The potential impact of climate change on Australia's soil organic carbon resources

BACKGROUND: Soil organic carbon (SOC) represents a significant pool of carbon within the biosphere. Climatic shifts in temperature and precipitation have a major influence on the decomposition and amount of SOC stored within an ecosystem and that released into the atmosphere. We have linked net primary production (NPP) algorithms, which include the impact of enhanced atmospheric CO(2 )on plant growth, to the SOCRATES terrestrial carbon model to estimate changes in SOC for the Australia continent between the years 1990 and 2100 in response to climate changes generated by the CSIRO Mark 2 Global Circulation Model (GCM). RESULTS: We estimate organic carbon storage in the topsoil (0–10 cm) of the Australian continent in 1990 to be 8.1 Gt. This equates to 19 and 34 Gt in the top 30 and 100 cm of soil, respectively. By the year 2100, under a low emissions scenario, topsoil organic carbon stores of the continent will have increased by 0.6% (49 Mt C). Under a high emissions scenario, the Australian continent becomes a source of CO(2 )with a net reduction of 6.4% (518 Mt) in topsoil carbon, when compared to no climate change. This is partially offset by the predicted increase in NPP of 20.3% CONCLUSION: Climate change impacts must be studied holistically, requiring integration of climate, plant, ecosystem and soil sciences. The SOCRATES terrestrial carbon cycling model provides realistic estimates of changes in SOC storage in response to climate change over the next century, and confirms the need for greater consideration of soils in assessing the full impact of climate change and the development of quantifiable mitigation strategies

Detecting autozygosity through runs of homozygosity: A comparison of three autozygosity detection algorithms

<p>Abstract</p> <p>Background</p> <p>A central aim for studying runs of homozygosity (ROHs) in genome-wide SNP data is to detect the effects of autozygosity (stretches of the two homologous chromosomes within the same individual that are identical by descent) on phenotypes. However, it is unknown which current ROH detection program, and which set of parameters within a given program, is optimal for differentiating ROHs that are truly autozygous from ROHs that are homozygous at the marker level but vary at unmeasured variants between the markers.</p> <p>Method</p> <p>We simulated 120 Mb of sequence data in order to know the true state of autozygosity. We then extracted common variants from this sequence to mimic the properties of SNP platforms and performed ROH analyses using three popular ROH detection programs, PLINK, GERMLINE, and BEAGLE. We varied detection thresholds for each program (e.g., prior probabilities, lengths of ROHs) to understand their effects on detecting known autozygosity.</p> <p>Results</p> <p>Within the optimal thresholds for each program, PLINK outperformed GERMLINE and BEAGLE in detecting autozygosity from distant common ancestors. PLINK's sliding window algorithm worked best when using SNP data pruned for linkage disequilibrium (LD).</p> <p>Conclusion</p> <p>Our results provide both general and specific recommendations for maximizing autozygosity detection in genome-wide SNP data, and should apply equally well to research on whole-genome autozygosity burden or to research on whether specific autozygous regions are predictive using association mapping methods.</p

Fine-mapping of common genetic variants associated with colorectal tumor risk identified potential functional variants

Genome-wide association studies (GWAS) have identified many common single nucleotide polymorphisms (SNPs) associated with colorectal cancer risk. These SNPs may tag correlated variants with biological importance. Fine-mapping around GWAS loci can facilitate detection of functional candidates and additional independent risk variants. We analyzed 11,900 cases and 14,311 controls in the Genetics and Epidemiology of Colorectal Cancer Consortium and the Colon Cancer Family Registry. To fine-map genomic regions containing all known common risk variants, we imputed high-density genetic data from the 1000 Genomes Project. We tested single-variant associations with colorectal tumor risk for all variants spanning genomic regions 250-kb upstream or downstream of 31 GWAS-identified SNPs (index SNPs). We queried the University of California, Santa Cruz Genome Browser to examine evidence for biological function. Index SNPs did not show the strongest association signals with colorectal tumor risk in their respective genomic regions. Bioinformatics analysis of SNPs showing smaller P-values in each region revealed 21 functional candidates in 12 loci (5q31.1, 8q24, 11q13.4, 11q23, 12p13.32, 12q24.21, 14q22.2, 15q13, 18q21, 19q13.1, 20p12.3, and 20q13.33). We did not observe evidence of additional independent association signals in GWAS-identified regions. Our results support the utility of integrating data from comprehensive fine-mapping with expanding publicly available genomic databases to help clarify GWAS associations and identify functional candidates that warrant more onerous laboratory follow-up. Such efforts may aid the eventual discovery of disease-causing variant(s).National Institutes of Health; National Cancer Institute; U.S. Department of Health and Human Services

Lying down frequency as a discomfort index in heat stressed Holstein bull calves

Abstract Changes in lying behaviour in response to extreme ambient temperatures have not been examined in dairy calves so far. In this study, lying time, and frequency of lying down were investigated in shaded (n = 8) and non-shaded (n = 8) Holstein bull calves during a 5-d period [temperature, average/max (°C); Day 1 (control, all calves shaded): 22.9/29.4, Day 2 (heat stress day): 28.3/38.8, Day 3: 26.2/33.5, Day 4: 23.7/28.7, and Day 5: 21.2/24.7]. The thermal environment around the calves was characterized by the temperature–humidity index (THI). A three-dimension accelerometer was used to record posture of the calves and lying time and lying down frequency were analysed with 4-h sampling intervals. On Day 1 no differences were found in THI between the shaded and non-shaded environments. On Days 2, 3 and 4 maximal and average THI were higher in the shaded than those recorded for the non-shaded environment. On Day5 no significant differences in THI were observed between calf environments. A similar diurnal pattern of lying time and lying down frequency was observed in both groups. Lying times were shorter during the afternoon (P = 0.003); however, no group differences were found in lying time (P = 0.551). During the daytime (between 8:00 and 20:00), the frequency of lying down was 50, 33, and 41% higher, respectively, than during the nighttime on Days 2, 3 and 4 (P < 0.001, P = 0.011, and P < 0.001). On the heat stress day, non-shaded calves changed posture 88.4 and 76.6% more often than shaded ones between 8:00 and 12:00 and 12:00 and 16:00, respectively (P < 0.001 for both intervals). Similar group differences were observed for Day 3 between 8:00 and 12:00 (71.2%) and Day 4 between 12:00 and 16:00 (76.6%), respectively (P = 0.003, and P = 0.001). On Day 5, there was no difference between groups (P = 0.732). As indicated by our results, heat stress causes changes in lying down frequency and lying time in dairy calves. Supplemental shading reduces discomfort as indicated by lying down frequency, but not by lying time