A review of spatial causal inference methods for environmental and epidemiological applications

The scientific rigor and computational methods of causal inference have had great impacts on many disciplines, but have only recently begun to take hold in spatial applications. Spatial casual inference poses analytic challenges due to complex correlation structures and interference between the treatment at one location and the outcomes at others. In this paper, we review the current literature on spatial causal inference and identify areas of future work. We first discuss methods that exploit spatial structure to account for unmeasured confounding variables. We then discuss causal analysis in the presence of spatial interference including several common assumptions used to reduce the complexity of the interference patterns under consideration. These methods are extended to the spatiotemporal case where we compare and contrast the potential outcomes framework with Granger causality, and to geostatistical analyses involving spatial random fields of treatments and responses. The methods are introduced in the context of observational environmental and epidemiological studies, and are compared using both a simulation study and analysis of the effect of ambient air pollution on COVID-19 mortality rate. Code to implement many of the methods using the popular Bayesian software OpenBUGS is provided

Track A Basic Science

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138319/1/jia218438.pd

Recent advances in pharmacology

xii+501hlm.;21c

Evaluation of single-nucleotide polymorphisms in CAPN1 for association with meat tenderness in cattle

Micromolar calcium activated neutral protease (CAPN1) was evaluated as a candidate gene for a quantitative trait locus (QTL) on BTA29 affecting meat tenderness by characterization of nucleotide sequence variation in the gene. Single-nucleotide polymorphisms (SNP) were identified by sequencing all 22 exons and 19 of the 21 introns in two sires (Piedmontese X Angus located at the U.S. Meat Animal Research Centre in Clay Centre, NE; Jersey X Limousin located at AgResearch in New Zealand) of independent resource populations previously shown to be segregating meat tenderness QTL on BTA29. The majority of the 38 SNP were found in introns or were synonymous substitutions in the coding regions, with two exceptions. Exons 14 and 9 contained SNP that were predicted to alter the protein sequence by the substitution of isoleucine for valine in Domain III of the protein, and alanine for glycine in Domain II of the protein. The resource populations were genotyped for these two SNP in addition to six intronic polymorphisms and two silent substitutions. Analysis of genotypes and shear force values in both populations revealed a difference between paternal CAPN1 alleles in which the allele encoding isoleucine at position 530 and glycine at position 316 associated with decreased meat tenderness (increased shear force values) relative to the allele encoding valine at position 530 and alanine at position 316 (P < 0.05). The association of maternal alleles with meat tenderness phenotypes is consistent with the hypothesis of CAPN1 as the gene underlying the QTL effect in two independent resource populations and presents the possibility of using these markers for selective breeding to reduce the numbers of animals with unfavourable meat tenderness traits