A genome-wide epistatic network underlies the molecular architecture of continuous color variation of body extremities.

Deciphering the molecular architecture of coat coloration for a better understanding of the biological mechanisms underlying pigmentation still remains a challenge. We took advantage of a rabbit French experimental population in which both a pattern and a gradient of coloration from white to brown segregated within the himalayan phenotype. The whole experimental design was genotyped using the high density Affymetrix® AxiomOrcun™ SNP Array and phenotyped into 6 different groups ordered from the lighter to the darker. Genome-wide association analyses pinpointed an oligogenic determinism, under recessive and additive inheritance, involving genes already known in melanogenesis (ASIP, KIT, MC1R, TYR), and likely processed pseudogenes linked to ribosomal function, RPS20 and RPS14. We also identified (i) gene-gene interactions through ASIP:MC1R affecting light cream/beige phenotypes while KIT:RPS responsible of dark chocolate/brown colors and (ii) a genome-wide epistatic network involving several others coloration genes such as POT1 or HPS5. Finally, we determined the recessive inheritance of the English spotting phenotype likely involving a copy number variation affecting at least the end of the coding sequence of the KIT gene. Our analyses of coloration as a continuous trait allowed us to go beyond much of the established knowledge through the detection of additional genes and gene-gene interactions that may contribute to the molecular architecture of the coloration phenotype.

Identification of genomic regions affecting production traits in pigs divergently selected for feed efficiency.

BACKGROUND: Feed efficiency is a major driver of the sustainability of pig production systems. Understanding the biological mechanisms that underlie these agronomic traits is an important issue for environment questions and farms' economy. This study aimed at identifying genomic regions that affect residual feed intake (RFI) and other production traits in two pig lines divergently selected for RFI during nine generations (LRFI, low RFI; HRFI, high RFI). RESULTS: We built a whole dataset of 570,447 single nucleotide polymorphisms (SNPs) in 2426 pigs with records for 24 production traits after both imputation and prediction of genotypes using pedigree information. Genome-wide association studies (GWAS) were performed including both lines (global-GWAS) or each line independently (LRFI-GWAS and HRFI-GWAS). Forty-five chromosomal regions were detected in the global-GWAS, whereas 28 and 42 regions were detected in the HRFI-GWAS and LRFI-GWAS, respectively. Among these 45 regions, only 13 were shared between at least two analyses, and only one was common between the three GWAS but it affects different traits. Among the five quantitative trait loci (QTL) detected for RFI, two were close to QTL for meat quality traits and two pinpointed novel genomic regions that harbor candidate genes involved in cell proliferation and differentiation processes of gastrointestinal tissues or in lipid metabolism-related signaling pathways. In most cases, different QTL regions were detected between the three designs, which suggests a strong impact of the dataset structure on the detection power and could be due to the changes in allelic frequencies during the establishment of lines. CONCLUSIONS: In addition to efficiently detecting known and new QTL regions for feed efficiency, the combination of GWAS carried out per line or simultaneously using all individuals highlighted chromosomal regions that affect production traits and presented significant changes in allelic frequencies across generations. Further analyses are needed to estimate whether these regions correspond to traces of selection or result from genetic drift.

The impact of training on data from genetically-related lines on the accuracy of genomic predictions for feed efficiency traits in pigs.

BACKGROUND: Most genomic predictions use a unique population that is split into a training and a validation set. However, genomic prediction using genetically heterogeneous training sets could provide more flexibility when constructing the training sets in small populations. The aim of our study was to investigate the potential of genomic prediction of feed efficiency related traits using training sets that combine animals from two different, but genetically-related lines. We compared realized prediction accuracy and prediction bias for different training set compositions for five production traits. RESULTS: Genomic breeding values (GEBV) were predicted using the single-step genomic best linear unbiased prediction method in six scenarios applied iteratively to two genetically-related lines (i.e. 12 scenarios). The objective for all scenarios was to predict GEBV of pigs in the last three generations (~ 400 pigs, G7 to G9) of a given line. For each line, a control scenario was set up with a training set that included only animals from that line (target line). For all traits, adding more animals from the other line to the training set did not increase prediction accuracy compared to the control scenario. A small decrease in prediction accuracies was found for average daily gain, backfat thickness, and daily feed intake as the number of animals from the target line decreased in the training set. Including more animals from the other line did not decrease prediction accuracy for feed conversion ratio and residual feed intake, which were both highly affected by selection within lines. However, prediction biases were systematic for these cases and might be reduced with bivariate analyses. CONCLUSIONS: Our results show that genomic prediction using a training set that includes animals from genetically-related lines can be as accurate as genomic prediction using a training set from the target population. With combined reference sets, accuracy increased for traits that were highly affected by selection. Our results provide insights into the design of reference populations, especially to initiate genomic selection in small-sized lines, for which the number of historical samples is small and that are developed simultaneously. This applies especially to poultry and pig breeding and to other crossbreeding schemes.

Embryonic environment and transgenerational effects in quail.

BACKGROUND: Environmental exposures, for instance to chemicals, are known to impact plant and animal phenotypes on the long term, sometimes across several generations. Such transgenerational phenotypes were shown to be promoted by epigenetic alterations such as DNA methylation, an epigenetic mark involved in the regulation of gene expression. However, it is yet unknown whether transgenerational epigenetic inheritance of altered phenotypes exists in birds. The purpose of this study was to develop an avian model to investigate whether changes to the embryonic environment had a transgenerational effect that could alter the phenotypes of third-generation offspring. Given its impact on the mammalian epigenome and the reproductive system in birds, genistein was used as an environment stressor. RESULTS: We compared several third-generation phenotypes of two quail "epilines", which were obtained from genistein-injected eggs (Epi+) or from untreated eggs (Epi-) from the same founders. A "mirrored" crossing strategy was used to minimize between-line genetic variability by maintaining similar ancestor contributions across generations in each line. Three generations after genistein treatment, a significant difference in the sexual maturity of the females, which, after three generations, could not be attributed to direct maternal effects, was observed between the lines, with Epi+ females starting to lay eggs later. Adult body weight was significantly affected by genistein treatment applied in a previous generation, and a significant interaction between line and sex was observed for body weight at 3 weeks. Behavioral traits, such as evaluating the birds' reaction to social isolation, were also significantly affected by genistein treatment. Yet, global methylation analyses revealed no significant difference between the epilines. CONCLUSIONS: These findings demonstrate that embryonic environment affects the phenotype of offspring three generations later in quail. While one cannot rule out the existence of some initial genetic variability between the lines, the mirrored animal design should have minimized its effects, and thus, the observed differences in animals of the third generation may be attributed, at least partly, to transgenerational epigenetic phenomena.

New gene functions in megakaryopoiesis and platelet formation.

Platelets are the second most abundant cell type in blood and are essential for maintaining haemostasis. Their count and volume are tightly controlled within narrow physiological ranges, but there is only limited understanding of the molecular processes controlling both traits. Here we carried out a high-powered meta-analysis of genome-wide association studies (GWAS) in up to 66,867 individuals of European ancestry, followed by extensive biological and functional assessment. We identified 68 genomic loci reliably associated with platelet count and volume mapping to established and putative novel regulators of megakaryopoiesis and platelet formation. These genes show megakaryocyte-specific gene expression patterns and extensive network connectivity. Using gene silencing in Danio rerio and Drosophila melanogaster, we identified 11 of the genes as novel regulators of blood cell formation. Taken together, our findings advance understanding of novel gene functions controlling fate-determining events during megakaryopoiesis and platelet formation, providing a new example of successful translation of GWAS to function.

Stratifying type 2 diabetes cases by BMI identifies genetic risk variants in LAMA1 and enrichment for risk variants in lean compared to obese cases.

Common diseases such as type 2 diabetes are phenotypically heterogeneous. Obesity is a major risk factor for type 2 diabetes, but patients vary appreciably in body mass index. We hypothesized that the genetic predisposition to the disease may be different in lean (BMI<25 Kg/m²) compared to obese cases (BMI≥30 Kg/m²). We performed two case-control genome-wide studies using two accepted cut-offs for defining individuals as overweight or obese. We used 2,112 lean type 2 diabetes cases (BMI<25 kg/m²) or 4,123 obese cases (BMI≥30 kg/m²), and 54,412 un-stratified controls. Replication was performed in 2,881 lean cases or 8,702 obese cases, and 18,957 un-stratified controls. To assess the effects of known signals, we tested the individual and combined effects of SNPs representing 36 type 2 diabetes loci. After combining data from discovery and replication datasets, we identified two signals not previously reported in Europeans. A variant (rs8090011) in the LAMA1 gene was associated with type 2 diabetes in lean cases (P = 8.4×10⁻9, OR = 1.13 [95% CI 1.09-1.18]), and this association was stronger than that in obese cases (P = 0.04, OR = 1.03 [95% CI 1.00-1.06]). A variant in HMG20A--previously identified in South Asians but not Europeans--was associated with type 2 diabetes in obese cases (P = 1.3×10⁻8, OR = 1.11 [95% CI 1.07-1.15]), although this association was not significantly stronger than that in lean cases (P = 0.02, OR = 1.09 [95% CI 1.02-1.17]). For 36 known type 2 diabetes loci, 29 had a larger odds ratio in the lean compared to obese (binomial P = 0.0002). In the lean analysis, we observed a weighted per-risk allele OR = 1.13 [95% CI 1.10-1.17], P = 3.2×10⁻¹4. This was larger than the same model fitted in the obese analysis where the OR = 1.06 [95% CI 1.05-1.08], P = 2.2×10⁻¹6. This study provides evidence that stratification of type 2 diabetes cases by BMI may help identify additional risk variants and that lean cases may have a stronger genetic predisposition to type 2 diabetes.

Kidney and recipient weight incompatibility reduces long-term graft survival.

Long-term function of kidney allografts depends on multiple variables, one of which may be the compatibility in size between the graft and the recipient. Here, we assessed the long-term consequences of the ratio of the weight of the kidney to the weight of the recipient (KwRw ratio) in a multicenter cohort of 1189 patients who received a transplant between 1995 and 2006. The graft filtration rate increased by a mean of 5.74 ml/min between the third and sixth posttransplantation months among patients with a low KwRw ratio (<2.3 g/kg; P<0.0001). In this low KwRw ratio group, the graft filtration rate remained stable between 6 months and 7 years but then decreased at a mean rate of 3.17 ml/min per yr (P<0.0001). In addition, low KwRw ratios conferred greater risk for proteinuria, more antihypertensive drugs, and segmental or global glomerulosclerosis. Moreover, a KwRw ratio<2.3 g/kg associated with a 55% increased risk for transplant failure by 2 years of follow-up. In conclusion, incompatibility between graft and recipient weight is an independent predictor of long-term graft survival, suggesting that avoiding kidney and recipient weight incompatibility may improve late clinical outcome after kidney transplantation.

Common variants in FTO, MC4R, TMEM18, PRL, AIF1, and PCSK1 show evidence of association with adult obesity in the Greek population.

Twenty-four single-nucleotide polymorphisms (SNPs) have been reproducibly associated with obesity. We performed a follow-up study for obesity in the Greek adult population. A total of 510 obese and 469 lean adults were genotyped for 24 SNPs. We tested the association with obesity status using logistic regression and we evaluated the combined genetic risk of 24 SNPs by calculating the area under the receiver-operating characteristic (ROC) curves. We nominally replicated the association with obesity (BMI ≥30 kg/m(2)) of six SNPs in or near the FTO, MC4R, TMEM18, PRL, AIF1, and PCSK1 loci (1.28 ≤ odds ratio (OR) ≤ 1.35; 0.004 ≤ P ≤ 0.043). The discrimination ability for obesity was slightly stronger (P = 9.59 × 10(-6)) when the genetic information of the 24 SNPs was added to nongenetic risk factors (area under the curve (AUC) = 0.722) in comparison with nongenetic factors analyzed alone (AUC = 0.685). Our data suggest that SNPs in or near the FTO, MC4R, TMEM18, PRL, AIF1, and PCSK1 loci contribute to obesity risk in the Greek population.

A genome-wide association study identifies rs2000999 as a strong genetic determinant of circulating haptoglobin levels.

Haptoglobin is an acute phase inflammatory marker. Its main function is to bind hemoglobin released from erythrocytes to aid its elimination, and thereby haptoglobin prevents the generation of reactive oxygen species in the blood. Haptoglobin levels have been repeatedly associated with a variety of inflammation-linked infectious and non-infectious diseases, including malaria, tuberculosis, human immunodeficiency virus, hepatitis C, diabetes, carotid atherosclerosis, and acute myocardial infarction. However, a comprehensive genetic assessment of the inter-individual variability of circulating haptoglobin levels has not been conducted so far.We used a genome-wide association study initially conducted in 631 French children followed by a replication in three additional European sample sets and we identified a common single nucleotide polymorphism (SNP), rs2000999 located in the Haptoglobin gene (HP) as a strong genetic predictor of circulating Haptoglobin levels (P(overall) = 8.1 × 10(-59)), explaining 45.4% of its genetic variability (11.8% of Hp global variance). The functional relevance of rs2000999 was further demonstrated by its specific association with HP mRNA levels (ß = 0.23 ± 0.08, P = 0.007). Finally, SNP rs2000999 was associated with decreased total and low-density lipoprotein cholesterol in 8,789 European children (P(total cholesterol) = 0.002 and P(LDL) = 0.0008).Given the central position of haptoglobin in many inflammation-related metabolic pathways, the relevance of rs2000999 genotyping when evaluating haptoglobin concentration should be further investigated in order to improve its diagnostic/therapeutic and/or prevention impact.

Lack of association of CD36 SNPs with early onset obesity: a meta-analysis in 9,973 European subjects.

A recent study suggested that four CD36 polymorphisms (namely rs3211867, rs3211883, rs3211908, and rs1527483) were associated with an increased risk of obesity, an increased BMI and percentage of body fat in European adolescents. We first attempted to confirm these results in three independent case-control genome-wide association studies (GWAS) data totaling 3,509 subjects of French and German origin, but we were unable to find any association of these variants with early onset obesity risk. We then genotyped the four CD36 single-nucleotide polymorphisms (SNPs) in a large population-based study of 4,667 Finnish subjects and we did not replicate any of the recently reported associations with BMI. By combining all available data in a meta-analysis (N = 9,973), we found no evidence for an association of the reported four variants in CD36 with increased obesity risk or increased BMI (0.07 ≤ P values ≤ 0.93). Finally, we assessed the contribution of the full CD36 locus gene variation to obesity risk in 3,509 subjects and we did not detect any significant association with obesity after correction for multiple testing. In summary, we were unable to confirm the recently reported association of variants in CD36 with early onset obesity in populations of European ancestry.

Genetic variant in HK1 is associated with a proanemic state and A1C but not other glycemic control-related traits.

OBJECTIVE: A1C is widely considered the gold standard for monitoring effective blood glucose levels. Recently, a genome-wide association study reported an association between A1C and rs7072268 within HK1 (encoding hexokinase 1), which catalyzes the first step of glycolysis. HK1 deficiency in erythrocytes (red blood cells [RBCs]) causes severe nonspherocytic hemolytic anemia in both humans and mice. RESEARCH DESIGN AND METHODS: The contribution of rs7072268 to A1C and the RBC-related traits was assessed in 6,953 nondiabetic European participants. We additionally analyzed the association with hematologic traits in 5,229 nondiabetic European individuals (in whom A1C was not measured) and 1,924 diabetic patients. Glucose control-related markers other than A1C were analyzed in 18,694 nondiabetic European individuals. A type 2 diabetes case-control study included 7,447 French diabetic patients. RESULTS: Our study confirms a strong association between the rs7072268-T allele and increased A1C (beta = 0.029%; P = 2.22 x 10(-7)). Surprisingly, despite adequate study power, rs7072268 showed no association with any other markers of glucose control (fasting- and 2-h post-OGTT-related parameters, n = 18,694). In contrast, rs7072268-T allele decreases hemoglobin levels (n = 13,416; beta = -0.054 g/dl; P = 3.74 x 10(-6)) and hematocrit (n = 11,492; beta = -0.13%; P = 2.26 x 10(-4)), suggesting a proanemic effect. The T allele also increases risk for anemia (836 cases; odds ratio 1.13; P = 0.018). CONCLUSIONS: HK1 variation, although strongly associated with A1C, does not seem to be involved in blood glucose control. Since HK1 rs7072268 is associated with reduced hemoglobin levels and favors anemia, we propose that HK1 may influence A1C levels through its anemic effect or its effect on glucose metabolism in RBCs. These findings may have implications for type 2 diabetes diagnosis and clinical management because anemia is a frequent complication of the diabetes state.