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Jessica Tyrrell

Publications and source records attributed to Jessica Tyrrell.

5 recordsLinked to original sources
bioRxiv2016

Quantifying the extent to which index event biases influence large genetic association studies

As genetic association studies increase in size to 100,000s of individuals, subtle biases may influence conclusions. One possible bias is \"index event bias\" (IEB), also called \"collider bias\", caused by the stratification by, or enrichment for, disease status when testing associations between gene variants and a disease-associated trait. We first provided a statistical framework for quantifying IEB then identified real examples of IEB in a range of study and analytical designs. We observed evidence of biased associations for some disease alleles and genetic risk scores, even in population-based studies. For example, a genetic risk score consisting of type 2 diabetes variants was associated with lower BMI in 113,203 type 2 diabetes controls from the population based UK Biobank study (-0.010 SDs BMI per allele, P=5E-4), entirely driven by IEB. Three of 11 individual type 2 diabetes risk alleles, and 10 of 25 hypertension alleles were associated with lower BMI at p<0.05 in UK Biobank when analyzing disease free individuals only, of which six hypertension alleles remained associated at p<0.05 after correction for IEB. Our analysis suggested that the associations between CCND2 and TCF7L2 diabetes risk alleles and BMI could (at least partially) be explained by IEB. Variants remaining associated after correction may be pleiotropic and include those in CYP17A1 (allele associated with hypertension risk and lower BMI). In conclusion, IEB may result in false positive or negative associations in very large studies stratified or strongly enriched for/against disease cases.

Genetics↗
bioRxiv2016

Genome-wide association analyses in &gt;119,000 individuals identifies thirteen morningness and two sleep duration loci

Disrupted circadian rhythms and reduced sleep duration are associated with several human diseases, particularly obesity and type 2 diabetes, but little is known about the genetic factors influencing these heritable traits. We performed genome-wide association studies of self-reported chronotype (morning/evening person) and self-reported sleep duration in 128,266 White British individuals from the UK Biobank study. Sixteen variants were associated with chronotype (P<5x10-8), including variants near the known circadian rhythm genes RGS16 (1.21 odds of morningness [95%CI 1.15, 1.27], P=3x10-12) and PER2 (1.09 odds of morningness [95%CI 1.06, 1.12], P=4x10-10). The PER2 signal has previously been associated with iris function. We sought replication using self-reported data from 89,823 23andMe participants; thirteen of the chronotype signals remained significant at P<5x10-8 on meta-analysis and eleven of these reached P<0.05 in the same direction in the 23andMe study. For sleep duration, we replicated one known signal in PAX8 (2.6 [95%CIs 1.9, 3.2] minutes per allele P=5.7x10-16) and identified and replicated two novel associations at VRK2 (2.0 [95% CI: 1.3, 2.7] minutes per allele, P=1.2x10-9; and 1.6 [95% CI: 1.1, 2.2] minutes per allele, P=7.6x10-9). Although we found genetic correlation between chronotype and BMI (rG=0.056, P=0.048); undersleeping and BMI (rG=0.147, P=1x10-5) and oversleeping and BMI (rG=0.097, P=0.039), Mendelian Randomisation analyses provided no consistent evidence of causal associations between BMI or type 2 diabetes and chronotype or sleep duration. Our study provides new insights into the biology of sleep and circadian rhythms in humans.

Genetics↗
bioRxiv2016

HUMAN LONGEVITY IS INFLUENCED BY MANY GENETIC VARIANTS: EVIDENCE FROM 75,000 UK BIOBANK PARTICIPANTS

Variation in human lifespan is 20 to 30% heritable but few genetic variants have been identified. We undertook a Genome Wide Association Study (GWAS) using age at death of parents of middle-aged UK Biobank participants of European decent (n=75,244 with fathers and/or mothers data). Genetic risk scores for 19 phenotypes (n=777 proven variants) were also tested.\n\nGenotyped variants (n=845,997) explained 10.2% (SD=1.3%) of combined parental longevity. In GWAS, a locus in the nicotine receptor CHRNA3 - previously associated with increased smoking and lung cancer - was associated with paternal age at death, with each protective allele (rs1051730[G]) being associated with 0.03 years later age at fathers death (p=3x10-8). Offspring of longer lived parents had more protective alleles (lower genetic risk scores) for coronary artery disease, systolic blood pressure, body mass index, cholesterol and triglyceride levels, type-1 diabetes, inflammatory bowel disease and Alzheimers disease. In candidate gene analyses, variants in the TOMM40/APOE locus were associated with longevity (including rs429358, p=3x10-5), but FOXO variants were not associated.\n\nThese results support a multiple protective factors model for achieving longer lifespans in humans, with a prominent role for cardiovascular-related pathways. Several of these genetically influenced risks, including blood pressure and tobacco exposure, are potentially modifiable.

Genomics↗
bioRxiv2015

Maternal genome-wide association study identifies a fasting glucose variant associated with offspring birth weight

Genome-wide association studies (GWAS) of birth weight have focused on fetal genetics, while relatively little is known about how maternal genetic variation influences fetal growth. We aimed to identify maternal genetic variants associated with birth weight that could highlight potentially relevant maternal determinants of fetal growth.\n\nWe meta-analysed GWAS data on up to 8.7 million SNPs in up to 86,577 women of European descent from the Early Growth Genetics (EGG) Consortium and the UK Biobank. We used structural equation modelling (SEM) and analyses of mother-child pairs to quantify the separate maternal and fetal genetic effects.\n\nMaternal SNPs at 10 loci (MTNR1B, HMGA2, SH2B3, KCNAB1, L3MBTL3, GCK, EBF1, TCF7L2, ACTL9 and CYP3A7) showed evidence of association with offspring birth weight at P<5x10-8. The SEM analyses showed at least 7 of the 10 associations were consistent with effects of the maternal genotype acting via the intrauterine environment, rather than via effects of shared alleles with the fetus. Variants, or correlated proxies, at many of the loci had been previously associated with adult traits, including fasting glucose (MTNR1B, GCK and TCF7L2) and sex hormone levels (CYP3A7), and one (EBF1) with gestational duration.\n\nThe identified associations indicate effects of maternal glucose, cytochrome P450 activity and gestational duration, and potential effects of maternal blood pressure and immune function on fetal growth. Further characterization of these associations, for example in mechanistic and causal analyses, will enhance understanding of the potentially modifiable maternal determinants of fetal growth, with the goal of reducing the morbidity and mortality associated with low and high birth weights.

Genetics↗
bioRxiv2015

Genetic evidence that lower circulating FSH levels lengthen menstrual cycle, increase age at menopause, and impact reproductive health: a UK Biobank study

Study question: How does a genetic variant altering follicle stimulating hormone (FSH) levels, which we identified as associated with length of menstrual cycle, more widely impact reproductive health?\n\nSummary answer: The T allele of the FSHB promoter polymorphism (rs10835638) results in longer menstrual cycles and later menopause and, while having detrimental effects on fertility, is protective against endometriosis.\n\nWhat is known already: The FSHB promoter polymorphism (rs10835638) affects levels of FSHB transcription and, as a result, levels of FSH. FSH is required for normal fertility and genetic variants at the FSHB locus are associated with age at menopause and polycystic ovary syndrome (PCOS).\n\nStudy design, size, duration: We conducted a genetic association study using cross-sectional data from the UK Biobank.\n\nParticipants/materials, setting, methods: We included white British individuals aged 40-69 years in 2006-2010, included in the May 2015 release of genetic data from UK Biobank. We conducted a genome-wide association study (GWAS) in 9,534 individuals to identify genetic variants associated with length of menstrual cycle. We tested the FSH lowering T allele of the FSHB promoter polymorphism (rs10835638) for associations with 29 reproductive phenotypes in up to 63,350 individuals.\n\nMain results and the role of chance: In the GWAS for menstrual cycle length, only variants near the FSHB gene reached genome-wide significance (P<5x10-8). The FSH-lowering T allele of the FSHB promoter polymorphism (rs10835638G>T; MAF 0.16) was associated with longer menstrual cycles (0.16 s.d. (approx. 1 day) per minor allele; 95% CI 0.12-0.20; P=6x10-16), later age at menopause (0.13 years per minor allele; 95% CI 0.04-0.22; P=5.7x10-3), greater female nulliparity (OR=1.06; 95% CI 1.02-1.11; P=4.8x10-3) and lower risk of endometriosis (OR=0.79; 95% CI 0.69-0.90; P=4.1x10-4). The FSH-lowering T allele was not associated more generally with other reproductive illnesses or conditions and we did not replicate associations with male infertility or PCOS.\n\nLimitations, reasons for caution: The data included might be affected by recall bias. Women with a cycle length recorded were aged over 40 and were approaching menopause, however we did not find evidence that this affected the results. Many of the illnesses had relatively small sample sizes and so we may have been under-powered to detect an effect.\n\nWider implications of the findings: We found a strong novel association between a genetic variant that lowers FSH levels and longer menstrual cycles, at a locus previously robustly associated with age at menopause. The variant was also associated with nulliparity and endometriosis risk. We conclude that lifetime differences in circulating levels of FSH between individuals can influence menstrual cycle length and a range of reproductive outcomes, including menopause timing, infertility, endometriosis and PCOS.

Genetics↗