Semiparametric theory and empirical processes in causal inference

In this paper we review important aspects of semiparametric theory and empirical processes that arise in causal inference problems. We begin with a brief introduction to the general problem of causal inference, and go on to discuss estimation and inference for causal effects under semiparametric models, which allow parts of the data-generating process to be unrestricted if they are not of particular interest (i.e., nuisance functions). These models are very useful in causal problems because the outcome process is often complex and difficult to model, and there may only be information available about the treatment process (at best). Semiparametric theory gives a framework for benchmarking efficiency and constructing estimators in such settings. In the second part of the paper we discuss empirical process theory, which provides powerful tools for understanding the asymptotic behavior of semiparametric estimators that depend on flexible nonparametric estimators of nuisance functions. These tools are crucial for incorporating machine learning and other modern methods into causal inference analyses. We conclude by examining related extensions and future directions for work in semiparametric causal inference

An Extremes of Phenotype Approach Confirms Significant Genetic Heterogeneity in Patients with Ulcerative Colitis

Background and Aims: Ulcerative colitis [UC] is a major form of inflammatory bowel disease globally. Phenotypic heterogeneity is defined by several variables including age of onset and disease extent. The genetics of disease severity remains poorly understood. To further investigate this, we performed a genome wide association [GWA] study using an extremes of phenotype strategy. Methods: We conducted GWA analyses in 311 patients with medically refractory UC [MRUC], 287 with non-medically refractory UC [nonMRUC] and 583 controls. Odds ratios [ORs] were calculated for known risk variants comparing MRUC and non-MRUC, and controls. Results: MRUC–control analysis had the greatest yield of genome-wide significant single nucleotide polymorphisms [SNPs] [2018], including lead SNP = rs111838972 [OR = 1.82, p = 6.28 × 10−9] near MMEL1 and a locus in the human leukocyte antigen [HLA] region [lead SNP = rs144717024, OR = 12.23, p = 1.7 × 10−19]. ORs for the lead SNPs were significantly higher in MRUC compared to non-MRUC [p < 9.0 × 10−6]. No SNPs reached significance in the non-MRUC–control analysis (top SNP, rs7680780 [OR 2.70, p = 5.56 × 10−8). We replicate findings for rs4151651 in the Complement Factor B [CFB] gene and demonstrate significant changes in CFB gene expression in active UC. Detailed HLA analyses support the strong associations with MHC II genes, particularly HLA-DQA1, HLA-DQB1 and HLA-DRB1 in MRUC. Conclusions: Our MRUC subgroup replicates multiple known UC risk variants in contrast to non-MRUC and demonstrates significant differences in effect sizes compared to those published. Non-MRUC cases demonstrate lower ORs similar to those published. Additional risk and prognostic loci may be identified by targeted recruitment of individuals with severe disease.Sally Mortlock, Anton Lord, Grant Montgomery, Martha Zakrzewski, Lisa A.Simms, Krupa Krishnaprasad, Katherine Hanigan, James D. Doecke, Alissa Walsh, Ian C. Lawrance, Peter A.Bampton, Jane M. Andrews, Gillian Mahy, Susan J. Connor, Miles P.Sparrow, Sally Bell, Timothy H. Florin, Jakob Begun, Richard B. Gearry, Graham L. Radford-Smit

Crystallization of PNMT, the adrenaline-synthesizing enzyme, is critically dependent on a high protein concentration

Phenylethanolamine N-methyltransferase, PNMT, utilizes the methylating cofactor S-adenosyl-L-methionine to catalyse the synthesis of adrenaline. Human PNMT has been crystallized in complex with an inhibitor and the cofactor product S-adenosyl-L-homocysteine using the hanging-drop technique with PEG 6000 and lithium chloride as precipitant. A critical requirement for crystallization was a high enzyme concentration (>90 mg ml(-1)) and cryocrystallography was used for high-quality data measurement. Diffraction data measured from a cryocooled crystal extend to a resolution of 2.3 Angstrom. Cryocooled crystals belong to space group P4(3)2(1)2 and have unit-cell parameters a = b = 94.3, c = 187.7 Angstrom

Efficiency of profile likelihood in semi-parametric models

Semi-parametric model, Profile likelihood, Two-phase outcome-dependent sampling, Efficiency, M-estimator, Maximum likelihood estimator, Efficient score, Efficient information bound,

Variation in synonymous codon use and DNA polymorphism within the Drosophila genome.

A strong negative correlation between the rate of amino-acid substitution and codon usage bias in Drosophila has been attributed to interference between positive selection at nonsynonymous sites and weak selection on codon usage. To further explore this possibility we have investigated polymorphism and divergence at three kinds of sites: synonymous, nonsynonymous and intronic in relation to codon bias in D. melanogaster and D. simulans. We confirmed that protein evolution is one of the main explicative parameters for interlocus codon bias variation (r2~ 40%). However, intron or synonymous diversities, which could have been expected to be good indicators of local interference [here defined as the additional increase of drift due to selection on tightly linked sites, also called `genetic draft¿ by Gillespie (2000)] did not covary significantly with codon bias or with protein evolution. Concurrently, levels of polymorphism were reduced in regions of low recombination rates whereas codon bias was not. Finally, while nonsynonymous diversities were very well correlated between species, neither synonymous nor intron diversities observed in D. melanogaster were correlated with those observed in D. simulans. All together, our results suggest that the selective constraint on the protein is a stable component of gene evolution while local interference is not. The pattern of variation in genetic draft along the genome therefore seems to be instable through evolutionary times and should therefore be considered as a minor determinant of codon bias variance. We argue that selective constraints for optimal codon usage are likely to be correlated with selective constraints on the protein, both between codons within a gene, as previously suggested, and also between genes within a genome