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Li, M. L.

Publications and source records attributed to Li, M. L..

3 recordsLinked to original sources

Application of the Bradley-Terry model to quantify components of sperm competition

In many species with female sperm storage, ejaculates from multiple males overlap in the female reproductive tract, making sperm competitive ability a key component of male reproductive fitness and a target of rapid evolutionary change in the underlying genes. Here, we used controlled laboratory assays of Drosophila melanogaster sperm competition, with doubly-mated females and paternity assignment of offspring, to ask whether a Bradley-Terry framework can effectively summarize and predict competitive outcomes. The Bradley-Terry model is a probabilistic approach that estimates a latent "ability" score for each contestant based on outcomes of pairwise contests, and thus is naturally suited to data from sperm competition, which are intrinsically pairwise. We selected five distinct male genotypes: four carried strongly expressed RFP or GFP markers that allowed us to distinguish their heterozygous offspring under UV illumination, and the fifth was Canton-S, a standard wild-type genotype that served as our reference. Using Canton-S females, we assayed all 20 ordered pairwise combinations of first and second male, recorded successful double matings, and quantified the offspring sired by each male. We then extended the Bradley-Terry model to estimate genotype-specific competitive success separately for first-male "defense" (fertilization success following initial mating, also called "P1") and second-male "offense" (fertilization success following a remating, also called "P2"). This framework provides a flexible and efficient way to integrate results across large arrays of pairwise mating tests and to derive predictive scores for sperm competitive performance.

evolutionary biology↗

A systems genetics approach identifies roles for proteasome factors in heart development and congenital heart defects

Congenital heart defects (CHDs) occur in about 1% of live births and are the leading cause of infant death due to birth defects. While there have been remarkable efforts to pursue large-scale whole-exome and genome sequencing studies on CHD patient cohorts, it is estimated that these approaches have thus far accounted for only about 50% of the genetic contribution to CHDs. We sought to take a new approach to identify genetic causes of CHDs. By combining analyses of genes that are under strong selective constraint along with published embryonic heart transcriptomes, we identified over 200 new candidate genes for CHDs. We utilized protein-protein interaction (PPI) network analysis to identify a functionally-related subnetwork consisting of known CHD genes as well as genes encoding proteasome factors, in particular POMP, PSMA6, PSMA7, PSMD3, and PSMD6. We used CRISPR screening in zebrafish embryos to preliminarily identify roles for the PPI subnetwork genes in heart development. We then used CRISPR to create new mutant zebrafish strains for two of the proteasome genes in the subnetwork: pomp and psmd6. Phenotypic analyses confirm critical roles for pomp and psmd6 in heart development. In particular, we find defects in myocardial cell shapes and in outflow tract development in pomp and psmd6 mutant zebrafish embryos, and these phenotypes have been observed in other zebrafish CHD-gene mutants. Our study provides a novel systems genetics approach to further our understanding of the genetic causes of human CHDs. Author SummaryCongenital heart defects (CHDs) are birth defects resulting in the abnormal structure and function of the heart. Genetic mutations are a significant cause of CHDs. Many studies have used genome sequencing of CHD patients and their families to gain knowledge of the mutations that cause CHDs. However, these studies have only found about 50 percent of the genes involved in CHDs. Here, we take a new approach to identifying genes that are required for heart development and that may cause CHDs, generating a list of over 200 candidate genes. Using multiple data systems, including human exome sequences, mouse transcriptomes, and protein-protein interactions, we identify a small group of related potential CHD genes that includes multiple genes encoding proteasome factors. These factors are known to be important for assembling the proteasome, a large molecular machine that eliminates unneeded or damaged proteins from the cell, but which has not been shown to contribute to CHD. We use a CRISPR-based approach in zebrafish to specifically eliminate some of these candidate genes and reveal new roles for proteasome genes in heart development. We show that loss of proteasome gene functions leads to zebrafish heart defects that resemble those seen in other zebrafish CHD-gene mutants. This study shows that a proteasome gene family contributes to heart development, advancing our understanding of the causes of CHDs. By increasing our understanding of the genetic causes of CHDs, our work should lead to better screening, more accurate diagnoses, and, ultimately, better treatments for these disorders.

genetics↗

weIMPUTE: A User-Friendly Web-Based Genotype Imputation Platform

Genotype imputation is a critical preprocessing step in genome-wide association studies (GWAS), enhancing statistical power for detecting associated single nucleotide polymorphisms (SNPs) by increasing marker size. In response to the needs of researchers seeking user-friendly graphical tools for imputation without requiring informatics or computer expertise, we have developed weIMPUTE, a web-based imputation graphical user interface (GUI). Unlike existing genotype imputation software, weIMPUTE supports multiple imputation software, including SHAPEIT, Eagle, Minimac4, Beagle, and IMPUTE2, while encompassing the entire workflow, from quality control to data format conversion. This comprehensive platform enables both novices and experienced users to readily perform imputation tasks. For reference genotype data owners, weIMPUTE can be installed on a server or workstation, facilitating web-based imputation services without data sharing. weIMPUTE represents a versatile imputation solution for researchers across various fields, offering the flexibility to create personalized imputation servers on different operating systems.

bioinformatics↗