Biology subjects

Njolstad, P. R.

Publications and source records attributed to Njolstad, P. R..

15 recordsLinked to original sources

bioRxiv2025

FEMA-Long: Modeling unstructured covariances for discovery of time-dependent effects in large-scale longitudinal datasets

While linear mixed-effects (LME) models are common for analyzing longitudinal data, most users rely on random intercepts or simple stationary covariance, due to unavailability of computationally tractable solutions. Here, we extend the Fast and Efficient Mixed-Effects Algorithm (FEMA) and present FEMA-Long, a computationally tractable approach to flexibly modeling longitudinal covariance suitable for high-dimensional data. FEMA-Long can: i) model unstructured covariance, ii) model covariates as smooth functions using splines, iii) discover time-dependent effects of covariates with spline interactions, and iv) use these flexible longitudinal modeling strategies to perform longitudinal genome-wide association studies and discover time-dependent genetic effects, in a computationally scalable manner, suitable for high-dimensional data. Through extensive simulations, we show that estimates from FEMA-Long are accurate, while being up to several thousand times faster and with minimal carbon footprint. To show the utility of FEMA-Long for discovering novel biological signal, using data from the Norwegian Mother, Father and Child Cohort Study (MoBa), we performed a longitudinal genome-wide association study with non-linear SNP-by-time interaction on length, weight, and BMI of 68,273 infants with up to six measurements in the first year of life. We found dynamic patterns of random effects including time-varying heritability and genetic correlations, as well as several genetic variants showing time-dependent effects, highlighting the applicability of FEMA-Long to enable novel discoveries. FEMA-Long is available at: https://github.com/cmig-research-group/cmig_tools. Author summaryMost large-scale datasets have complexities such as repeated measures, related individuals, or other dependencies across samples, preventing the use of standard regression approaches for analysis. In such circumstances, linear mixed-effects modeling is often employed. However, for high-dimensional datasets, fitting these models is quite challenging. Further, most standard uses of linear mixed-effects modeling focus on simpler covariance models, which may not hold. Here, we introduce FEMA-Long, a novel computationally efficient analytical framework for fitting linear mixed-effects models with time-varying random effects, as well as allowing the effect of the covariates to change smoothly over time by using splines. This is particularly relevant when, for example, studying the effect of genetic variants on phenotypes, where the effects could be non-linear over time. The FEMA-Long framework allows time-varying heritability as well as discovery of genetic variants that show time-dependent effects. By performing a genome-wide association study on data from the Norwegian Mother, Father and Child Cohort Study (MoBa) using FEMA-Long, we show the discovery of genetic variants with time-dependent effects on infant length, weight, and BMI during the first year of life. Our results highlight the potential of using FEMA-Long to make novel discoveries that can lead to biological insights on the genetics of complex traits as well as improve the potential of using genetics for personalized prediction.

genetics↗

bioRxiv2023

ProHap enables proteomic database generation accounting for population diversity

Amid the advances in genomics, the availability of large reference panels of human haplotypes is key to account for human diversity within and across populations. However, mass spectrometry-based proteomics does not benefit from this information. To address this gap, we introduce ProHap, a Python-based tool that constructs protein sequence databases from phased genotypes of reference panels. ProHap empowers researchers to account for haplotypic diversity in proteomic searches.

bioinformatics↗

bioRxiv2023

Mechanisms of the FTO locus association with obesity: Irx3 controls a sumoylation-dependent switch between adipogenesis and osteogenesis

BackgroundIRX3 is implicated in genetic predisposition to obesity via the FTO variant locus. IRX3 shows FTO risk allele-dependent upregulation specifically during early adipogenesis, leading to a shift from energy-dissipation to fat storage in mature adipocytes. However, how changes in IRX3 expression at one developmental stage affect cellular phenotype at a later stage remains unclear. We here hypothesize that IRX3 regulates adipocyte development via transcriptional modulation of epigenetic reprogramming factors. MethodsWe combined ChIP-, ATAC- and RNA-sequencing to map direct Irx3 target genes in regions of open chromatin during early adipogenesis of wild-type and Irx3-KO preadipocytes. Gene ontology analyses was performed to identify significantly enriched biological pathways. Denaturing western blotting was used to assess sumoylation levels, and the inhibitor ML-792 was used to specifically block sumoylation. Luciferase assays were performed to estimate effects of ML-792 on Ppar{gamma} activity. Bodipy lipid staining, immunofluorescence and qPCR were employed to assess adipogenic differentiation in 3D culture. Alkaline phosphatase and Alizarine Red S staining, as well as immunofluorescence and qPCR were used to assess osteogenic differentiation in 3D culture. ResultsWe identified more than 300 Irx3 binding sites in preadipocytes, and these were almost exclusively restricted to promoter regions, with a strong enrichment of genes related to sumoylation, histone modifications and chromatin remodeling. Genes from every step of the sumoylation cycle were bound by Irx3 and differentially expressed in response to Irx3-KO, leading to increased global sumoylation levels in the KO cells. Irx3 ablation and elevated sumoylation inhibited Ppar{gamma} activity and adipogenic differentiation in preadipocytes, both of which could be restored by pharmacological inhibition of sumoylation. The Irx3-KO cells demonstrated reduced epigenetic suppression against osteogenesis, resulting in increased osteogenesis in 3D culture. Finally, osteogenesis induced by Irx3 ablation could partially be reversed by inhibition of sumoylation. ConclusionsOur study has uncovered IRX3 as a novel upstream regulator of sumoylation, and a potent controller of epigenetic regulators, both directly and indirectly via suppressing global sumoylation levels. This study indicates that the FTO locus promotes obesity via IRX3-mediated suppression of sumoylation, which promotes adipogenic commitment and differentiation through epigenetic programming. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=61 SRC="FIGDIR/small/562662v1_figu1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@115feaborg.highwire.dtl.DTLVardef@102a6c0org.highwire.dtl.DTLVardef@111bd1corg.highwire.dtl.DTLVardef@1969061_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2023

Structural properties of the HNF-1A transactivation domain

Hepatocyte nuclear factor 1 (HNF-1A) is a transcription factor with important gene regulatory roles in pancreatic {beta}-cells. HNF1A gene variants are associated with a monogenic form of diabetes (HNF1A-MODY) or an increased risk for type 2 diabetes. While several pancreatic target genes of HNF-1A have been described, a lack of knowledge regarding the structure-function relationships in HNF-1A prohibits a detailed understanding of HNF-1A-mediated gene transcription, which is important for precision medicine and improved patient care. Therefore, we aimed to characterize the understudied transactivation domain (TAD) of HNF-1A in vitro. We present a bioinformatic approach to dissect the TAD sequence, analyzing protein structure, sequence composition, sequence conservation, and the existence of protein interaction motifs. Moreover, we developed the first protocol for the recombinant expression and purification of the HNF-1A TAD. Small-angle X-ray scattering and synchrotron radiation circular dichroism suggested a disordered conformation for the TAD. Furthermore, we present functional data on HNF-1A undergoing liquid-liquid phase separation, which is in line with in silico predictions and may be of biological relevance for gene transcriptional processes in pancreatic {beta}-cells.

biochemistry↗

bioRxiv2023

A systematic mapping of the genomic and proteomic variation associated with monogenic diabetes

AimsMonogenic diabetes is characterized as a group of diseases caused by rare variants in single genes. Multiple genes have been described to be responsible for monogenic diabetes, but the information on the variants is not unified among different resources. In this work, we aimed to develop an automated pipeline that collects all the genetic variants associated with monogenic diabetes from different resources, unify the data and translate the genetic sequences to the proteins. MethodsThe pipeline developed in this work is written in Python with the use of Jupyter notebook. It consists of 6 modules that can be implemented separately. The translation step is performed using the ProVar tool also written in Python. All the code along with the intermediate and final results is available for public access and reuse. ResultsThe resulting database had 2701 genomic variants in total and was divided into two levels: the variants reported to have an association with monogenic diabetes and the variants that have evidence of pathogenicity. Of them, 2565 variants were found in the ClinVar database and the rest 136 were found in the literature showing that the overlap between resources is not absolute. ConclusionsWe have developed an automated pipeline for collecting and harmonizing data on genetic variants associated with monogenic diabetes. Furthermore, we have translated variant genetic sequences into protein sequences accounting for all protein isoforms and their variants. This allows researchers to consolidate information on variant genes and proteins associated with monogenic diabetes and facilitates their study using proteomics or structural biology. Our open and flexible implementation using Jupyter notebooks enables tailoring and modifying the pipeline and its application to other rare diseases. Research in contextO_LIMonogenic diabetes is a group of Mendelian diseases with an autosomal-dominant pattern of inheritance. C_LIO_LIMonogenic diabetes is mainly caused by rare genetic variants that are usually evaluated manually. C_LIO_LIThe data on the variants are stored in several resources and are not unified in terms of the genomic coordinates, alleles, and variant annotation. C_LIO_LIWhat can be done for the systematic evaluation of the variants and their protein consequences? C_LIO_LIIn this work, we have created an automated Jupyter notebook-based pipeline for the collection and unification of the variants associated with monogenic diabetes. C_LIO_LIThe database of the genetic variants was created and translated to all possible variant protein sequences. C_LIO_LIThese results will be used for the analysis of proteomics data and protein structure modeling. C_LI

bioinformatics↗

bioRxiv2022

Identifying Protein Haplotypes by Mass Spectrometry

The non-random distribution of alleles of common genomic variants produces haplotypes, which are fundamental in medical and population genetic studies. Consequently, protein-coding genes with different co-occurring sets of alleles can encode different amino acid sequences: protein haplotypes. These protein haplotypes are present in biological samples, and detectable by mass spectrometry, but are not accounted for in proteomic searches. Consequently, the impact of haplotypic variation on the results of proteomic searches, and the discoverability of peptides specific to haplotypes remain unknown. Here, we study how common genetic haplotypes influence the proteomic search space and investigate the possibility to match peptides containing multiple amino acid substitutions to a publicly available data set of mass spectra. We found that for 9.96 % of the discoverable amino acid substitutions encoded by common haplotypes, two or more substitutions may co-occur in the same peptide after tryptic digestion of the protein haplotypes. We identified 342 spectra that matched to such multi-variant peptides, and out of the 4,251 amino acid substitutions identified, 6.63 % were covered by multi-variant peptides. However, the evaluation of the reliability of these matches remains challenging, suggesting that refined error rate estimation procedures are needed for such complex proteomic searches. As these become available and the ability to analyze protein haplotypes increases, we anticipate that proteomics will provide new information on the consequences of common variation, across tissues and time.

bioinformatics↗

bioRxiv2022

Extending protein interaction networks using proteoforms and small molecules

Biological network analysis is used to interpret modern high-throughput biomedical data sets in terms of biological functions and pathways. However, the results greatly depend on the topological characteristics of the underlying network, commonly composed of nodes representing genes or proteins that are connected by edges when interacting. In this study, we build biological networks accounting for small molecules, protein isoforms and post-translational modifications. We highlight how these change the global structure of the network and how the connectedness of pathway-based networks is altered. Our findings highlight the importance of carefully crafting the networks for network analysis to better represent the reality of biological systems.

bioinformatics↗

bioRxiv2022

The Norwegian Mother, Father, and Child cohort study (MoBa) genotyping data resource: MoBaPsychGen pipeline v.1

BackgroundThe Norwegian Mother, Father, and Child Cohort Study (MoBa) is a population-based pregnancy cohort, which includes approximately 114,500 children, 95,200 mothers, and 75,200 fathers. Genotyping of MoBa has been conducted through multiple research projects, spanning several years; using varying selection criteria, genotyping arrays, and genotyping centres. MoBa contains numerous interrelated families, which necessitated the implementation of a family-based quality control (QC) pipeline that verifies and accounts for diverse types of relatedness. MethodsThe MoBaPsychGen pipeline, comprising pre-imputation QC, phasing, imputation, and post-imputation QC, was developed based on current best-practice protocols and implemented to account for the complex structure of the MoBa genotype data. The pipeline includes QC on both single nucleotide polymorphism (SNP) and individual level. Phasing and imputation were performed using the publicly available Haplotype Reference Consortium release 1.1 panel as a reference. Information from the Medical Birth Registry of Norway and MoBa questionnaires were used to identify biological sex, year of birth, reported parent-offspring (PO) relationships, and multiple births (only available in the offspring generation). ResultsIn total, 207,569 unique individuals (90% of the unique individuals included in the study) and 6,981,748 autosomal SNPs passed the MoBaPsychGen pipeline. A further 174,462 chromosome X and 3,200 PAR SNPs are available in a subset of these individuals (N = 204,913 and 135,593, respectively). The relatedness checks performed throughout the pipeline allowed identification of within-generation and across-generation first-degree, second-degree, and third-degree relatives. The individuals passing post-imputation QC comprised 64,471 families ranging in size from singletons to 84 unique individuals (singletons are included as families as other family members may not have been genotyped, imputed, or passed post-imputation QC). The relationships identified include 287 monozygotic twin pairs, 22,884 full siblings, 117,004 PO pairs, 23,299 second-degree relative pairs, and 10,828 third-degree relative pairs. DiscussionMoBa contains a highly complex relatedness structure, with a variety of family structures including singletons, PO duos, full (mother, father, child) PO trios, nuclear families, blended families, and extended families. The availability of robustly quality-controlled genetic data for such a large cohort with a unique extended family structure will allow many novel research questions to be addressed. Furthermore, the MoBaPsychGen pipeline has potential utility in similar cohorts.

genetics↗

bioRxiv2021

Structural and biophysical characterization of HNF-1A as a tool to study MODY3 diabetes variants

Hepatocyte nuclear factor 1A (HNF-1A) is a transcription factor expressed in several embryonic and adult tissues, modulating expression of numerous target genes. Pathogenic variants in the HNF1A gene cause maturity-onset diabetes of the young 3 (MODY3 or HNF1A MODY), characterized by dominant inheritance, age of onset before 25-35 years of age, and pancreatic {beta}-cell dysfunction. A precise diagnosis alters management as insulin can be exchanged with sulfonylurea tablets and genetic counselling differs from polygenic forms of diabetes. More knowledge on mechanisms of HNF-1A function and the level of pathogenicity of the numerous HNF1A variants identified by exome sequencing is required for precise diagnostics. Here, we have structurally and biophysically characterized an HNF-1A protein containing both the DNA binding domain and the dimerization domain. We also present a novel approach to characterize HNF-1A variants. The folding and DNA binding capacity of two established MODY3 HNF-1A variant proteins (P112L, R263C) and one variant of unknown significance (N266S) were determined. All three variants showed reduced functionality compared to the wild-type protein. While the R263C and N266S variants displayed reduced binding to an HNF-1A target promoter, the P112L variant was unstable in vitro and in cells. Our results support and mechanistically explain disease causality for all investigated variants and allow for the dissection of structurally unstable and DNA binding defective variants. This points towards structural and biochemical investigation of HNF-1A being a valuable aid in reliable variant classification needed for precision diagnostics and management.

biochemistry↗

bioRxiv2020

The position of single-base deletions in the VNTR sequence of the carboxyl ester lipase (CEL) gene determines pathogenicity

Variable number of tandem repeat (VNTR) sequences present in the genome can have functional consequences that contribute to human disease. This is the case for the CEL gene, which encodes the digestive enzyme carboxyl ester lipase. CEL has a VNTR located in exon 11, and rare single-base deletions (DELs) within this region cause MODY8, an inherited disorder characterized by exocrine pancreatic dysfunction and diabetes. Here, we have studied how the position of single-base deletions within the CEL VNTR affects the proteins pathogenic properties. We investigated four naturally occurring CEL variants with single-base deletions in different VNTR segments (DEL1, DEL4, DEL9, DEL13), of which only DEL1 and DEL4 have been observed in MODY8 patients. When expressed in a cellular model system, only DEL1 and DEL4 exhibited significantly reduced secretion and increased intracellular aggregation compared to normal CEL. We found that all DEL variants had slightly decreased enzymatic activity and that their level of O-glycosylation was affected. Moreover, only DEL1 and DEL4 significantly increased endoplasmic reticulum (ER) stress. In conclusion, CEL single-base deletion variants have the highest pathogenic potential when the mutational event has taken place in the proximal VNTR part, resulting in the longest aberrant protein tails. Thus, DEL1 and DEL4 are pathogenic CEL variants, whereas we consider DEL13 as benign and DEL9 as likely benign. These findings have implications for our understanding of how CEL mutations cause pancreatic disease through protein misfolding and proteotoxicity, leading to ER stress and activation of the unfolded protein response.

cell biology↗

bioRxiv2020

SeeCiTe: a method to assess CNV calls from SNP arrays using trio data

MotivationSingle nucleotide polymorphism (SNP) genotyping arrays remain an attractive platform for assaying copy number variants (CNVs) in large population-wide cohorts. However current tools for calling CNVs are still prone to extensive false positive calls when applied to biobank scale arrays. Moreover, there is a lack of methods exploiting cohorts with trios available (e.g. nuclear family) to assist in quality control and downstream analyses following the calling. ResultsWe developed SeeCiTe (Seeing Cnvs in Trios), a novel CNV quality control tool that post-processes output from current CNV calling tools exploiting child-parent trio data to classify calls in quality categories and provide a set of visualizations for each putative CNV call in the offspring. We apply it to the Norwegian Mother, Father, and Child Cohort Study (MoBa) and show that SeeCiTe improves the specificity and sensitivity compared to the common empiric filtering strategies. To our knowledge it is the first tool that utilizes probe-level CNV data in trios to systematically highlight potential artefacts and visualize signal intensities in a streamlined fashion suitable for biobank scale studies. Availability and ImplementationThe software is implemented in R with the source code freely available at https://github.com/aksenia/SeeCiTe. ContactKsenia.Lavrichenko@mpi.nl, Stefan.Johansson@uib.no or Inge.Jonassen@uib.no

bioinformatics↗

bioRxiv2020

Haplotype-based heritability estimations reveal gestational duration as a maternal trait and fetal size measurements at birth as fetal traits in human pregnancy

AbstractGenotype-based approaches for the estimation of SNP-based narrow-sense heritability [Formula] have limited utility in pregnancy-related outcomes due to confounding by the shared alleles between mother and child. Here, we propose a haplotype-based approach to estimate the genetic variance attributable to three haplotypes - maternal transmitted [Formula], maternal non-transmitted [Formula] and paternal transmitted [Formula] in mother-child pairs. We show through extensive simulations that our haplotype-based approach outperforms the conventional and contemporary approaches for resolving the contribution of maternal and fetal effects, particularly when m1 and p1 have different effects in the offspring. We apply this approach to estimate the explicit and relative maternal-fetal genetic contribution to the phenotypic variance of gestational duration and gestational duration adjusted fetal size measurements at birth in 10,375 mother-child pairs. The results reveal that variance of gestational duration is mainly attributable to m1 and m2 ([Formula]= 17.3%, S. E. = 5.2%; [Formula] = 12.2%, S. E. = 5.2%; [Formula] = 0.0%, S. E. = 5.0%). In contrast, variance of fetal size measurements at birth are mainly attributable to m1 and p1 ([Formula] = 18.6 - 36.4%, [Formula] = 0.0 - 5.2% and [Formula] = 4.4 - 13.6%). Our results suggest that gestational duration and fetal size measurements are primarily genetically determined by the maternal and fetal genomes, respectively. In addition, a greater contribution of m1 as compared to m2 and p1 [Formula] to birth length and head circumference suggests a substantial influence of correlated maternal-fetal genetic effects on these traits. Our newly developed approach provides a direct and robust alternative for resolving explicit maternal and fetal genetic contributions to the phenotypic variance of pregnancy-related outcomes.

genetics↗

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

Genome-wide association study reveals a dynamic role of common genetic variation in infant and early childhood growth

Infant and childhood growth are dynamic processes characterized by drastic changes in fat mass and body mass index (BMI) at distinct developmental stages. To elucidate how genetic variation influences these processes, we performed the first genome-wide association study (GWAS) of BMI measurements at 12 time points from birth to eight years of age (9,286 children, 74,105 measurements) in the Norwegian Mother and Child Cohort Study (MoBa) with replication in 5,235 children (41,502 measurements). We identified five loci associated with BMI at distinct developmental stages with different patterns of association. Notably, we identified a novel transient effect in the leptin receptor (LEPR) locus, with no effect at birth, increasing effect on BMI in infancy, peaking at 6-12 months (rs2767486, P6m = 2.0 x 10-21, {beta}6m = 0.16) and little effect after age five. A similar transient effect was found near the leptin gene (LEP), peaking at 1.5 years of age (rs10487505, P1.5y = 1.3 x 10-8, {beta}1.5y = 0.079). Both signals are protein quantitative trait loci (pQTLs) for soluble LEPR and LEP in plasma in adults and independent from signals associated with other adult traits mapped to the respective genes, suggesting novel key roles of common variation in the leptin signaling pathway for healthy infant growth. Hence, our longitudinal analysis uncovers a complex and dynamic influence of common variation on BMI during infant and early childhood growth, dominated by the LEP-LEPR axis in infancy.

genetics↗

bioRxiv2018

PathwayMatcher: multi-omics pathway mapping and proteoform network generation

BackgroundMapping biomedical data to functional knowledge is an essential task in bioinformatics and can be achieved by querying identifiers, e.g. gene sets, in pathway knowledgebases. However, the isoform and post-translational modification states of proteins are lost when converting input and pathways into gene-centric lists.\n\nFindingsBased on the Reactome knowledgebase, we built a network of protein-protein interactions accounting for the documented isoform and modification statuses of proteins. We then implemented a command line application called PathwayMatcher (github.com/PathwayAnalysisPlatform/PathwayMatcher) to query this network. PathwayMatcher supports multiple types of omics data as input, and outputs the possibly affected biochemical reactions, subnetworks, and pathways.\n\nConclusionsPathwayMatcher enables refining the network-representation of pathways by including isoform and post-translational modifications. The specificity of pathway analyses is hence adapted to different levels of granularity and it becomes possible to distinguish interactions between different forms of the same protein.

bioinformatics↗