Financial Transaction Tax: Small is Beautiful

The case for taxing financial transactions merely to raise more revenues from the financial sector is not particularly strong. Better alternatives to tax the financial sector are likely to be available. However, a tax on financial transactions could be justified in order to limit socially undesirable transactions when more direct means of doing so are unavailable for political or practical reasons. Some financial transactions are indeed likely to do more harm than good, especially when they contribute to the systemic risk of the financial system. However, such a financial transaction tax should be very small, much smaller than the negative externalities in question, because it is a blunt instrument that also drives out socially useful transactions. There is a case for taxing over-the-counter derivative transactions at a somewhat higher rate than exchange-based derivative transactions. More targeted remedies to drive out socially undesirable transactions should be sought in parallel, which would allow, after their implementation, to reduce or even phase out financialtransaction taxes

PedHunter 2.0 and its usage to characterize the founder structure of the Old Order Amish of Lancaster County

<p>Abstract</p> <p>Background</p> <p>Because they are a closed founder population, the Old Order Amish (OOA) of Lancaster County have been the subject of many medical genetics studies. We constructed four versions of Anabaptist Genealogy Database (AGDB) using three sources of genealogies and multiple updates. In addition, we developed PedHunter, a suite of query software that can solve pedigree-related problems automatically and systematically.</p> <p>Methods</p> <p>We report on how we have used new features in PedHunter to quantify the number and expected genetic contribution of founders to the OOA. The queries and utility of PedHunter programs are illustrated by examples using AGDB in this paper. For example, we calculated the number of founders expected to be contributing genetic material to the present-day living OOA and estimated the mean relative founder representation for each founder. New features in PedHunter also include pedigree trimming and pedigree renumbering, which should prove useful for studying large pedigrees.</p> <p>Results</p> <p>With PedHunter version 2.0 querying AGDB version 4.0, we identified 34,160 presumed living OOA individuals and connected them into a 14-generation pedigree descending from 554 founders (332 females and 222 males) after trimming. From the analysis of cumulative mean relative founder representation, 128 founders (78 females and 50 males) accounted for over 95% of the mean relative founder contribution among living OOA descendants.</p> <p>Discussion/Conclusions</p> <p>The OOA are a closed founder population in which a modest number of founders account for the genetic variation present in the current OOA population. Improvements to the PedHunter software will be useful in future studies of both the OOA and other populations with large and computerized genealogies.</p

Common Variants in 40 Genes Assessed for Diabetes Incidence and Response to Metformin and Lifestyle Intervention in the Diabetes Prevention Program

OBJECTIVE: Genome-wide association studies have begun to elucidate the genetic architecture of type 2 diabetes. We examined whether single nucleotide polymorphisms (SNPs) identified through targeted complementary approaches affect diabetes incidence in the at-risk population of the Diabetes Prevention Program (DPP) and whether they influence a response to preventive interventions. RESEARCH DESIGN AND METHODS: We selected SNPs identified by prior genome-wide association studies for type 2 diabetes and related traits, or capturing common variation in 40 candidate genes previously associated with type 2 diabetes, implicated in monogenic diabetes, encoding type 2 diabetes drug targets or drug-metabolizing/transporting enzymes, or involved in relevant physiological processes. We analyzed 1,590 SNPs for association with incident diabetes and their interaction with response to metformin or lifestyle interventions in 2,994 DPP participants. We controlled for multiple hypothesis testing by assessing false discovery rates. RESULTS: We replicated the association of variants in the metformin transporter gene $SLC47A1$ with metformin response and detected nominal interactions in the AMP kinase (AMPK) gene $STK11$, the AMPK subunit genes $PRKAA1$ and $PRKAA2$, and a missense SNP in $SLC22A1$, which encodes another metformin transporter. The most significant association with diabetes incidence occurred in the AMPK subunit gene $PRKAG2$ (hazard ratio 1.24, 95% CI 1.09–1.40, P = 7 × 10$^{−4}$). Overall, there were nominal associations with diabetes incidence at 85 SNPs and nominal interactions with the metformin and lifestyle interventions at 91 and 69 mostly nonoverlapping SNPs, respectively. The lowest $P$ values were consistent with experiment-wide 33% false discovery rates. CONCLUSIONS: We have identified potential genetic determinants of metformin response. These results merit confirmation in independent samples

Genetic modulation of lipid profiles following lifestyle modification or metformin treatment: The Diabetes Prevention Program

Weight-loss interventions generally improve lipid profiles and reduce cardiovascular disease risk, but effects are variable and may depend on genetic factors. We performed a genetic association analysis of data from 2,993 participants in the Diabetes Prevention Program to test the hypotheses that a genetic risk score (GRS) based on deleterious alleles at 32 lipid-associated single-nucleotide polymorphisms modifies the effects of lifestyle and/or metformin interventions on lipid levels and nuclear magnetic resonance (NMR) lipoprotein subfraction size and number. Twenty-three loci previously associated with fasting LDL-C, HDL-C, or triglycerides replicated (P = 0.04–1×10−17). Except for total HDL particles (r = −0.03, P = 0.26), all components of the lipid profile correlated with the GRS (partial |r| = 0.07–0.17, P = 5×10−5–1×10−19). The GRS was associated with higher baseline-adjusted 1-year LDL cholesterol levels (β = +0.87, SEE±0.22 mg/dl/allele, P = 8×10−5, Pinteraction = 0.02) in the lifestyle intervention group, but not in the placebo (β = +0.20, SEE±0.22 mg/dl/allele, P = 0.35) or metformin (β = −0.03, SEE±0.22 mg/dl/allele, P = 0.90; Pinteraction = 0.64) groups. Similarly, a higher GRS predicted a greater number of baseline-adjusted small LDL particles at 1 year in the lifestyle intervention arm (β = +0.30, SEE±0.012 ln nmol/L/allele, P = 0.01, Pinteraction = 0.01) but not in the placebo (β = −0.002, SEE±0.008 ln nmol/L/allele, P = 0.74) or metformin (β = +0.013, SEE±0.008 nmol/L/allele, P = 0.12; Pinteraction = 0.24) groups. Our findings suggest that a high genetic burden confers an adverse lipid profile and predicts attenuated response in LDL-C levels and small LDL particle number to dietary and physical activity interventions aimed at weight loss

Common Variants in 40 Genes Assessed for Diabetes Incidence and Response to Metformin and Lifestyle Intervention in the Diabetes Prevention Program

OBJECTIVE: Genome-wide association studies have begun to elucidate the genetic architecture of type 2 diabetes. We examined whether single nucleotide polymorphisms (SNPs) identified through targeted complementary approaches affect diabetes incidence in the at-risk population of the Diabetes Prevention Program (DPP) and whether they influence a response to preventive interventions. RESEARCH DESIGN AND METHODS: We selected SNPs identified by prior genome-wide association studies for type 2 diabetes and related traits, or capturing common variation in 40 candidate genes previously associated with type 2 diabetes, implicated in monogenic diabetes, encoding type 2 diabetes drug targets or drug-metabolizing/transporting enzymes, or involved in relevant physiological processes. We analyzed 1,590 SNPs for association with incident diabetes and their interaction with response to metformin or lifestyle interventions in 2,994 DPP participants. We controlled for multiple hypothesis testing by assessing false discovery rates. RESULTS: We replicated the association of variants in the metformin transporter gene SLC47A1 with metformin response and detected nominal interactions in the AMP kinase (AMPK) gene STK11, the AMPK subunit genes PRKAA1 and PRKAA2, and a missense SNP in SLC22A1, which encodes another metformin transporter. The most significant association with diabetes incidence occurred in the AMPK subunit gene PRKAG2 (hazard ratio 1.24, 95% CI 1.09-1.40, P = 7 × 10(-4)). Overall, there were nominal associations with diabetes incidence at 85 SNPs and nominal interactions with the metformin and lifestyle interventions at 91 and 69 mostly nonoverlapping SNPs, respectively. The lowest P values were consistent with experiment-wide 33% false discovery rates. CONCLUSIONS: We have identified potential genetic determinants of metformin response. These results merit confirmation in independent samples