A panel comprising of 84 Turkish winter wheat landraces (LR) and 73 modern varieties (MV) was analyzed with genome wide association study (GWAS) to identify genes/genomic regions associated with increased yield under favorable and drought conditions. In addition, selective sweep analysis was conducted to detect signatures of selection in the winter wheat genome driving the differentiation between LR and MV, to gather an understanding of genomic regions linked to adaptation and yield improvement. The panel was genotyped with 25 K wheat SNP array and phenotyped for agronomic traits for two growing seasons (2018 and 2019) in Konya, Turkey. Year 2018 was treated as drought environment due to very low precipitation prior to heading whereas year 2019 was considered as a favorable season. GWAS conducted with SNPs and haplotype blocks using mixed linear model identified 18 genomic regions in the vicinities of known genes i.e., TaERF3-3A, TaERF3-3B, DEP1-5A, FRIZZY PANICLE-2D, TaSnRK23-1A, TaAGL6-A, TaARF12-2A, TaARF12-2B, WAPO1, TaSPL16-7D, TaTGW6-A1, KAT-2B, TaOGT1, TaSPL21-6B, TaSBEIb, trs1/WFZP-A, TaCwi-A1-2A and TaPIN1-7A associated with grain yield (GY) and yield related traits. Haplotype-based GWAS identified five haplotype blocks (H1A-42, H2A-71, H4A-48, H7B-123 and H7B-124), with the favorable haplotypes showing a yield increase of > 700 kg/ha in the drought season. SNP-based GWAS, detected only one larger effect genomic region on chromosome 7B, in common with haplotype-based GWAS. On an average, the percentage variation (PV) explained by haplotypes was 8.0% higher than PV explained by SNPs for all the investigated traits. Selective sweep analysis detected 39 signatures of selection between LR and MV of which 15 were within proximity of known functional genes controlling flowering (PRR-A1, PPR-D1, TaHd1-6B), GY and GY components (TaSus2-2B, TaGS2-B1, AG1-1A/WAG1-1A, DUO-A1, DUO-B1, AG2-3A/WAG2-3A, TaLAX1, TaSnRK210-4A, FBP, TaLAX1, TaPIL1 and AP3-1-7A/WPA3-7A) and 10 regions underlying various transcription factors and regulatory genes. The study outcomes contribute to utilization of LR in breeding winter wheat.

通过全基因组关联研究 (GWAS) 对由 84 个土耳其冬小麦地方品种 (LR) 和 73 个现代品种 (MV) 组成的小组进行了分析，以确定与有利和干旱条件下产量增加相关的基因/基因组区域。此外，还进行了选择性扫描分析，以检测冬小麦基因组中驱动 LR 和 MV 分化的选择特征，以了解与适应和产量提高相关的基因组区域。该小组使用 25 K 小麦 SNP 阵列进行了基因分型，并对土耳其科尼亚两个生长季节（2018 年和 2019 年）的农艺性状进行了表型分析。由于抽穗前降水量极少，2018 年被视为干旱环境，而 2019 年则被视为有利季节。使用混合线性模型对 SNP 和单倍型块进行 GWAS，鉴定了已知基因附近的 18 个基因组区域，即TaERF3-3A、TaERF3-3B 、 DEP1-5A、FRIZZY PANICLE-2D、TaSnRK23-1A、TaAGL6-A 、 TaARF12- 2A、TaARF12-2B、WAPO1、TaSPL16-7D、TaTGW6-A1、KAT-2B、TaOGT1、TaSPL21-6B、TaSBEIb、trs1/WFZP-A、TaCwi-A1-2A和TaPIN1-7A与谷物产量 (GY) 相关和产量相关性状。基于单倍型的 GWAS 确定了 5 个单倍型块（H1A-42、H2A-71、H4A-48、H7B-123 和 H7B-124），其中有利的单倍型显示干旱季节产量增加 > 700 公斤/公顷。基于 SNP 的 GWAS 只检测到 7B 染色体上一个较大的效应基因组区域，与基于单倍型的 GWAS 相同。平均而言，对于所有调查性状，由单倍型解释的变异百分比 (PV) 比由 SNP 解释的 PV 高 8.0%。 选择性扫描分析检测到 LR 和 MV 之间的 39 个选择特征，其中 15 个与控制开花的已知功能基因（ PRR-A1 、 PPR-D1 、 TaHd1-6B ）、GY 和 GY 成分（ TaSus2 -2B、 TaGS2-B1）接近、 AG1-1A/WAG1-1A 、 DUO-A1 、 DUO-B1、AG2-3A/WAG2-3A 、 TaLAX1、TaSnRK210-4A、FBP、TaLAX1 、 TaPIL1和AP3-1-7A/WPA3-7A ）和 10 个区域潜在的各种转录因子和调节基因。研究成果有助于LR在冬小麦育种中的应用。