Biology subjects

Hofmeister, R.

Publications and source records attributed to Hofmeister, R..

6 recordsLinked to original sources

bioRxiv2026

Sex differences in the genetic basis of human recombination within 190,000 parent-child pairs

Structured AbstractO_ST_ABSIntroductionC_ST_ABSSexual reproduction uses a specialized cell division called meiosis, in which a single round of DNA replication is followed by two cell divisions to create hap-loid gametes. Genetic recombination in meiosis assures faithful segregation of chromosomes and establishes patterns of genetic linkage and inheritance. Meiotic recombination is thus a fundamental genomic process that shapes major features of the genomic landscape, influences mutation, and creates genetic diversity. RationaleThe genetic basis of between-individual variation in the occurrence and genomic location of meiotic recombination events remains poorly understood in humans. In particular, we lack an understanding of whether the same DNA regions shape vari-ation in recombination rates across studies. We address this by curating 112,144 maternal and 78,653 paternal meiosis events (parent-child pairs) across cohorts from four countries, creating the largest dataset of its kind. ResultsWe identify 58 independent genomic regions associated with the occurrence and genomic location of recombination events, 24 of which are unique across phe-notypes. Of the 24 unique regions, 6 are novel and specific to female meiotic recombination harboring genes linked to ovarian function, a 7th novel region is linked to male recombination. Many regions have significant sex-dependent ef-fects across studies. We estimate the between-sex genetic correlation for meiotic recombination rate to be 0.374 (0.072 SD), which captures the extent to which the genetic factors influencing a trait in one sex also impact the same trait in the other. 30-40% of the individual-level variation in recombination rate is at-tributable to DNA markers, of which only 37.7% (2.7% SD) and 47.1% (3.8% SD) is attributable to previously known global modulators of meiotic recombi-nation, in females and males respectively. We create a polygenic predictor that significantly predicts recombination rates of both women and men after adjusting for age and year of birth. We find significant correlations between female meiotic recombination, reproductive outcomes and reproductive aging, after adjusting for study ascertainment. ConclusionsIn the largest meta-analysis to date of human meiotic recombination, we identify novel loci with sex-specific effects that influence male and female recombination rate across multiple cohorts.

genomics↗

bioRxiv2026

Mapping corpus callosum architecture: developmental, genetic, and cognitive correlates in youth

The corpus callosum (CC), the largest interhemispheric white-matter tract, plays a central role in higher-order cognitive functions and is frequently altered in neurodevelopmental and psychiatric conditions. However, most diffusion MRI studies rely on tract-averaged measures, which obscure spatially specific microstructural variation along the tract that may hold biologically and functionally meaningful information. This study aimed to provide a fine-grained characterisation of spatial variation in callosal structure and to determine its developmental, genetic, and cognitive correlates. This study leveraged multimodal data from the Philadelphia Neurodevelopmental Cohort (PNC), comprising 1342 participants aged 8-21 years. Diffusion MRI and tractography-based segmentation were used to extract fine-grained spatial variation in diffusion metrics along seven callosal tracts. A candidate gene approach targeted four genes (DCC, CDH2, AKT3, and GLI3) previously linked to the neurobiological mechanisms underlying CC formation. Five cognitive factors were derived from the Penn Computerized Neurocognitive Battery using factor analysis. A two-stage functional data analysis approach to examine global and local associations between along-tract diffusion metrics and age, candidate genetic variants, and behavioural outcomes. Results revealed distinct midline-to-cortical variations of age-related diffusion metrics change across callosal subdivisions. Frontal and parietal heteromodal callosal pathways showed pronounced distal-segment maturation (F = 13 - 23, p [≤] 2.8x10e-16), whereas posterior sensorimotor and occipital callosal tracts exhibited more stable age associations along their lengths (F = 4.2 - 4.3, p = 10e-3). Genetic variations in callosal axon-guidance genes (ROBO1, IQCJ-SCHIP1, NRP1 and DCC) were associated with spatial variation in callosal diffusion metrics, particularly in anterior (rostrum) and posterior callosal subdivisions (isthmus and splenium) (F = 4.29 - 18.61, p-values = 2.2e-16 - 1.4e-04). These regions correspond to early-forming callosal compartments, suggesting that prenatal axon- guidance mechanisms leave enduring spatial patterns on callosal organisation. Finally, spatial variation in callosal microstructure was significantly associated with behavioural performance (F = 2.9 - 21.3, P = 2.8x10e-16 - 0.04), with the strongest and most spatially heterogeneous effects observed for complex cognition and executive functioning. Across all analyses, functional data models generally outperformed tract-averaged linear models, supporting the value of explicitly preserving spatial variation along callosal tracts. Our findings converge on the CC as a spatially differentiated structure in which early genetic modulators and developmental constraints shape region- and segment-specific microstructural architecture that is behaviourally relevant through childhood and adolescence.

neuroscience↗

bioRxiv2025

Separating direct, indirect and parent-of-origin genetic effects in the human population

Here, we present a novel approach to estimate the degree to which the phenotypic effect of a DNA locus is attributable to four components: alleles in the child (direct genetic effects), alleles in the mother and the father (indirect genetic effects), or is dependent upon the parent from which it is inherited (parent-of-origin, PofO effects). Applying our model, JODIE, to 30,000 child-mother-father trios with phased DNA information from the Estonian Biobank (EstBB) and the Norwegian Mother, Father, Child Cohort (MoBa), we jointly estimate the phenotypic variance attributable to these four effects unbiased of assortative mating (AM) for height, body mass index (BMI) and childhood educational test score (EA). For all three traits, direct effects make the largest contribution to the genetic effect variance. But we find that parental indirect genetic effects make an equivalent combined contribution, and that there is a non-zero PofO effect variance for all traits. We calculate the heritability that would be obtained at the populationlevel in the absence of AM for common DNA loci, and show that the proportional contribution of direct effects to these heritability values can be calculated as 64.0% for EA in MoBa, 77.1% and 63.4% for height in MoBa and EstBB, and 81.2% and 88.0% for BMI in MoBa and EstBB. Additionally, using within-family genome-wide association testing, we identify 276 independently associated DNA regions that replicate across two additional biobanks, which all show a genotype-phenotype relationship that reflects an interplay of direct, indirect and PofO effects. Determining how direct, parental and PofO genetic effects combine across loci genome-wide to influence human phenotypic variation requires joint modeling of parental and child genotypes alongside the parental origin of loci and here, we make the first attempt to do this in the human population.

genetics↗

bioRxiv2023

Improving population scale statistical phasing with whole-genome sequencing data

Haplotype estimation, or phasing, has gained significant traction in large-scale projects due to its valuable contributions to population genetics, variant analysis, and the creation of reference panels for imputation and phasing of new samples. To scale with the growing number of samples, haplotype estimation methods designed for population scale rely on highly optimized statistical models to phase genotype data, and usually ignore read-level information. Statistical methods excel in resolving common variants, however, they still struggle at rare variants due to the lack of statistical information. In this study we introduce SAPPHIRE, a new method that leverages whole-genome sequencing data to enhance the precision of haplotype calls produced by statistical phasing. SAPPHIRE achieves this by refining haplotype estimates through the realignment of sequencing reads, particularly targeting low-confidence phase calls. Our findings demonstrate that SAPPHIRE significantly enhances the accuracy of haplotypes obtained from state of the art methods and also provides the subset of phase calls that are validated by sequencing reads. Finally, we show that our method scales to large data sets by its successful application to the extensive 3.6 Petabytes of sequencing data of the last UK Biobank 200,031 sample release.

genomics↗

bioRxiv2022

Imputation of low-coverage sequencing data from 150,119 UK Biobank genomes

Recent work highlights the advantages of low-coverage whole genome sequencing (lcWGS), followed by genotype imputation, as a cost-effective genotyping technology for statistical and population genetics. The release of whole genome sequencing data for 150,119 UK Biobank (UKB) samples represents an unprecedented opportunity to impute lcWGS with high accuracy. However, despite recent progress1,2, current methods struggle to cope with the growing numbers of samples and markers in modern reference panels, resulting in unsustainable computational costs. For instance, the imputation cost for a single genome is 1.11{pound} using GLIMPSE v1.1.1 (GLIMPSE1) on the UKB research analysis platform (RAP) and rises to 242.8{pound} using QUILT v1.0.4. To overcome this computational burden, we introduce GLIMPSE v2.0.0 (GLIMPSE2), a major improvement of GLIMPSE, that scales sublinearly in both the number of samples and markers. GLIMPSE2 imputes a low-coverage genome from the UKB reference panel for only 0.08{pound} in compute cost while retaining high accuracy for both ancient and modern genomes, particularly at rare variants (MAF < 0.1%) and for very low-coverage samples (0.1x-0.5x).

bioinformatics↗

bioRxiv2020

Efficient phasing and imputation of low-coverage sequencing data using large reference panels

Low-coverage whole genome sequencing followed by imputation has been proposed as a cost-effective genotyping approach for disease and population genetics studies. However, its competitiveness against SNP arrays is undermined as current imputation methods are computationally expensive and unable to leverage large reference panels. Here, we describe a method, GLIMPSE, for phasing and imputation of low-coverage sequencing datasets from modern reference panels. We demonstrate its remarkable performance across different coverages and human populations. It achieves imputation of a full genome for less than $1, outperforming existing methods by orders of magnitude, with an increased accuracy of more than 20% at rare variants. We also show that 1x coverage enables effective association studies and is better suited than dense SNP arrays to access the impact of rare variations. Overall, this study demonstrates the promising potential of low-coverage imputation and suggests a paradigm shift in the design of future genomic studies.

bioinformatics↗