The KSR2 -rs7973260 Polymorphism is Associated with Metabolic Phenotypes,but Not Psychological Phenotypes,in Chinese Elders

Abstract

Objective:

To examine the associations between genetic variants of KSR2 (kinase suppressor of RAS)-rs7973260, RAPGEF6 (guanine nucleotide exchange factor 6)-rs3756290, LOC105377703-rs4481363, and subjective well-being (SWB) and depressive symptoms (DSs) in Chinese elders, which were recently associated in a genome-wide association study conducted in Caucasians. The pleiotropic effects of KSR2-rs7973260 on metabolic phenotypes were also explored.

Materials and Methods:

We used data from 1788 older individuals aged 70-84 years from the aging arm of the Rugao Longevity and Aging Study, a population-based cohort study conducted in the Jiangsu province of China.

Results:

No significant distributions of genotype frequencies were observed between life-satisfied and -unsatisfied groups across those with the three polymorphisms. The level of SWB components (positive affect, negative affect, and affect balance) and DSs did not differ among genotypes of the three variants. However, the presence of GA+AA of KSR2-rs7973260 was significantly higher in the metabolic syndrome (MetS), severe hypertriglyceridemia (HTG), and diabetes groups than in control groups (43.7% vs. 37.6%, 46.4% vs. 37.6%, 45.8% vs. 37.9%, respectively). The A allele of rs7973260 was associated with increased risk of MetS, severe HTG, and diabetes with an odds ratios (95% confidence intervals) of 1.289 (1.002-1.658), 1.438 (1.076-1.921), and 1.384 (1.022-1.875), which remained significant after multiple adjustments.

Conclusion:

Rs7973260, rs3756290, and rs4481363 were not associated with SWB and DSs in Chinese elders. However, the KSR2-rs7973260 A allele exhibited pleiotropic effects on some metabolic phenotypes in Chinese elders. These effects should be validated in future studies.

Introduction

Subjective well-being (SWB), a major global assessment for quality of life, can be broadly classified into cognitive and affective facets (Diener et al., 1999; Diener, 2000). Overall life satisfaction (LS) is used to evaluate cognitive happiness, while positive affect (PA), negative affect (NA), and affect balance (AB) are used to evaluate affective facets (Diener, 2000). Studies have shown that there are strong associations between LS, NA, and depressive symptoms (DSs) (perceived low affect and pessimistic view of life) (Steptoe et al., 2015; Smoller, 2016). Twin studies (Kendler et al., 2011; Sadler et al., 2011) have provided considerable support that genetic factors play important roles in individuals' well-being and depression, explained by additive effects of multiple genetic variants (Rietveld et al., 2013).

In 2016, seven genetic variants associated with SWB and DSs were identified in a genome-wide association study (GWAS) conducted in a Caucasian population (Okbay et al., 2016). Three variants (rs7973260 in KSR2 (kinase suppressor of RAS) gene, rs3756290 near RAPGEF6 (guanine nucleotide exchange factor 6) gene, and rs4481363 near the LOC105377703 gene) have minor allele frequencies (MAF) higher than 0.05 in the Han Chinese population, based on a 1000-genome database, which were not in linkage disequilibrium. These variants were chosen to validate associations with SWB and DSs in a Han Chinese population in this study.

Twenty-seven rare functional mutations of the KSR2 gene were recently linked to energy balance and insulin sensitivity in a British population (Pearce et al., 2013), while a common variant rs10444502 near the KSR2 gene was associated with low-density lipoprotein (LDL) and triglyceride (TG) levels in a Croatian population (Zemunik et al., 2009). Another variant rs764157 near KSR2 was associated with metabolic syndrome (MetS) and levels of TG and blood pressure in a GWAS conducted in an African population (Tekola-Ayele et al., 2015). KRS2 regulates multiple signaling pathways by binding kinases such as Raf and AMP-activated protein kinase. KSR2 also significantly reduces MEKK3-induced NF-kappa-B activation in the IL1B (147720)-mediated proinflammatory pathway and downregulates MEKK3-induced IL8 production in response to IL1B stimulation (Channavajhala et al., 2005).

Genetic variants of KRS2 were also recently associated with psychological and metabolic phenotypes in a Caucasian population (Pearce et al., 2013; Okbay et al., 2016). RAPGEF6 is a protein-coding gene that acts as the guanine nucleotide exchange factor for Ras-related protein1 and Ras-related protein2 downstream of MRAS in the plasma membrane (Kuiperij et al., 2003). A de novo exonic microdeletion was discovered involving RAPGEF6 in a patient with schizophrenia (Xu et al., 2008). As for LOC105377703 (GeneID = 105377703), the function and relationship of this gene with certain diseases are yet to be explored. In this study, we aimed to examine the associations between KSR2-rs7973260, RAPGEF6-rs3756290, and LOC105377703-rs4481363 with SWB and DSs in a Chinese elderly population. In addition, we explored the pleiotropic effects of KSR2-rs7973260 on metabolic phenotypes.

Materials and Methods

Subjects

Data were drawn from the aging arm of the Rugao Longevity and Aging Study (RuLAS, conducted between November 2014 and December 2014) described in detail elsewhere (Liu et al., 2015). Overall, 1788 participants (75.4 ± 3.9 years) aged 70-84 years old were randomly recruited from the 31 villages of Jiang'an townships in Rugao, China. This study consisted of 1769 individuals with complete information on their psychological and metabolic phenotypes. Informed consent was obtained from all participants in this study. The Human Ethnics Committee of Fudan University School of Life Sciences approved the research.

Psychological phenotypes

SWB was measured by combining LS and Bradburn's Affect Balance Scale (ABS). LS was scaled from the following question: “How satisfied are you with your life as a whole?” Answers ranged from very dissatisfied or dissatisfied, to neither satisfied nor dissatisfied, or satisfied to very satisfied (Myers and Diener, 1996). In this study, LS was used as a categorical variable, describing satisfied (very satisfied, satisfied, or fair, as the reference group) versus unsatisfied (unsatisfied or very unsatisfied). The Bradburn's ABS (NM, 1969), based on the definition of happiness (subscaled with PA, NA, and AB) and details about measure of ABS on the Rugao Longevity and Aging Study, have been previously described by Liu et al., (2014). Depression symptoms were measured by the Chinese version of the 15-item Geriatric Depression Scale (GDS-15), consisting of 15 yes or no questions (Yesavage, 1988). In this study, the Cronbach's alpha of GDS-15 was 0.718 (Zhi et al., 2016).

Metabolic phenotypes

Blood specimens were drawn after overnight fasting, subjected to centrifugation immediately, and analyzed within 8 h for biochemical factors (TG levels, total cholesterol, and glucose) in the laboratory of Rugao People's Hospital. In this study, based on the definition of the National Cholesterol Education Program (NCEP, 2001) improved threshold, an individual was considered to have MetS if he or she had the following measures for three or more of the five component traits: waist circumference >90 cm for men or >88 cm for women, fasting plasma glucose (FPG) ≥100 mg/dL(5.6 mM), plasma TG levels ≥150 mg/dL (1.7 mM), high-density lipoprotein (HDL) cholesterol <40 mg/dL for men or <50 mg/dL for women, systolic blood pressure (SBP) ≥140 mmHg, or diastolic blood pressure (DBP) ≥90 mmHg. Severe hypertriglyceridemia (HTG) was defined as plasma TG ≥2.27 mM, (NCEP, 2002). Diabetes was defined as FPG ≥7.0 mM and FPG <7.0; body mass index (BMI) was calculated by weight (kg)/height (m)². Hypertension was defined as SBP of ≥140 mmHg, DBP of ≥90 mmHg, or antihypertensive drug use.

Covariates

The following sociodemographic characteristics were included in this study: age (age <74 years, age 75≤ to <79 years, age ≥80 years), gender (male, female), occupation (farmers and others), marital status [current married, other (never married, divorced, separated, or widowed)], and literacy [illiterate or literate (≥1 years of education)]. The following lifestyle factors were included in this study: smoking habits (never smoked and current or former smoker) and drinking habits (never drank and current or former drinker). Perceived self-reported mental health was assessed using questions that asked the participant as follows: “In general, compared with other people of the same age, would you say that your mental health is excellent, very good, good, fair, or poor?” This factor was used as a categorical variable, distinguishing between good (excellent, very good, or good, as the reference group) and poor (fair or poor).

Genotyping

Genotyping of KSR2-rs7973260, RAPGEF6-rs3756290, and LOC105377703-rs4481363 were accomplished using TaqMan assays (Thermo Fisher Scientific, MA). Total PCR volume of 10 μL included 10 ng genomic DNA, 5 μL TaqMan Universal Master Mix (Applied Biosystems), 0.2 μL TaqMan SNP Genotyping Assay Mix, and 2.5 μL RNase. Cycling conditions included one cycle of 95°C for 10 min and 40 cycles at 95°C for 15 s and 60°C for 60 s. Negative controls were included to avoid contamination. Fluorescence was detected using an ABI 7900HT and the alleles were scored using Sequence Detection Software (Thermo Fisher Scientific, MA). In addition to quality control samples included in each batch by the laboratory, blinded quality control samples were included to monitor the reproducibility of the genotyping assays. The concordance of duplicate samples was >99%.

Statistical analyses

Data were presented as the mean ± standard deviation (SD) or the percentage. Student's t test or analysis of variance was used for normally distributed continuous variables and nonparametric Mann-Whitney test was used for nonnormally distributed continuous variables. Chi-square tests were used for categorical variables. A deviation from Hardy-Weinberg equilibrium for the genetic variants of rs7973260, rs3756290, and rs4481363 was tested by a chi-squared test. Values were considered statistically significant at p < 0.05. All p values were two sided.

To estimate the association of the genetic variants to the risk of metabolic phenotypes, odds ratios (ORs) and 95% confidence intervals (CI) were derived from binary logistic regression models using SPSS version 19.0 (SPSS Inc., Chicago, IL). In this study, three models were performed. Crude ORs were calculated in model 1. Model 2 added demographic variables, age and gender, to model 1. Variables of sociodemographic characteristics, lifestyle factors, and mental health status were added to model 3.

Results

The genotype distributions for KSR2-rs7973260, RAPGEF6-rs3756290, and LOC105377703-rs4481363 polymorphisms are shown in Table 1. The genotype data distributions for three polymorphic loci did not deviate significantly from the Hardy-Weinberg equilibrium expectations. The allele frequencies of rs7973260 A, rs3756290 T, and rs4481363 C were 21.7%, 46.7%, and 17.9% in this Han Chinese population, respectively. Since the proportion of AA genotype of rs7973260 (4.6%) and CC of rs4481363 (3.2%) was lower than 5%, we used the dominant genetic model of the rare allele in the analysis. No significant distribution of genotype frequencies was observed between life-satisfied and life-unsatisfied groups for the rs7973260, rs3756290, and rs4481363 polymorphisms. SWB components (PA, NA, and AB) and DSs did not differ across genotypes of the three variants.

Table 1.

Single Nucleotide Polymorphisms and Subjective Well-Being Components in Elderly Chinese Participants

	rs4481363			rs7973260			rs3756290
	AA	CC+AC	p	GG	AG+AA	p	CC	CT	TT	p
All samples	1705 (96.8)	56 (3.2)		1075 (61.3)	679 (38.7)		476 (28.0)	861 (50.4)	364 (21.6)
LS n (%)
Satisfied	1013 (67.2)	495 (32.8)	0.94	942 (61.5)	578 (38.5)	0.719	409 (28.0)	733 (50.2)	317 (21.7)	0.82
Unsatisfied	159 (67.7)	76 (32.3)		141 (60.3)	93 (39.7)		64 (28.3)	117 (51.8)	45 (19.9)
PA	3.39 ± 1.24	3.27 ± 1.34	0.085	3.37 ± 1.29	3.33 ± 1.27	0.555	3.3 ± 1.29	3.38 ± 1.25	3.31 ± 1.34	0.455
NA	0.81 ± 1.18	0.87 ± 1.29	0.346	0.84 ± 1.21	0.82 ± 1.23	0.715	0.89 ± 1.25	0.8 ± 1.17	0.78 ± 1.2	0.338
AB	7.57 ± 1.85	7.40 ± 2.06	0.083	7.52 ± 1.94	7.51 ± 1.92	0.872	7.41 ± 1.95	7.58 ± 1.88	7.53 ± 1.94	0.306
DS	2.49 ± 2.24	2.53 ± 2.49	0.748	2.47 ± 2.26	2.57 ± 2.41	0.358	2.56 ± 2.29	2.48 ± 2.33	2.39 ± 2.26	0.591

AB, affect balance; DS, depressive symptom; GDS-15 score; LS, life satisfaction; NA, negative affect; PA, positive affect.

We did not observe significant associations between KSR2-rs7973260 and RAPGEF6-rs3756290 and metabolic phenotypes. GA+AA of KSR2-rs7973260 was significantly higher in MetS, severe HTG, and diabetes groups than in control groups (43.7% vs. 37.6%, 46.4% vs. 37.6%, 45.8% vs. 37.9%, respectively) (Table 2). A allele of rs7973260 was associated with an increased risk of MetS, severe HTG, and diabetes with ORs (95% CIs) of 1.289 (1.002-1.658), 1.438 (1.076-1.921), and 1.384 (1.022-1.875) (Table 3). The associations remained after adjustments for the potential cofounders of sex and age, and after further adjustments for sex, age, smoking habits, drinking habits, education, occupation, marital status, perceived mental health, PA, LS, and DS. We did not observe an association between rs7973260 and metabolic phenotypes, such as Tc, HDL, LDL, TG, glucose, blood pressure, and BMI (Table 4).

Table 2.

Demographic and Clinical Characteristics of the Participants Segregated by rs7973260 (n = 1754)

Characteristics n (%)	GG	GA+AA	p
Gender
Male	492 (60.4)	323 (39.6)	0.461
Female	583 (62.1)	356 (37.9)
Age group			0.784
70-74	515 (61.2)	326 (38.8)
75-79	365 (60.5)	238 (39.5)
80-84	195 (62.9)	115 (37.1)
Marital status			0.942
Married	370 (61.5)	232 (38.5)
Other	698 (61.3)	441 (38.7)
Literacy			0.456
Illiterate	557 (60.6)	362 (39.4)
Literate	502 (62.4)	303 (37.6)
Occupation			0.393
Farmer	948 (61)	605 (39)
Other	112 (64.4)	62 (35.6)
Smoking habits			0.907
Nonsmoker	787 (61)	504 (39)
Smoker	152 (61)	97 (39)
Ever-smoker	119 (62.6)	71 (37.4)
Drinking habits			0.655
Nondrinker	746 (61)	477 (39)
Drinker	198 (60.4)	130 (39.6)
Ever-drinker	117 (64.3)	65 (35.7)
Perceived mental health			0.112
Good	791 (62.4)	476 (37.6)
Poor	272 (58.2)	195 (41.8)
Metabolism			0.048
Normal	896 (62.4)	539 (37.6)
MetS	169 (56.3)	131 (43.7)
Glucose			0.035
FPG <7.0	939 (62.1)	573 (37.9)
FPG ≥7.0	103 (54.2)	87 (45.8)
Severe HTG			0.014
PTG <2.27	960 (62.4)	579 (37.6)
PTG ≥2.27	113 (53.6)	98 (46.4)
Blood pressure			0.190
Normal pressure	234 (64.5)	129 (35.5)
Hypertension	831 (60.7)	538 (39.3)
BMI			0.954
≤23.9	536 (61.3)	339 (38.7)
24-27.9	382 (61.8)	236 (38.2)
≥28	133 (60.7)	86 (39.3)

BMI, body mass index; FPG, fasting plasma glucose; HTG, hypertriglyceridemia; MetS, metabolic syndrome.

Table 3.

Odds Ratios of Bivariate Logistic Analysis Between rs7973260 Genotype and Fasting Plasma Glucose, Plasma Triglyceride, and Metabolic Syndrome in Elderly Chinese Participants

	MetS OR (95% CI)	p	High TG 2.27+OR (95% CI)	p	Diabetes OR (95% CI)	p
GA+AA/GG (Ref)	1		1		1
Model 1	1.289 (1.002-1.658)^a	0.049	1.438 (1.076-1.921)^a	0.014	1.384 (1.022-1.875)^a	0.036
Model 2	1.342 (1.036-1.739)^a	0.026	1.468 (1.095-1.967)^a	0.010	1.395 (1.029-1.892)^a	0.032
Model 3	1.361 (1.043-1.776)^a	0.023	1.499 (1.110-2.033)^a	0.008	1.420 (1.037-1.944)^a	0.029

Model 1, unadjusted model; Model 2, adjusted for sex and age; Model 3, adjusted for sex, age, smoking habits, drinking habits, literacy, occupation, marital status, perceived mental health, PA, LS, and GDS-15 scores; FPG <7 and plasma TG <2.27 were used as reference.

p < 0.05.

CI, confidence intervals; OR, odds ratio.

Table 4.

Physical Characteristics of Participants Segregated by rs7973260 Status

Characteristics	GG	GA+AA	p
Mean ± SD
Age (years)	75.39 ± 3.95	75.3 ± 3.85	0.642
Tc (mM)	5.13 ± 0.97	5.11 ± 0.92	0.758^a
HDL (mM)	1.48 ± 0.34	1.45 ± 0.31	0.079
LDL (mM)	2.79 ± 0.74	2.8 ± 0.68	0.889
TG (mM)	1.35 ± 0.88	1.46 ± 1.06	0.069^a
Glucose (mM)	5.77 ± 1.43	5.88 ± 1.5	0.125
SBP (mmHg)	155 ± 25.53	153.95 ± 26.68	0.363^a
DBP (mmHg)	81.64 ± 13.1	80.83 ± 13.67	0.424^a
Waist (cm)	81.82 ± 12.68	82.67 ± 14.48	0.197
BMI (kg/m²)	23.85 ± 4.15	23.86 ± 4.45	0.964

Examined by Mann-Whitney test.

DBP, diastolic blood pressure; HDL, high-density lipoprotein; LDL, low-density lipoprotein; SD, standard deviation; SBP, systolic blood pressure.

Discussion

In this study, we validated associations of three genetic loci, KSR2-rs7973260, RAPGEF6-rs3756290, and LOC105377703-rs4481363, with psychological phenotypes in a Chinese population for the first time. We found no association between these factors and LS, PA, NA, AB, or DSs in the studied Han Chinese population. We also explored the pleiotropic effects of KSR2-rs7973260 on metabolic phenotypes and found, for the first time, that A allele increases the risk of MetS, severe HTG, and diabetes.

We did not find the associations between the three genetic variants and SWB phenotypes that were most recently observed in Caucasians (Okbay et al., 2016), (1) perhaps due to the differences in genetics between the Han Chinese and Caucasian populations. For the seven common polymorphisms associated with SWB or DSs in Caucasians in the original GWAS conducted by Okbay et al. (Diener and Chan, 2011), only KSR2-rs7973260, RAPGEF6-rs3756290, and LOC105377703-rs4481363 (aside from a polymorphism rs4346787 in complete linkage disequilibrium) were high frequency polymorphisms in our participants. However, according to the 1000-genome database, KSR2-rs7973260, RAPGEF6-rs3756290, and LOC105377703-rs4481363 show large MAF differences between Caucasians and Chinese populations (17.7% vs. 25.2%, 25.3% vs. 50%, 45.5% vs. 19.9%, respectively). (2) As a heterogeneous phenotype, SWB includes a continuous profile of affective evaluations and subjective life assessments (Bartels, 2015; Diener and Chan, 2011), which reflect global assessments for the major domains in life and underpin diverse information for a broad range of behaviors and health regarding physical and mental health, social relationships, leisure, culture, life circumstances, and subjective states (Bartels, 2015; Steptoe et al., 2015; Rapacciuolo et al., 2016; Rico-Uribe et al., 2016; Rose and Lonsdale, 2016). (3) This study recruited elders. It is possible that different genes are involved in regulating happiness or depression at different ages. (4) The sample size of this study was too small to have enough statistical power to detect a weak effect size of the studied polymorphism (i.e., the β (sem) of rs375620 correlated with SWB was only −0.0177(0.0031) in the original GWAS conducted by Okbay (Diener and Chan, 2011)).

Although we expected to find the relationship between genetic variant of KSR2 and metabolic phenotypes, such as MetS, severe HTG, and diabetes, the link between KSR2-rs7973260 and these effects is indeed novel. As an intracellular scaffold protein, KSR2 is involved in multiple signal pathways. Studies (Greenhill, 2014; Pilbrow, 2014) have reported that the interaction between KSR2 and AMPK under certain conditions is associated with abnormalities in energy homeostasis and metabolism, which include obesity, high insulin levels, and impaired glucose tolerance, based on experiments in Ksr2 knockout mice (Brommage et al., 2008; Costanzo-Garvey et al., 2009) and in humans. Pearce et al. (2013) identified multiple rare variants in KSR2 that altered the function of KSR2 and led to predisposal to obesity and severe insulin resistance. Specifically, the authors found a total of 27 different KSR2 mutations in 2.1% of severely obese individuals and 1.0% of normal weight controls, of which 23 mutations were unique among obese individuals. Clinical characterization of obese individuals and family members with KSR2 mutations showed that they had hyperphagia in childhood, lower heart rates, and reduced basal metabolic rates when compared with obese controls and those with severe insulin resistance. The findings suggest that multiple rare variants with large effect sizes collectively influence predisposition to metabolic abnormities.

According to the observations of Okbay [8], the β of the KSR2-rs7973260 that correlated with DSs is 0.0306, while the MAF is 19% in Caucasians, suggesting that rs7973260 is a common, weak effect-sized polymorphism. Indeed, other common genetic variants in KSR2 have been corroborated by some GWAS studies for metabolism phenotypes. Zemunik et al. (2009) found that rs4767631 and rs10444502 in KSR2 (with a population frequency of 31.3% of the effect allele) are associated with plasma LDL levels and rs10444502 (with a population frequency of 28% of the effect allele) is associated with total cholesterol levels in those from the island of Korčula. Tekola-Ayele et al. (2015) reported that common genetic variant rs764157 near KSR2 gene affect MetS, plasma TG, and blood pressure in an African population. These studies and our study indicate that, not only multiple rare variants but also common variants of KSR2 gene may influence the population variations of metabolic phenotypes.

The metabolic phenotype-related rs7973260, which we identified, is located in the first intron of the KSR2 gene, which might just be a marker. Genetic variants in the first intron are particularly important in the pathogenesis of complex traits. The first intron may harbor elements regulating transcription such as transcription enhancers and silencers (Lio et al., 2002; Tokuhiro et al., 2003). The functional variant in linkage with KSR2 requires further study.

Limitations of the study should be mentioned. First, we investigated the genotype-phenotype associations of only three common polymorphisms of the aforementioned three genes, which may have missed the effects of other common and rare variants. Second, the sample size of this study is relatively small, which may limit the efficacy of the genetic associations.

Conclusions

We did not find significant associations between KSR2-rs7973260, RAPGEF6-rs3756290, and LOC105377703-rs4481363 polymorphisms, and SWB and DSs in a Chinese Han population. However, we did find a pleiotropic effect of KSR2-rs7973260 on the risk of MetS, severe HTG, and diabetes. These preliminary observations should be validated in Chinese participants and in other populations. The functional significance of these effects should also be further explored.

Ethical Approval

The Human Ethnics Committee of School of Life Sciences of Fudan University, Shanghai, China, approved this study. Written consent was obtained from all participants before the study.

Footnotes

Acknowledgments

We acknowledge all participants involved in the study. This work was supported by grants from the National Natural Science Foundation (81670465, 81571372, and 81260180), the Shanghai Municipal Natural Science Foundation (16ZR1439600), and the International Cooperation Project of Ministry of Science and Technology (2014DFA32830).

Authors' Contributions

Y.W. and T.M. were responsible for managing the participant database and retrieving follow-up information; they also contributed to article preparation, in the analyses of data and in drafting the article. Y.Z., X.C., and S.Y. supervised the ongoing research, taking part in the initiation of the study and contributing to article preparation. H.W., J.C., and X.J. contributed to analyses of data and in article preparation. X.J. and X.W. conceived the study, participated in its design and coordination, and helped draft the article. All authors read and approved the final article.

Author Disclosure Statement

No competing financial interests exist.

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