Genes and Weightlifting Performance

A recent case-control study identified 28 DNA polymorphisms associated with strength athlete status. However, studies of genotype-phenotype design are required to support those findings. The aim of the present study was to investigate both individually and in combination the association of 28 genetic markers with weightlifting performance in Russian athletes and to replicate the most significant findings in an independent cohort of Japanese athletes. Genomic DNA was collected from 53 elite Russian (31 men and 22 women, 23.3 ± 4.1 years) and 100 sub-elite Japanese (53 men and 47 women, 21.4 ± 4.2 years) weightlifters, and then genotyped using PCR or micro-array analysis. Out of 28 DNA polymorphisms, LRPPRC rs10186876 A, MMS22L rs9320823 T, MTHFR rs1801131 C, and PHACTR1 rs6905419 C alleles positively correlated (p < 0.05) with weightlifting performance (i.e., total lifts in snatch and clean and jerk in official competitions adjusted for sex and body mass) in Russian athletes. Next, using a polygenic approach, we found that carriers of a high (6–8) number of strength-related alleles had better competition results than carriers of a low (0–5) number of strength-related alleles (264.2 (14.7) vs. 239.1 (21.9) points; p = 0.009). These findings were replicated in the study of Japanese athletes. More specifically, Japanese carriers of a high number of strength-related alleles were stronger than carriers of a low number of strength-related alleles (212.9 (22.6) vs. 199.1 (17.2) points; p = 0.0016). In conclusion, we identified four common gene polymorphisms individually or in combination associated with weightlifting performance in athletes from East European and East Asian geographic ancestries.

Identifying individuals with high risk of Alzheimer’s disease using polygenic risk scores is most accurate when using all genetic information.

There is little agreement regarding the approach and optimal p-value threshold of SNPs to calculate genetic risk scores for Alzheimer’s disease (AD). This reflects a fundamental underlying debate on the polygenic versus oligogenic disease architecture. We re-investigated the assumptions underlying the choice of specific p-value thresholds defining genetic loci used to determine polygenic risk scores (PRS). We find the optimal p-value threshold for SNP selection is 0.1, which supports the polygenic architecture of AD. We found that previous studies supporting an oligogenic model of AD did not take account of the reduction of APOE-ε4 allele frequency in older individuals, which skewed the results towards lower p-value thresholds and eclipsed the contribution of genes associated to AD with higher p-values. The polygenic approach to AD is also effective to identify individuals at high or low AD risk, when only APOE-ε3 homozygous individuals are considered. We also introduce the standardisation of PRS against a population data which ensures comparability of the PRS between studies. In conclusion, our work demonstrates that AD is fundamentally a polygenic disease and that stratifying populations for AD risk best takes the full PRS score into account.

Polygenic approaches to detect gene–environment interactions when external information is unavailable

The exploration of ‘gene–environment interactions’ (G × E) is important for disease prediction and prevention. The scientific community usually uses external information to construct a genetic risk score (GRS), and then tests the interaction between this GRS and an environmental factor (E). However, external genome-wide association studies (GWAS) are not always available, especially for non-Caucasian ethnicity. Although GRS is an analysis tool to detect G × E in GWAS, its performance remains unclear when there is no external information. Our ‘adaptive combination of Bayes factors method’ (ADABF) can aggregate G × E signals and test the significance of G × E by a polygenic test. We here explore a powerful polygenic approach for G × E when external information is unavailable, by comparing our ADABF with the GRS based on marginal effects of SNPs (GRS-M) and GRS based on SNP × E interactions (GRS-I). ADABF is the most powerful method in the absence of SNP main effects, whereas GRS-M is generally the best test when single-nucleotide polymorphisms main effects exist. GRS-I is the least powerful test due to its data-splitting strategy. Furthermore, we apply these methods to Taiwan Biobank data. ADABF and GRS-M identified gene × alcohol and gene × smoking interactions on blood pressure (BP). BP-increasing alleles elevate more BP in drinkers (smokers) than in nondrinkers (nonsmokers). This work provides guidance to choose a polygenic approach to detect G × E when external information is unavailable.

Pharmacogenetics of antidepressant response: a polygenic approach

BackgroundMajor depressive disorder (MDD) has a high personal and socio-economic burden and more than 60% of patients fail to achieve remission with the first antidepressant. The biological mechanisms behind antidepressant response are only partially known but genetic factors play a relevant role. A combined predictor across genetic variants may be useful to investigate this complex trait.MethodsPolygenic risk scores (PRS) were used to estimate multi-allelic contribution to: 1) antidepressant efficacy; 2) its overlap with MDD and schizophrenia. We constructed PRS and tested whether these predicted symptom improvement or remission from the GENDEP study (n=736) to the STAR*D study (n=1409) and vice-versa, including the whole sample or only patients treated with escitalopram or citalopram. Using summary statistics from Psychiatric Genomics Consortium for MDD and schizophrenia, we tested whether PRS from these disorders predicted symptom improvement in GENDEP, STAR*D, and five further studies (n=3756).ResultsNo significant prediction of antidepressant efficacy was obtained from PRS in GENDEP/STAR*D but this analysis might have been underpowered. There was no evidence of overlap in the genetics of antidepressant response with either MDD or schizophrenia, either in individual studies or a meta-analysis. Stratifying by antidepressant did not alter the results.DiscussionWe identified no significant predictive effect using PRS between pharmacogenetic studies. The genetic liability to MDD or schizophrenia did not predict response to antidepressants, suggesting differences between the genetic component of depression and treatment response. Larger or more homogeneous studies will be necessary to obtain a polygenic predictor of antidepressant response.

A Polygenic Approach to the Study of Polygenic Diseases

Polygenic diseases are caused by the joint contribution of a number of independently acting or interacting polymorphic genes; the individual contribution of each gene may be small or even unnoticeable. The carriage of certain combinations of genes can determine the occurrence of clinically heterogeneous forms of the disease and treatment efficacy. This review describes the approaches used in a polygenic analysis of data in medical genomics, in particular, pharmacogenomics, aimed at identifying the cumulative effect of genes. This effect may result from the summation of gains of different genes or be caused by the epistatic interaction between the genes. Both cases are undoubtedly of great interest in investigating the nature of polygenic diseases. The means that allow one to discriminate between these two possibilities are discussed. The methods for searching for combinations of alleles of different genes associated with the polygenic phenotypic traits of the disease, as well as the methods for presenting and validating the results, are described and compared. An attempt is made to evaluate the applicability of the existing methods to an epistasis analysis. The results obtained by the authors using the APSampler software are described and summarized.