Estimation of cell-free fetal DNA fraction from maternal plasma based on linkage disequilibrium information

Cell-free fetal DNA fraction (FF) in maternal plasma is a key parameter affecting the performance of noninvasive prenatal testing (NIPT). Accurate quantitation of FF plays a pivotal role in these tests. However, there are few methods that could determine FF with high accuracy using shallow‐depth whole‐genome sequencing data. In this study, we hypothesized that the actual FF in maternal plasma should be proportional to the discrepancy rate between the observed genotypes and inferred genotypes based on the linkage disequilibrium rule in certain polymorphism sites. Based on this hypothesis, we developed a method named Linkage Disequilibrium information-based cell-free Fetal DNA Fraction (LDFF) to accurately quantify FF in maternal plasma. This method achieves a high performance and outperforms existing methods in the fetal DNA fraction estimation. As LDFF is a gender-independent method and developed on shallow-depth samples, it can be easily incorporated into routine NIPT test and may enhance the current NIPT performance.

Reporting Correct p Values in VEGAS Analyses

VEGAS (versatile gene-based association study) is a popular methodological framework to perform gene-based tests based on summary statistics from single-variant analyses. The approach incorporates linkage disequilibrium information from reference panels to account for the correlation of test statistics. The gene-based test can utilize three different types of tests. In 2015, the improved framework VEGAS2, using more detailed reference panels, was published. Both versions provide user-friendly web- and offline-based tools for the analysis. However, the implementation of the popular top-percentage test is erroneous in both versions. The p values provided by VEGAS2 are deflated/anti-conservative. Based on real data examples, we demonstrate that this can increase substantially the rate of false-positive findings and can lead to inconsistencies between different test options. We also provide code that allows the user of VEGAS to compute correct p values.

Reporting correct p-values in VEGAS analyses

VEGAS (versatile gene-based association study) is a popular methodological framework to perform gene-based tests based on summary statistics from single-variant analyses. The approach incorporates linkage disequilibrium information from reference panels to account for the correlation of test statistics. The gene-based test can utilize three different types of tests. In 2015, the improved framework VEGAS2, using more detailed reference panels, was published. Both versions provide user-friendly web- and offline-based tools for the analysis. However, the implementation of the popular top-percentage test is erroneous in both versions. The p-values provided by VEGAS2 are deflated/anti-conservative. Based on real data examples, we demonstrate that this can increase substantially the rate of false positive findings and can lead to inconsistencies between different test options. We also provide code that allows the user of VEGAS to compute correct p-values.

Selection signatures in worldwide Sheep populations

The diversity of populations in domestic species offers great opportunities to study genome response to selection. The recently published Sheep HapMap dataset is a great example of characterization of the world wide genetic diversity in sheep. In this study, we re-analyzed the Sheep HapMap dataset to identify selection signatures in worldwide sheep populations. Compared to previous analyses, we made use of statistical methods that (i) take account of the hierarchical structure of sheep populations, (ii) make use of linkage disequilibrium information and (iii) focus specifically on either recent or older selection signatures. We show that this allows pinpointing several new selection signatures in the sheep genome and distinguishing those related to modern breeding objectives and to earlier post-domestication constraints. The newly identified regions, together with the ones previously identified, reveal the extensive genome response to selection on morphology, color and adaptation to new environments.