Genetic risk sum score comprised of common polygenic variation is associated with body mass index

Assessing the prediction of type 2 diabetes risk using polygenic and clinical risk scores in South Asian study populations

BACKGROUND

Genome-wide polygenic risk scores (PRS) have shown high specificity and sensitivity in predicting type 2 diabetes (T2D) risk in Europeans. However, the PRS-driven information and its clinical significance in non-Europeans are underrepresented. We examined the predictive efficacy and transferability of PRS models using variant information derived from genome-wide studies of Asian Indians (AIs) (PRSAI) and Europeans (PRSEU) using 13,974 AI individuals.

METHODS

Weighted PRS models were constructed and analyzed on 4602 individuals from the Asian Indian Diabetes Heart Study/Sikh Diabetes Study (AIDHS/SDS) as discovery/training and test/validation datasets. The results were further replicated in 9372 South Asian individuals from UK Biobank (UKBB). We also assessed the performance of each PRS model by combining data of the clinical risk score (CRS).

RESULTS

Both genetic models (PRSAI and PRSEU) successfully predicted the T2D risk. However, the PRSAI revealed 13.2% odds ratio (OR) 1.80 [95% confidence interval (CI) 1.63-1.97; p = 1.6 × 10-152] and 12.2% OR 1.38 (95% CI 1.30-1.46; p = 7.1 × 10-237) superior performance in AIDHS/SDS and UKBB validation sets, respectively. Comparing individuals of extreme PRS (ninth decile) with the average PRS (fifth decile), PRSAI showed about two-fold OR 20.73 (95% CI 10.27-41.83; p = 2.7 × 10-17) and 1.4-fold OR 3.19 (95% CI 2.51-4.06; p = 4.8 × 10-21) higher predictability to identify subgroups with higher genetic risk than the PRSEU. Combining PRS and CRS improved the area under the curve from 0.74 to 0.79 in PRSAI and 0.72 to 0.75 in PRSEU.

CONCLUSION

Our data suggest the need for extending genetic and clinical studies in varied ethnic groups to exploit the full clinical potential of PRS as a risk prediction tool in diverse study populations.

Metabolic Syndrome: An Overview on Its Genetic Associations and Gene-Diet Interactions

Metabolic syndrome (MetS) is a cluster of cardiometabolic risk factors that includes central obesity, hyperglycemia, hypertension, and dyslipidemias and whose inter-related occurrence may increase the odds of developing type 2 diabetes and cardiovascular diseases. MetS has become one of the most studied conditions, nevertheless, due to its complex etiology, this has not been fully elucidated. Recent evidence describes that both genetic and environmental factors play an important role on its development. With the advent of genomic-wide association studies, single nucleotide polymorphisms (SNPs) have gained special importance. In this review, we present an update of the genetics surrounding MetS as a single entity as well as its corresponding risk factors, considering SNPs and gene-diet interactions related to cardiometabolic markers. In this study, we focus on the conceptual aspects, diagnostic criteria, as well as the role of genetics, particularly on SNPs and polygenic risk scores (PRS) for interindividual analysis. In addition, this review highlights future perspectives of personalized nutrition with regard to the approach of MetS and how individualized multiomics approaches could improve the current outlook.

Interaction between genetic susceptibility to obesity and food intake on BMI in Finnish school-aged children

Diet modulates the genetic risk of obesity, but the modulation has been rarely studied using genetic risk scores (GRSs) in children. Our objectives were to identify single nucleotide polymorphisms (SNPs) that drive the interaction of specific foods with obesity and combine these into GRSs. Genetic and food frequency data from Finnish Health in Teens study was utilized. In total, 1142 11-year-old subjects were genotyped on the Metabochip array. BMI-GRS with 30 well-known SNPs was computed and the interaction of individual SNPs with food items and their summary dietary scores were examined in relation to age- and sex-specific BMI z-score (BMIz). The whole BMI-GRS interacted with several foods on BMIz. We identified 7-11 SNPs responsible for each interaction and these were combined into food-specific GRS. The most predominant interaction was witnessed for pizza (p < 0.001): the effect on BMIz was b - 0.130 (95% CI - 0.23; - 0.031) in those with low-risk, and 0.153 (95% CI 0.072; 0.234) in high-risk. Corresponding, but weaker interactions were verified for sweets and chocolate, sugary juice drink, and hamburger and hotdog. In total 5 SNPs close to genes NEGR1, SEC16B, TMEM18, GNPDA2, and FTO were shared between these interactions. Our results suggested that children genetically prone to obesity showed a stronger association of unhealthy foods with BMIz than those with lower genetic susceptibility. Shared SNPs of the interactions suggest common differences in metabolic gene-diet interactions, which warrants further investigation.

Interactive effects of the low-carbohydrate diet score and genetic risk score on Hypo-HDL-cholesterolemia among Korean adults: A cross-sectional analysis from the Ansan and Ansung Study of the Korean Genome and Epidemiology Study

This cross-sectional study investigated the interaction between the genetic risk score (GRS) and abnormal high-density lipoprotein (HDL) cholesterol lipid levels, which are modified by low-carbohydrate diets (LCDs) and their effects on the prevalence of hypo-HDL-cholesterolemia (hypo-HDL-C) in Korean adults. Baseline data were obtained from the Ansan and Ansung study of the Korean Genome and Epidemiology Study (KoGES), conducted from 2001 to 2002, that targeted 8,314 Korean adults aged 40-69 years, including old men (47.6%) and women (52.4%), and whole genomic single nucleotide polymorphism (SNP) genotyping was performed. We identified 18 SNPs significantly associated with hypo-HDL-C in the proximity of several genes, including LPL, APOA5, LIPC, and CETP, and calculated the GRS. The low-carbohydrate diet score (LCDS) was calculated on the basis of energy intake information from food frequency questionnaires. Furthermore, we performed multivariable-adjusted logistic modeling to examine the odds ratio (OR) for hypo-HDL-C across tertiles of LCDS and GRS, adjusted for several covariates. Among participants in the highest GRS tertile, those in the highest tertile of the LCDS had a significantly lower risk of hypo-HDL-C (OR: 0.759, 95% CI (confidence interval): 0.625-0.923) than those in the lowest tertile of the LCDS. In the joint effect model, the group with the lowest GRS and highest LCDS was found to have the lowest risk of hypo-HDL-C prevalence. This study suggests that individuals with a high genetic risk for low HDL concentrations may have a beneficial effect on a lower intake of carbohydrates.

Polygenic risk scores: An overview from bench to bedside for personalised medicine

Since the first polygenic risk score (PRS) in 2007, research in this area has progressed significantly. The increasing number of SNPs that have been identified by large scale GWAS analyses has fuelled the development of a myriad of PRSs for a wide variety of diseases and, more recently, to PRSs that potentially identify differential response to specific drugs. PRSs constitute a composite genomic biomarker and potential applications for PRSs in clinical practice encompass risk prediction and disease screening, early diagnosis, prognostication, and drug stratification to improve efficacy or reduce adverse drug reactions. Nevertheless, to our knowledge, no PRSs have yet been adopted into routine clinical practice. Beyond the technical considerations of PRS development, the major challenges that face PRSs include demonstrating clinical utility and circumnavigating the implementation of novel genomic technologies at scale into stretched healthcare systems. In this review, we discuss progress in developing disease susceptibility PRSs across multiple medical specialties, development of pharmacogenomic PRSs, and future directions for the field.

Effect of dietary fat intake and genetic risk on glucose and insulin-related traits in Brazilian young adults

PURPOSE

The development of metabolic diseases such as type 2 diabetes (T2D) is closely linked to a complex interplay between genetic and dietary factors. The prevalence of abdominal obesity, hyperinsulinemia, dyslipidaemia, and high blood pressure among Brazilian adolescents is increasing and hence, early lifestyle interventions targeting these factors might be an effective strategy to prevent or slow the progression of T2D.

METHODS

We aimed to assess the interaction between dietary and genetic factors on metabolic disease-related traits in 200 healthy Brazilian young adults. Dietary intake was assessed using 3-day food records. Ten metabolic disease-related single nucleotide polymorphisms (SNPs) were used to construct a metabolic-genetic risk score (metabolic-GRS).

RESULTS

We found significant interactions between the metabolic-GRS and total fat intake on fasting insulin level (Pinteraction = 0.017), insulin-glucose ratio (Pinteraction = 0.010) and HOMA-B (Pinteraction = 0.002), respectively, in addition to a borderline GRS-fat intake interaction on HOMA-IR (Pinteraction = 0.051). Within the high-fat intake category [37.98 ± 3.39% of total energy intake (TEI)], individuals with ≥ 5 risk alleles had increased fasting insulin level (P = 0.021), insulin-glucose ratio (P = 0.010), HOMA-B (P = 0.001) and HOMA-IR (P = 0.053) than those with < 5 risk alleles.

CONCLUSION

Our study has demonstrated a novel GRS-fat intake interaction in young Brazilian adults, where individuals with higher genetic risk and fat intake had increased glucose and insulin-related traits than those with lower genetic risk. Large intervention and follow-up studies with an objective assessment of dietary factors are needed to confirm our findings.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s40200-021-00863-7.

Genetic underpinnings of regional adiposity distribution in African Americans: Assessments from the Jackson Heart Study

BACKGROUND

African ancestry individuals with comparable overall anthropometric measures to Europeans have lower abdominal adiposity. To explore the genetic underpinning of different adiposity patterns, we investigated whether genetic risk scores for well-studied adiposity phenotypes like body mass index (BMI) and waist circumference (WC) also predict other, less commonly measured adiposity measures in 2420 African American individuals from the Jackson Heart Study.

METHODS

Polygenic risk scores (PRS) were calculated using GWAS-significant variants extracted from published studies mostly representing European ancestry populations for BMI, waist-hip ratio (WHR) adjusted for BMI (WHRBMIadj), waist circumference adjusted for BMI (WCBMIadj), and body fat percentage (BF%). Associations between each PRS and adiposity measures including BF%, subcutaneous adiposity tissue (SAT), visceral adiposity tissue (VAT) and VAT:SAT ratio (VSR) were examined using multivariable linear regression, with or without BMI adjustment.

RESULTS

In non-BMI adjusted models, all phenotype-PRS were found to be positive predictors of BF%, SAT and VAT. WHR-PRS was a positive predictor of VSR, but BF% and BMI-PRS were negative predictors of VSR. After adjusting for BMI, WHR-PRS remained a positive predictor of BF%, VAT and VSR but not SAT. WC-PRS was a positive predictor of SAT and VAT; BF%-PRS was a positive predictor of BF% and SAT only.

CONCLUSION

These analyses suggest that genetically driven increases in BF% strongly associate with subcutaneous rather than visceral adiposity and BF% is strongly associated with BMI but not central adiposity-associated genetic variants. How common genetic variants may contribute to observed differences in adiposity patterns between African and European ancestry individuals requires further study.

The Disruptive Effects of Estrogen Removal before Puberty on Risk for Binge Eating in Female Rats

Recent research suggests that estrogen is protective against binge eating in adult females, and that pubertal estrogen may be critical for these effects. Nonetheless, to date, no study has examined the role of pubertal estrogen in adult binge eating phenotypes in females, potentially due to difficulties experimentally manipulating estrogen in humans to examine causal effects. We used a novel animal model to examine whether estrogen removal prior to puberty (via pre-pubertal ovariectomy (P-OVX)) increases rates of binge eating prone (BEP) phenotypes in adulthood in females. A total of 77 P-OVX and 79 intact rats were followed from pre-puberty into adulthood and phenotyped for BEP status in adulthood. Results showed significantly increased rates (~2-8x higher) of adult BEP phenotypes in P-OVX as compared to intact rats. Findings confirm that estrogen removal substantially increases later risk for binge eating in females, potentially by disrupting typical adolescent brain development.

Interaction between Metabolic Genetic Risk Score and Dietary Fatty Acid Intake on Central Obesity in a Ghanaian Population

Obesity is a multifactorial condition arising from the interaction between genetic and lifestyle factors. We aimed to assess the impact of lifestyle and genetic factors on obesity-related traits in 302 healthy Ghanaian adults. Dietary intake and physical activity were assessed using a 3 day repeated 24 h dietary recall and global physical activity questionnaire, respectively. Twelve single nucleotide polymorphisms (SNPs) were used to construct 4-SNP, 8-SNP and 12-SNP genetic risk scores (GRSs). The 4-SNP GRS showed significant interactions with dietary fat intakes on waist circumference (WC) (Total fat, P_interaction = 0.01; saturated fatty acids (SFA), P_interaction = 0.02; polyunsaturated fatty acids (PUFA), P_interaction = 0.01 and monounsaturated fatty acids (MUFA), P_interaction = 0.01). Among individuals with higher intakes of total fat (>47 g/d), SFA (>14 g/d), PUFA (>16 g/d) and MUFA (>16 g/d), individuals with ≥3 risk alleles had a significantly higher WC compared to those with <3 risk alleles. This is the first study of its kind in this population, suggesting that a higher consumption of dietary fatty acid may have the potential to increase the genetic susceptibility of becoming centrally obese. These results support the general dietary recommendations to decrease the intakes of total fat and SFA, to reduce the risk of obesity, particularly in individuals with a higher genetic predisposition to central obesity.

The Challenge of Stratifying Obesity: Attempts in the Quebec Family Study

Background and aims: Obesity is a major health problem worldwide. Given the heterogeneous obesity phenotype, an optimal obesity stratification would improve clinical management. Since obesity has a strong genetic component, we aimed to develop a polygenic risk score (PRS) to stratify obesity according to the genetic background of the individuals. Methods: A total of 231 single nucleotide polymorphisms (SNP) significantly associated to body mass index (BMI) from 21 genome-wide association studies were genotyped or imputed in 881 subjects from the Quebec Family Study (QFS). The population was randomly split into discovery (80%; n = 704) and validation (20%; n = 177) samples with similar obesity (BMI ≥ 30) prevalence (27.8% and 28.2%, respectively). Family-based associations with obesity were tested for every SNP in the discovery sample and a weighed and continuous PRS₂₃₁ was constructed. Generalized linear mixed effects models were used to test the association of PRS₂₃₁ with obesity in the QFS discovery sample and validated in the QFS replication sample. Furthermore, the Fatty Acid Sensor (FAS) Study (n = 141; 27.7% obesity prevalence) was used as an independent sample to replicate the results. Results: The linear trend test demonstrated a significant association of PRS₂₃₁ with obesity in the QFS discovery sample (OR_trend = 1.19 [95% CI, 1.14-1.24]; P = 2.0x10^-16). We also found that the obesity prevalence was significantly greater in the higher PRS₂₃₁ quintiles compared to the lowest quintile. Significant and consistent results were obtained in the QFS validation sample for both the linear trend test (OR_trend = 1.16 [95% CI, 1.07-1.26]; P = 6.7x10^-4), and obesity prevalence across quintiles. These results were partially replicated in the FAS sample (OR_trend = 1.12 [95% CI, 1.02-1.24]; P = 2.2x10^-2). PRS₂₃₁ explained 7.5%, 3.2%, and 1.2% of BMI variance in QFS discovery, QFS validation, and FAS samples, respectively. Conclusions: These results revealed that genetic background in the form of a 231 BMI-associated PRS has a significant impact on obesity, but a limited potential to accurately stratify it. Further studies are encouraged on larger populations.

Health risk prediction models incorporating personality data: Motivation, challenges, and illustration

The age of "big data" in health has ushered in an era of prediction models promising to forecast individual health events. Although many models focus on enhancing the predictive power of medical risk factors with genomic data, a recent proposal is to augment traditional health predictors with psychosocial data, such as personality measures. In this article we provide a general overview of the medical risk prediction models and then discuss the rationale for integrating personality data. We suggest three principles that should guide work in this area if personality data is ultimately to be useful within risk prediction as it is actually practiced in the health care system. These include (a) prediction of specific, priority health outcomes; (b) sufficient incremental validity beyond established biomedical risk factors; and (c) technically responsible model-building that does not overfit the data. We then illustrate the application of these principles in the development of a personality-augmented prediction model for the occurrence of mild cognitive impairment, designed for a primary care setting. We evaluate the results, drawing conclusions for the direction an iterative, programmatic approach would need to take to eventually achieve clinical utility. Although there is great potential for personality measurement to play a key role in the coming era of risk prediction models, the final section reviews the many challenges that must be faced in real-world implementation. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

CRISPR/Cas9, the Powerful New Genome-Editing Tool for Putative Therapeutics in Obesity

The molecular technology known as clustered regularly interspaced palindromic repeats (CRISPR)/CRISPR-associated protein (Cas) is revolutionizing the field of medical research and deepening our understanding of numerous biological processes. The attraction of CRISPR/Cas9 lies in its ability to efficiently edit DNA or modulate gene expression in living eukaryotic cells and organisms, a technology that was once considered either too expensive or scientifically risky. CRISPR/Cas9 has been successfully applied in agriculture to develop the next generation of disease-resistant plants. Now, the capability of gene editing has been translated to the biomedical area, focusing on the future of medicine faced with drug-resistant microbes by selectively targeting genes involved in antibiotic resistance, for example, or finding the ultimate strategy for cancer or HIV. In this regard, it was recently demonstrated that an injection of cancer-fighting CRISPR-modified white blood cells in a patient suffering from metastatic lung cancer could lead to promising results. Researchers and bioethicists are debating questions about the regulation of CRISPR/Cas9 that must be addressed. While legal challenges surround the use of this technique for genetically modifying cell lines in humans, we review the basic understanding of CRISPR/Cas9 and discuss how this technology could represent a candidate for treatment of non-communicable diseases in nutrition, such as obesity.

Primer in Genetics and Genomics, Article 7-Multifactorial Concepts: Gene-Gene Interactions

Most common disorders affecting human health are not attributable to simple Mendelian (single-gene) inheritance patterns. Rather, the risk of developing a complex disease is often the result of interactions across genes, whereby one gene modifies the phenotype of another gene. These types of interactions can occur between two or more genes and are referred to as epistasis. There are five major types of epistatic interactions, but in human genetics, additive epistasis is most often discussed and includes both positive and negative subtypes. Detecting epistatic interactions can be quite difficult because seemingly unrelated genes can interact with and influence each other. As a result of this complexity, statistical geneticists are constantly developing new methods to enhance detection, but there are disadvantages to each proposed method. In this article, we explore the concept of epistasis, discuss different types of epistatic interactions, and provide a brief introduction to statistical methods researchers use to uncover sets of epistatic interactions. Then, we consider Alzheimer's disease as an exemplar for a disease with epistatic effects. Finally, we provide helpful resources, where nurses can learn more about epistasis in order to incorporate these methods into their own program of research.

Development of multivariable models to predict change in Body Mass Index within a clinical trial population of psychotic individuals

Many antipsychotics promote weight gain, which can lead to non-compliance and relapse of psychosis. By developing models that accurately identify individuals at greater risk of weight gain, clinicians can make informed treatment decisions and target intervention measures. We examined clinical, genetic and expression data for 284 individuals with psychosis derived from a previously published randomised controlled trial (IMPACT). These data were used to develop regression and classification models predicting change in Body Mass Index (BMI) over one year. Clinical predictors included demographics, anthropometrics, cardiac and blood measures, diet and exercise, physical and mental health, medication and BMI outcome measures. We included genetic polygenic risk scores (PRS) for schizophrenia, bipolar disorder, BMI, waist-hip-ratio, insulin resistance and height, as well as gene co-expression modules generated by Weighted Gene Co-expression Network Analysis (WGCNA). The best performing predictive models for BMI and BMI gain after one year used clinical data only, which suggests expression and genetic data do not improve prediction in this cohort.

Mediterranean Diet Adherence and Genetic Background Roles within a Web-Based Nutritional Intervention: The Food4Me Study

Mediterranean Diet (MedDiet) adherence has been proven to produce numerous health benefits. In addition, nutrigenetic studies have explained some individual variations in the response to specific dietary patterns. The present research aimed to explore associations and potential interactions between MedDiet adherence and genetic background throughout the Food4Me web-based nutritional intervention. Dietary, anthropometrical and biochemical data from volunteers of the Food4Me study were collected at baseline and after 6 months. Several genetic variants related to metabolic risk features were also analysed. A Genetic Risk Score (GRS) was derived from risk alleles and a Mediterranean Diet Score (MDS), based on validated food intake data, was estimated. At baseline, there were no interactions between GRS and MDS categories for metabolic traits. Linear mixed model repeated measures analyses showed a significantly greater decrease in total cholesterol in participants with a low GRS after a 6-month period, compared to those with a high GRS. Meanwhile, a high baseline MDS was associated with greater decreases in Body Mass Index (BMI), waist circumference and glucose. There also was a significant interaction between GRS and the MedDiet after the follow-up period. Among subjects with a high GRS, those with a high MDS evidenced a highly significant reduction in total carotenoids, while among those with a low GRS, there was no difference associated with MDS levels. These results suggest that a higher MedDiet adherence induces beneficial effects on metabolic outcomes, which can be affected by the genetic background in some specific markers.

Predicting Polygenic Obesity Using Genetic Information

Except in rare cases, obesity tends to be a consequence of both an unhealthy lifestyle and a genetic susceptibility to gain weight. With more than 200 common genetic variants identified, there is a growing interest in developing personalized preventive and treatment strategies to predict an individual's obesity risk. We review the literature on the prediction of obesity and show that models based on the established genetic variants have poorer predictive ability than traditional predictors, such as family history of obesity and childhood obesity. Current findings suggest that opportunities for precision medicine in common obesity may be limited.

Relationships between GAT1 and PTSD, Depression, and Substance Use Disorder

Post-traumatic stress disorder (PTSD), Major Depressive Disorder (MDD), and Substance Use Disorder (SUD) have large public health impacts. Therefore, researchers have attempted to identify those at greatest risk for these phenotypes. PTSD, MDD, and SUD are in part genetically influenced. Additionally, genes in the glutamate and gamma-aminobutyric acid (GABA) system are implicated in the encoding of emotional and fear memories, and thus may impact these phenotypes. The current study examined the associations of single nucleotide polymorphisms in GAT1 individually, and at the gene level, using a principal components (PC) approach, with PTSD, PTSD comorbid with MDD, and PTSD comorbid with SUD in 486 combat-exposed veterans.  Findings indicate that several GAT1 SNPs, as well as one of the GAT1 PCs, was associated with PTSD, with and without MDD and SUD comorbidity. The present study findings provide initial insights into one pathway by which shared genetic risk influences PTSD-MDD and PTSD-SUD comorbidities, and thus identify a high-risk group (based on genotype) on whom prevention and intervention efforts should be focused.

A Weighted SNP Correlation Network Method for Estimating Polygenic Risk Scores

Polygenic scores are useful for examining the joint associations of genetic markers. However, because traditional methods involve summing weighted allele counts, they may fail to capture the complex nature of biology. Here we describe a network-based method, which we call weighted SNP correlation network analysis (WSCNA), and demonstrate how it could be used to generate meaningful polygenic scores. Using data on human height in a US population of non-Hispanic whites, we illustrate how this method can be used to identify SNP networks from GWAS data, create network-specific polygenic scores, examine network topology to identify hub SNPs, and gain biological insights into complex traits. In our example, we show that this method explains a larger proportion of the variance in human height than traditional polygenic score methods. We also identify hub genes and pathways that have previously been identified as influencing human height. In moving forward, this method may be useful for generating genetic susceptibility measures for other health related traits, examining genetic pleiotropy, identifying at-risk individuals, examining gene score by environmental effects, and gaining a deeper understanding of the underlying biology of complex traits.

Strong gender-specific additive effects of the NYD-SP18 and FTO variants on BMI values

The role of the FTO gene in obesity development is well established in populations around the world. The NYD-SP18 variant has been suggested to have a similar effect on BMI, but the role of this gene in determining BMI has not yet been verified. The objective of our study was to confirm the association between NYD-SP18 rs6971019 SNP and BMI in the Slavic population and to analyze i) the gender-specific effects of NYD-SP18 on BMI and ii) the simultaneous effect of FTO rs17817449 and NYD-SP18 on BMI. We analyzed a sample of a large adult population based on the post-MONICA study (1,191 males and 1,368 females). Individuals were analyzed three times over 9 years. NYD-SP18 rs6971019 SNP is related to BMI in males (2000/1 GG 28.3+/-3.7 kg/m(2) vs. +A 27.5+/-3.7 kg/m(2) P<0.0005; in other examinations P<0.05 and <0.005), but not in females (all P values over 0.48 in all three examinations). Further analysis revealed the significant additive effect (but not the interaction) of FTO and NYD-SP18 SNPs on BMI in males (all P<0.01). These results suggest that association between NYD-SP18 rs6971019 SNP and BMI may be restricted to males. Furthermore, variants within NYD-SP18 and FTO genes revealed a significant additive effect on BMI values in males.

Lack of association between genotype score and sprint/power performance in the Japanese population

OBJECTIVES

This study aimed to examine the association between a total genotype score (TGS) based on previously published genetic polymorphism candidates and differences in sprint/power performance.

DESIGN

Case-control association study.

METHODS

We analysed 21 polymorphisms, which have previously been associated with sprint/power performance and related phenotypes, in 211 Japanese sprint/power track and field athletes (77 regional, 72 national, and 62 international athletes) and 649 Japanese controls using the TaqMan SNP genotyping assay. We calculated the TGS (maximum value of 100 for the theoretically optimal polygenic score) for the 21 polymorphisms.

RESULTS

All groups exhibited similar TGSs (control: 55.9±7.2, regional: 55.1±7.1, national: 56.1±7.4, and international: 56.0±7.8, p=0.827 by one-way analysis of variance). Nine of the 21 polymorphisms had the same direction of effect (odds ratio >1.0) as in previous studies, while 12 had the opposite direction of effect (odds ratio <1.0). Three polymorphisms (rs699 in AGT, rs41274853 in CNTFR, and rs7832552 in TRHR), which had the same direction of effect as in previous studies, were associated with international sprint/power athlete status (p<0.05). However, after multiple testing corrections, the statistical significance of these polymorphisms was not retained.

CONCLUSIONS

These results suggest that TGSs based on the 21 previously published sprint/power performance-associated polymorphisms did not influence the sprint/power athlete status of Japanese track and field athletes. However, our results maintain the possibility that three of these polymorphisms might be associated with sprint/power performance.

Childhood body mass index and risk of schizophrenia in relation to childhood age, sex and age of first contact with schizophrenia

Childhood leanness is associated with an increased risk of schizophrenia, but the effects of gender, age at anthropometric measurements and age at first diagnosis on this relationship are unclear. The present study aimed at elucidating these associations.

METHODS

Population-based cohort study with childhood anthropometric measures obtained annually from the age of 7 to 13 years in 253,353 Danes born 1930-1976 and followed to 31 December 2010. During this period, 4936 were registered with schizophrenia. The associations of childhood BMI with risk of schizophrenia were estimated with Cox regression models.

RESULTS

Childhood BMI was significantly inversely associated with risk of schizophrenia, however with different patterns among boys and girls. In boys, childhood BMI had an inverse non-linear association with schizophrenia risk dependent on age at diagnosis; in particular, a surprisingly strong association was found between leanness and later onset of schizophrenia. In girls, the risk of schizophrenia decreased linearly with increasing BMI z-score (HR: 0.93; 95% CI: 0.88-0.98). In both boys and girls, birth weight was inversely associated with later risk. In girls, but not in boys, birth weight appeared to significantly modify the associations; there was a somewhat stronger inverse association in the lowest birth weight category.

CONCLUSION

Birth weight as well as childhood BMI at ages 7 through 13 years is associated with risk of schizophrenia in both genders, but with a particular high risk of late-onset in lean boys irrespective of birth weight, and in lean girls with low birth weight. If replicated, these observations may inform preventive efforts build on schizophrenia trajectories rooted in early life.

Assessment of first and second degree relatives of individuals with bipolar disorder shows increased genetic risk scores in both affected relatives and young At-Risk Individuals

Recent studies have revealed the polygenic nature of bipolar disorder (BP), and identified common risk variants associated with illness. However, the role of common polygenic risk in multiplex families has not previously been examined. The present study examined 249 European-ancestry families from the NIMH Genetics Initiative sample, comparing subjects with narrowly defined BP (excluding bipolar II and recurrent unipolar depression; n = 601) and their adult relatives without BP (n = 695). Unrelated adult controls (n = 266) were from the NIMH TGEN control dataset. We also examined a prospective cohort of young (12-30 years) offspring and siblings of individuals with BPI and BPII disorder (at risk; n = 367) and psychiatrically screened controls (n = 229), ascertained from five sites in the US and Australia and assessed with standardized clinical protocols. Thirty-two disease-associated SNPs from the PGC-BP Working Group report (2011) were genotyped and additive polygenic risk scores (PRS) derived. We show increased PRS in adult cases compared to unrelated controls (P = 3.4 × 10(-5) , AUC = 0.60). In families with a high-polygenic load (PRS score ≥32 in two or more subjects), PRS distinguished cases with BPI/SAB from other relatives (P = 0.014, RR = 1.32). Secondly, a higher PRS was observed in at-risk youth, regardless of affected status, compared to unrelated controls (GEE-χ(2) = 5.15, P = 0.012). This report is the first to explore common polygenic risk in multiplex families, albeit using only a small number of robustly associated risk variants. We show that individuals with BP have a higher load of common disease-associated variants than unrelated controls and first-degree relatives, and illustrate the potential utility of PRS assessment in a family context.

A Genetic Network Associated With Stress Resistance, Longevity, and Cancer in Humans

Human longevity and diseases are likely influenced by multiple interacting genes within a few biologically conserved pathways. Using long-lived smokers as a phenotype (n = 90)-a group whose survival may signify innate resilience-we conducted a genome-wide association study comparing them to smokers at ages 52-69 (n = 730). These results were used to conduct a functional interaction network and pathway analysis, to identify single nucleotide polymorphisms that collectively related to smokers' longevity. We identified a set of 215 single nucleotide polymorphisms (all of which had p <5×10(-3) in the genome-wide association study) that were located within genes making-up a functional interaction network. These single nucleotide polymorphisms were then used to create a weighted polygenic risk score that, using an independent validation sample of nonsmokers (N = 6,447), was found to be significantly associated with a 22% increase in the likelihood of being aged 90-99 (n = 253) and an over threefold increase in the likelihood of being a centenarian (n = 4), compared with being at ages 52-79 (n = 4,900). Additionally, the polygenic risk score was also associated with an 11% reduction in cancer prevalence over up to 18 years (odds ratio: 0.89, p = .011). Overall, using a unique phenotype and incorporating prior knowledge of biological networks, this study identified a set of single nucleotide polymorphisms that together appear to be important for human aging, stress resistance, cancer, and longevity.

Polygenic risk scores in imaging genetics: Usefulness and applications

Genetic factors account for up to 80% of the liability for schizophrenia and bipolar disorder. Genome-wide association studies (GWAS) have successfully identified several single nucleotide polymorphisms (SNPs) and genes associated with increased risk for both disorders. Single SNP analyses alone do not address the overall genomic or polygenic architecture of psychiatric disorders as the amount of phenotypic variation explained by each GWAS-supported SNP is small whereas the number of SNPs/regions underlying risk for illness is thought to be very large. The polygenic risk score models the aggregate effect of alleles associated with disease status present in each individual and allows us to utilise the power of large GWAS to be applied robustly in small samples. Here we make the case that risk prediction, intervention and personalised medicine can only benefit with the inclusion of polygenic risk scores in imaging genetics research.

A genetic risk score combining 32 SNPs is associated with body mass index and improves obesity prediction in people with major depressive disorder

BACKGROUND

Obesity is strongly associated with major depressive disorder (MDD) and various other diseases. Genome-wide association studies have identified multiple risk loci robustly associated with body mass index (BMI). In this study, we aimed to investigate whether a genetic risk score (GRS) combining multiple BMI risk loci might have utility in prediction of obesity in patients with MDD.

METHODS

Linear and logistic regression models were conducted to predict BMI and obesity, respectively, in three independent large case-control studies of major depression (Radiant, GSK-Munich, PsyCoLaus). The analyses were first performed in the whole sample and then separately in depressed cases and controls. An unweighted GRS was calculated by summation of the number of risk alleles. A weighted GRS was calculated as the sum of risk alleles at each locus multiplied by their effect sizes. Receiver operating characteristic (ROC) analysis was used to compare the discriminatory ability of predictors of obesity.

RESULTS

In the discovery phase, a total of 2,521 participants (1,895 depressed patients and 626 controls) were included from the Radiant study. Both unweighted and weighted GRS were highly associated with BMI (P < 0.001) but explained only a modest amount of variance. Adding 'traditional' risk factors to GRS significantly improved the predictive ability with the area under the curve (AUC) in the ROC analysis, increasing from 0.58 to 0.66 (95% CI, 0.62-0.68; χ(2) = 27.68; P < 0.0001). Although there was no formal evidence of interaction between depression status and GRS, there was further improvement in AUC in the ROC analysis when depression status was added to the model (AUC = 0.71; 95% CI, 0.68-0.73; χ(2) = 28.64; P <0.0001). We further found that the GRS accounted for more variance of BMI in depressed patients than in healthy controls. Again, GRS discriminated obesity better in depressed patients compared to healthy controls. We later replicated these analyses in two independent samples (GSK-Munich and PsyCoLaus) and found similar results.

CONCLUSIONS

A GRS proved to be a highly significant predictor of obesity in people with MDD but accounted for only modest amount of variance. Nevertheless, as more risk loci are identified, combining a GRS approach with information on non-genetic risk factors could become a useful strategy in identifying MDD patients at higher risk of developing obesity.

A genetic risk tool for obesity predisposition assessment and personalized nutrition implementation based on macronutrient intake

There is little evidence about genetic risk score (GRS)-diet interactions in order to provide personalized nutrition based on the genotype. The aim of the study was to assess the value of a GRS on obesity prediction and to further evaluate the interactions between the GRS and dietary intake on obesity. A total of 711 seekers of a Nutrigenetic Service were examined for anthropometric and body composition measurements and also for dietary habits and physical activity. Oral epithelial cells were collected for the identification of 16 SNPs (related with obesity or lipid metabolism) using DNA zip-coded beads. Genotypes were coded as 0, 1 or 2 according to the number of risk alleles, and the GRS was calculated by adding risk alleles with such a criterion. After being adjusted for gender, age, physical activity and energy intake, the GRS demonstrated that individuals carrying >7 risk alleles had in average 0.93 kg/m(2) of BMI, 1.69 % of body fat mass, 1.94 cm of waist circumference and 0.01 waist-to-height ratio more than the individuals with ≤7 risk alleles. Significant interactions for GRS and the consumption of energy, total protein, animal protein, vegetable protein, total fat, saturated fatty acids, polyunsaturated fatty acids, total carbohydrates, complex carbohydrates and fiber intake on adiposity traits were found after adjusted for confounders variables. The GRS confirmed that the high genetic risk group showed greater values of adiposity than the low risk group and demonstrated that macronutrient intake modifies the GRS association with adiposity traits.

Variation in SLC1A1 is related to combat-related posttraumatic stress disorder

Candidate gene studies have yet to investigate the glutamate system, the primary excitatory neurotransmitter of the HPA-axis related to PTSD risk. We investigated 13 SNPs in the glutamate transporter gene (SLC1A1) in relation to PTSD among combat-exposed veterans. Participants (n=418) completed a diagnostic interview and provided a blood sample for DNA isolation and genotyping. A subset of participants (n=391) had severity and combat exposure data available. In the primary logistic regression gender and rs10739062 were significant predictors of PTSD diagnosis (OR=0.50; OR=1.43). In the linear regression analysis, combat exposure was the only significant predictor (β=0.16) of severity. A computed genetic risk sum score was significant in relation to PTSD diagnosis (OR=1.15) and severity scores (β=0.14) above and beyond the effects of combat exposure. This study provides preliminary support for the relationship of glutamate transporter polymorphisms to PTSD risk and the need for further genetic studies within this system.

Inflammation-related genetic variants predict toxicity following definitive radiotherapy for lung cancer

Definitive radiotherapy improves locoregional control and survival in inoperable non-small cell lung cancer (NSCLC) patients. However, radiation-induced toxicities (pneumonitis/esophagitis) are common dose-limiting inflammatory conditions. We therefore conducted a pathway-based analysis to identify inflammation-related SNPs associated with radiation-induced pneumonitis or esophagitis. 11,930 SNPs were genotyped in 201 stage I-III NSCLC patients treated with definitive radiotherapy. Validation was performed in an additional 220 NSCLC cases. After validation, 19 SNPs remained significant. A polygenic risk score (PRS) was generated to summarize the effect from validated SNPs. Significant improvements in discriminative ability were observed by adding the PRS into the clinical/epidemiological variable-based model. We then used 277 lymphoblastoid cell-lines to assess radiation sensitivity and eQTL relationships of the identified SNPs. Three genes (PRKCE,DDX58 and TNFSF7) were associated with radiation sensitivity. We concluded that inflammation-related genetic variants could contribute to the development of radiation-induced toxicities. These loci could assist in predicting those unfavorable events.

Strong influence of dietary intake and physical activity on body fatness in elderly Japanese men: age-associated loss of polygenic resistance against obesity

Genome-wide association studies identified single nucleotide polymorphisms (SNPs) associated with body mass index (BMI) in middle-aged populations; however, it is unclear whether these SNPs are associated with body fatness in elderly people. We examined the association between genetic risk score (GRS) from BMI-associated SNPs and body fatness in elderly Japanese men. We also examined the contribution of GRS, dietary macronutrient intake, and physical activity to body fatness by different age groups. GRS was calculated from 10 BMI-associated SNPs in 84 middle-aged (30-64 years) and 97 elderly (65-79 years) Japanese men; subjects were divided into low, middle, and high GRS groups. Dietary macronutrient intake was assessed using a questionnaire, and physical activity was evaluated using both a questionnaire and an accelerometer. The middle-aged individuals with a high GRS had greater BMI; waist circumference; and total abdominal fat, visceral fat, and subcutaneous fat areas than the middle-aged individuals with low GRS, whereas the indicators were not different between the GRS groups in elderly individuals. Multiple linear regression analysis showed that GRS was the strongest predictor of BMI, total abdominal fat, and visceral fat in the middle-aged group, whereas fat, alcohol, and protein intakes or vigorous-intensity physical activity were more strongly associated with these indicators than was GRS in the elderly group. These results suggest that GRS from BMI-associated SNPs is not predictive of body fatness in elderly Japanese men. The stronger contribution of dietary macronutrient intake and physical activity to body fatness may attenuate the genetic predisposition in elderly men.

On the association of common and rare genetic variation influencing body mass index: a combined SNP and CNV analysis

Background

As the architecture of complex traits incorporates a widening spectrum of genetic variation, analyses integrating common and rare variation are needed. Body mass index (BMI) represents a model trait, since common variation shows robust association but accounts for a fraction of the heritability. A combined analysis of single nucleotide polymorphisms (SNP) and copy number variation (CNV) was performed using 1850 European and 498 African-Americans from the Study of Addiction: Genetics and Environment. Genetic risk sum scores (GRSS) were constructed using 32 BMI-validated SNPs and aggregate-risk methods were compared: count versus weighted and proxy versus imputation.

Results

The weighted SNP-GRSS constructed from imputed probabilities of risk alleles performed best and was highly associated with BMI (p = 4.3×10⁻¹⁶) accounting for 3% of the phenotypic variance. In addition to BMI-validated SNPs, common and rare BMI/obesity-associated CNVs were identified from the literature. Of the 84 CNVs previously reported, only 21-kilobase deletions on 16p12.3 showed evidence for association with BMI (p = 0.003, frequency = 16.9%), with two CNVs nominally associated with class II obesity, 1p36.1 duplications (OR = 3.1, p = 0.009, frequency 1.2%) and 5q13.2 deletions (OR = 1.5, p = 0.048, frequency 7.7%). All other CNVs, individually and in aggregate, were not associated with BMI or obesity. The combined model, including covariates, SNP-GRSS, and 16p12.3 deletion accounted for 11.5% of phenotypic variance in BMI (3.2% from genetic effects). Models significantly predicted obesity classification with maximum discriminative ability for morbid-obesity (p = 3.15×10⁻¹⁸).

Conclusion

Results show that incorporating validated effect sizes and allelic probabilities improve prediction algorithms. Although rare-CNVs did not account for significant phenotypic variation, results provide a framework for integrated analyses.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-368) contains supplementary material, which is available to authorized users.

Correlation analysis of genetic admixture and social identification with body mass index in a Native American community

OBJECTIVES

Body mass index (BMI) is a well-known measure of obesity with a multitude of genetic and non-genetic determinants. Identifying the underlying factors associated with BMI is difficult because of its multifactorial etiology that varies as a function of geoethnic background and socioeconomic setting. Thus, we pursued a study exploring the influence of the degree of Native American admixture on BMI (as well as weight and height individually) in a community sample of Native Americans (n = 846) while accommodating a variety of socioeconomic and cultural factors.

METHODS

Participants' degree of Native American (NA) ancestry was estimated using a genome-wide panel of markers. The participants also completed an extensive survey of cultural and social identity measures: the Indian Culture Scale (ICS) and the Orthogonal Cultural Identification Scale (OCIS). Multiple linear regression was used to examine the relation between these measures and BMI.

RESULTS

Our results suggest that BMI is correlated positively with the proportion of NA ancestry. Age was also significantly associated with BMI, while gender and socioeconomic measures (education and income) were not. For the two cultural identity measures, the ICS showed a positive correlation with BMI, while OCIS was not associated with BMI.

CONCLUSIONS

Taken together, these results suggest that genetic and cultural environmental factors, rather than socioeconomic factors, account for a substantial proportion of variation in BMI in this population. Further, significant correlations between degree of NA ancestry and BMI suggest that admixture mapping may be appropriate to identify loci associated with BMI in this population.

Whole-genome pathway analysis on 132,497 individuals identifies novel gene-sets associated with body mass index

Whole genome pathway analysis is a powerful tool for the exploration of the combined effects of gene-sets within biological pathways. This study applied Interval Based Enrichment Analysis (INRICH) to perform whole-genome pathway analysis of body-mass index (BMI). We used a discovery set composed of summary statistics from a meta-analysis of 123,865 subjects performed by the GIANT Consortium, and an independent sample of 8,632 subjects to assess replication of significant pathways. We examined SNPs within nominally significant pathways using linear mixed models to estimate their contribution to overall BMI heritability. Six pathways replicated as having significant enrichment for association after correcting for multiple testing, including the previously unknown relationships between BMI and the Reactome regulation of ornithine decarboxylase pathway, the KEGG lysosome pathway, and the Reactome stabilization of P53 pathway. Two non-overlapping sets of genes emerged from the six significant pathways. The clustering of shared genes based on previously identified protein-protein interactions listed in PubMed and OMIM supported the relatively independent biological effects of these two gene-sets. We estimate that the SNPs located in examined pathways explain ∼20% of the heritability for BMI that is tagged by common SNPs (3.35% of the 16.93% total).

Pitfalls of predicting complex traits from SNPs

The success of genome-wide association studies (GWASs) has led to increasing interest in making predictions of complex trait phenotypes, including disease, from genotype data. Rigorous assessment of the value of predictors is crucial before implementation. Here we discuss some of the limitations and pitfalls of prediction analysis and show how naive implementations can lead to severe bias and misinterpretation of results.

Replication study for the association of 9 East Asian GWAS-derived loci with susceptibility to type 2 diabetes in a Japanese population

Aims

East Asian genome-wide association studies (GWAS) for type 2 diabetes identified 8 loci with genome-wide significance, and 2 loci with a borderline association. However, the associations of these loci except MAEA locus with type 2 diabetes have not been evaluated in independent East Asian cohorts. We performed a replication study to investigate the association of these susceptibility loci with type 2 diabetes in an independent Japanese population.

Methods

We genotyped 7,379 Japanese participants (5,315 type 2 diabetes and 2,064 controls) for each of the 9 single nucleotide polymorphisms (SNPs), rs7041847 in GLIS3, rs6017317 in FITM2−R3HDML−HNF4A, rs6467136 near GCCI−PAX4, rs831571 near PSMD6, rs9470794 in ZFAND3, rs3786897 in PEPD, rs1535500 in KCNK16, rs16955379 in CMIP, and rs17797882 near WWOX. Because the sample size in this study was not sufficient to replicate single SNP associations, we constructed a genetic risk score (GRS) by summing a number of risk alleles of the 9 SNPs, and examined the association of the GRS with type 2 diabetes using logistic regression analysis.

Results

With the exception of rs1535500 in KCNK16, all SNPs had the same direction of effect (odds ratio [OR]>1.0) as in the original reports. The GRS constructed from the 9 SNPs was significantly associated with type 2 diabetes in the Japanese population (p = 4.0 × 10^-4, OR = 1.05, 95% confidence interval: 1.02–1.09). In quantitative trait analyses, rs16955379 in CMIP was nominally associated with a decreased homeostasis model assessment of β-cell function and with increased fasting plasma glucose, but neither the individual SNPs nor the GRS showed a significant association with the glycemic traits.

Conclusions

These results indicate that 9 loci that were identified in the East Asian GWAS meta-analysis have a significant effect on the susceptibility to type 2 diabetes in the Japanese population.

Interaction between polygenic risk for cigarette use and environmental exposures in the Detroit Neighborhood Health Study

Cigarette smoking is influenced both by genetic and environmental factors. Until this year, all large-scale gene identification studies on smoking were conducted in populations of European ancestry. Consequently, the genetic architecture of smoking is not well described in other populations. Further, despite a rich epidemiologic literature focused on the social determinants of smoking, few studies have examined the moderation of genetic influences (for example, gene-environment interactions) on smoking in African Americans. In the Detroit Neighborhood Health Study (DNHS), a sample of randomly selected majority African American residents of Detroit, we constructed a genetic risk score (GRS), in which we combined top (P-value <5 × 10(-7)) genetic variants from a recent meta-analysis conducted in a large sample of African Americans. Using regression (effective n=399), we first tested for association between the GRS and cigarettes per day, attempting to replicate the findings from the meta-analysis. Second, we examined interactions with three social contexts that may moderate the genetic association with smoking: traumatic events, neighborhood social cohesion and neighborhood physical disorder. Among individuals who had ever smoked cigarettes, the GRS significantly predicted the number of cigarettes smoked per day and accounted for ~3% of the overall variance in the trait. Significant interactions were observed between the GRS and number of traumatic events experienced, as well as between the GRS and average neighborhood social cohesion; the association between genetic risk and smoking was greater among individuals who had experienced an increased number of traumatic events in their lifetimes, and diminished among individuals who lived in a neighborhood characterized by greater social cohesion. This study provides support for the utility of the GRS as an alternative approach to replication of common polygenic variation, and in gene-environment interaction, for smoking behaviors. In addition, this study indicates that environmental determinants have the potential to both exacerbate (traumatic events) and diminish (neighborhood social cohesion) genetic influences on smoking behaviors.

Genetic risk prediction: individualized variability in susceptibility to toxicants

Genetic risk prediction uses genetic data to individualize prediction of outcome or effect from a known harmful toxicant. Several examples of toxicogenetics (usually binary traits) are discussed, reflecting largely Mendelian traits before the Human Genome Project began in 1990. Numerous complexities of the genome and what constitutes "a gene" have emerged during these past two decades. Examples of toxicogenomics (continuous outcomes, gradients) are examined. Most xenobiotic-induced environmental diseases resemble human complex diseases or other multifactorial traits such as height; these traits result from hundreds of low-effect genes. Consequently, uncovering an association between a trait and a genetic variant in a large cohort can provide important information about underlying biology; however, screening for a specific variant in an individual worker or patient has poor predictive value and little clinical utility. Individualized risk assessment for toxicants that cause environmental diseases, although a lofty goal, remains to be achieved.

Interactions of genetic and environmental risk factors with respect to body fat mass in children: results from the ALSPAC study

OBJECTIVE

To evaluate if percentile-specific effects of genetic, environmental and lifestyle obesity risk factors on body mass index (BMI) might reflect gene-environment interactions with respect to the development of overweight.

DESIGN AND METHODS

Retrospective study with data of 2,346 children from the Avon Longitudinal Study of Parents and Children (ALSPAC), using quantile regression with body fat mass index (FMI) for children at the age of 9 years as outcome variable. We assessed interactions of an "obesity-risk-allele-score" with environmental and nutritional factors.

RESULTS

There was no evidence of interactions between the obesity-risk-allele score and the environmental variables except for maternal overweight. However, we found a significant interaction with respect to intake of mono- and polyunsaturated fatty acids at the age of 7. In children with low intake, genetic risk was associated with increasing effect sizes by FMI percentile.

CONCLUSIONS

Our results suggest an interaction between a low dietary content of unsaturated fatty acids and genetic risk factors for overweight on FMI. This effect is likely to be stronger in children with higher FMI.

Obesity polymorphisms identified in genome-wide association studies interact with n-3 polyunsaturated fatty acid intake and modify the genetic association with adiposity phenotypes in Yup'ik people

n-3 Polyunsaturated fatty acids (n-3 PUFAs) have anti-obesity effects that may modulate risk of obesity, in part, through interactions with genetic factors. Genome-wide association studies (GWAS) have identified genetic variants associated with body mass index (BMI); however, the extent to which these variants influence adiposity through interactions with n-3 PUFAs remains unknown. We evaluated 10 highly replicated obesity GWAS single nucleotide polymorphisms (SNPs) for individual and cumulative associations with adiposity phenotypes in a cross-sectional sample of Yup'ik people (n = 1,073) and evaluated whether genetic associations with obesity were modulated by n-3 PUFA intake. A genetic risk score (GRS) was calculated by adding the BMI-increasing alleles across all 10 SNPs. Dietary intake of n-3 PUFAs was estimated using nitrogen stable isotope ratio (δ(15)N) of red blood cells, and genotype-phenotype analyses were tested in linear models accounting for familial correlations. GRS was positively associated with BMI (p = 0.012), PBF (p = 0.022), ThC (p = 0.025), and waist circumference (p = 0.038). The variance in adiposity phenotypes explained by the GRS included BMI (0.7 %), PBF (0.3 %), ThC (0.7 %), and WC (0.5 %). GRS interactions with n-3 PUFAs modified the association with adiposity and accounted for more than twice the phenotypic variation (~1-2 %), relative to GRS associations alone. Obesity GWAS SNPs contribute to adiposity in this study population of Yup'ik people and interactions with n-3 PUFA intake potentiated the risk of fat accumulation among individuals with high obesity GRS. These data suggest the anti-obesity effects of n-3 PUFAs among Yup'ik people may, in part, be dependent upon an individual's genetic predisposition to obesity.

Power and predictive accuracy of polygenic risk scores

Polygenic scores have recently been used to summarise genetic effects among an ensemble of markers that do not individually achieve significance in a large-scale association study. Markers are selected using an initial training sample and used to construct a score in an independent replication sample by forming the weighted sum of associated alleles within each subject. Association between a trait and this composite score implies that a genetic signal is present among the selected markers, and the score can then be used for prediction of individual trait values. This approach has been used to obtain evidence of a genetic effect when no single markers are significant, to establish a common genetic basis for related disorders, and to construct risk prediction models. In some cases, however, the desired association or prediction has not been achieved. Here, the power and predictive accuracy of a polygenic score are derived from a quantitative genetics model as a function of the sizes of the two samples, explained genetic variance, selection thresholds for including a marker in the score, and methods for weighting effect sizes in the score. Expressions are derived for quantitative and discrete traits, the latter allowing for case/control sampling. A novel approach to estimating the variance explained by a marker panel is also proposed. It is shown that published studies with significant association of polygenic scores have been well powered, whereas those with negative results can be explained by low sample size. It is also shown that useful levels of prediction may only be approached when predictors are estimated from very large samples, up to an order of magnitude greater than currently available. Therefore, polygenic scores currently have more utility for association testing than predicting complex traits, but prediction will become more feasible as sample sizes continue to grow.

Recently there has been much interest in combining multiple genetic markers into a single score for predicting disease risk. Even if many of the individual markers have no detected effect, the combined score could be a strong predictor of disease. This has allowed researchers to demonstrate that some diseases have a strong genetic basis, even if few actual genes have been identified, and it has also revealed a common genetic basis for distinct diseases. These analyses have so far been performed opportunistically, with mixed results. Here I derive formulae based on the heritability of disease and size of the study, allowing researchers to plan their analyses from a more informed position. I show that discouraging results in some previous studies were due to the low number of subjects studied, but a modest increase in study size would allow more successful analysis. However, I also show that, for genetics to become useful for predicting individual risk of disease, hundreds of thousands of subjects may be needed to estimate the gene effects. This is larger than most existing studies, but will become more common in the near future, so that gene scores will become more useful for predicting disease than has appeared to date.

Genetic risk prediction in a small cohort of healthy adults in Atlanta

Compared with single markers, polygenic scores that evaluate the joint effects of multiple trait-associated variants are more effective in explaining the variance of traits and risk of diseases. In total, 182 CHDWB (Emory-Georgia Tech Center for Health Discovery and Well Being study) adults were genotyped to investigate the common variant contributions to three traits (height, BMI, serum triglycerides) and three diseases (coronary artery disease (CAD), type 2 diabetes (T2D) and asthma). Association was contrasted between weighted and simple allelic sum polygenic scores with quantitative traits, and with the Framingham risk scores for CAD and T2D. Although the cohort size is two or three orders of magnitude smaller than typical discovery cohorts, we were able to detect significant associations and to explain up to 5% of the traits by the genetic risk scores, despite a strong influence of outliers. An unexpected finding was that CAD-associated single nucleotide polymorphisms (SNPs) explain a significant amount of the variation for total serum cholesterol. Forward step-wise sequential addition of SNPs into the regression model showed that the top-ranked SNPs explain a large proportion of variance, whereas inclusion of gender and ethnicity also affect the performance of polygenic scores.

Genetic predisposition to higher body mass index or type 2 diabetes and leukocyte telomere length in the Nurses' Health Study

BACKGROUND

Although cross-sectional studies have linked higher body mass index (BMI) and type 2 diabetes (T2D) to shortened telomeres, whether these metabolic conditions play a causal role in telomere biology is unknown. We therefore examined whether genetic predisposition to higher BMI or T2D was associated with shortened leukocyte telomere length (LTL).

METHODOLOGY

We conducted an analysis of 3,968 women of European ancestry aged 43-70 years from the Nurses' Health Study, who were selected as cases or controls in genome-wide association studies and studies of telomeres and disease. Pre-diagnostic relative telomere length in peripheral blood leukocytes, collected in 1989-1990, was measured by quantitative PCR. We combined information from multiple risk variants by calculating genetic risk scores based on 32 polymorphisms near 32 loci for BMI, and 36 polymorphisms near 35 loci for T2D.

FINDINGS

After adjustment for age and case-control status, there was no association between the BMI genetic risk score and LTL (β per standard deviation increase: -0.01; SE: 0.02; P = 0.52). Similarly, the T2D genetic score was not associated with LTL (β per standard deviation increase: -0.006; SE: 0.02; P = 0.69).

CONCLUSIONS

In this population of middle-aged and older women of European ancestry, those genetically predisposed to higher BMI or T2D did not possess shortened telomeres. Although we cannot exclude weak or modest effects, our findings do not support a causal relation of strong magnitude between these metabolic conditions and telomere dynamics.

Genetic predisposition to higher body mass index or type 2 diabetes and leukocyte telomere length in the Nurses' Health Study

BACKGROUND

Although cross-sectional studies have linked higher body mass index (BMI) and type 2 diabetes (T2D) to shortened telomeres, whether these metabolic conditions play a causal role in telomere biology is unknown. We therefore examined whether genetic predisposition to higher BMI or T2D was associated with shortened leukocyte telomere length (LTL).

METHODOLOGY

We conducted an analysis of 3,968 women of European ancestry aged 43-70 years from the Nurses' Health Study, who were selected as cases or controls in genome-wide association studies and studies of telomeres and disease. Pre-diagnostic relative telomere length in peripheral blood leukocytes, collected in 1989-1990, was measured by quantitative PCR. We combined information from multiple risk variants by calculating genetic risk scores based on 32 polymorphisms near 32 loci for BMI, and 36 polymorphisms near 35 loci for T2D.

FINDINGS

After adjustment for age and case-control status, there was no association between the BMI genetic risk score and LTL (β per standard deviation increase: -0.01; SE: 0.02; P = 0.52). Similarly, the T2D genetic score was not associated with LTL (β per standard deviation increase: -0.006; SE: 0.02; P = 0.69).

CONCLUSIONS

In this population of middle-aged and older women of European ancestry, those genetically predisposed to higher BMI or T2D did not possess shortened telomeres. Although we cannot exclude weak or modest effects, our findings do not support a causal relation of strong magnitude between these metabolic conditions and telomere dynamics.

Development and evaluation of a genetic risk score for obesity

Multi-locus profiles of genetic risk, so-called "genetic risk scores," can be used to translate discoveries from genome-wide association studies into tools for population health research. We developed a genetic risk score for obesity from results of 16 published genome-wide association studies of obesity phenotypes in European-descent samples. We then evaluated this genetic risk score using data from the Atherosclerosis Risk in Communities (ARIC) cohort GWAS sample (N = 10,745, 55% female, 77% white, 23% African American). Our 32-locus GRS was a statistically significant predictor of body mass index (BMI) and obesity among ARIC whites [for BMI, r = 0.13, p<1 × 10(-30); for obesity, area under the receiver operating characteristic curve (AUC) = 0.57 (95% CI 0.55-0.58)]. The GRS predicted differences in obesity risk net of demographic, geographic, and socioeconomic information. The GRS performed less well among African Americans. The genetic risk score we derived from GWAS provides a molecular measurement of genetic predisposition to elevated BMI and obesity.[Supplemental materials are available for this article. Go to the publisher's online edition of Biodemography and Social Biology for the following resource: Supplement to Development & Evaluation of a Genetic Risk Score for Obesity.].

Serotonin system gene polymorphisms are associated with impulsivity in a context dependent manner

Impulsivity is a risk factor for adverse outcomes and characterizes several psychiatric disorders and risk for suicide. There is strong evidence that genetic variation influences individual differences in impulsivity, but the details are not yet understood. There is growing interest in better understanding the context dependency of genetic effects that is reflected in studies examining gender specificity, gene×environment interaction and epistasis (gene-gene interaction). In a cross-sectional study we examined whether polymorphisms in six serotonin system candidate genes and the experience of early life trauma (age 0-12) were associated with individual differences in impulsivity in a non-clinical sample of Caucasian university students (N=424). We specifically tested potential gender specific, gene-gene, and gene×environment (early life trauma) effects. In our main analyses with Barratt Impulsiveness Scale (BIS-11) total score, there were significant (i.e. p<.01 and False Discovery Rate <.10) interactions between (1) gender and TPH2 (rs1386483) genotype; (2) gender and HTR2A (rs6313) genotype; and epistatic interactions among (3) 5-HTTLPR and MAOA uVNTR; (4) 5-HTTLPR and rs6313 and (5) HTR1B (rs6296) and rs6313 genotypes. Our results strongly support the explicit investigation of context dependent genetic effects on impulsivity and may help to resolve some of the conflicting reports in the literature.

Obesity susceptibility loci on body mass index and weight loss in Spanish adolescents after a lifestyle intervention

OBJECTIVE

To estimate the contribution of 9 obesity-related polymorphisms and a genetic predisposition score (GPS) on anthropometric and biochemical variables before and after a weight loss intervention program in overweight/obese Spanish adolescents.

STUDY DESIGN

Overweight/obese adolescents (n = 168; 12-16 years) participating in the EVASYON program were genotyped for 9 obesity-related single nucleotide polymorphisms in the FTO, MC4R, TMEM18, IL6, PPARG, and ADIPQ genes.

RESULTS

At baseline, the GPS showed a significant association with body mass index-standard deviation score (BMI-SDS) and fat mass. After 3 months of intervention, this GPS also showed a relationship with the variation of both anthropometric measurements. After adjusting for baseline BMI-SDS, subjects with a lower GPS had a greater improvement on metabolic profile, as well as a better response to physical activity, compared with those subjects with a higher GPS.

CONCLUSIONS

The GPS seems to have an important relationship with BMI-SDS and fat mass both at baseline and after a 3-month weight loss lifestyle intervention. Obese and overweight adolescents with a lower GPS have a greater benefit of weight loss after 3 months of a multidisciplinary lifestyle intervention.

Polygenic effects of common single-nucleotide polymorphisms on life span: when association meets causality

Recently we have shown that the human life span is influenced jointly by many common single-nucleotide polymorphisms (SNPs), each with a small individual effect. Here we investigate further the polygenic influence on life span and discuss its possible biological mechanisms. First we identified six sets of prolongevity SNP alleles in the Framingham Heart Study 550K SNPs data, using six different statistical procedures (normal linear, Cox, and logistic regressions; generalized estimation equation; mixed model; gene frequency method). We then estimated joint effects of these SNPs on human survival. We found that alleles in each set show significant additive influence on life span. Twenty-seven SNPs comprised the overlapping set of SNPs that influenced life span, regardless of the statistical procedure. The majority of these SNPs (74%) were within genes, compared to 40% of SNPs in the original 550K set. We then performed a review of current literature on functions of genes closest to these 27 SNPs. The review showed that the respective genes are largely involved in aging, cancer, and brain disorders. We concluded that polygenic effects can explain a substantial portion of genetic influence on life span. Composition of the set of prolongevity alleles depends on the statistical procedure used for the allele selection. At the same time, there is a core set of longevity alleles that are selected with all statistical procedures. Functional relevance of respective genes to aging and major diseases supports causal relationships between the identified SNPs and life span. The fact that genes found in our and other genetic association studies of aging/longevity have similar functions indicates high chances of true positive associations for corresponding genetic variants.

Meta-analyses of genome-wide linkage scans of anxiety-related phenotypes

Genetic factors underlying trait neuroticism, reflecting a tendency towards negative affective states, may overlap genetic susceptibility for anxiety disorders and help explain the extensive comorbidity amongst internalizing disorders. Genome-wide linkage (GWL) data from several studies of neuroticism and anxiety disorders have been published, providing an opportunity to test such hypotheses and identify genomic regions that harbor genes common to these phenotypes. In all, 11 independent GWL studies of either neuroticism (n=8) or anxiety disorders (n=3) were collected, which comprised of 5341 families with 15 529 individuals. The rank-based genome scan meta-analysis (GSMA) approach was used to analyze each trait separately and combined, and global correlations between results were examined. False discovery rate (FDR) analysis was performed to test for enrichment of significant effects. Using 10 cM intervals, bins nominally significant for both GSMA statistics, P(SR) and P(OR), were found on chromosomes 9, 11, 12, and 14 for neuroticism and on chromosomes 1, 5, 15, and 16 for anxiety disorders. Genome-wide, the results for the two phenotypes were significantly correlated, and a combined analysis identified additional nominally significant bins. Although none reached genome-wide significance, an excess of significant P(SR)P-values were observed, with 12 bins falling under a FDR threshold of 0.50. As demonstrated by our identification of multiple, consistent signals across the genome, meta-analytically combining existing GWL data is a valuable approach to narrowing down regions relevant for anxiety-related phenotypes. This may prove useful for prioritizing emerging genome-wide association data for anxiety disorders.

Genomewide Association Studies in Cardiovascular Disease—An Update 2011

BACKGROUND

Genomewide association studies have led to an enormous boost in the identification of susceptibility genes for cardiovascular diseases. This review aims to summarize the most important findings of recent years.

CONTENT

We have carefully reviewed the current literature (PubMed search terms: “genome wide association studies,” “genetic polymorphism,” “genetic risk factors,” “association study” in connection with the respective diseases, “risk score,” “transcriptome”).

SUMMARY

Multiple novel genetic loci for such important cardiovascular diseases as myocardial infarction, hypertension, heart failure, stroke, and hyperlipidemia have been identified. Given that many novel genetic risk factors lie within hitherto-unsuspected genes or influence gene expression, these findings have inspired discoveries of biological function. Despite these successes, however, only a fraction of the heritability for most cardiovascular diseases has been explained thus far. Forthcoming techniques such as whole-genome sequencing will be important to close the gap of missing heritability.