Biology subjects

Lepamets, M.

Publications and source records attributed to Lepamets, M..

7 recordsLinked to original sources

bioRxiv2022

Omics-informed CNV calls reduce false positive rate and improve power for CNV-trait associations

Copy number variations (CNV) are believed to play an important role in a wide range of complex traits but discovering such associations remains challenging. Whilst whole genome sequencing (WGS) is the gold standard approach for CNV detection, there are several orders of magnitude more samples with available genotyping microarray data. Such array data can be exploited for CNV detection using dedicated software (e.g., PennCNV), however these calls suffer from elevated false positive and negative rates. In this study, we developed a CNV quality score that weights PennCNV calls (pCNV) based on their likelihood of being true positive. First, we established a measure of pCNV reliability by leveraging evidence from multiple omics data (WGS, transcriptomics and methylomics) obtained from the same samples. Next, we built a predictor of omics-confirmed pCNVs, termed omics-informed quality score (OQS), using only PennCNV software output parameters. Promisingly, OQS assigned to pCNVs detected in close family members was up to 35% higher than the OQS of pCNVs not carried by other relatives (P < 3.0-10-90), outperforming other scores. Finally, in an association study of four anthropometric traits in 89,516 Estonian Biobank samples, the use of OQS led to a relative increase in the trait variance explained by CNVs of up to 34% compared to raw pCNVs or previous quality scores. Overall, we put forward a flexible framework to improve any CNV detection method leveraging multi-omics evidence, applied it to improve PennCNV calls and demonstrated its utility by improving the statistical power for downstream association analyses.

bioinformatics↗

bioRxiv2020

Genome-wide study identifies association between HLA-B*55:01 and penicillin allergy

BackgroundHypersensitivity reactions to drugs are often unpredictable and can be life-threatening, underscoring a need for understanding their underlying mechanisms and risk factors. The extent to which germline genetic variation influences the risk of commonly reported drug allergies such as penicillin allergy remains largely unknown. MethodsWe extracted data from the electronic health records of 52,000 Estonian and 500,000 UK biobank participants to study the role of genetic variation in the occurrence of penicillin hypersensitivity reactions. We used imputed SNP to HLA typing data from up to 22,554 and 488,377 individuals from the Estonian and UK cohorts, respectively, to further fine-map the human leukocyte antigen (HLA) association and replicated our results in two additional cohorts involving a total of 1.14 million individuals. ResultsGenome-wide meta-analysis of penicillin allergy revealed a significant association located in the HLA region on chromosome 6. The signal was further fine-mapped to the HLA-B*55:01 allele (OR 1.47 95% CI 1.37-1.58, P-value 4.63x10-26) and confirmed by independent replication in two cohorts. The meta-analysis of all four cohorts in the study revealed a strong association of HLA-B*55:01 allele with penicillin allergy (OR 1.33 95% CI 1.29-1.37, P-value 2.23x10-72). In silico follow-up suggests a potential effect on T lymphocytes at HLA-B*55:01. ConclusionWe present the first robust evidence for the role of an allele of the major histocompatibility complex (MHC) I gene HLA-B in the occurrence of penicillin allergy.

genetics↗

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

Gene content of the fish-hunting cone snail Conus consors

BackgroundConus consors is a fish-hunting cone snail that lives in the tropical waters of the Indo-Pacific region. Cone snails have attracted scientific interest for the amazing potency of their venom, which consists of a complex mixture of small proteins known as conopeptides, many of which act as ion channel and receptor modulators with high selectivity.\n\nResultsWe have analysed publicly available transcriptomic sequences from 8 tissues of Conus consors and complemented the transcriptome data with the data from genomic DNA reads. We identified 17,715 full-length protein sequences from the transcriptome. In addition, we predicted 168 full-length or partial conopeptide sequences and characterized gene structures of several conopeptide superfamilies.

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

Polygenic prediction of breast cancer: comparison of genetic predictors and implications for screening

BackgroundPublished genetic risk scores for breast cancer (BC) so far have been based on a relatively small number of markers and are not necessarily using the full potential of large-scale Genome-Wide Association Studies. This study aims to identify an efficient polygenic predictor for BC based on best available evidence and to assess its potential for personalized risk prediction and screening strategies.\n\nMethodsFour different genetic risk scores (two already published and two newly developed) and their combinations (metaGRS) are compared in the subsets of two population-based biobank cohorts: the UK Biobank (UKBB, 3157 BC cases, 43,827 controls) and Estonian Biobank (EstBB, 317 prevalent and 308 incident BC cases in 32,557 women). In addition, correlations between different genetic risk scores and their associations with BC risk factors are studied in both cohorts.\n\nResultsThe metaGRS that combines two genetic risk scores (metaGRS2 - based on 75 and 898 Single Nucleotide Polymorphisms, respectively) has the strongest association with prevalent BC status in both cohorts. One standard deviation difference in the metaGRS2 corresponds to an Odds Ratio = 1.6 (95% CI 1.54 to 1.66, p = 9.7*10-135) in the UK Biobank and accounting for family history marginally attenuates the effect (Odds Ratio = 1.58, 95% CI 1.53 to 1.64, p = 9.1*10-129). In the EstBB cohort, the hazard ratio of incident BC for the women in the top 5% of the metaGRS2 compared to women in the lowest 50% is 4.2 (95% CI 2.8 to 6.2, p = 8.1*10-13). The different GRSs are only moderately correlated with each other and are associated with different known predictors of BC. The classification of genetic risk for the same individual may vary considerably depending on the chosen GRS.\n\nConclusionsWe have shown that metaGRS2 that combines on the effects of more than 900 SNPs provides best predictive ability for breast cancer in two different population-based cohorts. The strength of the effect of metaGRS2 indicates that the GRS could potentially be used to develop more efficient strategies for breast cancer screening for genotyped women.

genetics↗

bioRxiv2017

The complement system supports normal postnatal development and gonadal function in both sexes

Male and female infertility are clinically managed and classified as distinct diseases, and relatively little is known about mechanisms of gonadal function common to both sexes. We used genome-wide genetic analysis on 74,896 women and men to find rare genetic variants that modulate gonadal function in both sexes. This uncovered an association with variants disrupting CSMD1, a complement regulatory protein located on 8p23, in a genomic region with an exceptional evolution. We found that Csmd1 knockout mice display a diverse array of gonadal defects in both sexes, and in females, impaired mammary gland development that leads to increased offspring mortality. The complement pathway is significantly disrupted in Csmd1 mice, and further disruption of the complement pathway from joint inactivation of C3 leads to more extreme reproductive defects. Our results can explain a novel human genetic association with infertility and implicate the complement system in the normal development of postnatal tissues.

developmental biology↗