13 research outputs found
[Molecular genetic and genomic approaches to studying evolution and adaptation in birds] ΠΠΎΠ»Π΅ΠΊΡΠ»ΡΡΠ½ΠΎ-Π³Π΅Π½Π΅ΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΈ Π³Π΅Π½ΠΎΠΌΠ½ΡΠ΅ ΠΏΠΎΠ΄Ρ ΠΎΠ΄Ρ ΠΊ ΠΈΠ·ΡΡΠ΅Π½ΠΈΡ ΡΠ²ΠΎΠ»ΡΡΠΈΠΈ ΠΈ Π°Π΄Π°ΠΏΡΠ°ΡΠΈΠΈ ΠΏΡΠΈΡ
The review summarizes information on the advances in molecular genetic and genomic approaches to elucidate the main points in the evolutionary history of birds (class Aves) adapted to a wide variety of habitats.
Π ΠΎΠ±Π·ΠΎΡΠ΅ ΠΎΠ±ΠΎΠ±ΡΠ΅Π½Ρ ΡΠ²Π΅Π΄Π΅Π½ΠΈΡ ΠΎ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΡΡ
ΠΌΠΎΠ»Π΅ΠΊΡΠ»ΡΡΠ½ΠΎ-Π³Π΅Π½Π΅ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈ Π³Π΅Π½ΠΎΠΌΠ½ΡΡ
ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ΠΎΠ² Π΄Π»Ρ Π²ΡΡΡΠ½Π΅Π½ΠΈΡ ΠΎΡΠ½ΠΎΠ²Π½ΡΡ
ΠΌΠΎΠΌΠ΅Π½ΡΠΎΠ² Π² ΡΠ²ΠΎΠ»ΡΡΠΈΠΎΠ½Π½ΠΎΠΉ ΠΈΡΡΠΎΡΠΈΠΈ ΠΏΡΠΈΡ (ΠΊΠ»Π°ΡΡ Aves), Π°Π΄Π°ΠΏΡΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
ΠΊ ΡΠ°ΠΌΡΠΌ ΡΠ°Π·Π½ΠΎΠΎΠ±ΡΠ°Π·Π½ΡΠΌ ΡΡΠ»ΠΎΠ²ΠΈΡΠΌ ΠΎΠ±ΠΈΡΠ°Π½ΠΈΡ
[Genomic assessment of breeding bulls] ΠΠ΅Π½ΠΎΠΌΠ½Π°Ρ ΠΎΡΠ΅Π½ΠΊΠ° ΠΏΠ»Π΅ΠΌΠ΅Π½Π½ΡΡ Π±ΡΠΊΠΎΠ²
The review considers aspects of genomic assessment of breeding bulls based on the use of molecular genetic markers and, in particular, SNP markers for determining the breeding value of animals.
Π ΠΎΠ±Π·ΠΎΡΠ΅ ΡΠ°ΡΡΠΌΠΎΡΡΠ΅Π½Ρ Π°ΡΠΏΠ΅ΠΊΡΡ Π³Π΅Π½ΠΎΠΌΠ½ΠΎΠΉ ΠΎΡΠ΅Π½ΠΊΠΈ ΠΏΠ»Π΅ΠΌΠ΅Π½Π½ΡΡ
Π±ΡΠΊΠΎΠ² Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ ΠΌΠΎΠ»Π΅ΠΊΡΠ»ΡΡΠ½ΠΎ-Π³Π΅Π½Π΅ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΌΠ°ΡΠΊΠ΅ΡΠΎΠ² ΠΈ, Π² ΡΠ°ΡΡΠ½ΠΎΡΡΠΈ, SNP-ΠΌΠ°ΡΠΊΠ΅ΡΠΎΠ² Π΄Π»Ρ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΠΏΠ»Π΅ΠΌΠ΅Π½Π½ΠΎΠΉ ΡΠ΅Π½Π½ΠΎΡΡΠΈ ΠΆΠΈΠ²ΠΎΡΠ½ΡΡ
[Towards advanced biotechnological developments to realize the genetic potential of egg-type poultry] ΠΠ°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ Π±ΠΈΠΎΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΎΠΊ Π΄Π»Ρ ΡΠ΅Π°Π»ΠΈΠ·Π°ΡΠΈΠΈ Π³Π΅Π½Π΅ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΏΠΎΡΠ΅Π½ΡΠΈΠ°Π»Π° ΡΠΈΡΠ½ΠΎΠΉ ΠΏΡΠΈΡΡ
Realization of the genetic potential of laying hens makes it feasible to achieve the maximum possible yield of egg products against, while using effective feed compositions and various feed additives. Implementation of molecular genetic technologies for the analysis of intestinal microbiota and the expression of key genes for productivity and resistance is an important tool in studying mechanisms of the effects of feed preparations on microorganism of birds. Within the framework of the project for the development of modern biotechnologies to assess gene expression, we carried out an experiment to assess influence of human recombinant interferon alpha-2b on the expression of genes for productivity and immunity in laying hens. A positive effect of the additive on the immune system of birds and the effectiveness of molecular genetic technologies for assessing the expression of key genes and the use of the studied additives in feeding of egg-type poultry have been shown.
Π Π΅Π°Π»ΠΈΠ·Π°ΡΠΈΡ Π³Π΅Π½Π΅ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΏΠΎΡΠ΅Π½ΡΠΈΠ°Π»Π° ΠΊΡΡ-Π½Π΅ΡΡΡΠ΅ΠΊ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ Π΄ΠΎΡΡΠΈΠ³Π°ΡΡ ΠΌΠ°ΠΊΡΠΈΠΌΠ°Π»ΡΠ½ΠΎ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΡΠΉ Π²ΡΡ
ΠΎΠ΄ ΡΠΈΡΠ½ΠΎΠΉ ΠΏΡΠΎΠ΄ΡΠΊΡΠΈΠΈ Π½Π° ΡΠΎΠ½Π΅ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΡΡ
ΠΊΠΎΠΌΠΏΠΎΠ·ΠΈΡΠΈΠΉ ΠΊΠΎΡΠΌΠΎΠ² ΠΈ ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
ΠΊΠΎΡΠΌΠΎΠ²ΡΡ
Π΄ΠΎΠ±Π°Π²ΠΎΠΊ. ΠΠ½Π΅Π΄ΡΠ΅Π½ΠΈΠ΅ ΠΌΠΎΠ»Π΅ΠΊΡΠ»ΡΡΠ½ΠΎ-Π³Π΅Π½Π΅ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ Π΄Π»Ρ Π°Π½Π°Π»ΠΈΠ·Π° ΠΌΠΈΠΊΡΠΎΠ±ΠΈΠΎΡΡ ΠΊΠΈΡΠ΅ΡΠ½ΠΈΠΊΠ° ΠΈ ΡΠΊΡΠΏΡΠ΅ΡΡΠΈΠΈ ΠΊΠ»ΡΡΠ΅Π²ΡΡ
Π³Π΅Π½ΠΎΠ² ΠΏΡΠΎΠ΄ΡΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ ΠΈ ΡΠ΅Π·ΠΈΡΡΠ΅Π½ΡΠ½ΠΎΡΡΠΈ ΡΠ²Π»ΡΠ΅ΡΡΡ Π²Π°ΠΆΠ½ΡΠΌ ΠΈΠ½ΡΡΡΡΠΌΠ΅Π½ΡΠΎΠΌ Π² ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΈ ΠΌΠ΅Ρ
Π°Π½ΠΈΠ·ΠΌΠΎΠ² Π²ΠΎΠ·Π΄Π΅ΠΉΡΡΠ²ΠΈΡ ΠΊΠΎΡΠΌΠΎΠ²ΡΡ
ΠΏΡΠ΅ΠΏΠ°ΡΠ°ΡΠΎΠ² Π½Π° ΠΌΠ°ΠΊΡΠΎΠΎΡΠ³Π°Π½ΠΈΠ·ΠΌ ΠΏΡΠΈΡΡ. Π ΡΠ°ΠΌΠΊΠ°Ρ
ΠΏΡΠΎΠ΅ΠΊΡΠ° ΠΏΠΎ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠ΅ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ
Π±ΠΈΠΎΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ Π΄Π»Ρ ΠΎΡΠ΅Π½ΠΊΠΈ ΡΠΊΡΠΏΡΠ΅ΡΡΠΈΠΈ Π³Π΅Π½ΠΎΠ² Π½Π°ΠΌΠΈ ΠΎΡΡΡΠ΅ΡΡΠ²Π»Π΅Π½ ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½Ρ ΠΏΠΎ ΠΈΠ·ΡΡΠ΅Π½ΠΈΡ ΡΠ΅Π»ΠΎΠ²Π΅ΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΠ΅ΠΊΠΎΠΌΠ±ΠΈΠ½Π°Π½ΡΠ½ΠΎΠ³ΠΎ ΠΈΠ½ΡΠ΅ΡΡΠ΅ΡΠΎΠ½Π° Π°Π»ΡΡΠ°-2b Π½Π° ΡΠΊΡΠΏΡΠ΅ΡΡΠΈΡ Π³Π΅Π½ΠΎΠ² ΠΏΡΠΎΠ΄ΡΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ ΠΈ ΠΈΠΌΠΌΡΠ½ΠΈΡΠ΅ΡΠ° Ρ ΠΊΡΡ-Π½Π΅ΡΡΡΠ΅ΠΊ. ΠΠΎΠΊΠ°Π·Π°Π½Ρ ΠΏΠΎΠ»ΠΎΠΆΠΈΡΠ΅Π»ΡΠ½ΠΎΠ΅ Π²Π»ΠΈΡΠ½ΠΈΠ΅ ΠΏΡΠ΅ΠΏΠ°ΡΠ°ΡΠ° Π½Π° ΠΈΠΌΠΌΡΠ½Π½ΡΡ ΡΠΈΡΡΠ΅ΠΌΡ ΠΏΡΠΈΡ ΠΈ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ ΠΌΠΎΠ»Π΅ΠΊΡΠ»ΡΡΠ½ΠΎ-Π³Π΅Π½Π΅ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ Π΄Π»Ρ ΠΎΡΠ΅Π½ΠΊΠΈ ΡΠΊΡΠΏΡΠ΅ΡΡΠΈΠΈ ΠΊΠ»ΡΡΠ΅Π²ΡΡ
Π³Π΅Π½ΠΎΠ² ΠΈ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ ΠΈΠ·ΡΡΠ°Π΅ΠΌΡΡ
Π΄ΠΎΠ±Π°Π²ΠΎΠΊ Π² ΠΊΠΎΡΠΌΠ»Π΅Π½ΠΈΠΈ ΡΠΈΡΠ½ΠΎΠΉ ΠΏΡΠΈΡΡ
[Genetic variation of the NCAPG-LCORL locus in chickens of local breeds based on SNP genotyping data] ΠΠ΅Π½Π΅ΡΠΈΡΠ΅ΡΠΊΠ°Ρ ΠΈΠ·ΠΌΠ΅Π½ΡΠΈΠ²ΠΎΡΡΡ Π»ΠΎΠΊΡΡΠ° NCAPG-LCORL Ρ ΠΊΡΡ Π»ΠΎΠΊΠ°Π»ΡΠ½ΡΡ ΠΏΠΎΡΠΎΠ΄ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ Π΄Π°Π½Π½ΡΡ SNP-Π³Π΅Π½ΠΎΡΠΈΠΏΠΈΡΠΎΠ²Π°Π½ΠΈΡ
Using SNP analysis, genomic variation of the NCAPG-LCORL locus in chickens of 49 gene pool breeds and crossbreds from the Genetic Collection of Rare and Endangered Chicken Breeds was analyzed. Genotyping was performed using an Illumina Chicken 60K SNP iSelect BeadChip. As a result of SNP scanning, five significant SNPs were identified in the NCAPG-LCORL region in all breeds and crossbreds of the analyzed groups of chickens for GGA4. Cluster analysis of admixture models revealed a subdivision of individuals according to their origin at K = 5. Chickens of the egg and meat types formed two separate clusters, which is consistent with the results of genotype frequencies. When analyzing genetic differentiation between groups of chickens with different utility types on the basis of pairwise FST values, significant differences (p < 0.05) were found for the group of egg-type chickens in comparison with meat-type (0.330), dual purpose (meat-egg, 0.178), game (0.225 ) and dual purpose (egg-meat, 0.237) chickens, as well as for meat-type relative to fancy chickens (0.153). The results showed that the compared groups differ genetically from each other, which is confirmed by the data on genotype frequencies. The population specificity of the linkage disequilibrium structure at the NCAPG-LCORL locus was revealed for 11 chicken breeds.
Π Ρ
ΠΎΠ΄Π΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Ρ ΠΏΠΎΠΌΠΎΡΡΡ Π°Π½Π°Π»ΠΈΠ·Π° ΠΎΠ΄Π½ΠΎΠ½ΡΠΊΠ»Π΅ΠΎΡΠΈΠ΄Π½ΠΎΠ³ΠΎ ΠΏΠΎΠ»ΠΈΠΌΠΎΡΡΠΈΠ·ΠΌΠ° (SNP) Π±ΡΠ»Π° ΠΏΡΠΎΠ°Π½Π°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π° Π³Π΅Π½ΠΎΠΌΠ½Π°Ρ ΠΈΠ·ΠΌΠ΅Π½ΡΠΈΠ²ΠΎΡΡΡ Π»ΠΎΠΊΡΡΠ° NCAPG-LCORL Ρ ΠΊΡΡ 49 Π³Π΅Π½ΠΎΡΠΎΠ½Π΄Π½ΡΡ
ΠΏΠΎΡΠΎΠ΄ ΠΈ Π³ΠΈΠ±ΡΠΈΠ΄Π½ΡΡ
ΡΠΎΡΠΌ ΠΈΠ· Β«ΠΠ΅Π½Π΅ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΊΠΎΠ»Π»Π΅ΠΊΡΠΈΠΈ ΡΠ΅Π΄ΠΊΠΈΡ
ΠΈ ΠΈΡΡΠ΅Π·Π°ΡΡΠΈΡ
ΠΏΠΎΡΠΎΠ΄ ΠΊΡΡΒ». ΠΠ΅Π½ΠΎΡΠΈΠΏΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΠ»ΠΈ Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΡΠΈΠΏΠ° Illumina Chicken 60K SNP iSelect BeadChip. Π ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ΅ SNP-ΡΠΊΠ°Π½ΠΈΡΠΎΠ²Π°Π½ΠΈΡ Ρ Π²ΡΠ΅Ρ
ΠΏΠΎΡΠΎΠ΄ ΠΈ Π³ΠΈΠ±ΡΠΈΠ΄ΠΎΠ² Π°Π½Π°Π»ΠΈΠ·ΠΈΡΡΠ΅ΠΌΡΡ
Π³ΡΡΠΏΠΏ ΠΊΡΡ Π½Π° GGA4 Π² ΡΠ΅Π³ΠΈΠΎΠ½Π΅, Π²ΠΊΠ»ΡΡΠ°ΡΡΠ΅ΠΌ NCAPG-LCORL, ΠΈ Π² ΠΎΠ±Π»Π°ΡΡΠΈ ΡΡΠ΄ΠΎΠΌ Ρ ΡΡΠΈΠΌ ΡΠ΅Π³ΠΈΠΎΠ½ΠΎΠΌ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΎ ΠΏΡΡΡ Π·Π½Π°ΡΠΈΠΌΡΡ
SNPs, ΠΊΠΎΡΠΎΡΡΠ΅ ΠΌΠΎΠ³ΡΡ Π±ΡΡΡ ΠΊΠ°Π½Π΄ΠΈΠ΄Π°ΡΠ°ΠΌΠΈ Π΄Π»Ρ ΡΠ΅Π»Π΅ΠΊΡΠΈΠΈ Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΠΌΠ°ΡΠΊΠ΅ΡΠΎΠ² (MAS). ΠΠ»Π°ΡΡΠ΅ΡΠ½ΡΠΉ Π°Π½Π°Π»ΠΈΠ· Π°Π΄ΠΌΠΈΠΊΡ-ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ ΠΎΠ±Π½Π°ΡΡΠΆΠΈΠ» ΡΠ°Π·Π΄Π΅Π»Π΅Π½ΠΈΠ΅ ΠΎΡΠΎΠ±Π΅ΠΉ ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²Π΅Π½Π½ΠΎ ΠΈΡ
ΠΏΡΠΎΠΈΡΡ
ΠΎΠΆΠ΄Π΅Π½ΠΈΡ ΠΏΡΠΈ Π=5. ΠΡΡΡ ΡΠΈΡΠ½ΠΎΠ³ΠΎ ΠΈ ΠΌΡΡΠ½ΠΎΠ³ΠΎ Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΠΏΡΠΎΠ΄ΡΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ ΡΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π»ΠΈ Π΄Π²Π° ΠΎΠ±ΠΎΡΠΎΠ±Π»Π΅Π½Π½ΡΡ
ΠΊΠ»Π°ΡΡΠ΅ΡΠ°, ΡΡΠΎ ΡΠΎΠ³Π»Π°ΡΡΠ΅ΡΡΡ Ρ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ°ΠΌΠΈ ΡΠ°ΡΡΠΎΡ Π³Π΅Π½ΠΎΡΠΈΠΏΠΎΠ². ΠΡΠΈ Π°Π½Π°Π»ΠΈΠ·Π΅ Π³Π΅Π½Π΅ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ Π΄ΠΈΡΡΠ΅ΡΠ΅Π½ΡΠΈΠ°ΡΠΈΠΈ ΠΌΠ΅ΠΆΠ΄Ρ Π³ΡΡΠΏΠΏΠ°ΠΌΠΈ ΠΊΡΡ ΡΠ°Π·Π»ΠΈΡΠ½ΠΎΠ³ΠΎ Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΠΏΡΠΎΠ΄ΡΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΏΠΎΠΏΠ°ΡΠ½ΡΡ
FST-Π·Π½Π°ΡΠ΅Π½ΠΈΠΉ ΠΎΡΠΌΠ΅ΡΠ΅Π½Ρ Π΄ΠΎΡΡΠΎΠ²Π΅ΡΠ½ΡΠ΅ ΡΠ°Π·Π»ΠΈΡΠΈΡ (p < 0,05) Π΄Π»Ρ Π³ΡΡΠΏΠΏΡ ΠΊΡΡ ΡΠΈΡΠ½ΠΎΠ³ΠΎ Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΠΏΡΠΎΠ΄ΡΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ Π² ΡΡΠ°Π²Π½Π΅Π½ΠΈΠΈ Ρ ΠΌΡΡΠ½ΡΠΌΠΈ (0,330), ΠΌΡΡΠΎ-ΡΠΈΡΠ½ΡΠΌΠΈ (0,178), Π±ΠΎΠΉΡΠΎΠ²ΡΠΌΠΈ (0,225) ΠΈ ΡΠΈΡΠ½ΠΎ-ΠΌΡΡΠ½ΡΠΌΠΈ (0,237), Π° ΡΠ°ΠΊΠΆΠ΅ Π΄Π»Ρ ΠΊΡΡ ΠΌΡΡΠ½ΠΎΠ³ΠΎ Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΠΏΡΠΎΠ΄ΡΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ ΠΎΡΠ½ΠΎΡΠΈΡΠ΅Π»ΡΠ½ΠΎ Π΄Π΅ΠΊΠΎΡΠ°ΡΠΈΠ²Π½ΡΡ
(0,153). Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΏΠΎΠΊΠ°Π·Π°Π»ΠΈ, ΡΡΠΎ ΡΡΠ°Π²Π½ΠΈΠ²Π°Π΅ΠΌΡΠ΅ Π³ΡΡΠΏΠΏΡ ΠΎΡΠ»ΠΈΡΠ°ΡΡΡΡ Π³Π΅Π½Π΅ΡΠΈΡΠ΅ΡΠΊΠΈ Π΄ΡΡΠ³ ΠΎΡ Π΄ΡΡΠ³Π°, ΡΡΠΎ ΠΏΠΎΠ΄ΡΠ²Π΅ΡΠΆΠ΄Π°Π΅ΡΡΡ Π΄Π°Π½Π½ΡΠΌΠΈ ΠΎ ΡΠ°ΡΡΠΎΡΠ°Ρ
Π³Π΅Π½ΠΎΡΠΈΠΏΠΎΠ². ΠΡΡΠ²Π»Π΅Π½Π° ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠΎΠ½Π½Π°Ρ ΡΠΏΠ΅ΡΠΈΡΠΈΡΠ½ΠΎΡΡΡ ΡΡΡΡΠΊΡΡΡΡ Π½Π΅ΡΠ°Π²Π½ΠΎΠ²Π΅ΡΠΈΡ ΠΏΠΎ ΡΡΠ΅ΠΏΠ»Π΅Π½ΠΈΡ (LD) ΠΏΠΎ Π»ΠΎΠΊΡΡΡ NCAPG-LCORL Π΄Π»Ρ 11 ΠΏΠΎΡΠΎΠ΄ ΠΊΡΡ
Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950β2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021
Background: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020β21 COVID-19 pandemic period.
Methods: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution.
Findings: Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62Β·8% [95% UI 60Β·5β65Β·1] decline), and increased during the COVID-19 pandemic period (2020β21; 5Β·1% [0Β·9β9Β·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4Β·66 million (3Β·98β5Β·50) global deaths in children younger than 5 years in 2021 compared with 5Β·21 million (4Β·50β6Β·01) in 2019. An estimated 131 million (126β137) people died globally from all causes in 2020 and 2021 combined, of which 15Β·9 million (14Β·7β17Β·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22Β·7 years (20Β·8β24Β·8), from 49Β·0 years (46Β·7β51Β·3) to 71Β·7 years (70Β·9β72Β·5). Global life expectancy at birth declined by 1Β·6 years (1Β·0β2Β·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15Β·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7Β·89 billion (7Β·67β8Β·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39Β·5% [28Β·4β52Β·7]) and south Asia (26Β·3% [9Β·0β44Β·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92Β·2%) of 204 nations.
Interpretation: Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic.
Funding: Bill & Melinda Gates Foundation
Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990β2021: a systematic analysis for the Global Burden of Disease Study 2021
Background: Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. Methods: The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble modelβa modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimatesβwith alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2Β·5th and 97Β·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortalityβwhich includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. Findings: The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94Β·0 deaths (95% UI 89Β·2β100Β·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271Β·0 deaths [250Β·1β290Β·7] per 100 000 population) and Latin America and the Caribbean (195Β·4 deaths [182Β·1β211Β·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48Β·1 deaths [47Β·4β48Β·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23Β·2 deaths [16Β·3β37Β·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1Β·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8Β·3 years (6Β·7β9Β·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0Β·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3Β·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. Interpretation: Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. Funding: Bill & Melinda Gates Foundation