Inimese mikrobioomi mõjutavad faktorid ning seosed naiste tervisega

Väitekirja elektrooniline versioon ei sisalda publikatsiooneViimastel aastatel on inimese mikrobioomi ja tervise vaheliste seoste uurimine olnud väga aktuaalseks teemaks teaduses. Mikrobioomi olulisus väljendub nii ainevahetusprotsesside, kui ka närvi- ja immuunsüsteemi töös ning toimimises. Mikrobioomi ennast mõjutavad mitmed erinevad faktorid, mille alla kuuluvad muuhulgas vanus, toitumine, ravimite tarbimine ja füüsiline aktiivsus. Geneetika mõju meie tervisele ning selle roll erinevate haiguste juures on uuritud pikalt kuid samas on mikrobioomi ning inimesegeneetika vaheliste seoste uurimine alles algusjärgus. Osalesime oma teadustöö raames suure ülemaailmse konsortsiumi (MiBioGen) töös, mis koondab 24 kohordi ja rohkem kui 18 000 indiviidi geeni ja mikrobioomiandmeid, et leida seoseid soolestiku mikrobioomi ning inimese geneetilise varieeruvuse vahel. Töös leiti assotsiatsioonid 31 geenilookuse ja mikrobioomi vahel ning kinnitati varasemalt mitmetes teadustöödes leitud seos laktaasi kodeeriva geeni LCT ning bakteriperekonna Bifidobacterium vahel. Meie teadustöö teine pool uuris seoseid mikrobioomi ning naiste tervise vahel. Keskendusime seoste uurimisele soolestiku mikrobioomi ning polütsüstiliste munasarjade sündroomi (PCOS) vahel, mis on üheks enimlevinumaks endokriinhaiguseks viljakas eas olevatel naistel ja millega kaasnevad sageli probleemid viljakusega. Nägime, et mikrobioomi ning PCOS’i omavaheline seos toimib tõenäoliselt läbi ainevahetusprotsesside ning eeldiabeedi diagnoosiga PCOS naiste seas on mikroobikoosluse mitmekesisus väiksem võrreldes metaboolselt tervete PCOS naistega. Viimaks uurisime, endomeetriumi mikroobikooslust viljakusprobleemidega naiste seas ning nägime, et bakteriperekond Lactobacillus omab sellele olulist mõju. See tulemus võimaldab tulevikus potentsiaalselt kasutada Lactobacillus’t kui biomarkerit viljakusprobleemide tuvastamiseks. Kokkuvõtvalt anname oma teadustööga panuse mikrobioomi kui uurimissuuna edasisse arengusse, näidates uusi seoseid mikrobioomi ning inimese tervise vahel, mis annavad aluse edasiste uurimistööde läbiviimiseksThe human microbiome is one of the most important scientific discoveries in human healthcare in recent decades. Microbiome influences our metabolism, immune system as well as nervous system. Meanwhile, microbiome itself is affected by various factors including diet, medication, and physical activity. It has become a widely known knowledge that human phenotype and different disease states are dependent on our genetics. However, association between human genetics and microbiome are less studied. An international consortium MiBioGen with data from more than 18,000 individuals was formed with the aim to study the effect of host genetics on gut microbiome composition. We identified 31 associations between genetic loci and microbiome as well as confirmed an association between a gene responsible for lactase production (LCT) and Bifidobacterium – an association seen in previous publications as well as replicated in new research studies. In addition, our work searched for links between female health and microbiome. Namely, we focused on one of the most prevalent female endocrine and metabolic disorders called polycystic ovary syndrome (PCOS) and additionally investigated the microbial composition of the endometrium. Our work with PCOS and gut microbiome revealed that the effects of microbiome on PCOS work through metabolic processes and prediabetic women with PCOS diagnosis have lower bacterial diversity compared to their healthy counterparts. Finally, we show that the genus Lactobacillus has an enormous impact on the composition of endometrial microbiome and could potentially be used as a biomarker in clinical work to help identify possible causes behind infertility problems. In conclusion, with our work we contribute to the microbiome research field by bringing new knowledge into the interplay between microbiome and genetics as well as female health that in the future would provide an impetus for further in-depth research to fully understand the role of microbiome in human health and disease.https://www.ester.ee/record=b549474

Harvade variantide analüüs polütsüstiliste munasarjade sündroomiga seotud geneetiliste variantide tuvastamiseks

Polütsüstiliste munasarjade sündroom (ingl k Polycystic Ovary Syndrome, PCOS) on maailmas laialt levinud endokriinne komplekshaigus reproduktiivses eas olevatel naistel. Haigust iseloomustavad paljud erinevad tunnused nagu näiteks hüperandrogenism, munasarjade tsüstiline morfoloogia, insuliinresistentsus ning ülekaal või rasvumine. Haiguse etioloogia on siiani jäänud ebaselgeks, selle leidmiseks on läbi viidud mitmeid sagedastele variantidele keskenduvaid ülegenoomseid assotsiatsiooniuuringuid, mis on pakkunud mitmeid kandidaatgeene. Üheks uuemaks viisiks leida siiani tabamatuks jäänud PCOS-i peidetud pärilikkust on harvade variantide analüüs, mis on läbiviidud ka käesoleva magistritöö raames, kasutades selleks Eesti populatsioonist pärit indiviide

A saturated map of common genetic variants associated with human height

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40–50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes1. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel2) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10–20% (14–24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries.publishedVersionPeer reviewe

The gut microbiome in polycystic ovary syndrome and its association with metabolic traits

Abstract Context: Despite the gut microbiome being widely studied in metabolic diseases, its role in polycystic ovary syndrome (PCOS) has been scarcely investigated. Objective: Compare the gut microbiome in late fertile age women with and without PCOS and investigate whether changes in the gut microbiome correlate with PCOS-related metabolic parameters. Design: Prospective, case–control study using the Northern Finland Birth Cohort 1966. Setting: General community. Participants: A total of 102 PCOS women and 201 age- and body mass index (BMI)-matched non-PCOS control women. Clinical and biochemical characteristics of the participants were assessed at ages 31 and 46 and analyzed in the context of gut microbiome data at the age of 46. Intervention: (s): None Main outcome measure(s): Bacterial diversity, relative abundance, and correlations with PCOS-related metabolic measures. Results: Bacterial diversity indices did not differ significantly between PCOS and controls (Shannon diversity P = .979, unweighted UniFrac P = .175). Four genera whose balance helps to differentiate between PCOS and non-PCOS were identified. In the whole cohort, the abundance of 2 genera from Clostridiales, Ruminococcaceae UCG-002, and Clostridiales Family XIII AD3011 group, were correlated with several PCOS-related markers. Prediabetic PCOS women had significantly lower alpha diversity (Shannon diversity P = .018) and markedly increased abundance of genus Dorea (false discovery rate = 0.03) compared with women with normal glucose tolerance. Conclusions: PCOS and non-PCOS women at late fertile age with similar BMI do not significantly differ in their gut microbial profiles. However, there are significant microbial changes in PCOS individuals depending on their metabolic health

A saturated map of common genetic variants associated with human height

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries

A saturated map of common genetic variants associated with human height.

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes1. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel2) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries

Large-scale association analyses identify host factors influencing human gut microbiome composition

To study the effect of host genetics on gut microbiome composition, the MiBioGen consortium curated and analyzed genome-wide genotypes and 16S fecal microbiome data from 18,340 individuals (24 cohorts). Microbial composition showed high variability across cohorts: only 9 of 410 genera were detected in more than 95% of samples. A genome-wide association study of host genetic variation regarding microbial taxa identified 31 loci affecting the microbiome at a genome-wide significant (P < 5 x 10(-8)) threshold. One locus, the lactase (LCT) gene locus, reached study-wide significance (genome-wide association study signal: P = 1.28 x 10(-20)), and it showed an age-dependent association with Bifidobacterium abundance. Other associations were suggestive (1.95 x 10(-10) < P < 5 x 10(-8)) but enriched for taxa showing high heritability and for genes expressed in the intestine and brain. A phenome-wide association study and Mendelian randomization identified enrichment of microbiome trait loci in the metabolic, nutrition and environment domains and suggested the microbiome might have causal effects in ulcerative colitis and rheumatoid arthritis

A saturated map of common genetic variants associated with human height

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40–50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes1. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel2) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10–20% (14–24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries.</p