Biology subjects

Craig, S. J. C.

Publications and source records attributed to Craig, S. J. C..

3 recordsLinked to original sources

bioRxiv2023

The Complete Sequence and Comparative Analysis of Ape Sex Chromosomes

Apes possess two sex chromosomes--the male-specific Y and the X shared by males and females. The Y chromosome is crucial for male reproduction, with deletions linked to infertility1. The X chromosome carries genes vital for reproduction and cognition2. Variation in mating patterns and brain function among great apes suggests corresponding differences in their sex chromosomes. However, due to their highly repetitive nature and incomplete reference assemblies, ape sex chromosomes have been challenging to study. Here, using the methodology developed for the telomere-to-telomere (T2T) human genome, we produced gapless assemblies of the X and Y chromosomes for five great apes (chimpanzee, bonobo, gorilla, Bornean and Sumatran orangutans) and a lesser ape, the siamang gibbon. These assemblies allowed us to untangle the intricacies of ape sex chromosome evolution. We found that, compared to the Xs, the ape Ys vary greatly in size and have low alignability and high levels of structural rearrangements. This divergence on the Y arises from the accumulation of lineage-specific ampliconic regions, palindromes, transposable elements, and satellites. Our analysis of Y chromosome genes revealed expansions of multi-copy gene families and signatures of purifying selection. Thus, the Y exhibits dynamic evolution, while the X is more stable. Mapping short-read sequencing data to these assemblies revealed diversity and selection patterns on sex chromosomes of >100 great ape individuals. These reference assemblies are expected to inform human evolution and conservation genetics of nonhuman apes, all of which are endangered species.

genomics↗

bioRxiv2023

In vivo detection of DNA secondary structures using Permanganate/S1 Footprinting with Direct Adapter Ligation and Sequencing (PDAL-Seq)

DNA secondary structures are essential elements of the genomic landscape, playing a critical role in regulating various cellular processes. These structures refer to G-quadruplexes, cruciforms, Z-DNA or H-DNA structures, amongst others (collectively called non-B DN), which DNA molecules can adopt beyond the B conformation. DNA secondary structures have significant biological roles, and their landscape is dynamic and can rearrange due to various factors, including changes in cellular conditions, temperature, and DNA-binding proteins. Understanding this dynamic nature is crucial for unraveling their functions in cellular processes. Detecting DNA secondary structures remains a challenge. Conventional methods, such as gel electrophoresis and chemical probing, have limitations in terms of sensitivity and specificity. Emerging techniques, including next-generation sequencing and single-molecule approaches, offer promise but face challenges since these techniques are mostly limited to only one type of secondary structure. Here we describe an updated version of a technique permanganate/S1 nuclease footprinting, which uses potassium permanganate to trap single-stranded DNA regions as found in non-B structures, in combination with S1 nuclease digest and adapter ligation to detect genome-wide non-B formation. To overcome technical hurdles, we combined this method with direct adapter ligation and sequencing (PDAL-Seq). Furthermore, we established a user-friendly pipeline available on Galaxy to standardize PDAL-Seq data analysis. This optimized method allows the analysis of many types of DNA secondary structures that form in a living cell and will advance our knowledge of their roles in health and disease.

molecular biology↗

bioRxiv2019

Polygenic risk score based on weight gain trajectories is a strong predictor of childhood obesity

Obesity is highly heritable, yet only a small fraction of its heritability has been attributed to specific genetic variants. These variants are traditionally ascertained from genome-wide association studies (GWAS), which utilize samples with tens or hundreds of thousands of individuals for whom a single summary measurement (e.g., BMI) is collected. An alternative approach is to focus on a smaller, more deeply characterized sample in conjunction with advanced statistical models that leverage detailed phenotypes. Here we use novel functional data analysis (FDA) techniques to capitalize on longitudinal growth information and construct a polygenic risk score (PRS) for obesity in children followed from birth to three years of age. This score, comprised of 24 single nucleotide polymorphisms (SNPs), is significantly higher in children with (vs. without) rapid infant weight gain--a predictor of obesity later in life. Using two independent cohorts, we show that genetic variants identified in early childhood are also informative in older children and in adults, consistent with early childhood obesity being predictive of obesity later in life. In contrast, PRSs based on SNPs identified by adult obesity GWAS are not predictive of weight gain in our cohort of children. Our research provides an example of a successful application of FDA to GWAS. We demonstrate that a deep, statistically sophisticated characterization of a longitudinal phenotype can provide increased statistical power to studies with relatively small sample sizes. This study shows how FDA approaches can be used as an alternative to the traditional GWAS. Author SummaryFinding genetic variants that confer an increased risk of developing a particular disease has long been a focus of modern genetics. Genome wide association studies (GWAS) have catalogued single nucleotide polymorphisms (SNPs) associated with a variety of complex diseases in humans, including obesity, but by and large have done so using increasingly large samples-- tens or even hundreds of thousands of individuals, whose phenotypes are thus often only superficially characterized. This, in turn, may hide the intricacies of the genetic influence on disease. GWAS findings are also usually study-population dependent. We found that genetic risk scores based on SNPs from large adult obesity studies are not predictive of the propensity to gain weight in very young children. However, using a small cohort of a few hundred children deeply characterized with growth trajectories between birth and two years, and leveraging such trajectories through novel functional data analysis (FDA) techniques, we were able to produce a strong childhood obesity genetic risk score.

genetics↗