Genome-Wide Association Analysis of Semen Characteristics in Piétrain Boars.

Since artificial insemination is common practice in pig breeding, the quality and persistence of the semen are decisive for the usability of individual boars. In the current study, genome-wide association analyses were performed to investigate the genetic variability underlying phenotypic variations in semen characteristics. These traits comprise sperm morphology and sperm motility under different temporal and thermal storage conditions, in addition to standard semen quality parameters. Two consecutive samples of the fourth and fifth ejaculates from the same boar were comprehensively analyzed in a genotyped Piétrain boar population. A total of 13 genomic regions on different chromosomes were identified that contain single-nucleotide polymorphisms significantly associated with these traits. Subsequent analysis of the genomic regions revealed candidate genes described to be involved in spermatogenesis, such as FOXL3, GPER1, PDGFA, PRKAR1B, SNRK, SUN1, and TSPO, and sperm motility, including ARRDC4, CEP78, DNAAF5, and GPER1. Some of these genes were also associated with male fertility or infertility in mammals (e.g., CEP78, GPER1). The analyses based on these laboriously determined and valuable phenotypes contribute to a better understanding of the genetic background of male fertility traits in pigs and could prospectively contribute to the improvement of sperm quality through breeding approaches.

Microbial signature inferred from genomic breeding selection on milk urea concentration and its relation to proxies of nitrogen-utilization efficiency in Holsteins.

Increasing the nitrogen-utilization efficiency (NUE) of dairy cows by breeding selection would offer advantages from nutritional, environmental, and economic perspectives. Because data collection of NUE phenotypes is not feasible in large cow cohorts, the cow individual milk urea concentration (MU) has been suggested as an indicator trait. Considering the symbiotic interplay between dairy cows and their rumen microbiome, individual MU was thought to be influenced by host genetics and by the rumen microbiome, the latter in turn being partly attributed to host genetics. To enhance our knowledge of MU as an indicator trait for NUE, we aimed to identify differential abundant rumen microbial genera between Holstein cows with divergent genomic breeding values for MU (GBVMU; GBVHMU vs. GBVLMU, where H and L indicate high and low MU phenotypes, respectively). The microbial genera identified were further investigated for their correlations with MU and 7 additional NUE-associated traits in urine, milk, and feces in 358 lactating Holsteins. Statistical analysis of microbial 16S rRNA amplicon sequencing data revealed significantly higher abundances of the ureolytic genus Succinivibrionaceae UCG-002 in GBVLMU cows, whereas GBVHMU animals hosted higher abundances of Clostridia unclassified and Desulfovibrio. The entire discriminating ruminal signature of 24 microbial taxa included a further 3 genera of the Lachnospiraceae family that revealed significant correlations to MU values and were therefore proposed as considerable players in the GBVMU-microbiome-MU axis. The significant correlations of Prevotellaceae UCG-003, Anaerovibrio, Blautia, and Butyrivibrio abundances with MU measurements, milk nitrogen, and N content in feces suggested their contribution to genetically determined N-utilization in Holstein cows. The microbial genera identified might be considered for future breeding programs to enhance NUE in dairy herds.

Ruminal background of predisposed milk urea (MU) concentration in Holsteins.

Efforts to reduce nitrogen (N) emissions are currently based on the optimization of dietary- N supply at average herd N requirements. The implementation of the considerable individual differences and predispositions in N- use efficiency and N- excretion in breeding programs is hampered by the difficulty of data collection. Cow individual milk urea (MU) concentration has been proposed as an easy-to-measure surrogate trait, but recent studies questioned its predictive power. Therefore, a deeper understanding of the biological mechanisms underlying predisposed higher (HMUg) or lower (LMUg) MU concentration in dairy cows is needed. Considering the complex N- metabolism in ruminants, the distinction between HMUg and LMUg could be based on differences in (i) the rumen microbial community, (ii) the host-specific transcription processes in the rumen villi, and (iii) the host-microbe interaction in the rumen. Therefore, rumen fluid and rumen epithelial samples from 10 HMUg and 10 LMUg cows were analyzed by 16S sequencing and HiSeq sequencing. In addition, the effect of dietary-N reduction on ruminal shifts was investigated in a second step. In total, 10 differentially abundant genera (DAG) were identified between HMUg and LMUg cows, elucidating greater abundances of ureolytic Succinivibrionaceae_UCG-002 and Ruminococcaceae_unclassified in LMUg animals and enhanced occurrences of Butyvibrio in HMUg cows. Differential expression analysis revealed genes of the bovine Major Histocompatibility Complex (BOLA genes) as well as MX1, ISG15, and PRSS2 displaying candidates of MU predisposition that further attributed to enhanced immune system activities in LMUg cows. A number of significant correlations between microbial genera and host transcript abundances were uncovered, including strikingly positive correlations of BOLA-DRA transcripts with Roseburia and Lachnospiraceae family abundances that might constitute particularly prominent microbial-host interplays of MU predisposition. The reduction of feed-N was followed by 18 DAG in HMUg and 19 DAG in LMUg, depicting pronounced interest on Shuttleworthia, which displayed controversial adaption in HMUg and LMUg cows. Lowering feed-N further elicited massive downregulation of immune response and energy metabolism pathways in LMUg. Considering breeding selection strategies, this study attributed information content to MU about predisposed ruminal N-utilization in Holstein-Friesians.

Genomic characterization of the world's longest selection experiment in mouse reveals the complexity of polygenic traits.

BACKGROUND: Long-term selection experiments are a powerful tool to understand the genetic background of complex traits. The longest of such experiments has been conducted in the Research Institute for Farm Animal Biology (FBN), generating extreme mouse lines with increased fertility, body mass, protein mass and endurance. For >140 generations, these lines have been maintained alongside an unselected control line, representing a valuable resource for understanding the genetic basis of polygenic traits. However, their history and genomes have not been reported in a comprehensive manner yet. Therefore, the aim of this study is to provide a summary of the breeding history and phenotypic traits of these lines along with their genomic characteristics. We further attempt to decipher the effects of the observed line-specific patterns of genetic variation on each of the selected traits. RESULTS: Over the course of >140 generations, selection on the control line has given rise to two extremely fertile lines (>20 pups per litter each), two giant growth lines (one lean, one obese) and one long-distance running line. Whole genome sequencing analysis on 25 animals per line revealed line-specific patterns of genetic variation among lines, as well as high levels of homozygosity within lines. This high degree of distinctiveness results from the combined effects of long-term continuous selection, genetic drift, population bottleneck and isolation. Detection of line-specific patterns of genetic differentiation and structural variation revealed multiple candidate genes behind the improvement of the selected traits. CONCLUSIONS: The genomes of the Dummerstorf trait-selected mouse lines display distinct patterns of genomic variation harbouring multiple trait-relevant genes. Low levels of within-line genetic diversity indicate that many of the beneficial alleles have arrived to fixation alongside with neutral alleles. This study represents the first step in deciphering the influence of selection and neutral evolutionary forces on the genomes of these extreme mouse lines and depicts the genetic complexity underlying polygenic traits.

Identification of Genomic Regions Influencing N-Metabolism and N-Excretion in Lactating Holstein- Friesians.

Excreted nitrogen (N) of dairy cows contribute to environmental eutrophication. The main N-excretory metabolite of dairy cows is urea, which is synthesized as a result of N-metabolization in the liver and is excreted via milk and urine. Genetic variation in milk urea (MU) has been postulated but the complex physiology behind the trait as well as the tremendous diversity of processes regulating the N-metabolism impede the consistent determination of causal regions in the bovine genome. In order to map the genetic determinants affecting N-excretion, MU and eight other N-excretory metabolites in milk and urine were assessed in a genome-wide association study. Therefore phenotypes of 371 Holstein- Friesians were obtained in a trial on a dairy farm under near commercial conditions. Genotype data comprised SNP information of the Bovine 50K MD Genome chip (45,613 SNPs). Significantly associated genomic regions for MU concentration revealed GJA1 (BTA 9), RXFP1, and FRY1 (both BTA 12) as putative candidates. For milk urea yield (MUY) a promising QTL on BTA 17 including SH3D19 emerged, whereas RCAN2, CLIC5, ENPP4, and ENPP5 (BTA 23) are suggested to influence urinary urea concentration. Minor N-fractions in milk (MN) may be regulated by ELF2 and SLC7A11 (BTA 17), whilst ITPR2 and MYBPC1 (BTA 5), STIM2 (BTA 6), SGCD (BTA 7), SLC6A2 (BTA 18), TMCC2 and MFSD4A (BTA 16) are suggested to have an impact on various non-urea-N (NUN) fractions excreted via urine. Our results highlight genomic regions and candidate genes for N-excretory metabolites and provide a deeper insight into the predisposed component to regulate the N-metabolism in dairy cows.

Two mouse lines selected for large litter size display different lifetime fecundities.

We recently described two outbred mouse lines that were selected for large litter size at first delivery. However, lifetime fecundity appears to be economically more important for the husbandry of many polytocous species for which mouse lines might serve as bona fide animal models (e.g. for pigs). In the present study, we compared the lifetime fecundities of two highly fertile mouse lines (FL1 and FL2: >20 offspring/litter at first delivery) with those of an unselected control line (ctrl) and two lines that were selected for high body weight (DU6) and high protein mass (DU6P) without selection pressure on fertility. We tested the hypothesis that selection for large litter size at first parturition would also increase lifetime fecundity in mice, and we observed very large differences between lines. Whereas FL1 and ctrl delivered up to nine and ten litters, none of the DU6 and DU6P females gave birth to more than five litters. In line with this observation, FL1 delivered the most pups per lifetime (85.7/female). FL2 females produced the largest average litter sizes (20.4 pups/litter) in the first four litters; however, they displayed a reduced number of litters. With the exception of ctrl, litter sizes declined from litter to litter. Repeated delivery of litters with high offspring numbers did not affect the general health of FL females. The presented data demonstrate that two biodiverse, highly fertile mouse lines selected for large litter size at first delivery show different lifetime reproductive fitness levels. Thus, these mouse lines might serve as valuable mouse models for investigating lifetime productivity and longevity in farm animals.

Generalized gametic relationships for flexible analyses of parent-of-origin effects.

A class of epigenetic inheritance patterns known as genomic imprinting allows alleles to influence the phenotype in a parent-of-origin-specific manner. Various pedigree-based parent-of-origin analyses of quantitative traits have attempted to determine the share of genetic variance that is attributable to imprinted loci. In general, these methods require four random gametic effects per pedigree member to account for all possible types of imprinting in a mixed model. As a result, the system of equations may become excessively large to solve using all available data. If only the offspring have records, which is frequently the case for complex pedigrees, only two averaged gametic effects (transmitting abilities) per parent are required (reduced model). However, the parents may have records in some cases. Therefore, in this study, we explain how employing single gametic effects solely for informative individuals (i.e., phenotyped individuals), and only average gametic effects otherwise, significantly reduces the complexity compared with classical gametic models. A generalized gametic relationship matrix is the covariance of this mixture of effects. The matrix can also make the reduced model much more flexible by including observations from parents. Worked examples are present to illustrate the theory and a realistic body mass data set in mice is used to demonstrate its utility. We show how to set up the inverse of the generalized gametic relationship matrix directly from a pedigree. An open-source program is used to implement the rules. The application of the same principles to phased marker data leads to a genomic version of the generalized gametic relationships.

Genomic imprinting analyses identify maternal effects as a cause of phenotypic variability in type 1 diabetes and rheumatoid arthritis.

Imprinted genes, giving rise to parent-of-origin effects (POEs), have been hypothesised to affect type 1 diabetes (T1D) and rheumatoid arthritis (RA). However, maternal effects may also play a role. By using a mixed model that is able to simultaneously consider all kinds of POEs, the importance of POEs for the development of T1D and RA was investigated in a variance components analysis. The analysis was based on Swedish population-scale pedigree data. With P = 0.18 (T1D) and P = 0.26 (RA) imprinting variances were not significant. Explaining up to 19.00% (± 2.00%) and 15.00% (± 6.00%) of the phenotypic variance, the maternal environmental variance was significant for T1D (P = 1.60 × 10-24) and for RA (P = 0.02). For the first time, the existence of maternal genetic effects on RA was indicated, contributing up to 16.00% (± 3.00%) of the total variance. Environmental factors such as the social economic index, the number of offspring, birth year as well as their interactions with sex showed large effects.

Turning Observed Founder Alleles into Expected Relationships in an Intercross Population.

Pedigree-derived relationships for individuals from an intercross of several lines cannot easily account for the segregation variance that is mainly caused by loci with alternative alleles fixed in different lines. However, when all founders are genotyped for a large number of markers, such relationships can be derived for descendants as expected genomic relationships conditional on the observed founder allele frequencies. A tabular method was derived in detail for autosomes and the X-chromosome. As a case study, we analyzed litter size and body weights at three different ages in an advanced mouse intercross (29 generations, total pedigree size 19,266) between a line selected for high litter size (FL1) and a highly inbred control line (DUKsi). Approximately 60% of the total genetic variance was due to segregation variance. Estimated heritability values were 0.20 (0.03), 0.34 (0.04), 0.23 (0.03), 0.41 (0.03) and 0.47 (0.02) for litter size, litter weight and body weight at ages of 21, 42 and 63 days, respectively (standard errors in brackets). These values were between 12% and 65% higher than observed in analyses that treated founders as unrelated. Fields of applications include experimental populations (selection experiments or advanced intercross lines) with a limited number of founders, which can be genotyped at a reasonable cost. In principle any number of founder lines can be treated. Additional genotypes from individuals in later generations can be combined into a joint relationship matrix by capitalizing on previously published approaches.

Scanning the genomes of parents for imprinted loci acting in their un-genotyped progeny.

Depending on their parental origin, alleles at imprinted loci are fully or partially inactivated through epigenetic mechanisms. Their effects contribute to the broader class of parent-of-origin effects. Standard methodology for mapping imprinted quantitative trait loci in association studies requires phenotypes and parental origin of marker alleles (ordered genotypes) to be simultaneously known for each individual. As such, many phenotypes are known from un-genotyped offspring in ongoing breeding programmes (e.g. meat animals), while their parents have known genotypes but no phenotypes. By theoretical considerations and simulations, we showed that the limitations of standard methodology can be overcome in such situations. This is achieved by first estimating parent-of-origin effects, which then serve as dependent variables in association analyses, in which only imprinted loci give a signal. As a theoretical foundation, the regression of parent-of-origin effects on the number of B-alleles at a biallelic locus - representing the un-ordered genotype - equals the imprinting effect. The applicability to real data was demonstrated for about 1800 genotyped Brown Swiss bulls and their un-genotyped fattening progeny. Thus, this approach unlocks vast data resources in various species for imprinting analyses and offers valuable clues as to what extent imprinted loci contribute to genetic variability.

Testicular transcriptional signatures associated with high fertility.

Factors of high fertility are poorly described. The majority of transgenic or knockout models with a reproductive phenotype are subfertile or infertile phenotypes. Few genotypes have been linked to improved reproductive performance (0.2%) or increased litter size (1%). In this study, we used a unique mouse model, fertility line FL1, selected for 'high fertility' for more than 170 generations. This strain has almost doubled the number of littermates as well as their total birth weight accompanied by an elevated ovulation rate and increased numbers of corpora lutea compared to a randomly mated and unselected control line (Ctrl). Here, we investigate whether the gonadal tissue of FL1 males are affected by 'co-evolution' after more than 40 years of female-focused selection. Using microarrays, we analysed the testicular transcriptome of the FL1 and Ctrl mice. These data were also compared with previously published female gonadal transcriptional alterations. We detected alterations in testicular gene expression, which are partly associated with female reproductive performance. Thus, female-focused selection for litter size has not only affected the female side, but also has been manifested in transcriptional alterations on the male gonadal organ. This suggests consequences for the entire mouse lines in the long run and emphasizes the perspective of inevitably considering both genders about mechanisms of high fertility.

Selection for female traits of high fertility affects male reproductive performance and alters the testicular transcriptional profile.

BACKGROUND: Many genes important for reproductive performance are shared by both sexes. However, fecundity indices are primarily based on female parameters such as litter size. We examined a fertility mouse line (FL2), which has a considerably increased number of offspring and a total litter weight of 180% compared to a randomly bred control line (Ctrl) after more than 170 generations of breeding. In the present study, we investigated whether there might be a parallel evolution in males after more than 40 years of breeding in this outbred mouse model. RESULTS: Males of the fertility mouse line FL2 showed reduced sperm motility performance in a 5 h thermal stress experiment and reduced birth rate in the outbred mouse line. Transcriptional analysis of the FL2 testis showed the differential expression of genes associated with steroid metabolic processes (Cyp1b1, Cyp19a1, Hsd3b6, and Cyp21a1) and female fecundity (Gdf9), accompanied by 150% elevated serum progesterone levels in the FL2 males. Cluster analysis revealed the downregulation of genes of the kallikrein-related peptidases (KLK) cluster located on chromosome 7 in addition to alterations in gene expression with serine peptidase activity, e.g., angiotensinogen (Agt), of the renin-angiotensin system essential for ovulation. Although a majority of functional annotations map to female reproduction and ovulation, these genes are differentially expressed in FL2 testis. CONCLUSIONS: These data indicate that selection for primary female traits of increased litter size not only affects sperm characteristics but also manifests as transcriptional alterations of the male side likely with direct long-term consequences for the reproductive performance of the mouse line.

Reproductive performance primarily depends on the female genotype in a two-factorial breeding experiment using high-fertility mouse lines.

Mouse models showing an improved fertility phenotype are barely described in the literature. In the present study, we further characterized two outbred mouse models that have been selected for the phenotype 'high fertility' for more than 177 generations (fertility lines (FL) 1 and 2). In order to delineate the impact of males and females on fertility parameters, we performed a two-factorial breeding experiment by mating males and females of the three different genotypes (FL1, FL2, unselected control (Ctrl)) in all 9 possible combinations. Reproductive performance, such as number of offspring per litter or total birth weight of the entire pup, mainly depends on the female genotype. Although the reproductive performance of FL1 and FL2 is very similar, their phenotypes differ. FL2 animals of both genders are larger compared to FL1 and control animals. Females of the control line delivered offspring earlier compared to FL1 and FL2 dams. Males of FL1 are the lightest and the only ones who gained weight during the two weeks mating period. To address whether this effect is correlated with differing serum androgen levels, we measured the concentrations of testosterone, dehydroepiandrosterone, 4-androstenedione, androstanediol and dihydrotestosterone in males of all three lines by GC-MS. We measured serum testosterone between 5.0 and 6.4 ng/mL, whereas the concentrations of the other androgens were at least one order of magnitude lower, with no significant differences between the lines. Our data indicate that reproductive outcome largely depends on the genotype of the female in a two-factorial breeding experiment and supports previous findings that the phenotype 'high fertility' is warranted by using different physiological strategies.

Adrenal cortex expression quantitative trait loci in a German Holstein × Charolais cross.

BACKGROUND: The importance of the adrenal gland in regard to lactation and reproduction in cattle has been recognized early. Caused by interest in animal welfare and the impact of stress on economically important traits in farm animals the adrenal gland and its function within the stress response is of increasing interest. However, the molecular mechanisms and pathways involved in stress-related effects on economically important traits in farm animals are not fully understood. Gene expression is an important mechanism underlying complex traits, and genetic variants affecting the transcript abundance are thought to influence the manifestation of an expressed phenotype. We therefore investigated the genetic background of adrenocortical gene expression by applying an adaptive linear rank test to identify genome-wide expression quantitative trait loci (eQTL) for adrenal cortex transcripts in cattle. RESULTS: A total of 10,986 adrenal cortex transcripts and 37,204 single nucleotide polymorphisms (SNPs) were analysed in 145 F2 cows of a Charolais × German Holstein cross. We identified 505 SNPs that were associated with the abundance of 129 transcripts, comprising 482 cis effects and 17 trans effects. These SNPs were located on all chromosomes but X, 16, 24 and 28. Associated genes are mainly involved in molecular and cellular functions comprising free radical scavenging, cellular compromise, cell morphology and lipid metabolism, including genes such as CYP27A1 and LHCGR that have been shown to affect economically important traits in cattle. CONCLUSIONS: In this study we showed that adrenocortical eQTL affect the expression of genes known to contribute to the phenotypic manifestation in cattle. Furthermore, some of the identified genes and related molecular pathways were previously shown to contribute to the phenotypic variation of behaviour, temperament and growth at the onset of puberty in the same population investigated here. We conclude that eQTL analysis appears to be a useful approach providing insight into the molecular and genetic background of complex traits in cattle and will help to understand molecular networks involved.

Covariance Between Genotypic Effects and its Use for Genomic Inference in Half-Sib Families.

In livestock, current statistical approaches utilize extensive molecular data, e.g., single nucleotide polymorphisms (SNPs), to improve the genetic evaluation of individuals. The number of model parameters increases with the number of SNPs, so the multicollinearity between covariates can affect the results obtained using whole genome regression methods. In this study, dependencies between SNPs due to linkage and linkage disequilibrium among the chromosome segments were explicitly considered in methods used to estimate the effects of SNPs. The population structure affects the extent of such dependencies, so the covariance among SNP genotypes was derived for half-sib families, which are typical in livestock populations. Conditional on the SNP haplotypes of the common parent (sire), the theoretical covariance was determined using the haplotype frequencies of the population from which the individual parent (dam) was derived. The resulting covariance matrix was included in a statistical model for a trait of interest, and this covariance matrix was then used to specify prior assumptions for SNP effects in a Bayesian framework. The approach was applied to one family in simulated scenarios (few and many quantitative trait loci) and using semireal data obtained from dairy cattle to identify genome segments that affect performance traits, as well as to investigate the impact on predictive ability. Compared with a method that does not explicitly consider any of the relationship among predictor variables, the accuracy of genetic value prediction was improved by 10-22%. The results show that the inclusion of dependence is particularly important for genomic inference based on small sample sizes.

Mendelian sampling covariability of marker effects and genetic values.

BACKGROUND: Measures of the expected genetic variability among full-sibs are of practical relevance, such as in the context of mating decisions. An important application field in animal and plant breeding is the selection and allocation of mates when large or small amounts of genetic variability among offspring are desired, depending on user-specific goals. Estimates of the Mendelian sampling variance can be obtained by simulating gametes from parents with known diplotypes. Knowledge of recombination rates and additive marker effects is also required. In this study, we aimed at developing an exact method that can account for both additive and dominance effects. RESULTS: We derived parent-specific covariance matrices that exactly quantify the within-family (co-)variability of additive and dominance marker effects. These matrices incorporate prior knowledge of the parental diplotypes and recombination rates. When combined with additive marker effects, they allow the exact derivation of the Mendelian sampling (co-)variances of (estimated) breeding values for several traits, as well for the aggregate genotype. A comparative analysis demonstrated good average agreement between the exact values and the simulation results for a practical dataset (74,353 German Holstein cattle). CONCLUSIONS: The newly derived method is suitable for calculating the exact amount of intra-family variation of the estimated breeding values and genetic values (comprising additive and dominance effects).

Genome-Wide Association Study of a Varroa-Specific Defense Behavior in Honeybees (Apis mellifera).

Honey bees are exposed to many damaging pathogens and parasites. The most devastating is Varroa destructor, which mainly affects the brood. A promising approach for preventing its spread is to breed Varroa-resistant honey bees. One trait that has been shown to provide significant resistance against the Varroa mite is hygienic behavior, which is a behavioral response of honeybee workers to brood diseases in general. Here, we report the use of an Affymetrix 44K SNP array to analyze SNPs associated with detection and uncapping of Varroa-parasitized brood by individual worker bees (Apis mellifera). For this study, 22 000 individually labeled bees were video-monitored and a sample of 122 cases and 122 controls was collected and analyzed to determine the dependence/independence of SNP genotypes from hygienic and nonhygienic behavior on a genome-wide scale. After false-discovery rate correction of the P values, 6 SNP markers had highly significant associations with the trait investigated (α < 0.01). Inspection of the genomic regions around these SNPs led to the discovery of putative candidate genes.

On the relevance of technical variation due to building pools in microarray experiments.

BACKGROUND: Pooled samples are frequently used in experiments measuring gene expression. In this method, RNA from different individuals sharing the same experimental conditions and explanatory variables is blended and their concentrations are jointly measured. As a matter of principle, individuals are represented in equal shares in each pool. However, some degree of disproportionality may arise from the limits of technical precision. As a consequence a special kind of technical error occurs, which can be modelled by a respective variance component. Previously published theory - allowing for variable pool sizes - has been applied to four microarray gene expression data sets from different species in order to assess the practical relevance of this type of technical error in terms of significance and size of this variance component. RESULTS: The number of transcripts with a significant variance component due to imperfect blending was found to be 4329 (23 %) in mouse data and 7093 (49 %) in honey bees, but only 6 in rats and none whatsoever in human data. These results correspond to a false discovery rate of 5 % in each data set. The number of transcripts found to be differentially expressed between treatments was always higher when the blending error variance was neglected. Simulations clearly indicated overly-optimistic (anti-conservative) test results in terms of false discovery rates whenever this source of variability was not represented in the model. CONCLUSIONS: Imperfect equality of shares when blending RNA from different individuals into joint pools of variable size is a source of technical variation with relevance for experimental design, practice at the laboratory bench and data analysis. Its potentially adverse effects, incorrect identification of differentially expressed transcripts and overly-optimistic significance tests, can be fully avoided, however, by the sound application of recently established theory and models for data analysis.