Genetic predisposition to metabolically unfavourable adiposity and prostate cancer risk: A Mendelian randomization analysis

BACKGROUND

The associations of adiposity with aggressive prostate cancer risk are unclear. Using two-sample Mendelian randomization, we assessed the association of metabolically unfavourable adiposity (UFA), favourable adiposity (FA) and for comparison body mass index (BMI), with prostate cancer, including aggressive prostate cancer.

METHODS

We examined the association of these genetically predicted adiposity-related traits with risk of prostate cancer overall, aggressive and early onset disease using outcome summary statistics from the PRACTICAL consortium (including 15,167 aggressive cases).

RESULTS

In inverse-variance weighted models, there was little evidence that genetically predicted one standard deviation higher UFA, FA and BMI were associated with aggressive prostate cancer [OR: 0.85 (95% CI:0.61-1.19), 0.80 (0.53-1.23) and 0.97 (0.88-1.08), respectively]; these associations were largely consistent in sensitivity analyses accounting for horizontal pleiotropy. There was no strong evidence that genetically determined UFA, FA or BMI were associated with overall prostate cancer or early age of onset prostate cancer.

CONCLUSIONS

We did not find differences in the associations of UFA and FA with prostate cancer risk, which suggest that adiposity is unlikely to influence prostate cancer via the metabolic factors assessed; however, these did not cover some aspects related to metabolic health that may link obesity with aggressive prostate cancer, which should be explored in future studies.

Do sex hormones confound or mediate the effect of chronotype on breast and prostate cancer? A Mendelian randomization study

Morning-preference chronotype has been found to be protective against breast and prostate cancer. Sex hormones have been implicated in relation to chronotype and the development of both cancers. This study aimed to assess whether sex hormones confound or mediate the effect of chronotype on breast and prostate cancer using a Mendelian Randomization (MR) framework. Genetic variants associated with chronotype and sex hormones (total testosterone, bioavailable testosterone, sex hormone binding globulin, and oestradiol) (p<5×10-8) were obtained from published genome-wide association studies (n≤244,207 females and n≤205,527 males). These variants were used to investigate causal relationships with breast (nCases/nControls = 133,384/113,789) and prostate (nCases/nControls = 79,148/61,106) cancer using univariable, bidirectional and multivariable MR. In females, we found evidence for: I) Reduced risk of breast cancer per category increase in morning-preference (OR = 0.93, 95% CI:0. 88, 1.00); II) Increased risk of breast cancer per SD increase in bioavailable testosterone (OR = 1.10, 95% CI: 1.01, 1.19) and total testosterone (OR = 1.15, 95% CI:1.07, 1.23); III) Bidirectional effects between morning-preference and both bioavailable and total testosterone (e.g. mean SD difference in bioavailable testosterone = -0.08, 95% CI:-0.12, -0.05 per category increase in morning-preference vs difference in morning-preference category = -0.04, 95% CI: -0.08, 0.00 per SD increase in bioavailable testosterone). In males, we found evidence for: I) Reduced risk of prostate cancer per category increase in morning-preference (OR = 0.90, 95% CI: 0.83, 0.97) and II) Increased risk of prostate cancer per SD increase in bioavailable testosterone (OR = 1.22, 95% CI: 1.08, 1.37). No bidirectional effects were found between morning-preference and testosterone in males. While testosterone levels were causally implicated with both chronotype and cancer, there was inconsistent evidence for testosterone as a mediator of the relationship. The protective effect of morning-preference on both breast and prostate cancer is clinically interesting, although it may be difficult to effectively modify chronotype. Further studies are needed to investigate other potentially modifiable intermediates.

Differentially expressed genes reflect disease-induced rather than disease-causing changes in the transcriptome

Comparing transcript levels between healthy and diseased individuals allows the identification of differentially expressed genes, which may be causes, consequences or mere correlates of the disease under scrutiny. We propose a method to decompose the observational correlation between gene expression and phenotypes driven by confounders, forward- and reverse causal effects. The bi-directional causal effects between gene expression and complex traits are obtained by Mendelian Randomization integrating summary-level data from GWAS and whole-blood eQTLs. Applying this approach to complex traits reveals that forward effects have negligible contribution. For example, BMI- and triglycerides-gene expression correlation coefficients robustly correlate with trait-to-expression causal effects (rBMI = 0.11, PBMI = 2.0 × 10-51 and rTG = 0.13, PTG = 1.1 × 10-68), but not detectably with expression-to-trait effects. Our results demonstrate that studies comparing the transcriptome of diseased and healthy subjects are more prone to reveal disease-induced gene expression changes rather than disease causing ones.

Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression

Trait-associated genetic variants affect complex phenotypes primarily via regulatory mechanisms on the transcriptome. To investigate the genetics of gene expression, we performed cis- and trans-expression quantitative trait locus (eQTL) analyses using blood-derived expression from 31,684 individuals through the eQTLGen Consortium. We detected cis-eQTL for 88% of genes, and these were replicable in numerous tissues. Distal trans-eQTL (detected for 37% of 10,317 trait-associated variants tested) showed lower replication rates, partially due to low replication power and confounding by cell type composition. However, replication analyses in single-cell RNA-seq data prioritized intracellular trans-eQTL. Trans-eQTL exerted their effects via several mechanisms, primarily through regulation by transcription factors. Expression of 13% of the genes correlated with polygenic scores for 1,263 phenotypes, pinpointing potential drivers for those traits. In summary, this work represents a large eQTL resource, and its results serve as a starting point for in-depth interpretation of complex phenotypes.

Integrative genomic analysis implicates limited peripheral adipose storage capacity in the pathogenesis of human insulin resistance

Insulin resistance is a key mediator of obesity-related cardiometabolic disease, yet the mechanisms underlying this link remain obscure. Using an integrative genomic approach, we identify 53 genomic regions associated with insulin resistance phenotypes (higher fasting insulin levels adjusted for BMI, lower HDL cholesterol levels and higher triglyceride levels) and provide evidence that their link with higher cardiometabolic risk is underpinned by an association with lower adipose mass in peripheral compartments. Using these 53 loci, we show a polygenic contribution to familial partial lipodystrophy type 1, a severe form of insulin resistance, and highlight shared molecular mechanisms in common/mild and rare/severe insulin resistance. Population-level genetic analyses combined with experiments in cellular models implicate CCDC92, DNAH10 and L3MBTL3 as previously unrecognized molecules influencing adipocyte differentiation. Our findings support the notion that limited storage capacity of peripheral adipose tissue is an important etiological component in insulin-resistant cardiometabolic disease and highlight genes and mechanisms underpinning this link.

A principal component meta-analysis on multiple anthropometric traits identifies novel loci for body shape

Large consortia have revealed hundreds of genetic loci associated with anthropometric traits, one trait at a time. We examined whether genetic variants affect body shape as a composite phenotype that is represented by a combination of anthropometric traits. We developed an approach that calculates averaged PCs (AvPCs) representing body shape derived from six anthropometric traits (body mass index, height, weight, waist and hip circumference, waist-to-hip ratio). The first four AvPCs explain >99% of the variability, are heritable, and associate with cardiometabolic outcomes. We performed genome-wide association analyses for each body shape composite phenotype across 65 studies and meta-analysed summary statistics. We identify six novel loci: LEMD2 and CD47 for AvPC1, RPS6KA5/C14orf159 and GANAB for AvPC3, and ARL15 and ANP32 for AvPC4. Our findings highlight the value of using multiple traits to define complex phenotypes for discovery, which are not captured by single-trait analyses, and may shed light onto new pathways.

A reference panel of 64,976 haplotypes for genotype imputation

We describe a reference panel of 64,976 human haplotypes at 39,235,157 SNPs constructed using whole-genome sequence data from 20 studies of predominantly European ancestry. Using this resource leads to accurate genotype imputation at minor allele frequencies as low as 0.1% and a large increase in the number of SNPs tested in association studies, and it can help to discover and refine causal loci. We describe remote server resources that allow researchers to carry out imputation and phasing consistently and efficiently.

Meta-analysis of gene-level associations for rare variants based on single-variant statistics

Meta-analysis of genome-wide association studies (GWASs) has led to the discoveries of many common variants associated with complex human diseases. There is a growing recognition that identifying "causal" rare variants also requires large-scale meta-analysis. The fact that association tests with rare variants are performed at the gene level rather than at the variant level poses unprecedented challenges in the meta-analysis. First, different studies may adopt different gene-level tests, so the results are not compatible. Second, gene-level tests require multivariate statistics (i.e., components of the test statistic and their covariance matrix), which are difficult to obtain. To overcome these challenges, we propose to perform gene-level tests for rare variants by combining the results of single-variant analysis (i.e., p values of association tests and effect estimates) from participating studies. This simple strategy is possible because of an insight that multivariate statistics can be recovered from single-variant statistics, together with the correlation matrix of the single-variant test statistics, which can be estimated from one of the participating studies or from a publicly available database. We show both theoretically and numerically that the proposed meta-analysis approach provides accurate control of the type I error and is as powerful as joint analysis of individual participant data. This approach accommodates any disease phenotype and any study design and produces all commonly used gene-level tests. An application to the GWAS summary results of the Genetic Investigation of ANthropometric Traits (GIANT) consortium reveals rare and low-frequency variants associated with human height. The relevant software is freely available.

Sex-stratified genome-wide association studies including 270,000 individuals show sexual dimorphism in genetic loci for anthropometric traits

Given the anthropometric differences between men and women and previous evidence of sex-difference in genetic effects, we conducted a genome-wide search for sexually dimorphic associations with height, weight, body mass index, waist circumference, hip circumference, and waist-to-hip-ratio (133,723 individuals) and took forward 348 SNPs into follow-up (additional 137,052 individuals) in a total of 94 studies. Seven loci displayed significant sex-difference (FDR<5%), including four previously established (near GRB14/COBLL1, LYPLAL1/SLC30A10, VEGFA, ADAMTS9) and three novel anthropometric trait loci (near MAP3K1, HSD17B4, PPARG), all of which were genome-wide significant in women (P<5×10(-8)), but not in men. Sex-differences were apparent only for waist phenotypes, not for height, weight, BMI, or hip circumference. Moreover, we found no evidence for genetic effects with opposite directions in men versus women. The PPARG locus is of specific interest due to its role in diabetes genetics and therapy. Our results demonstrate the value of sex-specific GWAS to unravel the sexually dimorphic genetic underpinning of complex traits.

Genome-wide meta-analysis identifies 11 new loci for anthropometric traits and provides insights into genetic architecture

Approaches exploiting trait distribution extremes may be used to identify loci associated with common traits, but it is unknown whether these loci are generalizable to the broader population. In a genome-wide search for loci associated with the upper versus the lower 5th percentiles of body mass index, height and waist-to-hip ratio, as well as clinical classes of obesity, including up to 263,407 individuals of European ancestry, we identified 4 new loci (IGFBP4, H6PD, RSRC1 and PPP2R2A) influencing height detected in the distribution tails and 7 new loci (HNF4G, RPTOR, GNAT2, MRPS33P4, ADCY9, HS6ST3 and ZZZ3) for clinical classes of obesity. Further, we find a large overlap in genetic structure and the distribution of variants between traits based on extremes and the general population and little etiological heterogeneity between obesity subgroups.

A meta-analysis of the associations between common variation in the PDE8B gene and thyroid hormone parameters, including assessment of longitudinal stability of associations over time and effect of thyroid hormone replacement

OBJECTIVE

Common variants in PDE8B are associated with TSH but apparently without any effect on thyroid hormone levels that is difficult to explain. Furthermore, the stability of the association has not been examined in longitudinal studies or in patients on levothyroxine (l-T(4)).

DESIGN

Totally, four cohorts were used (n=2557): the Busselton Health Study (thyroid function measured on two occasions), DEPTH, EFSOCH (selective cohorts), and WATTS (individuals on l-T(4)).

METHODS

Meta-analysis to clarify associations between the rs4704397 single nucleotide polymorphism in PDE8B on TSH, tri-iodothyronine (T(3)), and T(4) levels.

RESULTS

Meta-analysis confirmed that genetic variation in PDE8B was associated with TSH (P=1.64 × 10(-10) 0.20 s.d./allele, 95% confidence interval (CI) 0.142, 0.267) and identified a possible new association with free T(4) (P=0.023, -0.07 s.d./allele, 95% CI -0.137, -0.01), no association was seen with free T(3) (P=0.218). The association between PDE8B and TSH was similar in 1981 (0.14 s.d./allele, 95% CI 0.04, 0.238) and 1994 (0.20 s.d./allele, 95% CI 0.102, 0.300) and even more consistent between PDE8B and free T(4) in 1981 (-0.068 s.d./allele, 95% CI -0.167, 0.031) and 1994 (-0.07 s.d./allele, 95% CI -0.170, 0.030). No associations were seen between PDE8B and thyroid hormone parameters in individuals on l-T(4).

CONCLUSION

Common genetic variation in PDE8B is associated with reciprocal changes in TSH and free T(4) levels that are consistent over time and lost in individuals on l-T(4). These findings identify a possible genetic marker reflecting variation in thyroid hormone output that will be of value in epidemiological studies and provides additional evidence that PDE8B is involved in TSH signaling in the thyroid.

Common lipid-altering gene variants are associated with therapeutic intervention thresholds of lipid levels in older people

AIMS

There are a large number of common genetic variants that have been robustly associated with low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, or triglyceride concentrations. The majority of these have been identified or confirmed in recent genome-wide association studies, but few studies have assessed the combined effect of known lipid variants. We hypothesized that these variants would influence both the need for interventions and myocardial infarction (MI) outcomes. We aimed to estimate combined effects of proven SNPs on LDL, HDL, and triglyceride concentrations and MI history in a representative older population.

METHODS AND RESULTS

In the InCHIANTI Study of Aging (age >or=65 years), we calculated individual dyslipidaemia risk allele counts for increased LDL (range 4-14, n = 594), reduced HDL (5-16, n = 635), and increased triglycerides (7-16, n = 611). Lipid levels were compared with ATPIII National Cholesterol Education Panel (NCEP) intervention guidelines. Individual variants and the APOE haplotype explained <2.1% of the variance in their respective lipid concentrations, with the exception of the CETP SNP rs1800775 and HDL levels (4.76%). Combined risk allele counts outperformed the largest single-SNP effects for LDL (explaining 7.1% of variance) and triglycerides (4.8%), but not HDL (3.4%). Risk alleles were divided as near as possible into quartiles. The 31% of respondents with 10 or more LDL increasing alleles were more likely to have LDL levels above the intervention threshold (OR 3.00, 95% CI 1.67-5.39, P = 2.5 x 10(-4)), compared with the 21% with 7 or less risk alleles. Similarly, the 35% with 13 or more triglyceride risk alleles were more likely to exceed NCEP intervention thresholds (OR 2.98, 95% CI 1.43-6.22, P = 0.004) compared with the 24% with 10 or less alleles. The number of individuals reporting an MI event was small (n = 67), but an event was more common in the 36% of respondents who had the highest combined risk allele score for all three lipids (OR 3.68, 95% CI 1.21-11.2, P = 0.021) compared with the lowest risk 22%.

CONCLUSION

In a representative older population, the cumulative effects of proven LDL- and triglyceride-altering genetic variants increased the odds of crossing the lipid-level threshold for therapeutic intervention by approximately three-fold.

Abnormal nephron development associated with a frameshift mutation in the transcription factor hepatocyte nuclear factor-1 beta

BACKGROUND

The transcription factor hepatocyte nuclear factor (HNF)-1 beta functions as a homodimer or as a heterodimer with the structurally related protein HNF-1 alpha. Both are expressed sequentially in rat kidney development, with HNF-1 beta being detected from the earliest inductory phases. HNF-1 beta gene mutations are associated with a unique disorder characterized by maturity-onset diabetes of the young (MODY) and early-onset and progressive nondiabetic renal dysfunction, which may lead to chronic renal failure.

METHODS

The HNF-1 beta gene was screened for mutations in six subjects with early-onset diabetes and a history of renal dysfunction in the subjects or their families.

RESULTS

A novel frameshift mutation in exon 4 of the HNF-1 beta gene and a deletion of CCTCT at codons 328 to 329 were detected in one subject. She was diagnosed as diabetic at the age of 21 in her second pregnancy. Glucose tolerance rapidly deteriorated over 18 months as a result of beta-cell dysfunction. The HNF-1 beta mutation arose de novo on a paternal chromosome and cosegregated with renal abnormalities in her family. The proband had bilateral small cysts in normal-sized kidneys and a reduced creatinine clearance of 66 mL/min (NR 80-120). Her first pregnancy was terminated at 17 weeks following an ultrasound diagnosis of bilateral, nonfunctioning cystic kidneys. Her first-born child had a small multicystic, dysplastic right kidney and a dysplastic left kidney with a reduced creatinine clearance (40 mL/min per 1.73 m2). Histologic examination of the large (5.8 vs. 1.4 g), polycystic fetal kidneys showed no normal nephrogenesis.

CONCLUSIONS

These studies indicate that HNF-1 beta plays a central role in normal kidney development and pancreatic beta-cell function, and suggest that one mechanism by which HNF-1 beta gene mutations may cause renal dysfunction are by their effects on nephron development.

A missense mutation in the hepatocyte nuclear factor 4 alpha gene in a UK pedigree with maturity-onset diabetes of the young

Maturity-onset diabetes of the young (MODY) is a monogenic subgroup of non-insulin dependent diabetes mellitus (NIDDM) characterised bylan early age of onset (< 25 years) and an autosomal dominant mode of inheritance. MODY is genetically heterogeneous with three different genes identified to date; hepatocyte nuclear factor 4 alpha (HNF-4 alpha) [MODY1], glucokinase [MODY2] and hepatocyte nuclear factor 1 alpha (HNF-1 alpha) [MODY3]. A nonsense mutation in the HNF-4 alpha gene has recently been shown to cause MODY in a single large North American pedigree (RW). We screened a large UK Caucasian MODY family which showed weak evidence of linkage to the MODY1 locus on chromosome 20q (lod score for ADA 0.68 at theta = 0) for mutations in the coding region of the HNF-4 alpha gene by direct sequencing. A missense mutation resulting in the substitution of glutamine for glutamic acid was identified in exon 7 (E276Q). The mutation was present in all of the diabetic members of the pedigree plus two unaffected subjects and was not detected in 75 normal control subjects or 95 UK Caucasian subjects with late-onset NIDDM. This is the first missense mutation to be described in the HNF-4 alpha gene.