A method to customize population-specific arrays for genome-wide association testing.

As an example of optimizing population-specific genotyping assays using a whole-genome sequence reference set, we detail the approach that followed to design the Axiom-NL array which is characterized by an improved imputation backbone based on the Genome of the Netherlands (GoNL) reference sequence and, compared with earlier arrays, a more comprehensive inclusion of SNPs on chromosomes X, Y, and the mitochondria. Common variants on the array were selected to be compatible with the Illumina Psych Array and the Affymetrix UK Biobank Axiom array. About 3.5% of the array (23 977 markers) represents SNPs from the GWAS catalog, including SNPs at FTO, APOE, Ion-channels, killer-cell immunoglobulin-like receptors, and HLA. Around 26 000 markers associated with common psychiatric disorders are included, as well as 6705 markers suggested to be associated with fertility and twinning. The platform can thus be used for risk profiling, detection of new variants, as well as ancestry determination. Results of coverage tests in 249 unrelated subjects with GoNL-based sequence data show that after imputation with 1000G as a reference, the median concordance between original and imputed genotypes is above 98%. The median imputation quality R2 for MAF thresholds of 0.001, 0.01, 0.05, and >0.05 are 0.05, 0.28, 0.80, 0.99, respectively, for the 1000G imputed SNPs, with a similar quality for the autosomes and X chromosome, showing a good genome-wide coverage for association studies after imputation.

Joint variant and de novo mutation identification on pedigrees from high-throughput sequencing data.

The analysis of whole-genome or exome sequencing data from trios and pedigrees has been successfully applied to the identification of disease-causing mutations. However, most methods used to identify and genotype genetic variants from next-generation sequencing data ignore the relationships between samples, resulting in significant Mendelian errors, false positives and negatives. Here we present a Bayesian network framework that jointly analyzes data from all members of a pedigree simultaneously using Mendelian segregation priors, yet providing the ability to detect de novo mutations in offspring, and is scalable to large pedigrees. We evaluated our method by simulations and analysis of whole-genome sequencing (WGS) data from a 17-individual, 3-generation CEPH pedigree sequenced to 50× average depth. Compared with singleton calling, our family caller produced more high-quality variants and eliminated spurious calls as judged by common quality metrics such as Ti/Tv, Het/Hom ratios, and dbSNP/SNP array data concordance, and by comparing to ground truth variant sets available for this sample. We identify all previously validated de novo mutations in NA12878, concurrent with a 7× precision improvement. Our results show that our method is scalable to large genomics and human disease studies.

Crops gone wild: evolution of weeds and invasives from domesticated ancestors.

The evolution of problematic plants, both weeds and invasives, is a topic of increasing interest. Plants that have evolved from domesticated ancestors have certain advantages for study. Because of their economic importance, domesticated plants are generally well-characterized and readily available for ecogenetic comparison with their wild descendants. Thus, the evolutionary history of crop descendants has the potential to be reconstructed in some detail. Furthermore, growing crop progenitors with their problematic descendants in a common environment allows for the identification of significant evolutionary differences that correlate with weediness or invasiveness. We sought well-established examples of invasives and weeds for which genetic and/or ethnobotanical evidence has confirmed their evolution from domesticates. We found surprisingly few cases, only 13. We examine our list for generalizations and then some selected cases to reveal how plant pests have evolved from domesticates. Despite their potential utility, crop descendants remain underexploited for evolutionary study. Promising evolutionary research opportunities for these systems are abundant and worthy of pursuit.

Proteomics reveals new salt responsive proteins associated with rice plasma membrane.

The signaling processes in plants that initiate cellular responses to biotic and abiotic factors are believed to be located in the plasma membrane (PM). A better understanding of the PM proteome response to environmental stresses might lead to new strategies for improving stress-tolerant crops. A sub-cellular proteomics approach was applied to monitor changes in abundance of PM-associated protein in response to salinity, a key abiotic stress affecting rice productivity worldwide. Proteome was extracted from a root plasma-membrane-rich fraction of a rice salt tolerant variety, IR651, grown under saline and normal conditions. Comparative two-dimensional electrophoresis revealed that 24 proteins were differentially expressed in response to salt stress. From these, eight proteins were identified by mass spectrometry analysis. Most of the proteins identified are likely to be PM-associated and are known to be involved in several important mechanisms of plant adaptation to salt stress. These include regulation of PM pumps and channels, membrane structure, oxidative stress defense, signal transduction, protein folding, and the methyl cycle. To investigate the correlation between mRNA and protein level in response to salinity, we performed quantitative Real-Time PCR analysis of three genes that were salt responsive at the protein level, including 1,4-Benzoquinone reductase, a putative remorin and a hypersensitive induced response protein. No concordance was detected between the changes in levels of gene and protein expression. Our results indicate that the proteomics approach is suitable for expression analysis of membrane associated proteins under salt stress.