Biology subjects

Sinsheimer, J. S.

Publications and source records attributed to Sinsheimer, J. S..

5 recordsLinked to original sources

bioRxiv2024

Distinct patterns of bioscience doctoral publication disparities by gender and race/ethnicity

The ability to address the lack of diversity in the Science, Technology, Engineering and Math workforce depends on inclusive and equitable training of doctoral students to succeed in the profession. An important metric used to assess equitable training in bioscience doctoral programs is the number of publications that result from a students research. The purpose of this study was to investigate whether there are demographic differences in publication rates among all students in a cohort of bioscience Ph.D. programs at the University of California Los Angeles who graduated between 2011 and 2019. Using institutional data and publication database queries, we determined the number of doctoral publications parsed by authorship position, and the timing of the first publication for each student. The resulting dataset was then analyzed for the relationships between publication categories and student gender, race/ethnicity, and citizenship status. We find that female students published significantly fewer total and co-author papers compared to male students, but had the same number of first-author publications. In contrast, students from underrepresented racial/ethnic groups had fewer first-author papers compared to students from well-represented groups, but similar numbers of total and co-author publications. Publication of the first doctoral paper occurred later for female versus male, and underrepresented versus well-represented students. These results provide evidence for distinct patterns of doctoral publication disparities by gender and race/ethnicity, offering insights into a key metric of bioscience student success and informing potential strategies to achieve equitable outcomes in bioscience doctoral education.

scientific communication and education↗

bioRxiv2022

High retention of genomic variation and fitness-related traits in the effective population of reintroduced wolves in Yellowstone National Park

Cooperatively breeding species exhibit numerous strategies to avoid mating with close relatives, inherently reducing effective population size. For species of management concern, accurate estimates of inbreeding and trait depression are crucial for the species future. We utilized genomic and pedigree data for Yellowstone National Park gray wolves to investigate the contributions of foundation stock lineages, genetic architecture of the effective population, and putative fitness consequences of inbreeding. Our dataset spans 25 years and seven generations since reintroduction, encompassing 152 nuclear families and 329 litters. We found over 87% of the pedigree foundation genomes persisted and report influxes of allelic diversity from two translocated wolves from a divergent source in Montana. As expected for group-living species, mean kinship significantly increased over time, although we found high retention of genetic variation. Strikingly, the effective population carried a significantly lower level of genome-wide inbreeding coefficients and autozygosity with shorter decays for linkage disequilibrium relative to the non-breeding population. Lifespan and heterozygosity were higher in the effective population, although individuals who had their first litter at an older age also had higher inbreeding coefficients. Our findings highlight genetic contributions to fitness, and the importance of effective population size and gene flow to counteract loss of genetic variation in a wild, free-ranging social carnivore. It is crucial for managers to mitigate factors that significantly reduce effective population size and genetic connectivity, which supports the dispersion of genetic variation that aids in rapid evolutionary responses to environmental challenges.

evolutionary biology↗

bioRxiv2021

Multivariate Genomewide Association Analysis with IHT

1In genome-wide association studies (GWAS), analyzing multiple correlated traits is potentially superior to conducting multiple univariate analyses. Standard methods for multivariate GWAS operate marker-by-marker and are computationally intensive. We present a penalized regression algorithm for multivariate GWAS based on iterative hard thresholding (IHT) and implement it in a convenient Julia package MendelIHT.jl (https://github.com/OpenMendel/MendelIHT.jl). In simulation studies with up to 100 traits, IHT exhibits similar true positive rates, smaller false positive rates, and faster execution times than GEMMAs linear mixed models and mv-PLINKs canonical correlation analysis. On UK Biobank data, our IHT software completed a 3-trait joint analysis in 20 hours and an 18-trait joint analysis in 53 hours, requiring up to 80GB of computer memory. In short, our software enables geneticists to fit a single regression model that simultaneously considers the effect of all SNPs and dozens of traits.

genetics↗

bioRxiv2020

A Fast Data-Driven Method for Genotyp Imputation, Phasing, and Local Ancestry Inference: MendelImpute.jl

1Current methods for genotype imputation and phasing exploit the sheer volume of data in haplotype reference panels and rely on hidden Markov models. Existing programs all have essentially the same imputation accuracy, are computationally intensive, and generally require pre-phasing the typed markers. We propose a novel data-mining method for genotype imputation and phasing that substitutes highly efficient linear algebra routines for hidden Markov model calculations. This strategy, embodied in our Julia program MendelImpute.jl, avoids explicit assumptions about recombination and population structure while delivering similar prediction accuracy, better memory usage, and an order of magnitude or better run-times compared to the fastest competing method. MendelImpute operates on both dosage data and unphased genotype data and simultaneously imputes missing genotypes and phase at both the typed and untyped SNPs. Finally, MendelImpute naturally extends to global and local ancestry estimation and lends itself to new strategies for data compression and hence faster data transport and sharing.

genetics↗

bioRxiv2019

Multivariate GWAS: Generalized Linear Models, Prior Weights, and Double Sparsity

1BackgroundConsecutive testing of single nucleotide polymorphisms (SNPs) is usually employed to identify genetic variants associated with complex traits. Ideally one should model all covariates in unison, but most existing analysis methods for genome-wide association studies (GWAS) perform only univariate regression. ResultsWe extend and efficiently implement iterative hard thresholding (IHT) for multiple regression, treating all SNPs simultaneously. Our extensions accommodate generalized linear models (GLMs), prior information on genetic variants, and grouping of variants. In our simulations, IHT recovers up to 30% more true predictors than SNP-by-SNP association testing, and exhibits a 2 to 3 orders of magnitude decrease in false positive rates compared to lasso regression. We also test IHT on the UK Biobank hypertension phenotypes and the Northern Finland Birth Cohort of 1966 cardiovascular phenotypes. We find that IHT scales to the large datasets of contemporary human genetics and recovers the plausible genetic variants identified by previous studies. ConclusionsOur real data analysis and simulation studies suggest that IHT can (a) recover highly correlated predictors, (b) avoid over-fitting, (c) deliver better true positive and false positive rates than either marginal testing or lasso regression, (d) recover unbiased regression coefficients, (e) exploit prior information and group-sparsity and (f) be used with biobank sized data sets. Although these advances are studied for GWAS inference, our extensions are pertinent to other regression problems with large numbers of predictors.

genomics↗