Transposable elements (TEs) are a major source of genome variation across the branches of life. Although TEs may play an adaptive role in their host’s genome, they are more often deleterious, and purifying selection is an important factor controlling their genomic loads. In contrast, life history, mating system, GC content, and RNAi pathways have been suggested to account for the disparity of TE loads in different species. Previous studies of fungal, plant, and animal genomes have reported conflicting results regarding the direction in which these genomic features drive TE evolution. Many of these studies have had limited power, however, because they studied taxonomically narrow systems, comparing only a limited number of phylogenetically independent contrasts, and did not address long-term effects on TE evolution. Here, we test the long-term determinants of TE evolution by comparing 42 nematode genomes spanning over 500 million years of diversification. This analysis includes numerous transitions between life history states, and RNAi pathways, and evaluates if these forces are sufficiently persistent to affect the long-term evolution of TE loads in eukaryotic genomes. Although we demonstrate statistical power to detect selection, we find no evidence that variation in these factors influence genomic TE loads across extended periods of time. In contrast, the effects of genetic drift appear to persist and control TE variation among species. We suggest that variation in the tested factors are largely inconsequential to the large differences in TE content observed between genomes, and only by these large-scale comparisons can we distinguish long-term and persistent effects from transient or random changes.

The past decade has witnessed the discovery of obesity-related genetic variants and their functions through genome-wide association studies. Combinations of risk alleles can influence obesity phenotypes with different degrees of effectiveness across various individuals by interacting with environmental factors. We examined the interaction between genetic variation and changes in dietary habits or exercise that influences body fat loss from a large Korean cohort (n = 8840). Out of 673 obesity-related SNPs, a total of 100 SNPs (37 for carbohydrate intake; 19 for fat intake; 44 for total calories intake; 25 for exercise onset) identified to have gene-environment interaction effect in generalized linear model were used to calculate genetic risk scores (GRS). Based on the GRS distribution, we divided the population into four levels, namely, “very insensitive”, “insensitive”, “sensitive”, and “very sensitive” for each of the four categories, “carbohydrate intake”, “fat intake”, “total calories intake”, and “exercise”. Overall, the mean body fat loss became larger when the sensitivity level was increased. In conclusion, genetic variants influence the effectiveness of dietary regimes for body fat loss. Based on our findings, we suggest a platform for personalized body fat management by providing the most suitable and effective nutrition or activity plan specific to an individual.

Largely driven by huge reductions in per-base costs, sequencing nucleic acids has become a near-ubiquitous technique in laboratories performing biological and biomedical research. Most of the effort goes to re-sequencing, but assembly of de novogenerated, raw sequence reads into contigs that span as much of the genome as possible is central to many projects. Although truly complete coverage is not realistically attainable, maximizing the amount of sequence that can be correctly assembled into contigs contributes to coverage. Here we compare three commonly used assembly algorithms (ABySS, Velvet and SOAPdenovo2), and show that empirical optimization of k-mer values has a disproportionate influence on de novo assembly of a eukaryotic genome, the nematode parasite Meloidogynechitwoodi. Each assembler was challenged with about 40 million Iluumina II paired-end reads, and assemblies performed under a range of k-mer sizes. In each instance, the optimal k-mer was 127, although based on N50 values,ABySS was more efficient than the others. That the assembly was not spurious was established using the “Core Eukaryotic Gene Mapping Approach”, which indicated that 98.79% of the M. chitwoodi genome was accounted for by the assembly. Subsequent gene finding and annotation are consistent with this and suggest that k-mer optimization contributes to the robustness of assembly.

Previous studies suggested that genetic, environmental factors and their interactions could affect body fat mass (BFM). However, studies describing these effects were performed at a single time point in a population. In this study, we investigated the interaction between genetic and environmental factors in affecting BFM and implicate the healthcare utilization of lifestyle modifications from a personalized and genomic perspective. We examined how nutritional intake or physical activity changes in the individuals affect BFM concerning the genetic composition. We conducted an observational study including 259 adult participants with single nucleotide polymorphism (SNP) genotyping and longitudinal lifestyle monitoring, including food consumption and physical activities, by following lifestyle modification guidance. The participants’ lifelog data on exercise and diet were collected through a wearable device for 3 months. Moreover, we measured anthropometric and serologic markers to monitor their potential changes through lifestyle modification. We examined the influence of genetic composition on body fat reduction induced by lifestyle changes using genetic risk scores (GRSs) of three phenotypes: GRS-carbohydrate (GRS-C), GRS-fat (GRS-F), and GRS-exercise (GRS-E). Our results showed that lifestyle modifications affected BFM more significantly in the high GRS class compared to the low GRS class, indicating the role of genetic factors affecting the efficiency of the lifestyle modification-induced BFM changes. Interestingly, the influence of exercise modification in the low GRS class with active lifestyle change was lower than that in the high GRS class with inactive lifestyle change (P = 0.022), suggesting the implication of genetic factors for efficient body fat control.