Biology subjects

Laisk, T.

Publications and source records attributed to Laisk, T..

7 recordsLinked to original sources

bioRxiv2026

Sex-specific associations between education-related genetic factors and fertility extend beyond educational attainment

Educational attainment (EA) is strongly associated with fertility, yet the mechanisms underlying this relationship remain poorly understood. Using data from ~40,000 women and ~10,000 men in the Estonian Biobank, we examine sex-specific associations between EA polygenic scores (EA PGS) and completed fertility. The EA PGS captures an individual's genetic predisposition to completing higher levels of education, and can also be constructed separately for cognitive and non-cognitive EA components. We find that EA PGS is negatively associated with fertility among women, with a significantly stronger association for the non-cognitive than the cognitive EA polygenic score. The association of EA PGS with fertility is only partly explained by EA and is moderated by EA and reproductive timing, changing sign across age-at-first-pregnancy strata. Within-family estimates are not attenuated relative to population estimates, consistent with a predominant role of direct genetic effects. Among men, associations between EA PGS and fertility are non-significant or slightly positive. Overall, education-related genetic variation is associated with fertility through pathways largely independent of EA, operating through sex-specific and timing-dependent mechanisms. These results contribute to our understanding of the micro-level mechanisms shaping macro-level demographic change.

genetics↗

bioRxiv2025

Biologically-Inspired Digital Histology for Deep Phenotyping of Placental Composition Changes Across Major Lesion Types

Placenta pathology provides diagnostic insights for understanding pregnancy complications and guides maternal and perinatal care. While placental abnormalities reflect both acute and chronic maternal-fetal health states, the organs spatial and temporal heterogeneity poses significant challenges for systematic histological analysis. To date, there has been no analysis of cell populations and their relationship to placental lesions due to the scale of annotations required by expert pathologists. The Histology Analysis Pipeline.PY (HAPPY), previously published by our group, is a hierarchical deep learning approach for quantitative single-cell resolution analysis of H&E slides. Here we apply HAPPY to analyse 130 slides from 62 live full-term pregnancies across healthy controls and four common placental lesion types (infarction, perivillous fibrin, avascular villi, and intervillous thrombosis). We computed cell-type and tissue-structure compositions, established the expected range of healthy variation, and quantified slide-level deviation from this reference using compositional data analysis. Our results reveal significant cellular composition differences between histologically normal and lesioned placentas, including increased extra-villous trophoblasts and leukocytes, coupled with decreases in Hofbauer cells. These changes accompanied distinctive tissue microstructural alterations, particularly increased fibrin deposition and changes to the villous structures. The magnitude of compositional deviation increased with infarction size but not with intervillous thrombosis. Importantly, many differences extend beyond visibly affected areas, indicating organ-wide adaptive responses rather than purely discrete focal pathologies. This quantitative characterisation provides insights into relationships between specific pathologies and placental structure, demonstrating the potential of AI-based methods to enhance conventional histopathological assessment for research and clinical practice.

pathology↗

bioRxiv2022

HAPPY: A deep learning pipeline for mapping cell-to-tissue graphs across placenta histology whole slide images

Accurate placenta pathology assessment is essential for managing maternal and newborn health, but the placentas heterogeneity and temporal variability pose challenges for histology analysis. To address this issue, we developed the Histology Analysis Pipeline.PY (HAPPY), a deep learning hierarchical method for quantifying the variability of cells and micro-anatomical tissue structures across placenta histology whole slide images. HAPPY differs from patch-based features or segmentation approaches by following an interpretable biological hierarchy, representing cells and cellular communities within tissues at a single-cell resolution across whole slide images. We present a set of quantitative metrics from healthy term placentas as a baseline for future assessments of placenta health and we show how these metrics deviate in placentas with clinically significant placental infarction. HAPPYs cell and tissue predictions closely replicate those from independent clinical experts and placental biology literature.

bioinformatics↗

bioRxiv2019

Leveraging biobank-scale rare and common variant analyses to identify ASPHD1 as the main driver of reproductive traits in the 16p11.2 locus

Whereas genome-wide association studies (GWAS) allowed identifying thousands of associations between variants and traits, their success rate in pinpointing causal genes has been disproportionately low. Here, we integrate biobank-scale phenotype data from carriers of a rare copy-number variant (CNV), Mendelian randomization and animal modeling to identify causative genes in a GWAS locus for age at menarche (AaM). We show that the dosage of the 16p11.2 BP4-BP5 interval is correlated positively with AaM in the UK and Estonian biobanks and 16p11.2 clinical cohorts, with a directionally consistent trend for pubertal onset in males. These correlations parallel an increase in reproductive tract disorders in both sexes. In support of these observations, 16p11.2 mouse models display perturbed pubertal onset and structurally altered reproductive organs that track with CNV dose. Further, we report a negative correlation between the 16p11.2 dosage and relative hypothalamic volume in both humans and mice, intimating a perturbation in the gonadotropin-releasing hormone (GnRH) axis. Two independent lines of evidence identified candidate causal genes for AaM; Mendelian randomization and agnostic dosage modulation of each 16p11.2 gene in zebrafish gnrh3:egfp models. ASPHD1, expressed predominantly in brain and pituitary gland, emerged as a major phenotype driver; and it is subject to modulation by KCTD13 to exacerbate GnRH neuron phenotype. Together, our data highlight the power of an interdisciplinary approach to elucidate disease etiologies underlying complex traits.

genomics↗

bioRxiv2019

The genetic architecture of sporadic and recurrent miscarriage

Miscarriage is a common complex trait that affects 10-25% of clinically confirmed pregnancies1,2. Here we present the first large-scale genetic association analyses with 69,118 cases from five different ancestries for sporadic miscarriage and 750 cases of European ancestry for recurrent miscarriage, and up to 359,469 female controls. We identify one genome-wide significant association on chromosome 13 (rs146350366, minor allele frequency (MAF) 1.2%, Pmeta=3.2x -8 (CI) 1.2-1.6) for sporadic miscarriage in our European ancestry meta-analysis (50,060 cases and 174,109 controls), located near FGF9 involved in pregnancy maintenance3 and progesterone production4. Additionally, we identified three genome-wide significant associations for recurrent miscarriage, including a signal on chromosome 9 (rs7859844, MAF=6.4%, Pmeta=1.3x -8 in controlling extravillous trophoblast motility5. We further investigate the genetic architecture of miscarriage with biobank-scale Mendelian randomization, heritability and, genetic correlation analyses. Our results implicate that miscarriage etiopathogenesis is partly driven by genetic variation related to gonadotropin regulation, placental biology and progesterone production.

genetics↗

bioRxiv2018

Endometrial receptivity revisited: endometrial transcriptome adjusted for tissue cellular heterogeneity

STUDY QUESTIONDoes cellular composition of the endometrial biopsy affect the gene expression profile of endometrial whole-tissue samples?\n\nSUMMARY ANSWERThe differences in epithelial and stromal cell proportions in endome-trial biopsies modify whole-tissue gene expression profiles, and also affect the results of differential expression analysis.\n\nWHAT IS ALREADY KNOWNEach cell type has its unique gene expression profile. The proportions of epithelial and stromal cells vary in endometrial tissue during the menstrual cycle, along with individual and technical variation due to the way and tools used to obtain the tissue biopsy.\n\nSTUDY DESIGN, SIZE, DURATIONUsing cell-population specific transcriptome data and computational deconvolution approach, we estimated the epithelial and stromal cell proportions in whole-tissue biopsies taken during early secretory and mid-secretory phases. The estimated cellular proportions were used as covariates in whole-tissue differential gene expression analysis. Endometrial transcriptomes before and after deconvolution were compared and analysed in biological context.\n\nPARTICIPANTS/MATERIAL, SETTING, METHODSPaired early- and mid-secretory endometrial biopsies were obtained from thirty-five healthy, regularly cycling, fertile volunteers, aged 23 to 36 years, and analysed by RNA sequencing. Differential gene expression analysis was performed using two approaches. In one of them, computational deconvolution was applied as an intermediate step to adjust for epithelial and stromal cells proportions in endometrial biopsy. The results were then compared to conventional differential expression analysis.\n\nMAIN RESULTS AND THE ROLE OF CHANCEThe estimated average proportions of stromal and epithelial cells in early secretory phase were 65% and 35%, and during mid-secre-tory phase 46% and 54%, respectively, that correlated well with the results of histological evaluation (r=0.88, p=1.1x10-6). Endometrial tissue transcriptomic analysis showed that approximately 26% of transcripts (n=946) differentially expressed in receptive endometrium in cell-type unadjusted analysis also remain differentially expressed after adjustment for biopsy cellular composition. However, the other 74% (n=2,645) become statistically non-significant after adjustment for biopsy cellular composition, underlining the impact of tissue heterogeneity on differential expression analysis. The results suggest new mechanisms involved in endometrial maturation involving genes like LINC01320, SLC8A1 and GGTA1P, described for the first time in context of endometrial receptivity.\n\nLIMITATIONS, REASONS FOR CAUTIONOnly dominant endometrial cell types were considered in gene expression profile deconvolution; however, other less frequent endometrial cell types also contribute to the whole-tissue gene expression profile.\n\nWIDER IMPLICATIONS OF THE FINDINGSThe better understanding of molecular processes during transition from pre-receptive to receptive endometrium serves to improve the effectiveness and personalization of assisted reproduction protocols. Biopsy cellular composition should be taken into account in future endometrial omics studies, where tissue heterogeneity could potentially influence the results.\n\nTRIAL REGISTRATION NON/A

genomics↗

bioRxiv2018

GWAS meta-analysis highlights the hypothalamic-pituitary-gonadal axis (HPG axis) in the genetic regulation of menstrual cycle length

The normal menstrual cycle requires a delicate interplay between the hypothalamus, pituitary, and ovary. Therefore, its length is an important indicator of female reproductive health. Menstrual cycle length has been shown to be partially controlled by genetic factors, especially in the follicle stimulating hormone beta-subunit (FSHB) locus. GWAS meta-analysis of menstrual cycle length in 44,871 women of European ancestry confirmed the previously observed association with the FSHB locus and identified four additional novel signals in, or near, the GNRH1, PGR, NR5A2 and INS-IGF2 genes. These findings confirm the role of the hypothalamic-pituitary-gonadal axis in the genetic regulation of menstrual cycle length, but also highlight potential novel local regulatory mechanisms, such as those mediated by IGF2.

genetics↗