effects was approximately equal to the square of the correlation between the trait measurements. snp a b sig snps c r d chr e % f region g h penk including a number snp significant at p < b false positive rate c number of significant snp in common between a chillp and p8fat gwas of the same type d correlation between allele effects of gwas of chillp and p8fat for the same method of data trimming e top chromosomes with largest number of snp significant at p < in descending order of number of significant snp f percent of snp with p < on the top chromosomes compared to all chromosomes g genomic region with the largest number of snp significant at p < h gene located to this genomic region i p8mean and chillp or p8fat respectively a b p ars-bfgl- btb- btb- hapmap55575- ars-bfgl- btb- btb- btb- btb- hapmap53460- hapmap42233- hapmap50089- ιhapmap55575- ars-bfgl- btb- hapmap32434- ua-ifasa- a allele frequency across the entire sample the effect of data trimming univariate trimming of the top and bottom 5% of data for chillp or p8fat decreased the similarity between the two gwas. the correlation of allele effects decreased to r =, a value similar to the square of the correlation between the chillp and p8fat measurements after trimming the top and bottom 5% of measurements. none of the significant snps were significant for both chillp and p8fat. the clusters of snp on bta were in different locations in the two gwas, similar to that found in the full data set, with a slightly greater distance between the clusters. the most significant snp in the chillp gwas was now on bta8: mb and the most significant snp in the p8fat gwas was on bta14: mb. bivariate trimming improved the correlation between allele effects for a gwas of p8fat and chillp trimming at diff < decreased the sample size by and respectively for the p8fat and chillp datasets but increased the correlation of allele effects to r =, a value similar to the square of the correlation between the chillp and p8fat measurements after trimming at diff <. the number of significant associations decreased for chillp and p8fat, but there was an increase in the number of snps that were significant in common. the chillp and p8fat gwas showed two clusters of snp, one at bta14: and one at bta14: mb, although there were more snp in the bta14: mb cluster than in the bta14: mb cluster for both gwas. bivariate trimming at diff < decreased the sample by for p8fat and for chillp and not only increased the correlation between allele effects it reduced the fpr for the experiment and generated smaller p-values for the snp. the correlation between allele effects between the gwas increased to r = which is approximately equal to the square of the correlation of the trait values trimmed at diff <. the number of significant snp nearly doubled, the number of significant snp in common increased to and there were snp in common at p <. on bta each of the gwas showed two clusters of snp separated by approximately mb, with both gwas showing snp significant in both clusters, and the snp hapmap25761-btc- was associated with p8fat with p = × 10- an alternative to trimming is to average the two trait measurements, which may reduce the number of highly divergent measurements and preserve as high a sample size as possible. overall, the correlation between chillp and the p8mean allele effects was r = and between p8fat and p8mean allele effects was r =. these are approximately equal to the square of the correlations between the trait measurements of chillp and p8mean and p8fat and p8mean. the number of significant snps were relatively low but a large proportion were shared and significant snps were shared between all three measurements. importantly, there was evidence for both clusters of significant snps associated to p8mean on bta however, there were fewer significant snp in each cluster, and the p-values were not as small. averaging the trait measurements was not as efficient as stringent bivariate trimming. the effect on discrete phenotypes when the phenotypes were coded as affected or unaffected, depending upon whether they exceeded the + s.d. threshold above the mean, the effect of misclassification was greater on these results, but bivariate trimming was able to mitigate these effects substantially. the correlation between allele effects for the full data set was r = and increased to r = for bivariate trimming at diff < the percentage discordantly scored individuals declined from to %. with discrete phenotypes the associations were more significant, and at a stringency of diff < there were significant snps in the chillp gwas, significant snps in the p8fat gwas. there were significant snps common to both of the gwas, four of these common snps were significant at the × 10- threshold, and one of these snps was significant at the × 10- threshold. obviously, with discrete phenotypes it is possible to exclude all discrepantly coded individuals, and all +a and a+ individuals could be removed to give exactly the same output from a gwas. after bivariate trimming at diff < and then removal of the discordant phenotypes, there were snps that were significant at p < × 10- and snps that were significant at p <, obtained with a sample of n = animals. a a numbers of individuals coded as unaffected or affected using a threshold of > + s.d. in the chillp and p8fat data, respectively b percentage of discrepant codes for both traits c correlation of allele effects for the discretised chillp and p8fat data discussion the error in measuring phenotypes had a strong effect on the profiles of significant snp identified in the gwas. there was a small overlap in the significant snps, the chromosomes with the largest number of significant snps were similar but not the same, and the region with the largest number of significant snps, occurring on bta in both of the trait measurements, was shifted by at least mb towards the telomere. follow up studies of this important qtl on bta could have ended up wasting time studying a non-overlapping set of genes. overall, the correlation in allele effects was moderate and was approximately equal to the square of the correlation between the trait measurements for all data sets. as the correlation between trait measurements declines below approximately r = between duplicate trait measurements, the similarity between gwas will decline rapidly. for qualitative traits using a threshold to determine whether an individual is affected, measurement error appeared to generate greater incompatibility between the gwas, possibly because it is a more powerful statistical design. these results suggest that one would expect to find minimal overlap in the significant snp from one study to the next if a different but correlated trait was used in a confirmation study, such as rib fat thickness or intramuscular fat percentage. the attempts to control measurement error were not all equally successful, and even small amounts of measurement error still caused differences in the gwas. univariate trimming, which is generally used to remove obviously erroneous outliers, such as a kg human, or a kg peregrine falcon, made the comparison between gwas worse. nevertheless, despite the increased divergence between measurements, the qtl on bta was still found in both gwas, except that the disparity in the location of the qtl in each gwas increased. bivariate trimming, even at an equivalent of the removal of around 10% of the most divergent measurements, showed an improvement in similarity between the gwas. at the most stringent level, the overall experimental statistics such as the fpr and the size of the p-values improved, so that smaller p-values were generated for the significant snp despite the decrease in sample size. more importantly, for the qtl on bta its location was clarified into two adjacent qtl, separated by mb. using the mean of the two measurements helped to reduce phenotypic error, also showed two qtl separated by mb on bta but the fpr was close to 100% and the p-values were substantially larger. this suggests that taking the average does not remove error, and it does not reduce error sufficiently that the errors will not affect a gwas study. nevertheless, even with the most stringent amounts of bivariate trimming, so that the trait measurements were very highly correlated, the list of significant snp of the two gwas did not coincide. this suggests that the deterministic methods used to analyse gwas data are sensitive to small differences when mapping quantitative traits and every effort needs to be made to improve the precision of trait measurement, or to develop methods of analysis that are not so sensitive to error. it is therefore recommended that trait values in gwas experiments be examined for repeatability before the experiment is performed. at present, duplicate, independent measures of the phenotype are not made for gwa studies, so routine bivariate trimming would not be possible for most data sets. for traits that do not have high repeatability, two or more independent measurements of the same trait should be obtained for all samples, and individuals genotyped that have highly correlated trait measurements. the major differences in the trait measurements in this study were that 1) a different group of individuals performed the measurement, and 2) the p8fat measurement was performed about an hour after slaughter on a warm carcass whereas the chillp measurement was performed after the carcass had been refrigerated, and would have been measured within hours of the p8fat measurement. although one cannot explicitly disentangle the effects of warm vs chiller and inspectors vs meat scientists, because the comparison was warm+inspectors vs cold+scientists, the differences between the mean chillp and p8fat were ~ mm and the regression coefficient of one measure on the other was nearly in the entire sample with a small standard error of estimation, which suggests that overall, there was little shrinkage in the fat thickness. furthermore, the plot of divergence suggests that most of the difference is due to strongly divergent measurements for a minority of samples, implicating operator error rather than systematic differences due to the chiller. nevertheless, given these factors of when, where and by whom the trait was measured, these measurement differences may be at the extreme end of divergence of measurement of the same trait, and studies of other traits may not be as strongly affected. however, until other analyses of independently performed repeated measurements are obtained one cannot be sure that the results obtained here are atypical or that they will represent a general phenomenon. more importantly, these results suggest that a wide variety of phenotypes may be subject to unreproducible results due to technical issues associated with phenotype measurement. in the measurement of fat thickness, fat layers are not uniformly thick and so slight differences in where an individual or group of scorers placed the ruler could potentially affect the measure obtained. some phenotypes are more likely to be affected by such measurement problems. for example, metabolite or hormone concentrations may show diurnal or weekly cycles or may show different values in different assays. a bone thickness or length will depend upon a landmark being identified and used consistently. a waist measurement, defined as the region of smallest circumference, might be made at a different location in each individual, or if measured at a particular part of the abdomen might not be the smallest circumference. these results also show that the effects of measurement accuracy are stronger on the coding of affected or unaffected status, such as when an individual passes a threshold. traits that may be affected by such thresholds are schizophrenia or obesity. these effects on gwas studies yield sobering implications for genomic selection or phenotype prediction. genomic selection or phenotype prediction uses ld between markers and traits in one study to predict the performance of a separate set of animals using their genotypes for the same panel of markers, usually a set of tens to hundreds of thousands of snps in a snp array. there is the implicit assumption in those studies that if large enough numbers of snps and large enough sample sizes of animals are used then high predictive accuracy will be obtained. genomic selection depends for its success not only on the significance of the most significant snps but includes loci with minimal evidence for significance and even snps with non-significant effects into these models. the results in this study show that evidence for larger qtl can appear to shift by mb along the chromosome, and the evidence for smaller qtl can disappear altogether, which will affect the list of snp used in these predictions. in addition, genomic selection analyses may be affected by trait measurement error because even after the majority of discordant data have been removed the allele effects in the two gwas still showed clear differences. these results suggest that the prediction accuracy of such genomic selection or prediction studies will only improve once better phenotypic measures are collected, either by double scoring the phenotypes or by more accurate machine based collection of precisely defined phenotypes.  it is recommended that trait values in gwas experiments be examined for repeatability before the experiment is performed. wherever possible, independent scorers should collect the repeated data. for traits that do not have high repeatability, two or more independent measurements of the same trait should be obtained for all samples. this threshold is suggested because the square of a decimal fraction begins to depart substantially from the original fraction below a value of, and the correlation between allele effects between two gwas is approximately equal to the square of the correlation between trait values. for prospective individuals in a gwas, only those with accurately measured trait values should be genotyped, such as those that have highly correlated trait measurements. 
despite the reduction in sample size due to trimming, more snp were significant. the correlation between allele effects was approximately equal to the square of the correlation between the trait measurements. for traits that do not have high repeatability, two or more independent measurements of the same trait should be obtained for all samples, and individuals genotyped that have highly correlated trait measurements. it is recommended that trait values in gwas experiments be examined for repeatability before the experiment is performed. in this report, the effects of two independent measurements of the same trait, subcutaneous fat thickness, were examined in gwas of individuals. there is an unspoken assumption that imprecision of measurement of phenotypes will not have large systematic effects on the location of significant associations in a genome wide association study. the trait values obtained by two independent groups working to the same trait definition were correlated with r =. univariate trimming of the top and bottom 5% of data, a method used to control for erroneous trait values, decreased the similarity between the gwas and increased the apparent shift of the qtl on bta stringent bivariate trimming of data, using only trait values that were similar to each other in the two data sets, substantially improved the correlation of trait values and allele effects in the gwas, and showed evidence for two qtl on bta separated by mb.
 there is an unspoken assumption that imprecision of measurement of phenotypes will not have large systematic effects on the location of significant associations in a genome wide association study. in this report, the effects of two independent measurements of the same trait, subcutaneous fat thickness, were examined in gwas of individuals.  the trait values obtained by two independent groups working to the same trait definition were correlated with r =. the allele effects obtained from the two analyses were only moderately correlated, with r =, and there was one significant association in common to the two measurements. the correlation between allele effects was approximately equal to the square of the correlation between the trait measurements. an important quantitative trait locus on bta appeared to be shifted distally by mb along the chromosome. the divergence in gwas was stronger with data coded into two discrete classes. univariate trimming of the top and bottom 5% of data, a method used to control for erroneous trait values, decreased the similarity between the gwas and increased the apparent shift of the qtl on bta stringent bivariate trimming of data, using only trait values that were similar to each other in the two data sets, substantially improved the correlation of trait values and allele effects in the gwas, and showed evidence for two qtl on bta separated by mb. despite the reduction in sample size due to trimming, more snp were significant. using the mean of the two measurements of the trait was not as efficient as bivariate trimming.  it is recommended that trait values in gwas experiments be examined for repeatability before the experiment is performed. for traits that do not have high repeatability, two or more independent measurements of the same trait should be obtained for all samples, and individuals genotyped that have highly correlated trait measurements. 