Haplotype based testing for a better understanding of the selective architecture

BACKGROUND

The identification of genomic regions affected by selection is one of the most important goals in population genetics. If temporal data are available, allele frequency changes at SNP positions are often used for this purpose. Here we provide a new testing approach that uses haplotype frequencies instead of allele frequencies.

RESULTS

Using simulated data, we show that compared to SNP based test, our approach has higher power, especially when the number of candidate haplotypes is small or moderate. To improve power when the number of haplotypes is large, we investigate methods to combine them with a moderate number of haplotype subsets. Haplotype frequencies can often be recovered with less noise than SNP frequencies, especially under pool sequencing, giving our test an additional advantage. Furthermore, spurious outlier SNPs may lead to false positives, a problem usually not encountered when working with haplotypes. Post hoc tests for the number of selected haplotypes and for differences between their selection coefficients are also provided for a better understanding of the underlying selection dynamics. An application on a real data set further illustrates the performance benefits.

CONCLUSIONS

Due to less multiple testing correction and noise reduction, haplotype based testing is able to outperform SNP based tests in terms of power in most scenarios.

Comparative analysis of Student's live online learning readiness during the coronavirus (COVID-19) pandemic in the higher education sector

Amid the coronavirus outbreak, many countries are facing a dramatic situation in terms of the global economy and human social activities, including education. The shutdown of schools is affecting many students around the world, with face-to-face classes suspended. Many countries facing the disastrous situation imposed class suspension at an early stage of the coronavirus outbreak, and Asia was one of the earliest regions to implement live online learning. Despite previous research on online teaching and learning, students' readiness to participate in the real-time online learning implemented during the coronavirus outbreak is not yet well understood. This study explored several key factors in the research framework related to learning motivation, learning readiness and student's self-efficacy in participating in live online learning during the coronavirus outbreak, taking into account gender differences and differences among sub-degree (SD), undergraduate (UG) and postgraduate (PG) students. Technology readiness was used instead of conventional online/internet self-efficacy to determine students' live online learning readiness. The hypothetical model was validated using confirmatory factor analysis (CFA). The results revealed no statistically significant differences between males and females. On the other hand, the mean scores for PG students were higher than for UG and SD students based on the post hoc test. We argue that during the coronavirus outbreak, gender differences were reduced because students are forced to learn more initiatively. We also suggest that students studying at a higher education degree level may have higher expectations of their academic achievement and were significantly different in their online learning readiness. This study has important implications for educators in implementing live online learning, particularly for the design of teaching contexts for students from different educational levels. More virtual activities should be considered to enhance the motivation for students undertaking lower-level degrees, and encouragement of student-to-student interactions can be considered.