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  1. Fulltext Polygenic risk prediction: why and when out-of-sample prediction R2 can exceed SNP-based heritability
    Wang X, Walker A, Revez JA, Ni G, Adams MJ, McIntosh AM, et al.
    Am J Hum Genet, 2023 Jul 06;110(7):1207-1215.
    PMID: 37379836 DOI: 10.1016/j.ajhg.2023.06.006
    In polygenic score (PGS) analysis, the coefficient of determination (R2) is a key statistic to evaluate efficacy. R2 is the proportion of phenotypic variance explained by the PGS, calculated in a cohort that is independent of the genome-wide association study (GWAS) that provided estimates of allelic effect sizes. The SNP-based heritability (hSNP2, the proportion of total phenotypic variances attributable to all common SNPs) is the theoretical upper limit of the out-of-sample prediction R2. However, in real data analyses R2 has been reported to exceed hSNP2, which occurs in parallel with the observation that hSNP2 estimates tend to decline as the number of cohorts being meta-analyzed increases. Here, we quantify why and when these observations are expected. Using theory and simulation, we show that if heterogeneities in cohort-specific hSNP2 exist, or if genetic correlations between cohorts are less than one, hSNP2 estimates can decrease as the number of cohorts being meta-analyzed increases. We derive conditions when the out-of-sample prediction R2 will be greater than hSNP2 and show the validity of our derivations with real data from a binary trait (major depression) and a continuous trait (educational attainment). Our research calls for a better approach to integrating information from multiple cohorts to address issues of between-cohort heterogeneity.
    Matched MeSH terms: Multifactorial Inheritance/genetics
  2. Fulltext Polygenic hazard score is associated with prostate cancer in multi-ethnic populations
    Huynh-Le MP, Fan CC, Karunamuni R, Thompson WK, Martinez ME, Eeles RA, et al.
    Nat Commun, 2021 02 23;12(1):1236.
    PMID: 33623038 DOI: 10.1038/s41467-021-21287-0
    Genetic models for cancer have been evaluated using almost exclusively European data, which could exacerbate health disparities. A polygenic hazard score (PHS1) is associated with age at prostate cancer diagnosis and improves screening accuracy in Europeans. Here, we evaluate performance of PHS2 (PHS1, adapted for OncoArray) in a multi-ethnic dataset of 80,491 men (49,916 cases, 30,575 controls). PHS2 is associated with age at diagnosis of any and aggressive (Gleason score ≥ 7, stage T3-T4, PSA ≥ 10 ng/mL, or nodal/distant metastasis) cancer and prostate-cancer-specific death. Associations with cancer are significant within European (n = 71,856), Asian (n = 2,382), and African (n = 6,253) genetic ancestries (p 
    Matched MeSH terms: Multifactorial Inheritance/genetics*
  3. Fulltext Polygenic risk scores for prediction of breast cancer risk in Asian populations
    Ho WK, Tai MC, Dennis J, Shu X, Li J, Ho PJ, et al.
    Genet Med, 2022 Mar;24(3):586-600.
    PMID: 34906514 DOI: 10.1016/j.gim.2021.11.008
    PURPOSE: Non-European populations are under-represented in genetics studies, hindering clinical implementation of breast cancer polygenic risk scores (PRSs). We aimed to develop PRSs using the largest available studies of Asian ancestry and to assess the transferability of PRS across ethnic subgroups.

    METHODS: The development data set comprised 138,309 women from 17 case-control studies. PRSs were generated using a clumping and thresholding method, lasso penalized regression, an Empirical Bayes approach, a Bayesian polygenic prediction approach, or linear combinations of multiple PRSs. These PRSs were evaluated in 89,898 women from 3 prospective studies (1592 incident cases).

    RESULTS: The best performing PRS (genome-wide set of single-nucleotide variations [formerly single-nucleotide polymorphism]) had a hazard ratio per unit SD of 1.62 (95% CI = 1.46-1.80) and an area under the receiver operating curve of 0.635 (95% CI = 0.622-0.649). Combined Asian and European PRSs (333 single-nucleotide variations) had a hazard ratio per SD of 1.53 (95% CI = 1.37-1.71) and an area under the receiver operating curve of 0.621 (95% CI = 0.608-0.635). The distribution of the latter PRS was different across ethnic subgroups, confirming the importance of population-specific calibration for valid estimation of breast cancer risk.

    CONCLUSION: PRSs developed in this study, from association data from multiple ancestries, can enhance risk stratification for women of Asian ancestry.

    Matched MeSH terms: Multifactorial Inheritance/genetics
  4. Fulltext Hair Cortisol in Twins: Heritability and Genetic Overlap with Psychological Variables and Stress-System Genes
    Rietschel L, Streit F, Zhu G, McAloney K, Frank J, Couvy-Duchesne B, et al.
    Sci Rep, 2017 Nov 10;7(1):15351.
    PMID: 29127340 DOI: 10.1038/s41598-017-11852-3
    Hair cortisol concentration (HCC) is a promising measure of long-term hypothalamus-pituitary-adrenal (HPA) axis activity. Previous research has suggested an association between HCC and psychological variables, and initial studies of inter-individual variance in HCC have implicated genetic factors. However, whether HCC and psychological variables share genetic risk factors remains unclear. The aims of the present twin study were to: (i) assess the heritability of HCC; (ii) estimate the phenotypic and genetic correlation between HPA axis activity and the psychological variables perceived stress, depressive symptoms, and neuroticism; using formal genetic twin models and molecular genetic methods, i.e. polygenic risk scores (PRS). HCC was measured in 671 adolescents and young adults. These included 115 monozygotic and 183 dizygotic twin-pairs. For 432 subjects PRS scores for plasma cortisol, major depression, and neuroticism were calculated using data from large genome wide association studies. The twin model revealed a heritability for HCC of 72%. No significant phenotypic or genetic correlation was found between HCC and the three psychological variables of interest. PRS did not explain variance in HCC. The present data suggest that HCC is highly heritable. However, the data do not support a strong biological link between HCC and any of the investigated psychological variables.
    Matched MeSH terms: Multifactorial Inheritance*
  5. Lack of evidence for intermolecular epistatic interactions between adiponectin and resistin gene polymorphisms in Malaysian male subjects
    Lau CH, Muniandy S
    Genet Mol Biol, 2012 Jan;35(1):38-44.
    PMID: 22481872
    Epistasis (gene-gene interaction) is a ubiquitous component of the genetic architecture of complex traits such as susceptibility to common human diseases. Given the strong negative correlation between circulating adiponectin and resistin levels, the potential intermolecular epistatic interactions between ADIPOQ (SNP+45T > G, SNP+276G > T, SNP+639T > C and SNP+1212A > G) and RETN (SNP-420C > G and SNP+299G > A) gene polymorphisms in the genetic risk underlying type 2 diabetes (T2DM) and metabolic syndrome (MS) were assessed. The potential mutual influence of the ADIPOQ and RETN genes on their adipokine levels was also examined. The rare homozygous genotype (risk alleles) of SNP-420C > G at the RETN locus tended to be co-inherited together with the common homozygous genotypes (protective alleles) of SNP+639T > C and SNP+1212A > G at the ADIPOQ locus. Despite the close structural relationship between the ADIPOQ and RETN genes, there was no evidence of an intermolecular epistatic interaction between these genes. There was also no reciprocal effect of the ADIPOQ and RETN genes on their adipokine levels, i.e., ADIPOQ did not affect resistin levels nor did RETN affect adiponectin levels. The possible influence of the ADIPOQ gene on RETN expression warrants further investigation.
    Matched MeSH terms: Multifactorial Inheritance
  6. Signatures of polygenic adaptation associated with climate across the range of a threatened fish species with high genetic connectivity
    Harrisson KA, Amish SJ, Pavlova A, Narum SR, Telonis-Scott M, Rourke ML, et al.
    Mol Ecol, 2017 Nov;26(22):6253-6269.
    PMID: 28977721 DOI: 10.1111/mec.14368
    Adaptive differences across species' ranges can have important implications for population persistence and conservation management decisions. Despite advances in genomic technologies, detecting adaptive variation in natural populations remains challenging. Key challenges in gene-environment association studies involve distinguishing the effects of drift from those of selection and identifying subtle signatures of polygenic adaptation. We used paired-end restriction site-associated DNA sequencing data (6,605 biallelic single nucleotide polymorphisms; SNPs) to examine population structure and test for signatures of adaptation across the geographic range of an iconic Australian endemic freshwater fish species, the Murray cod Maccullochella peelii. Two univariate gene-association methods identified 61 genomic regions associated with climate variation. We also tested for subtle signatures of polygenic adaptation using a multivariate method (redundancy analysis; RDA). The RDA analysis suggested that climate (temperature- and precipitation-related variables) and geography had similar magnitudes of effect in shaping the distribution of SNP genotypes across the sampled range of Murray cod. Although there was poor agreement among the candidate SNPs identified by the univariate methods, the top 5% of SNPs contributing to significant RDA axes included 67% of the SNPs identified by univariate methods. We discuss the potential implications of our findings for the management of Murray cod and other species generally, particularly in relation to informing conservation actions such as translocations to improve evolutionary resilience of natural populations. Our results highlight the value of using a combination of different approaches, including polygenic methods, when testing for signatures of adaptation in landscape genomic studies.
    Matched MeSH terms: Multifactorial Inheritance*
  7. Fulltext Genomic Dissection of Bipolar Disorder and Schizophrenia, Including 28 Subphenotypes
    Bipolar Disorder and Schizophrenia Working Group of the Psychiatric Genomics Consortium. Electronic address: [email protected], Bipolar Disorder and Schizophrenia Working Group of the Psychiatric Genomics Consortium
    Cell, 2018 Jun 14;173(7):1705-1715.e16.
    PMID: 29906448 DOI: 10.1016/j.cell.2018.05.046
    Schizophrenia and bipolar disorder are two distinct diagnoses that share symptomology. Understanding the genetic factors contributing to the shared and disorder-specific symptoms will be crucial for improving diagnosis and treatment. In genetic data consisting of 53,555 cases (20,129 bipolar disorder [BD], 33,426 schizophrenia [SCZ]) and 54,065 controls, we identified 114 genome-wide significant loci implicating synaptic and neuronal pathways shared between disorders. Comparing SCZ to BD (23,585 SCZ, 15,270 BD) identified four genomic regions including one with disorder-independent causal variants and potassium ion response genes as contributing to differences in biology between the disorders. Polygenic risk score (PRS) analyses identified several significant correlations within case-only phenotypes including SCZ PRS with psychotic features and age of onset in BD. For the first time, we discover specific loci that distinguish between BD and SCZ and identify polygenic components underlying multiple symptom dimensions. These results point to the utility of genetics to inform symptomology and potential treatment.
    Matched MeSH terms: Multifactorial Inheritance/genetics
  8. Fulltext Dimensionality reduction reveals fine-scale structure in the Japanese population with consequences for polygenic risk prediction
    Sakaue S, Hirata J, Kanai M, Suzuki K, Akiyama M, Lai Too C, et al.
    Nat Commun, 2020 03 26;11(1):1569.
    PMID: 32218440 DOI: 10.1038/s41467-020-15194-z
    The diversity in our genome is crucial to understanding the demographic history of worldwide populations. However, we have yet to know whether subtle genetic differences within a population can be disentangled, or whether they have an impact on complex traits. Here we apply dimensionality reduction methods (PCA, t-SNE, PCA-t-SNE, UMAP, and PCA-UMAP) to biobank-derived genomic data of a Japanese population (n = 169,719). Dimensionality reduction reveals fine-scale population structure, conspicuously differentiating adjacent insular subpopulations. We further enluciate the demographic landscape of these Japanese subpopulations using population genetics analyses. Finally, we perform phenome-wide polygenic risk score (PRS) analyses on 67 complex traits. Differences in PRS between the deconvoluted subpopulations are not always concordant with those in the observed phenotypes, suggesting that the PRS differences might reflect biases from the uncorrected structure, in a trait-dependent manner. This study suggests that such an uncorrected structure can be a potential pitfall in the clinical application of PRS.
    Matched MeSH terms: Multifactorial Inheritance/genetics*
  9. Fulltext Evaluating approaches for constructing polygenic risk scores for prostate cancer in men of African and European ancestry
    Darst BF, Shen J, Madduri RK, Rodriguez AA, Xiao Y, Sheng X, et al.
    Am J Hum Genet, 2023 Jul 06;110(7):1200-1206.
    PMID: 37311464 DOI: 10.1016/j.ajhg.2023.05.010
    Genome-wide polygenic risk scores (GW-PRSs) have been reported to have better predictive ability than PRSs based on genome-wide significance thresholds across numerous traits. We compared the predictive ability of several GW-PRS approaches to a recently developed PRS of 269 established prostate cancer-risk variants from multi-ancestry GWASs and fine-mapping studies (PRS269). GW-PRS models were trained with a large and diverse prostate cancer GWAS of 107,247 cases and 127,006 controls that we previously used to develop the multi-ancestry PRS269. Resulting models were independently tested in 1,586 cases and 1,047 controls of African ancestry from the California Uganda Study and 8,046 cases and 191,825 controls of European ancestry from the UK Biobank and further validated in 13,643 cases and 210,214 controls of European ancestry and 6,353 cases and 53,362 controls of African ancestry from the Million Veteran Program. In the testing data, the best performing GW-PRS approach had AUCs of 0.656 (95% CI = 0.635-0.677) in African and 0.844 (95% CI = 0.840-0.848) in European ancestry men and corresponding prostate cancer ORs of 1.83 (95% CI = 1.67-2.00) and 2.19 (95% CI = 2.14-2.25), respectively, for each SD unit increase in the GW-PRS. Compared to the GW-PRS, in African and European ancestry men, the PRS269 had larger or similar AUCs (AUC = 0.679, 95% CI = 0.659-0.700 and AUC = 0.845, 95% CI = 0.841-0.849, respectively) and comparable prostate cancer ORs (OR = 2.05, 95% CI = 1.87-2.26 and OR = 2.21, 95% CI = 2.16-2.26, respectively). Findings were similar in the validation studies. This investigation suggests that current GW-PRS approaches may not improve the ability to predict prostate cancer risk compared to the PRS269 developed from multi-ancestry GWASs and fine-mapping.
    Matched MeSH terms: Multifactorial Inheritance/genetics
  10. Fulltext A Meta-Analysis of Quantitative Trait Loci Associated with Multiple Disease Resistance in Rice (Oryza sativa L.)
    Kumar IS, Nadarajah K
    Plants (Basel), 2020 Nov 05;9(11).
    PMID: 33167299 DOI: 10.3390/plants9111491
    Rice blast, sheath blight and bacterial leaf blight are major rice diseases found worldwide. The development of resistant cultivars is generally perceived as the most effective way to combat these diseases. Plant disease resistance is a polygenic trait where a combinatorial effect of major and minor genes affects this trait. To locate the source of this trait, various quantitative trait loci (QTL) mapping studies have been performed in the past two decades. However, investigating the congruency between the reported QTL is a daunting task due to the heterogeneity amongst the QTLs studied. Hence, the aim of our study is to integrate the reported QTLs for resistance against rice blast, sheath blight and bacterial leaf blight and objectively analyze and consolidate the location of QTL clusters in the chromosomes, reducing the QTL intervals and thus identifying candidate genes within the selected meta-QTL. A total of twenty-seven studies for resistance QTLs to rice blast (8), sheath blight (15) and bacterial leaf blight (4) was compiled for QTL projection and analyses. Cumulatively, 333 QTLs associated with rice blast (114), sheath blight (151) and bacterial leaf blight (68) resistance were compiled, where 303 QTLs could be projected onto a consensus map saturated with 7633 loci. Meta-QTL analysis on 294 QTLs yielded 48 meta-QTLs, where QTLs with membership probability lower than 60% were excluded, reducing the number of QTLs within the meta-QTL to 274. Further, three meta-QTL regions (MQTL2.5, MQTL8.1 and MQTL9.1) were selected for functional analysis on the basis that MQTL2.5 harbors the highest number of QTLs; meanwhile, MQTL8.1 and MQTL9.1 have QTLs associated with all three diseases mentioned above. The functional analysis allows for determination of enriched gene ontology and resistance gene analogs (RGAs) and other defense-related genes. To summarize, MQTL2.5, MQTL8.1 and MQTL9.1 have a considerable number of R-genes that account for 10.21%, 4.08% and 6.42% of the total genes found in these meta-QTLs, respectively. Defense genes constitute around 3.70%, 8.16% and 6.42% of the total number of genes in MQTL2.5, MQTL8.1 and MQTL9.1, respectively. This frequency is higher than the total frequency of defense genes in the rice genome, which is 0.0096% (167 defense genes/17,272 total genes). The integration of the QTLs facilitates the identification of QTL hotspots for rice blast, sheath blight and bacterial blight resistance with reduced intervals, which helps to reduce linkage drag in breeding. The candidate genes within the promising regions could be utilized for improvement through genetical engineering.
    Matched MeSH terms: Multifactorial Inheritance
  11. Fulltext Congenital renal anomalies - a dissection observational study
    Bilodi, Arun Kumar .S, Gangadhar, M R
    Malaysian Journal of Medical Research, 2017;1(1):14-18.
    MyJurnal
    Anomaly is a congenital disorder. It is a medical condition that is present since birth. But the word congenital neither applies nor excludes genetic disorder. Congenital anomalies due to environmental factors are called as Teratogens. Infections, deficiency in the diet and toxins are environmental causes. Maternal folic acid deficiency may cause spina bifida. Intake of alcohol, and certain prescribed drugs like phenytoin may cause congenital anomalies or defects. Apart from physical anomalies, other types of congenital disorders are inborn errors of metabolism (Kumar, Abbas and Fausto, 2005). About 15% to 25% of anomalies are due to chromosomal factors or single gene factors, 8% to 12% anomalies are said to be due to environmental factors, 25% are said to be due to multifactorial inheritance 40% to 60% of anomalies are of unknown origin (Stevenson, 1993 ; Nelson and Holmes, 1984). Congenital anomalies are present since birth with structural deformity found immediately after birth or their presence may be detected by signs and symptoms later on (Holland and Brew, 1991). Congenital Anomalies are seen in 2% of population as major abnormality. There are two types of abnormalities, namely malformations where growth disturbances occur during embryogenesis and the other is deformation. It is late change that appears in a structure which was normal earlier (Roizen and Patterson, 2003).
    Matched MeSH terms: Multifactorial Inheritance
  12. Fulltext Identification of common genetic risk variants for autism spectrum disorder
    Grove J, Ripke S, Als TD, Mattheisen M, Walters RK, Won H, et al.
    Nat Genet, 2019 03;51(3):431-444.
    PMID: 30804558 DOI: 10.1038/s41588-019-0344-8
    Autism spectrum disorder (ASD) is a highly heritable and heterogeneous group of neurodevelopmental phenotypes diagnosed in more than 1% of children. Common genetic variants contribute substantially to ASD susceptibility, but to date no individual variants have been robustly associated with ASD. With a marked sample-size increase from a unique Danish population resource, we report a genome-wide association meta-analysis of 18,381 individuals with ASD and 27,969 controls that identified five genome-wide-significant loci. Leveraging GWAS results from three phenotypes with significantly overlapping genetic architectures (schizophrenia, major depression, and educational attainment), we identified seven additional loci shared with other traits at equally strict significance levels. Dissecting the polygenic architecture, we found both quantitative and qualitative polygenic heterogeneity across ASD subtypes. These results highlight biological insights, particularly relating to neuronal function and corticogenesis, and establish that GWAS performed at scale will be much more productive in the near term in ASD.
    Matched MeSH terms: Multifactorial Inheritance/genetics
  13. Fulltext European polygenic risk score for prediction of breast cancer shows similar performance in Asian women
    Ho WK, Tan MM, Mavaddat N, Tai MC, Mariapun S, Li J, et al.
    Nat Commun, 2020 07 31;11(1):3833.
    PMID: 32737321 DOI: 10.1038/s41467-020-17680-w
    Polygenic risk scores (PRS) have been shown to predict breast cancer risk in European women, but their utility in Asian women is unclear. Here we evaluate the best performing PRSs for European-ancestry women using data from 17,262 breast cancer cases and 17,695 controls of Asian ancestry from 13 case-control studies, and 10,255 Chinese women from a prospective cohort (413 incident breast cancers). Compared to women in the middle quintile of the risk distribution, women in the highest 1% of PRS distribution have a ~2.7-fold risk and women in the lowest 1% of PRS distribution has ~0.4-fold risk of developing breast cancer. There is no evidence of heterogeneity in PRS performance in Chinese, Malay and Indian women. A PRS developed for European-ancestry women is also predictive of breast cancer risk in Asian women and can help in developing risk-stratified screening programmes in Asia.
    Matched MeSH terms: Multifactorial Inheritance*
  14. Fulltext Joint analysis of psychiatric disorders increases accuracy of risk prediction for schizophrenia, bipolar disorder, and major depressive disorder
    Maier R, Moser G, Chen GB, Ripke S, Cross-Disorder Working Group of the Psychiatric Genomics Consortium, Coryell W, et al.
    Am J Hum Genet, 2015 Feb 05;96(2):283-94.
    PMID: 25640677 DOI: 10.1016/j.ajhg.2014.12.006
    Genetic risk prediction has several potential applications in medical research and clinical practice and could be used, for example, to stratify a heterogeneous population of patients by their predicted genetic risk. However, for polygenic traits, such as psychiatric disorders, the accuracy of risk prediction is low. Here we use a multivariate linear mixed model and apply multi-trait genomic best linear unbiased prediction for genetic risk prediction. This method exploits correlations between disorders and simultaneously evaluates individual risk for each disorder. We show that the multivariate approach significantly increases the prediction accuracy for schizophrenia, bipolar disorder, and major depressive disorder in the discovery as well as in independent validation datasets. By grouping SNPs based on genome annotation and fitting multiple random effects, we show that the prediction accuracy could be further improved. The gain in prediction accuracy of the multivariate approach is equivalent to an increase in sample size of 34% for schizophrenia, 68% for bipolar disorder, and 76% for major depressive disorders using single trait models. Because our approach can be readily applied to any number of GWAS datasets of correlated traits, it is a flexible and powerful tool to maximize prediction accuracy. With current sample size, risk predictors are not useful in a clinical setting but already are a valuable research tool, for example in experimental designs comparing cases with high and low polygenic risk.
    Matched MeSH terms: Multifactorial Inheritance/genetics*
  15. Fulltext Identification of novel breast cancer susceptibility loci in meta-analyses conducted among Asian and European descendants
    Shu X, Long J, Cai Q, Kweon SS, Choi JY, Kubo M, et al.
    Nat Commun, 2020 Mar 05;11(1):1217.
    PMID: 32139696 DOI: 10.1038/s41467-020-15046-w
    Known risk variants explain only a small proportion of breast cancer heritability, particularly in Asian women. To search for additional genetic susceptibility loci for breast cancer, here we perform a meta-analysis of data from genome-wide association studies (GWAS) conducted in Asians (24,206 cases and 24,775 controls) and European descendants (122,977 cases and 105,974 controls). We identified 31 potential novel loci with the lead variant showing an association with breast cancer risk at P 
    Matched MeSH terms: Multifactorial Inheritance/genetics
  16. Fulltext Seasonality shows evidence for polygenic architecture and genetic correlation with schizophrenia and bipolar disorder
    Byrne EM, Psychiatric Genetics Consortium Major Depressive Disorder Working Group, Raheja UK, Stephens SH, Heath AC, Madden PA, et al.
    J Clin Psychiatry, 2015 Feb;76(2):128-34.
    PMID: 25562672 DOI: 10.4088/JCP.14m08981
    OBJECTIVE: To test common genetic variants for association with seasonality (seasonal changes in mood and behavior) and to investigate whether there are shared genetic risk factors between psychiatric disorders and seasonality.

    METHOD: Genome-wide association studies (GWASs) were conducted in Australian (between 1988 and 1990 and between 2010 and 2013) and Amish (between May 2010 and December 2011) samples in whom the Seasonal Pattern Assessment Questionnaire (SPAQ) had been administered, and the results were meta-analyzed in a total sample of 4,156 individuals. Genetic risk scores based on results from prior large GWAS studies of bipolar disorder, major depressive disorder (MDD), and schizophrenia were calculated to test for overlap in risk between psychiatric disorders and seasonality.

    RESULTS: The most significant association was with rs11825064 (P = 1.7 × 10⁻⁶, β = 0.64, standard error = 0.13), an intergenic single nucleotide polymorphism (SNP) found on chromosome 11. The evidence for overlap in risk factors was strongest for schizophrenia and seasonality, with the schizophrenia genetic profile scores explaining 3% of the variance in log-transformed global seasonality scores. Bipolar disorder genetic profile scores were also associated with seasonality, although at much weaker levels (minimum P value = 3.4 × 10⁻³), and no evidence for overlap in risk was detected between MDD and seasonality.

    CONCLUSIONS: Common SNPs of large effect most likely do not exist for seasonality in the populations examined. As expected, there were overlapping genetic risk factors for bipolar disorder (but not MDD) with seasonality. Unexpectedly, the risk for schizophrenia and seasonality had the largest overlap, an unprecedented finding that requires replication in other populations and has potential clinical implications considering overlapping cognitive deficits in seasonal affective disorders and schizophrenia.

    Matched MeSH terms: Multifactorial Inheritance/genetics*
  17. Fulltext Prediction of Breast and Prostate Cancer Risks in Male BRCA1 and BRCA2 Mutation Carriers Using Polygenic Risk Scores
    Lecarpentier J, Silvestri V, Kuchenbaecker KB, Barrowdale D, Dennis J, McGuffog L, et al.
    J Clin Oncol, 2017 Jul 10;35(20):2240-2250.
    PMID: 28448241 DOI: 10.1200/JCO.2016.69.4935
    Purpose BRCA1/2 mutations increase the risk of breast and prostate cancer in men. Common genetic variants modify cancer risks for female carriers of BRCA1/2 mutations. We investigated-for the first time to our knowledge-associations of common genetic variants with breast and prostate cancer risks for male carriers of BRCA1/ 2 mutations and implications for cancer risk prediction. Materials and Methods We genotyped 1,802 male carriers of BRCA1/2 mutations from the Consortium of Investigators of Modifiers of BRCA1/2 by using the custom Illumina OncoArray. We investigated the combined effects of established breast and prostate cancer susceptibility variants on cancer risks for male carriers of BRCA1/2 mutations by constructing weighted polygenic risk scores (PRSs) using published effect estimates as weights. Results In male carriers of BRCA1/2 mutations, PRS that was based on 88 female breast cancer susceptibility variants was associated with breast cancer risk (odds ratio per standard deviation of PRS, 1.36; 95% CI, 1.19 to 1.56; P = 8.6 × 10-6). Similarly, PRS that was based on 103 prostate cancer susceptibility variants was associated with prostate cancer risk (odds ratio per SD of PRS, 1.56; 95% CI, 1.35 to 1.81; P = 3.2 × 10-9). Large differences in absolute cancer risks were observed at the extremes of the PRS distribution. For example, prostate cancer risk by age 80 years at the 5th and 95th percentiles of the PRS varies from 7% to 26% for carriers of BRCA1 mutations and from 19% to 61% for carriers of BRCA2 mutations, respectively. Conclusion PRSs may provide informative cancer risk stratification for male carriers of BRCA1/2 mutations that might enable these men and their physicians to make informed decisions on the type and timing of breast and prostate cancer risk management.
    Matched MeSH terms: Multifactorial Inheritance*
  18. Fulltext Association analysis identifies 65 new breast cancer risk loci
    Michailidou K, Lindström S, Dennis J, Beesley J, Hui S, Kar S, et al.
    Nature, 2017 Nov 02;551(7678):92-94.
    PMID: 29059683 DOI: 10.1038/nature24284
    Breast cancer risk is influenced by rare coding variants in susceptibility genes, such as BRCA1, and many common, mostly non-coding variants. However, much of the genetic contribution to breast cancer risk remains unknown. Here we report the results of a genome-wide association study of breast cancer in 122,977 cases and 105,974 controls of European ancestry and 14,068 cases and 13,104 controls of East Asian ancestry. We identified 65 new loci that are associated with overall breast cancer risk at P 
    Matched MeSH terms: Multifactorial Inheritance/genetics
  19. Fulltext Large scale multifactorial likelihood quantitative analysis of BRCA1 and BRCA2 variants: An ENIGMA resource to support clinical variant classification
    Parsons MT, Tudini E, Li H, Hahnen E, Wappenschmidt B, Feliubadaló L, et al.
    Hum Mutat, 2019 Sep;40(9):1557-1578.
    PMID: 31131967 DOI: 10.1002/humu.23818
    The multifactorial likelihood analysis method has demonstrated utility for quantitative assessment of variant pathogenicity for multiple cancer syndrome genes. Independent data types currently incorporated in the model for assessing BRCA1 and BRCA2 variants include clinically calibrated prior probability of pathogenicity based on variant location and bioinformatic prediction of variant effect, co-segregation, family cancer history profile, co-occurrence with a pathogenic variant in the same gene, breast tumor pathology, and case-control information. Research and clinical data for multifactorial likelihood analysis were collated for 1,395 BRCA1/2 predominantly intronic and missense variants, enabling classification based on posterior probability of pathogenicity for 734 variants: 447 variants were classified as (likely) benign, and 94 as (likely) pathogenic; and 248 classifications were new or considerably altered relative to ClinVar submissions. Classifications were compared with information not yet included in the likelihood model, and evidence strengths aligned to those recommended for ACMG/AMP classification codes. Altered mRNA splicing or function relative to known nonpathogenic variant controls were moderately to strongly predictive of variant pathogenicity. Variant absence in population datasets provided supporting evidence for variant pathogenicity. These findings have direct relevance for BRCA1 and BRCA2 variant evaluation, and justify the need for gene-specific calibration of evidence types used for variant classification.
    Matched MeSH terms: Multifactorial Inheritance
  20. Fulltext Breast Cancer Polygenic Risk Score and Contralateral Breast Cancer Risk
    Kramer I, Hooning MJ, Mavaddat N, Hauptmann M, Keeman R, Steyerberg EW, et al.
    Am J Hum Genet, 2020 11 05;107(5):837-848.
    PMID: 33022221 DOI: 10.1016/j.ajhg.2020.09.001
    Previous research has shown that polygenic risk scores (PRSs) can be used to stratify women according to their risk of developing primary invasive breast cancer. This study aimed to evaluate the association between a recently validated PRS of 313 germline variants (PRS313) and contralateral breast cancer (CBC) risk. We included 56,068 women of European ancestry diagnosed with first invasive breast cancer from 1990 onward with follow-up from the Breast Cancer Association Consortium. Metachronous CBC risk (N = 1,027) according to the distribution of PRS313 was quantified using Cox regression analyses. We assessed PRS313 interaction with age at first diagnosis, family history, morphology, ER status, PR status, and HER2 status, and (neo)adjuvant therapy. In studies of Asian women, with limited follow-up, CBC risk associated with PRS313 was assessed using logistic regression for 340 women with CBC compared with 12,133 women with unilateral breast cancer. Higher PRS313 was associated with increased CBC risk: hazard ratio per standard deviation (SD) = 1.25 (95%CI = 1.18-1.33) for Europeans, and an OR per SD = 1.15 (95%CI = 1.02-1.29) for Asians. The absolute lifetime risks of CBC, accounting for death as competing risk, were 12.4% for European women at the 10th percentile and 20.5% at the 90th percentile of PRS313. We found no evidence of confounding by or interaction with individual characteristics, characteristics of the primary tumor, or treatment. The C-index for the PRS313 alone was 0.563 (95%CI = 0.547-0.586). In conclusion, PRS313 is an independent factor associated with CBC risk and can be incorporated into CBC risk prediction models to help improve stratification and optimize surveillance and treatment strategies.
    Matched MeSH terms: Multifactorial Inheritance*
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